[Significance] The pig industry is a key sector of China's animal husbandry. With the continuous expansion of farming scale, traditional manual inspection methods can no longer meet the demands of modern production in terms of efficiency, accuracy, and animal welfare. In recent years, intelligent pig monitoring technologies based on multi-source data—such as images, depth information, sensors, and sound—have developed rapidly, providing new solutions for health monitoring, behavior recognition, weight assessment, and physiological state management during the farming process. As a crucial foundation for upgrading the pig industry toward intelligent and precise farming, it is of significant value to systematically review the current research status, application progress, and future trends of this technological system. [Progress] This paper focuses on the main research areas in intelligent pig monitoring, systematically summarizing the commonly used data types and their applications in farming scenarios from the perspective of matching data sources with application objectives. First, research based on infrared images mainly focuses on non-contact acquisition of body temperature information, which is used for disease early warning and health monitoring, offering clear advantages in reducing stress responses and increasing monitoring frequency. Second, visible-light images are widely applied in behavior recognition and health analysis, supporting automated identification and quantification of behaviors such as feeding, resting, and aggression, thereby facilitating dynamic understanding of pig herd behavior patterns and changes. Third, depth images and three-dimensional information demonstrate unique value in body measurement extraction and weight estimation, promoting the development of non-contact, continuous weight monitoring. Fourth, wearable sensors enable continuous monitoring of sows' health, lameness risk, and daily behavioral rhythms by recording physiological data such as body temperature, acceleration, and feeding activity in real time. Finally, audio signals, an emerging data type in recent years, have shown potential in monitoring abnormal sounds such as coughing, providing a new approach for the early detection of respiratory diseases. On this basis, this paper further summarizes the research and application of intelligent detection equipment. Current equipment presents a development trend of two aspects: one focuses on single indicators such as body temperature and weight, characterized by precise collection and rapid feedback; the other integrates multiple functions including image acquisition, body temperature detection, behavior recording, and identity recognition through mobile platforms such as inspection robots, enabling full-scenario and all-weather intelligent detection and improving the automation and refinement level of pig farm management. With the growth of industrial demand, various types of equipment are gradually moving from laboratories to commercialization, providing important support for intelligent breeding. [Conclusions and Prospects] Despite the rapid development of intelligent pig detection technology, multiple challenges still exist. At the data level, interference from lighting, occlusion, and noise in different scenarios can affect the stability of detection results; at the hardware level, some equipment suffers from high costs and needs improvement in reliability; at the model level, differences across pig farms, breeds, and growth stages still lead to insufficient adaptability; at the application level, data continuity, system stability, and equipment maintenance costs in large-scale scenarios require further optimization. These factors collectively restrict the large-scale promotion of intelligent detection technology in the industry. Future research directions will exhibit the following common trends: first, achieving contactless operation and multi-scenario adaptability to minimize disturbance to pigs and enhance stability in complex environments. Second, advancing the integration of multimodal data fusion and deep learning to establish stronger correlations among multi-source data such as images, sensors, and audio. Third, developing individualized health and growth models to provide a scientific basis for precision feeding and management.

[Objective] As a major grain crop, maize plays a critical role in global food security. The ears of maize serves as a key phenotypic trait, providing essential information on the plant's physiological and agronomic status. Its morphological characteristics, size, and color effectively reflect the plant's growth status and potential yield. Therefore, accurately acquiring images of maize ears in the field across different growth stages is crucial for breeding research and yield prediction. Traditional field detection of maize ears relies heavily on manual labor, which is not only inefficient and labor-intensive but also struggles to meet the high-throughput demands of modern precision breeding programs. There is an urgent need for efficient, automated detection technologies that can operate reliably under real-world field conditions. To address the requirement for efficient acquisition of maize ears phenotypic traits in field breeding work, the objectives of this research are to develop a robust object detection solution suitable for large-scale field environments. The main contribution of this research is the proposal of an improved CornYOLO model based on the YOLO11n (You Only Look Once 11) architecture, which was designed to enhance the detection accuracy and efficiency of maize ears in complex field environments. [Methods] Image data were acquired using a unmanned ground vehicle (UGV) equipped with a high-resolution panoramic camera, which traversed multiple experimental plots under varying lighting and growth conditions. A dataset containing 1 152 annotated samples was constructed, covering diverse ear morphologies and occlusion scenarios. Dynamic data augmentation techniques were applied during training to enhance the model's generalization capability. Three key enhancements were introduced to the YOLO11n detection framework. First, a Cross Stage Partial network with Dynamic Pointwise Spatial Attention (C2PDA) module was designed to replace the Cross Stage Partial with Pointwise Spatial Attention (C2PSA) module in the YOLO11 backbone network. This module enhances spatial discriminability and channel sensitivity in feature representation through the collaborative integration of a dynamic channel weighting mechanism and position-aware modeling. It significantly improves the model's performance in identifying maize ears under challenging field conditions such as occlusion of stems and leaves and multi-scale target distribution. Second, the spatial pyramid pooling – fast (SPPF) module in the original model was replaced with an feature refinement module (FRM ) to optimize multi-scale feature fusion. The FRM functions via directional feature decomposition and an adaptive attention mechanism. It captures fine-grained spatial structural information through horizontal and vertical bidirectional pooling and combines spatial-channel cooperative attention for dynamic feature calibration, thereby improving recognition accuracy across varying ear sizes and complex backgrounds. Finally, the unified intersection over union (UIoU) loss function was introduced to optimize bounding box regression accuracy. UIoU is an innovative loss function that emphasizes weight allocation among prediction boxes of different qualities. It adaptively adjusts the weight of each prediction box's loss term based on the IoU value or its monotonic function, assigning higher weights to lower-quality predictions to prioritize their optimization, while reducing weights for high-quality boxes to prevent over-optimization. [Results and Discussions] Experimental results demonstrate that CornYOLO achieved a mAP@50 of 89.3% on the validation set, with the F₁-Score increasing by 2.5 percentage points. Compared to widely used lightweight models including YOLOv3-tiny, YOLOv5n, YOLOv8n, YOLOv10n, Real-Time Detection Transformer (RT-DETR) and YOLOv13n, CornYOLO showed significantly superior detection performance in complex field environments, with mAP@50 improvements of 2.2, 1.9, 1.8, 5.7, 12.6 and 2.4 percentage points, respectively.. These results fully validate that CornYOLO can efficiently and accurately extract maize ear images under field conditions, providing a technical foundation for precise phenotypic evaluation and yield prediction. Furthermore, ablation studies were conducted, revealing the individual and combined contributions of each module: Introducing the C2PDA module improved the model's mAP@50 by 0.5 percentage points and the F₁-Score by 0.5 percentage points.. However, after incorporating the FRM module—which successfully enhanced multi-scale detection performance and increased the F₁-Score by 1.5 percentage points—the integration of these two modules resulted in the generation of a small number of low-quality detection boxes. The original loss function was inefficient in optimizing such boxes, resulting in no improvement in mAP@50 after the modification. To address this issue, the UIoU loss function was introduced. By dynamically adjusting weight assignments based on prediction quality, it significantly improved the regression performance for low-quality detection boxes, thereby enhancing the localization accuracy and convergence stability of the model in dense target scenarios. The final CornYOLO model exhibited excellent overall performance: compared to the original YOLO11n, the F₁-Score increased by 2.5 percentage points and mAP@50 improved by 1.1.The experimental results fully demonstrate that CornYOLO effectively enhances the detection capability for maize ears in complex field environments compared to the baseline YOLO11n model. [Conclusions] The CornYOLO model proposed in this study incorporates three key components—C2PDA, FRM, and UIoU—which significantly enhance both the efficiency and accuracy of maize ears detection in field environments. This approach enhances model convergence and localization performance in dense and occluded scenes. This enables the model to effectively and precisely identify maize ears under practical conditions, thereby providing reliable technical support for phenotypic analysis and yield prediction in maize breeding. Furthermore, it contributes to advancing intelligent extraction of maize ears information and offers a scalable solution for high-throughput field phenotyping. This work provides essential technical support for phenotypic analysis and yield prediction in maize breeding. Future work will focus on extending the model to other crop types and further optimizing inference efficiency for real-time deployment on mobile platforms.

[Objective] Most existing studies on cropping index extraction have primarily focused on cereal crops, whereas investigations targeting vegetable-based cropping systems remain relatively limited. Current national-scale cropping index products are largely designed for major cereal-producing regions, with model parameters calibrated according to the phenological characteristics and growth cycles of cereal crops. Such parameterization neglects the distinctive multi-season rotation patterns of open-field vegetable cultivation, leading to reduced accuracy in capturing the actual cropping dynamics of vegetable-growing areas. Consequently, these limitations hinder a comprehensive understanding of cropland utilization characteristics in regions characterized by intensive vegetable production. This study aims to improve the extraction method of the cropping index for vegetable cultivation systems and to reveal the characteristics of cropland use in typical regions with intensive vegetable multiple cropping. [Methods] This study employed Sentinel-2 MSI surface reflectance (SR) data released by the european space agency (ESA). A greedy algorithm was used to identify the optimal grid and orbit combination (47QRG, 61) covering Tonghai county. Based on this configuration, a total of 362 images acquired from 2020 to 2024 were compiled to construct a 5-day, 10 m spatial resolution time series. The normalized difference vegetation index (NDVI) time series was smoothed and reconstructed using the whittaker smoothing (WS) method. The cropland extent was defined using the cropland mask from the GLC_FCS10 land cover dataset. Active croplands and greenhouse-covered areas were further identified using the vegetation–soil–pigment indices and synthetic-aperture radar (SAR) time-series images (VSPS) and the advanced plastic greenhouse index (APGI). Non-active croplands and greenhouse areas were excluded to refine the open-field cropland boundaries. Subsequently, the second-order difference method was applied to detect NDVI peaks in the reconstructed time series, with rule-based constraints used to eliminate false peaks. The number of valid peaks per pixel was then used to calculate the annual cropping index of Tonghai county from 2020 to 2024, and its spatial distribution and spatiotemporal variations were analyzed. [Results and Discussions] Compared with conventional 10-day median or maximum value compositing approaches, the time-series reconstruction based on specific grid and orbit combinations provides a more accurate representation of crop growth dynamics and peak patterns. Validation using 338 ground samples of cropping index obtained from field surveys in 2024 demonstrated an overall accuracy of 89.94%, a Kappa coefficient of 0.84, MAE of 0.36, and RMSE of 0.11, indicating satisfactory reliability of the extracted results. From 2020 to 2024, the average cropping indices of croplands in Tonghai county were 221.45%, 217.80%, 275.37%, 232.41%, and 237.50%, respectively, reflecting a generally high level of land-use intensity. In 2020, 2021, 2023, and 2024, double cropping systems dominated, with triple cropping being secondary, whereas in 2022, triple cropping became predominant. Multi-season cropping (≥3 seasons) was mainly concentrated along the urban zones adjacent to Qilu lake, where abundant water resources provide favorable conditions for open-field vegetable cultivation. Interannual variations in the cropping index were largely driven by the alternation between double- and triple-cropping systems. Specifically, from 2020 to 2021, the cropping index decreased by 3.67%; cropland areas with decreased, unchanged, and increased indices accounted for 31.08%, 40.23%, and 28.69% of the total cropland area, respectively, with 10.45% of croplands shifting from triple to double cropping. From 2021 to 2022, the index increased substantially by 57.57%; decreased, unchanged, and increased areas accounted for 13.79%, 33.02%, and 53.18%, respectively, with 17.60% of croplands converting from double to triple cropping. Between 2022 and 2023, the index decreased by 42.96%, with corresponding area proportions of 47.96%, 35.06%, and 16.99%, and 16.62% of croplands shifting from triple to double cropping. From 2023 to 2024, the index slightly increased by 5.09%, with 27.49%, 39.19%, and 33.32% of croplands showing decreases, stability, and increases, respectively; 11.21% of croplands converted from double to triple cropping. Overall, the interannual variations were mainly influenced by the mutual transitions between double- and triple-cropping systems. [Conclusions] The results of this study provide valuable theoretical and technical references for cropland resource management, optimization of regional vegetable production, and the promotion of sustainable agricultural development in Tonghai county.

[Objective] Under abiotic stress conditions, rice plants become fragile and susceptible to disease infection. Accurate diagnosis and scientific prevention and control strategies for rice diseases are crucial for the prevention and control of rice diseases, even disasters such as blooding and high temperatures. However, under field natural environmental conditions, the identification of rice diseases is a challenging problem. There are various issues such as complex backgrounds, illumination change, occlusion, which make it extremely difficult to comprehensively obtain disease information, thus significantly increasing the difficulty of disease identification. This study aims to develop an efficient rice disease recognition model by integrating the efficient channel attention (ECA) mechanism with the MobileViT model, enhancing the accuracy of rice disease identification in the field. Additionally, we combine the rice disease knowledge graph to achieve precise diagnosis and generate scientifically grounded control prescriptions for effective disease management. [Methods] A total of 1 304 raw images of rice diseases were collected from different rice disease investigation and long-term monitoring points in Jiangsu province, at different periods of time, using mobile phones and cameras. 167 disease images from the rice leaf disease image samples (RLDIS) dataset were used to supplement the dataset. The raw images were accurately classified and preprocessed under the guidance of plant protection experts. A dataset containing 1 471 original images was constructed that includes seven types of rice diseases: bacterial leaf blight, false smut, leaf blast, bakanae disease, heart rot, grain discoloration, and panicle blast. Data augmentation techniques were used to expand the scale of the dataset, the number of images increased to 7 735 after data augmentation. To guarantee the model's superior generalization capabilities, the dataset was strategically divided into a training set, validation set and test set in a 7:1.5:1.5 ratio. A novel rice disease recognition model has been established by integrating the efficient channel attention (ECA) module into the MobileViT model. The recognition model architecture was optimized by improving convolutional structures, reconstructing Transformer encoding blocks, replacing activation function using SiLU. To verify the performance of the model, cross validation and ablation experiments were conducted. After establishing a highly accurate recognition model, the recognition model was combined with the rice disease knowledge graph to achieve accurate diagnosis of rice diseases and generate scientific prevention and control strategies. Finally, an intelligent rice disease diagnostic system was developed using the Flask framework and cloud computing technologies. [Results and Discussion] The results of the ablation study revealed that the model, which combined convolutional layer optimization, Transformer block reconstruction, and the integration of the ECA module, had outstanding performance.The overall precision, F₁-Score and recall rate achieved 97.27%, 97.32%, and 97.46%, respectively. In terms of accuracy, the improved model increased to 97.25%, representing an improvement of 2.3% over the original model (94.95%). To further verify the effectiveness of the improved model, various mainstream models such as Swin Transformer, TinyVit, Ghostnetv2, and Convnext were compared with the proposed model.The experimental results showed that the improved model outperformed the suboptimal model (TinyVit) by 0.92、1.43、0.95、1.32 percent points in overall accuracy, F₁-Score and recall rate, respectively. Moreover, the improved model showed significant advantages in terms of floating-point operations, model size, and parameter count, with a parameter count of only 6.02 MB, making it more suitable for deployment on hardware-constrained devices.The analysis results of confusion matrix and heatmap visualizations revealed that the improved model had significant improvements in the recognition accuracy for bacterial leaf blight, heart rot, grain discoloration, and panicle blast.The combination of rice disease knowledge graph and the improved model has achieved more accurate disease diagnosis results. Meanwhile, the disease prevention and control strategies were generated to guide rice disease prevention and control.In addition, to assess the rice disease intelligent diagnosis system, the system was used for field rice disease investigation, and compared with the diagnosis results of plant protection experts.The rice disease diagnosis system demonstrated an impressive accuracy rate of 98% during field application, with reliable real-time performance and stability. [Conclusion] The improved model demonstrated stronger robustness and stability.By integrating ECA module and reconstructing Transformer encoding blocks, the MobileViT model achieved noticeable improvements in precision, recall and F₁ score, while effectively reducing computational costs, leading to more efficient recognition capabilities of rice diseases in complex field environments. The application of the rice disease intelligent diagnosis system revealed that the system could achieve accurate rice disease diagnosis results, and generate disease prevention and control strategies for guide rice disease prevention and control. The system could meet the application requirements of accuracy, real-time and stability in field disease investigation. This method could effectively improve the prevention and control efficiency of rice diseases, waterlogging, high temperature and other disasters, providing significant technical support for improving the quality, efficiency, digitization and intelligence of rice production.

[Objective] The critical challenges are addressed in automated safflower harvesting, particularly the inefficiencies in path planning, suboptimal route quality, and limited decision-making capability under dynamic and complex environments. To solve these issues, the problem is formulated as a three-dimensional traveling salesman problem (3D TSP) and an enhanced reinforcement learning framework named actor-critic reinforcement learning pointer network (AC-RL-PtrNet) is proposed, specifically designed for deployment on intelligent safflower picking robots in agricultural settings. [Methods] First, to address the inherent limitations of conventional attention mechanisms in dynamic environments with complex spatial structures, an enhanced attention module was proposed based on the dynamic exponential moving average (DEMA) framework. By combining multi-head attention, spatial distance encoding, and adaptive exponential smoothing, the improved design allowed the model to better capture long-range dependencies and spatial context among safflowers. Meanwhile, to minimize computational cost while preserving inference quality, a structured pruning approach was adopted, which selectively removed redundant connections in the long short-term memory (LSTM) gates and fully connected layers. In parallel, the Critic network was redesigned to improve learning stability and accuracy. This was achieved through the inclusion of batch normalization, residual feature aggregation, and a multi-layer value estimation head, all of which contributed to a tighter Actor-Critic synergy during policy training. [Results and Discussions] To quantitatively assess the impact of each component, ablation experiments were conducted across various configurations. The results confirmed that each module contributed distinct benefits, while their combination yielded the highest improvements in both planning precision and inference efficiency. This coordinated Actor–Critic design effectively enhanced both trajectory quality and decision stability, which were critical in sequential robotic picking tasks. Experimental results also demonstrated that, compared with traditional swarm intelligence algorithms PSO, AC, and NSGA, the proposed AC-RL-PtrNet model achieved a planning time improvement ranging from –2.63% to 61.87% on the 25-target dataset and from 22.93% to 59.1% on the 31-target dataset. Meanwhile, the optimized paths were significantly shortened across different planning instances, indicating robust generalization capability under varied problem scales. Furthermore, field experiments provided concrete validation of the model's practical applicability. When deployed on a mobile picking robot in real safflower fields, the AC-RL-PtrNet achieved a 9.56% reduction in path length and 5.43% time saved for a 25-target picking task, and a 20.17% path reduction and 29.70% time saving for a 31-target scenario involving a different safflower variety. Overall, these results all indicated that the proposed method exhibited significant advantages in enhancing path planning efficiency and optimizing path quality. [Conclusions] Consequently, this study offers a practical solution for achieving efficient and robust automatic picking by safflower picking robots and provides new insights into solving 3D combinatorial optimization problems.

[Objective] When deep learning is applied to plant disease recognition tasks, model fine-tuning faces significant challenges, including limited computational resources and high parameter update overhead. Although traditional Low-Rank Adaptation (LoRA) methods effectively reduce parameter overhead, their strategy of assigning a uniform, fixed rank to all layers often overlooks the varying importance of different layers. This approach may still lead to constrained optimization in critical layers or resource waste in less significant ones. To address this limitation, a dynamic rank allocation (DRA) algorithm is proposes. The DRA algorithm is designed to evaluate and adjust the required parameter resources for each layer during training, aiming to enhance the accuracy of plant disease classification models while more efficiently balancing computational resources. [Methods] The experiments utilized public datasets, the wheat plant diseases dataset and the plants disease dataset. The wheat plant diseases dataset comprised 13 104 images covering 15 types of wheat diseases such as black rust and fusarium head blight as well as healthy plants, while the Plants Disease Dataset included 37 505 images of 26 types of plant diseases such as algal leaf spot, corn rust, and bacterial spot of tomato. These datasets were captured under varied lighting, different backgrounds, diverse angles, and at various stages of plant growth. A cross-layer feature similarity metric based on Centred Kernel Alignment (CKA) was introduced to quantify the representational correlation between different layers. Concurrently, a correction factor was constructed based on gradient information and activation intensity to measure the direct impact of each layer on the loss function. These two metrics were then fused using a weighted harmonic mean to generate a comprehensive importance score, which was subsequently used for the initial rank allocation. Furthermore, considering the effect of feature representation changes during training, a stability-triggered adaptive rank update strategy rank re-allocation (RRA) was proposed. This strategy monitored the average parameter change of the low-rank adapters during the training process to determine the convergence state. When this change fell below a specific threshold, the low-rank matrices were merged into the original weights, and the rank allocation table was then re-calculated and updated. This process ensured that more resources were allocated to critical layers, thereby achieving an optimized allocation of parameter resources across different layers. [Results and Discussions] Tests on four models (AlexNet, MobileNetV2, Regnety, and ConvNeXt) indicated that, compared to full-parameter fine-tuning, the proposed method reduced resource consumption to 0.42%, 2.46%, 3.56%, and 1.25%, respectively, while maintaining a comparable average accuracy. The RRA strategy demonstrated continuous parameter optimization throughout the model's training. On the ConvNeXt model, the trainable parameters on the plants disease dataset were progressively reduced from 18.34 to 9.26 MB, a reduction of nearly 50%. In comparison with the standard LoRA method (R=16), the method reduces the accuracy by 0.38, 0.40 and 0.05 percentage points on the wheat plant diseases dataset for AlexNet, MobileNetV2, and regnety, respectively, while resource consumption was reduced by 59.3%, 87.4% and 50.5%. Robustness was tested by applying perturbations to the test set, including Gaussian noise, random cropping, color jitter, and random rotation. The results showed that the model was most affected by color jitter and random rotation on the Plants Disease Dataset, with accuracy dropping by 6.02 and 5.11 percentage points, respectively. On the wheat plant diseases dataset, the model was more sensitive to random cropping and random rotation, with accuracy decreasing by 4.33 and 4.40 percentage points, respectively; the overall performance degradation remained within an acceptable range. When compared to other advanced low-rank methods such as AdaLoRA and DyLoRA under the same parameter budget, the DRA method exhibited higher accuracy. On the RegNetY model, the DRA method achieved an accuracy of 90.96% on the Plants Disease Dataset, which was 0.55 percentage points higher than AdaLoRA and 0.94 percentage points higher than DyLoRA. In terms of training efficiency on the Plants Disease Dataset, the DRA method required 43.5 minutes to reach its peak validation accuracy of 89.84%, whereas AdaLoRA required 52.3 minutes, representing a training time increase of approximately 20.23%. Regarding inference flexibility, the DyLoRA method was designed to generate a universal model capable of adapting to multiple rank configurations after a single training run, allowing for dynamic rank switching during inference based on hardware or latency requirements. The DRA method, however, did not possess this inference-time flexibility. It was focused on converging to a single, high-performance rank configuration for a specific task during the training phase. [Conclusions] The low-rank adaptive fine-tuning method proposed in this paper significantly reduced the number of model training parameters while ensuring plant disease recognition accuracy. Compared to traditional fixed-rank LoRA and other advanced low-rank optimization methods, it demonstrated distinct advantages, providing an effective pathway for efficient model deployment on resource-constrained devices.

[Objective] In the eel(Monopterus albus) farming system used in feed distribution research of mechanical arm, the challenges included slow path planning speeds, excessive trajectory redundancy, and suboptimal obstacle avoidance success rates within confined operational spaces. To mitigate these issues, an improved path planning algorithm, based on the bidirectional rapidly-exploring random tree star (BI-RRT*) algorithm, was proposed. The primary aim was to significantly enhance the motion efficiency and task success rate of robotic arms operating in complex, constrained environments. [Methods] This research developed a hybrid path planning algorithm, building upon the foundational BI-RRT* algorithm. This novel approach integrated an adaptive goal-biased strategy with an enhanced artificial potential field (APF) method. The algorithm's framework comprised three core components: a high-quality sampling strategy, an efficient search strategy, and a path optimization algorithm. For the high-quality sampling strategy, an adaptive goal-biased approach was introduced to overcome the limitations of inefficient random sampling and slow convergence rates characteristic of traditional BI-RRT algorithms in complex environments. This strategy dynamically adjusted the generation of sampling points, moving beyond purely random selection. Instead, it prioritized sampling regions in the vicinity of the target, guided by the target direction and a predefined bias probability. This mechanism substantially augmented the growth propensity of the search tree towards the target area, effectively reducing the stochasticity of random sampling and consequently accelerating the path search process. For the efficient search strategy, an improved APF concept was incorporated into the node expansion process. This integration aimed to further enhance search efficiency and prevent the algorithm from converging to local optima. Traditional APF methods were prone to generating local minima when navigating complex obstacle environments, often leading to path planning failures. To address this, the APF was refined to achieve superior integration with the BI-RRT framework. During each new node expansion, in addition to considering the inherent random exploration characteristics of BI-RRT, a directional attractive field was superimposed. This attractive field not only originated from the ultimate target point but also factored in the current growth orientation of the search tree and localized environmental information. Specifically, a composite attractive function was devised, which synergized the attractive force exerted by the target point on the current node with the attraction from potential "guide points". Concurrently, the computation of the repulsive field was optimized to more precisely delineate the geometry and proximity of obstacles, thereby circumventing common issues such as "oscillation" and "deadlock" prevalent in traditional APF. Through this methodology, the algorithm was able to more effectively steer the search tree to circumvent obstacles and rapidly converge towards the target region, significantly bolstering the directedness of the search and successfully preventing the algorithm from becoming ensnared in suboptimal local solutions. For the path optimization algorithm, following the generation of an initial feasible path, a greedy optimization strategy was employed for path pruning and smoothing. This was executed to yield an optimal path characterized by reduced length, enhanced smoothness, and improved conformity with the kinematic properties of the robotic arm. Path pruning was initially applied to eliminate redundant nodes; if a collision-free direct connection existed between two non-adjacent nodes, intermediate nodes were excised, thereby substantially abbreviating the path length. Subsequently, path smoothing techniques, such as B-spline curves or cubic spline interpolation, were introduced. These techniques facilitated the insertion of smooth curves between the pruned key nodes, thereby eradicating sharp angular turns within the path. This enabled the robotic arm to execute movements with greater stability and efficiency during actual operation, mitigating impact and vibration. This two-stage optimization procedure ensured that the final generated path was not merely feasible but also optimal across metrics of length, smoothness, and motion efficiency. [Results and Discussion] To comprehensively validate the performance of the proposed algorithm, a two-stage experimental verification was conducted. Initially, comparative simulations were performed in both two-dimensional (2D) and three-dimensional (3D) environments utilizing the MATLAB platform. These simulation scenarios were meticulously engineered to encompass three archetypal environments—simple, complex, and narrow passages—thereby emulating the diverse obstacle configurations potentially encountered in industrialized eel (Monopterus albus) aquaculture. The results unequivocally demonstrated that, concerning both path planning speed and quality, the improved BI-RRT* algorithm significantly surpassed RRT, APF-RRT, and traditional BI-RRT algorithms across all tested environments. These simulation outcomes robustly substantiated the theoretical superiority and inherent robustness of the improved BI-RRT* algorithm proposed in this study across varying complex environments. To further ascertain the engineering applicability and practical potential of the algorithm, an eel (Monopterus albus) feeding robotic arm simulation system was meticulously constructed based on the robot operating system (ROS) and MoveIt frameworks. This system precisely emulated the kinematics, dynamics, and obstacle distribution pertinent to an industrialized eel (Monopterus albus) aquaculture environment. During simulated continuous feeding tasks, the improved BI-RRT* algorithm consistently exhibited impressive and outstanding performance. Its average running time was merely 2.1 s, representing a substantial 41.6% reduction compared to the traditional BI-RRT. The average length of the planned path was recorded at only 1 680 mm, with an average of 180 nodes, indicating a significant reduction in path redundancy. Furthermore, the algorithm achieved an impressive obstacle avoidance success rate of 96% in complex confined spaces. These empirical findings not only validated the algorithm's effectiveness but also underscored its immense potential for practical engineering applications. The discussion section of this study also provided an in-depth analysis of the differential performance of the algorithm across various scenarios and explored prospective avenues for future optimization, such as its adaptability in dynamic obstacle environments and its potential for integration with advanced visual sensing systems. [Conclusions] The experimental results conclusively demonstrated that the improved BI-RRT* algorithm significantly enhanced the path planning efficiency and trajectory quality of robotic arms operating within confined spaces. It also exhibited exceptionally high reliability in obstacle avoidance, thereby effectively addressing the automated feeding requirements of industrialized eel (*Monopterus albus*) aquaculture. The algorithmic framework possessed considerable generality, offering valuable theoretical insights and technical precedents for resolving analogous robotic arm path planning challenges in other agricultural automation contexts.

[Objective] Lodging is a major agronomic constraint that adversely affects both yield and quality in field crops, with flax (Linum usitatissimum L.) being especially vulnerable due to its slender stems and susceptibility to wind and rainfall. Precise delineation of lodged areas from field imagery remains a significant challenge owing to the complex and heterogeneous morphology of lodging patterns, irregular and blurred boundaries, and substantial background interference from upright plants, weeds, and soil textures. These factors necessitate the development of a segmentation framework that combines high precision and strong boundary adherence with computational efficiency, enabling deployment on resource-constrained agricultural monitoring platforms. In response to this need, a lightweight yet accurate lodging segmentation approach based on an improved YOLOv11n-seg architecture was proposed. The proposed method is specifically designed to enhance fine-grained feature sensitivity, multi-scale representation capability, and boundary precision, while markedly reducing parameter count,giga floating-point operations (GFLOPs), and model size. [Methods] The proposed architecture integrated targeted modifications across the backbone, neck, and output stages. In the backbone, standard C3k2 modules were replaced with C3k2_SDW blocks, which combined a StarBlock structure with depthwise separable convolutions to reduce redundancy and computation without sacrificing spatial and contextual representational capacity. To counteract potential reductions in channel discrimination resulting from light-weighting, a multi-scale efficient channel attention (MS-ECA) mechanism was embedded within selected backbone layers, yielding C3k2_SDW_MS-ECA modules. These modules incorporated parallel convolution branches with varying kernel sizes to capture channel-wise dependencies across multiple receptive fields, thereby adaptively recalibrating lodging-related features with minimal computational overhead. In the neck, a bidirectional feature pyramid network (BiFPN) was introduced to facilitate efficient bidirectional information exchange between scales. By assigning normalized, trainable fusion weights, the BiFPN adaptively balanced contributions from low- and high-level feature maps, while a multi-stage semantic fusion strategy further enriched the integration of spatial details and contextual semantics, thereby improving the detection of small and fragmented lodged patches. At the output stage, a boundary refinement procedure was applied to the predicted masks, improving contour sharpness, enhancing boundary compactness, and mitigating false detections in complex visual environments.The experimental dataset comprised unmanned aerial vehicle (UAV) RGB imagery at a resolution of 4 032×2 268 pixels, acquired from flax fields in Dingxi, Gansu province. Lodged regions were manually annotated with polygonal masks. To increase robustness against variability in illumination, background complexity, and lodging morphology, data augmentation techniques—including random rotation, brightness and contrast adjustment, and blurring—were employed, expanding the dataset to 3 852 images. The dataset was divided into training, validation, and testing subsets in a 75%, 15% and 10% split. Model training was conducted with 640×640 pixel inputs for 300 epochs using stochastic gradient descent (initial learning rate 0.01, momentum 0.937, weight decay 0.0005) in PyTorch 2.0.0. Evaluation involved comparison with YOLACT, YOLOv7-seg, YOLOv8n-seg, and the original YOLOv11n-seg using precision (P), recall (R), mAP@0.5, mAP50–95, parameter count, GFLOPs, and model size. [Results and Discussions] Ablation experiments demonstrated the incremental contributions of each architectural component. Substituting C3k2 with C3k2_SDW reduced parameters from 2.83 M to 2.14 M and computation from 10.2 to 8.1 GFLOPs, with slight performance improvements. Incorporating BiFPN further lowered complexity to 1.68 M parameters and 7.7 GFLOPs, accompanied by notable gains in detection metrics. The addition of MS-ECA attention achieved the highest performance, delivering P of 92.6%, R of 92.0%, and mAP@0.5 of 95.2%, corresponding to improvements of 3.7 percentage points in Precision and 2.1 percentage points in mAP@0.5 over the YOLOv11n-seg baseline, without increasing model size. Qualitative Grad-CAM visualizations revealed more precise focus on lodging regions and reduced false activations in upright stems and non-lodged soil areas. Generalization capability was further validated on the public WE3DS agricultural segmentation dataset, where the proposed model achieved average improvements of 4.3, 1.9, and 2.6 percentage points in precision, recall, and mAP@0.5, respectively, compared to the baseline. [Conclusions] The improved YOLOv11n-seg architecture achieves a superior balance between accuracy and efficiency for flax lodging segmentation. By combining the C3k2_SDW_MS-ECA backbone, BiFPN with multi-stage semantic fusion in the neck, and output boundary refinement. This combination of high accuracy, lightweight design, and robust boundary delineation renders the model highly applicable to real-time, in-field deployment for intelligent lodging monitoring and precision agriculture. The results further suggest that the approach is transferable to broader agricultural segmentation tasks, providing a practical and scalable solution for modern smart farming applications.

[Objective] Maize leaf biomass is a key trait that reflects plant morphology, growth vigor, and physiological processes including photosynthetic production. Its dynamic changes can effectively characterize the growth status of maize. Accurate estimation of maize leaf biomass is crucial for accurately predicting maize yield and informing production management decisions. Extensive research on crop biomass estimation indicates that 3D point cloud data characterizing crop morphological structure, along with features derived therefrom, exhibit an extremely high correlation with crop biomass. However, traditional biomass prediction studies focus primarily on the population canopy scale, and lack effective prediction methods for biomass at the plant and organ scales. Research on non-destructive measurement methods for maize leaf biomass, based on 3D point clouds and machine learning, is conducted to address the demand for rapid acquisition of organ-level biomass information in maize cultivation and management research. [Methods] Maize leaf point cloud data were acquired using three techniques: multi-view stereo (MVS), LiDAR scanning, and 3D digitalization (DT). The leaf point clouds underwent preprocessing steps that included plant segmentation, denoising, mesh refinement, and uniform subsampling. Subsequently, morphological traits were extracted from the processed data, including leaf length, leaf area, bounding box dimensions, and the number of points contained within the leaf point clouds. Three machine learning methods—random forest (RF), gradient boosting regression tree (GBRT), and support vector regression (SVR)—as well as two deep learning methods—convolutional neural network (CNN) and fully connected neural network (FCNN)—were employed for predicting maize leaf dry weight. A point cloud-based maize leaf biomass prediction model was subsequently developed. This study utilized the mean squared error reduction method inherent to RF and the cumulative improvement method based on decision tree splits in GBRT to rank and visualize feature importance for optimal models. The resulting rankings were then visualized. Simultaneously, Pearson correlation analysis was used to analyze the correlations of the features from the fused dataset (integrating data from the three devices) as well as those from the DT data with maize leaf biomass. [Results and Discussions] The results demonstrated that, among the biomass prediction models developed in this study, the model based on Laser point cloud data and the FCNN method achieved the highest accuracy, with a mean absolute error (MAE) of 0.08 g, a mean absolute percentage error (MAPE) of 4.60%, a root mean square error (RMSE) of 0.10 g, and a coefficient of determination (R²) of 0.98. In the correlation analysis, the leaf area exhibited the strongest correlation with biomass (r = 0.92), followed by the number of points (r = 0.88), leaf width (r = 0.86), and leaf length (r = 0.77). In the feature importance ranking, the leaf area trait consistently ranked within the top two positions, whereas the number of points traits ranked among the top three in most cases. However, features such as the height of the leaf base from the ground, the horizontal distances from the leaf tip and apex to the stem, and the azimuth angle demonstrated low correlations with biomass and low feature importance. [Conclusions] Among all the maize leaf features investigated in this study, size-related traits (such as leaf area, point count, leaf length, and leaf width) had the greatest impact on the accuracy of biomass estimation. The utilization of high-resolution 3D point clouds of maize leaves, combined with machine learning methods, enabled a high-accuracy estimation of leaf dry weight and provided a novel approach for the non-destructive measurement of biomass in crop organs.

[Objective] Preventing and containing leaf diseases is a critical component of tea production, and accurate identification and localization of symptoms are essential for modern, automated plantation management. Field inspection in tea gardens poses distinctive challenges for vision-based algorithms: targets appeared at widely varying scales and morphologies under complex backgrounds and unfixed acquisition distances, which easily misled detectors. Models trained on standardized datasets with uniform distance and background often underperform, leading to false alarms and missed detections. To support method development under realistic constraints, YOLO-SADMFA (YOLO Switchable Atrous Dynamic Multi-scale Frequency-aware Adaptive), a detector based on the YOLOv11n backbone was proposed. The architecture aims to preserve fine details during repeated re-sampling (down- and up-sampling), strengthen modeling of lesions at varying scales, and refine multi-scale feature fusion. [Methods] The proposed architecture incorporates additional convolutional, feature extraction, upsampling, and detection head stages to better handle multi-scale representations, and introduces a DMF-Upsample (Dynamic Multi-scale Frequency-aware Upsample) module that performs upsampling through multi-scale feature analysis and dynamic frequency adjustment fusion. This module enables efficient multi-scale feature integration while effectively mitigating information loss during up- and down-sampling. Concretely, the DMF-Upsample analyzed multi-frequency responses from adjacent pyramid levels and fused them with dynamically learned frequency-selective weights, which preserved high-frequency lesion boundaries and textures while retaining low-frequency contextual structure such as leaf contours and global shading. A lightweight gating mechanism estimated per-location and per-channel coefficients to regulate the contribution of different bands, and a residual bypass preserved identity information to further reduce aliasing and oversmoothing introduced by repeated resampling. Furthermore, the baseline C3k2 block is replaced with a switchable atrous convolution (SAConv) module, which enhances multi-scale feature capture by combining outputs from different dilation rates and incorporates a weight locking mechanism weight-locking mechanism to improve model stability and performance. In practice, the SAConv aggregated parallel atrous branches at multiple dilation factors through learned coefficients under weight locking, which expanded the effective receptive field without sacrificing spatial resolution and suppressed gridding artifacts, while incurring modest parameter overhead. Lastly, an adaptive spatial feature fusion (ASFF) mechanism is integrated into the detection head, forming an ASFF-Head that learns spatially varying fusion weights across different feature scales, effectively filters conflicting information, and strengthens the model's robustness and overall detection accuracy. Together, these components formed a deeper yet efficient multi-scale pathway suited to complex field scenes. [Results and Discussions] Compared with the original YOLOv11n model, YOLO-SADMFA improved precision, recall, and mAP by 4.4, 8.4, and 3.7 percent points, respectively, indicating more reliable identification and localization across diverse field scenes. The detector was particularly effective for multi-scale targets where the lesion area occupied approximately 10%~65% of the image, reflecting the variability introduced by unfixed acquisition distance during tea garden patrols. Under low illumination and in complex backgrounds with occlusions and clutter, it maintained stable performance, reduced both missed detections and false alarms, and effectively distinguished disease categories with similar morphology and color. On edge computing devices, it sustained about 161 FPS, which met real-time requirements for mobile inspection robots and portable systems. These outcomes demonstrated strengthened robustness to background interference and improved sensitivity at extreme scales, which was consistent with practical demands where the acquisition distance was not fixed. From an ablation perspective, DMF-Upsample preserved high-frequency lesion boundaries while retaining low-frequency structural context after resampling, SAConv expanded receptive fields through multi-dilation aggregation under a weight-locking mechanism, and the ASFF-Head mitigated conflicts among feature pyramids. Their combination yielded cumulative gains in stability and accuracy. Qualitative analyses further supported the quantitative results: boundary localization improved for small, speckled lesions, large blotches were captured with fewer spurious edges, and distractors such as veins, shadows, and soil textures were less frequently misclassified, confirming the benefits of dynamic multi-scale frequency-aware fusion and adaptive spatial weighting in real field conditions. [Conclusions] The proposed YOLO-SADMFA effectively addressed the multi-scale disease detection challenge in complex tea garden environments, where acquisition distance was not fixed, lesion morphology and color were diverse, and cluttered backgrounds easily caused misjudgments and omissions. It significantly improved detection accuracy and robustness relative to the original YOLOv11n model across a wide range of target scales, and it maintained stable performance under low illumination and complex backgrounds typical of field inspections. It provided reliable technical support for automated tea leaf disease inspection systems by enabling accurate localization and identification of lesions in real operating conditions and by sustaining real-time inference on edge devices suitable for patrol-style deployment. It therefore had important application value for promoting the intelligent development of the tea industry, supporting large-scale, standardized, and continuous field monitoring and management, and offering a practical foundation for engineering implementation and subsequent optimization in real-world tea plantations.

[Objective] Rice-crayfish co-culture represents a highly efficient ecological agricultural system that simultaneously provides substantial economic returns and ecological benefits. Accurately obtaining spatial distribution information on rice-crayfish fields is of great importance for promoting the optimal allocation of agricultural resources, supporting ecological protection, and facilitating sustainable agricultural development. In regions characterized by complex terrain, fragmented plots, and diverse planting structures, traditional extraction approaches are often influenced by spectral confusion and indistinct field boundaries, making it difficult to achieve high-precision identification of rice-crayfish fields. The aim is to develop a deep learning model that integrates multi-temporal and multi-scale feature information to improve the accuracy of remote sensing identification of rice-crayfish fields. [Methods] Multi-temporal GF-2 satellite imagery was employed as the primary data source, and a deep learning-based extraction method named the dual-branch attention pyramid Network (DBAP-Net) was developed. The proposed model was established upon the U-Net framework and designed with a dual-branch encoder architecture to extract temporal features from different phenological stages, thereby fully capturing the spectral variations of rice-crayfish fields between the paddy flooding and rice-growing periods. The convolutional block attention module (CBAM) was embedded in each encoder layer and in the skip connections to adaptively adjust feature weights along both the channel and spatial dimensions, enhancing the network's capacity to emphasize critical spatial structures of rice-crayfish fields while effectively suppressing background noise and redundant information. During the feature fusion stage, an atrous spatial pyramid pooling (ASPP) module was incorporated to aggregate contextual information from multiple receptive fields through multi-scale atrous convolutions, improving the model's capability for multi-scale spatial information representation. [Results and Discussions] A quantitative performance evaluation of each DBAP-Net component was conducted through ablation experiments. The results demonstrated that the introduction of a dual-branch structure improved overall accuracy (OA), F₁-Score, Intersection over Union (IoU), and Matthews Correlation Coefficient (MCC) by 0.48, 0.57, 0.94, and 0.99 percentage points, respectively. Incorporating the CBAM module further enhanced these metrics by 0.52, 0.85, 1.40, and 1.20 percentage points, while the addition of the ASPP module yielded further increases of 0.59, 1.09, 1.81, and 1.49 percentage points, respectively. The DBAP-Net model achieved the highest comprehensive performance, with an OA of 94.45%, F₁-Score of 91.79%, IoU of 84.82%, and MCC of 87.60%. These values represented respective improvements of 1.83, 2.55, 4.25, and 3.98 percentage points compared with the baseline U-Net model. These findings indicated that each enhancement module made a substantial contribution to improving both feature representation and spatial boundary delineation. DBAP-Net was further compared with five representative semantic segmentation networks—U-Net, PSPNet, DeepLabV3+, SegFormer, and TransUNet—to comprehensively evaluate its generalization and segmentation performance. The results demonstrated that DBAP-Net consistently achieved higher overall accuracy, precision, F₁-Score, and IoU than all other comparison models. Specifically, compared with U-Net, PSPNet, and DeepLabV3+, the F₁-Scores increased by 2.55, 2.98, and 2.75 percentage points, while the IoU values improved by 4.25, 4.96, and 4.57 percentage points, respectively. In comparison with the more recent models, SegFormer and TransUNet, DBAP-Net's F₁-Score was higher by 3.09 and 1.67 percentage points, and its IoU was enhanced by 5.12 and 2.81 percentage points. Visualization of the segmentation results further revealed that DBAP-Net produced clear segmentation boundaries with complete fields, significantly reducing misclassification and omission rates. In contrast, other models exhibited varying degrees of boundary blurring and fragmentation. When applied across the entire study area, DBAP-Net demonstrated strong robustness and stability. The model achieved an overall accuracy of 96.00%, with a Kappa coefficient of 0.920. The producer's accuracy and user's accuracy were 95.13% and 97.38%, respectively. Compared with traditional approaches such as the seasonal water-difference method, random forest classification, and temporal index thresholding, DBAP-Net significantly improved both extraction precision and spatial completeness, particularly under conditions of complex terrain and fragmented agricultural landscapes. [Conclusions] DBAP-Net, by integrating multi-temporal spectral and multi-scale spatial information, significantly improves the accuracy and completeness of remote sensing extraction of rice-crayfish fields from high-resolution remote sensing imagery. The model provides a reliable and adaptable technical framework for fine-scale monitoring, precise mapping, and sustainable management of rice-crayfish co-culture systems, offering valuable methodological support for agricultural resource assessment and ecological protection.

Objective With the rapid advancement of agricultural modernization and unmanned operations, traditional harvesting processes in large-scale farms still suffer from low scheduling efficiency, uneven workload distribution, and suboptimal path planning. These limitations hinder the realization of intelligent and efficient agricultural production. Multi-machine collaborative operation scheduling and planning have become key technologies in intelligent farming management, aiming to optimize task allocation and path planning among multiple harvesters under time window and workload balance constraints. However, such problems belong to complex combinatorial optimization categories characterized by high dimensionality and nonlinearity. Conventional genetic algorithms (GA) often exhibit premature convergence and weak local search capabilities, resulting in suboptimal scheduling schemes. To address these challenges, this study focused on the collaborative harvesting operations of multiple combine harvesters across several fields and proposed an improved multi-traveling salesman problem genetic algorithm (IMTSP_GA) for integrated multi-machine scheduling and path planning. Methods A multi-machine cooperative scheduling model was constructed with the objective of minimizing the total operational time of all harvesters while considering time window and load-balancing constraints. The problem was modeled as a multi-traveling salesman problem (MTSP), in which each harvester was regarded as a traveling salesman responsible for a subset of field tasks. To solve the model, the proposed IMTSP_GA adopted a two-layer chromosome encoding structure: the first layer represented the visiting sequence of all task units, and the second layer defined the segmentation positions that allocated tasks to different machines, thereby forming feasible multi-harvester operation routes. To ensure both initial solution quality and population diversity, a hybrid initialization strategy combining sequential and random initialization was designed. Furthermore, a Q-learning-based adaptive mutation mechanism was introduced into the genetic operation process. By constructing a state–action–reward model based on the variation trend of fitness values, the algorithm dynamically selected mutation operators according to their historical performance, thus balancing global exploration and local exploitation. The overall process included chromosome encoding, fitness evaluation, group-based selection, crossover and mutation operations, and Q-learning-driven adaptive control. Based on the optimized scheduling scheme, the full-path planning for each harvester was divided into two stages: (1) in-field path planning, which used an internal spiral coverage method to reduce turning frequency and non-working time; and (2) road network path planning, which employed the Dijkstra algorithm to obtain globally shortest travel routes between fields. Results and Discussions A total of 25 farmlands were divided into 49 task units, and four John Deere 3588 harvesters were used for the simulation. Comparative experiments were performed among IMTSP_GA, standard GA, particle swarm optimization (PSO), and ant colony optimization (ACO). The results showed that the IMTSP_GA significantly outperformed other algorithms in terms of total operation time, convergence speed, and computational efficiency. Specifically, the total operational time was reduced by 4.48%, 5.32%, and 9.87% compared with GA, PSO, and ACO, respectively. The average runtime was 5.82 s, which was substantially shorter than GA (11.55 s) and PSO (10.70 s). The algorithm exhibited fast early convergence and effectively avoided premature stagnation. To further evaluate generalization capability, five classical traveling salesman problem (TSP) datasets—Berlin52, Eil76, Bier127, CH150, and KroB200—were tested. IMTSP_GA consistently achieved superior average solutions and shorter runtimes across all datasets, confirming its robustness and adaptability to different problem scales and complexities. Finally, full-process path planning was visualized based on the optimized scheduling results. The generated harvester routes were continuous and compact, ensuring reasonable task allocation and efficient transitions between fields, thereby validating the effectiveness of the proposed model and algorithm. Conclusions By integrating a Q-learning-based adaptive mutation mechanism, IMTSP_GA autonomously selects effective mutation strategies to enhance search performance and convergence stability. Meanwhile, the hybrid initialization strategy maintains population diversity and improves the quality of initial solutions. Simulation results verify that IMTSP_GA surpasses traditional GA, PSO, and ACO in solution quality, convergence performance, and computational efficiency. The method effectively reduces total operation time, optimizes harvester task allocation, and improves the coordination and efficiency of multi-machine operations. In future work, the research will be extended to more complex scenarios involving multi-region cooperation, task prioritization, and dynamic environmental factors. Reinforcement learning and online optimization techniques will be incorporated to achieve real-time scheduling and intelligent decision-making, thereby enhancing the adaptability and engineering applicability of the proposed method in large-scale intelligent agricultural systems.

[Objective] The YOLO-MDFR (You Only Look Once Version 12-MDFR), a lightweight detection algorithm was proposed based on an enhanced YOLOv12m, specifically designed for accurate identification of pepper leaf diseases and pests in complex natural environments. Pepper cultivation frequently faces challenges from diseases and pests, and early detection is critical for reducing yield losses. However, existing detection models often suffer from limitations such as insufficient feature extraction for subtle lesions, loss of edge information due to complex backgrounds, and high missed detection rates for small lesions. To address these issues, the model was systematically improved in three key aspects: backbone lightweighting, attention mechanism enhancement, and optimized multi-scale feature fusion, aiming to balance detection accuracy, model compactness, and real-time performance for field applications. ​ [Methods] The dataset was established in the primary pepper cultivation zone of Gangu County, Tianshui City, Gansu Province. The cultivated variety was the locally dominant Capsicum annuum L. var. conoides (Mill.). Data collection was conducted from March 15 to May 20, 2024. The collected samples included four categories of pepper leaves: healthy leaves, leaves damaged by thrips, leaves infected with tobacco mosaic virus exhibiting yellowing symptoms, and leaves affected by bacterial leaf spot. First, the original YOLOv12m backbone was replaced with an improved MobileNetV4 architecture to enhance lightweight performance while preserving feature extraction capability. Specifically, the original 5×5 standard convolutions in the bottleneck layers of MobileNetV4 were substituted with two sequential 3×3 depthwise separable convolutions. This design was based on the principle that two 3×3 convolutions achieve an equivalent receptive field (matching the 5×5 coverage) while reducing parameter count—depthwise separable convolutions further decompose spatial and channel convolution, minimizing redundant computations. Second, a novel Dimensional Frequency Reciprocal Attention Mixing Transformer (D-F-Ramit) module was introduced to enhance sensitivity to lesion boundaries and fine-grained textures. The module first converted feature maps from the spatial domain to the frequency domain using discrete cosine transform (DCT), capturing high-frequency components often lost in spatial-only attention. It then integrated three parallel branches: channel attention, spatial attention, and frequency-domain attention. Finally, a Residual Aggregation Gate-controlled Convolution (RAGConv) module was developed for the neck network. This module included a residual aggregation path to collect multi-layer feature information and a gate control unit that dynamically weighted feature components based on their relevance. The residual structure provided a direct gradient propagation path, alleviating gradient vanishing during backpropagation and ensuring efficient information transfer during feature fusion. A systematic experimental framework was established to comprehensively evaluate model performance: (1) Ablation studies were conducted using a controlled variable approach to verify the individual contributions of the improved MobileNetV4, D-F-Ramit, and RAGConv modules; (2) Lesion scale sensitivity analysis assessed detection performance across different lesion sizes, with emphasis on small-spot recognition; (3) Resolution impact analysis evaluated five common input resolutions (320×320–736×736) to explore the trade-offs among accuracy, speed, and computational efficiency; and (4) Embedded deployment validation involved model quantization and implementation on the Rockchip RK3588 platform to measure inference speed and power consumption on edge devices. [Results and Discussion] YOLO-MDFR achieved a mAP@0.5 of 95.6% on this dataset. Compared to YOLOv12m, it improved accuracy by 2.0%, reduced parameters by 61.5%, and lowered computational complexity by 68.5%. Real-time testing showed 43.4 FPS on an NVIDIA RTX 4060 GPU (CUDA 12.2) and 22.8 FPS on a Rockchip RK3588 embedded platform with only 3.5 W power consumption—suitable for battery-powered field devices. Lesion-scale analysis revealed 33.5% accuracy for <16×16 pixel lesions critical for early detection. Confusion matrix evaluation reduced misclassification, bacterial leaf spot/thrips damage misrates fell from 5.8% to 2.1%, and tobacco mosaic virus/healthy leaves from 3.2% to 1.5%, resulting in an overall 2.3% misrate. Experiments across varying input resolutions revealed a clear performance–resolution trade-off. As resolution increased from 320×320 to 736×736, mAP rose from 89.5% to 96.2%, showing diminishing returns beyond 512×512. Concurrently, computational cost grew roughly quadratically, reducing inference speed from 65.2 FPS to 35.1 FPS. [Conclusion] This study presents YOLO-MDFR, a lightweight detection model for identifying pepper leaf diseases and pests under complex natural conditions. By integrating an improved MobileNetV4 backbone, a multi-dimensional frequency reciprocal attention mixing transformer (D-F-Ramit), and a residual aggregation gate-controlled convolution (RAGConv) module, YOLO-MDFR outperforms mainstream detection models in both accuracy and efficiency. Systematic deployment experiments yielded optimized configurations for different application scenario. Despite its strong performance, the model shows limitations in robustness under extreme lighting, generalization to emerging diseases, and detection of small targets under occlusion. Future work will address these issues through ambient light data fusion, domain adaptation with semi-supervised learning, and binocular vision integration.

[Objective] With the widespread availability of high-resolution and multi-source remote sensing data, remote sensing-based crop classification has played an increasingly vital role in agricultural monitoring, yield estimation, and land use management. However, traditional pixel-level classification methods often struggle to achieve stable, high-precision classification under conditions of intra-plot heterogeneity, spectral confusion, and noise interference.Therefore, this study aimed to improve parcel-level crop classification accuracy and spatial consistency by constructing a multi-source feature fusion and ensemble learning framework, which exploits complementary spectral, spatial, temporal, and productivity characteristics to enhance robustness and generalization in multi-crop classification tasks. [Methods] To enhance field-level classification accuracy and spatial consistency, this study proposed and validated a crop classification method integrating field-scale feature extraction, feature selection, and Stacking ensemble learning. This approach aimed to fully leverage the complementarity of spectral, spatial, and temporal information through feature engineering and model fusion. The study area was located in Feicheng city, Shandong province. The data included multi-temporal Sentinel-2 optical imagery, Sentinel-1 SAR data, Gaofen remote sensing imagery, and parcel vector samples with crop_type attributes. All imagery underwent radiometric and atmospheric correction, projection registration, and cropping during preprocessing to ensure spatial consistency and temporal correspondence across sensors. The dataset was constructed at the field level, comprising 3 200 fields for model training and independent validation. This study systematically constructed four types of meta-features on the plot scale: First, raw bands and vegetation indices; second, spatial meta-features, including texture, morphology, and structural indicators calculated from high-resolution imagery to reflect internal spatial heterogeneity; third, temporal sequence meta-features, extracting vegetation indices, backscatter, and harmonic/temporal statistics from multi-temporal optical and SAR imagery to characterize crop growth cycles; fourth, crop primary productivity features to highlight differences in carbon fixation and biomass accumulation among crops. Subsequently, for the high-dimensional, multi-source feature set, a combined strategy of LightGBM and recursive feature elimination (RFE) was employed for feature importance assessment and selection. This retained a subset of features most critical to classification, enhancing model generalization and computational efficiency. Within the classification framework, a Stacking-based ensemble learning model was constructed. Base learners included random forest (RF), eXtreme gradient boosting (XGB), support vector machine (SVM), gradient boosting (GB), categorical boosting (CatBoost), adaptive boosting (ADA), back propagation (BP), K-nearest neighbors (KNN), and light gradient boosting machine (LightGBM). These base models learn and represent plot features from distinct perspectives, fully exploring nonlinear relationships among spectral, spatial, and temporal characteristics. During the meta-learner selection phase, to compare the impact of different feature fusion strategies on classification performance, XGBClassifier, LGBMClassifier, MLPClassifier, and LogisticRegression were selected as meta-learners for experimental comparison. By contrasting the classification outcomes of different meta-models under the same base model outputs, their contributions to improving feature fusion accuracy and stability differences were analyzed. During model training, hierarchical cross-validation was employed to mitigate bias caused by class imbalance. Overall accuracy (OA), Kappa coefficient, and F₁-Score served as primary evaluation metrics, while recall and precision rates for each crop category underwent systematic analysis. [Results and Discussions] The findings indicated that feature selection significantly impacted classification performance. By integrating LightGBM with feature selection strategies, a subset of 102 optimal features was identified. This subset included gross primary production (GPP), spectral features, vegetation indices, textural features, temporal features, and harmonic features. This approach effectively mitigated feature redundancy and multicollinearity issues, enhancing model stability and generalization capability. Among these, GPP-related features and vegetation indices from key growth stages (e.g., TVI, RDVI, GNDVI) demonstrated high discriminative power in distinguishing crop categories, fully reflecting the close coupling between remote sensing features and crop phenological information. The Stacking ensemble strategy demonstrated outstanding classification performance. Among various meta-learners, the Stacking model with XGBClassifier as the final learner achieved the highest classification accuracy (OA = 95.66%, Kappa = 0.900 6), showcasing exceptional ensemble generalization capability. It performed particularly well in identifying major crops like maize while maintaining good adaptability for less common crops. The method's advantage extended beyond accuracy gains to its comprehensive integration of complementary spectral, temporal, and spatial feature processing capabilities across base learners. The meta-learner adaptively synthesized multi-model outputs, enhancing classification stability and spatial consistency. Compared to traditional pixel-level classification followed by parcel reclassification, direct feature extraction and classification based on vector parcels effectively avoided edge blending and noise interference inherent in pixel-level methods, significantly improving parcel recognition stability and accuracy. Experimental results demonstrated that parcel-level classification outperformed pixel-level strategies in overall accuracy and Kappa coefficient, with superior spatial consistency and robustness in classification outcomes. [Conclusions] The "optimal feature subset + Stacking ensemble learning + parcel-level classification" method developed in this study demonstrates outstanding accuracy and stability in multi-source remote sensing crop identification, providing an efficient and feasible technical pathway for parcel-level classification in complex agricultural landscapes. Future work will integrate high-resolution time-series data with deep learning models to further enhance the method's cross-regional adaptability and crop monitoring capabilities.

[Objective] The leaf area index (Leaf Area Index, LAI) is a key biophysical parameter that reflects the canopy structure and photosynthetic capacity of crops. However, the inversion of winter wheat LAI from remote sensing data is often constrained by the limited availability of field measurements, leading to insufficient model generalization. Although radiative transfer model (RTM)-based simulations can expand the sample size, discrepancies between simulated and observed spectra persist due to simplified canopy and soil parameterizations. Conversely, purely data-driven generative models such as generative adversarial networks (GANs) can enhance sample diversity but often produce physically inconsistent samples in the absence of biophysical constraints. To address these issues, a physics-constrained PROSAIL-cGAN (conditional generative adversarial network) spectral sample augmentation method is proposed that integrates the PROSAIL model with cGAN to improve the accuracy and robustness of LAI inversion under small-sample conditions. The objective was to generate physically realistic spectral–parameter pairs and provide reliable data support for remote sensing–based monitoring of winter wheat growth. [Methods] The study area was located in Zouping city, Shandong province, a major winter wheat production region within the Huang-Huai-Hai Plain. A total of 133 field samples were collected during the jointing stage in April 2025 using an LAI-2200C canopy analyzer, with synchronous canopy spectra acquired simultaneously. A Sentinel-2A Level-2A image from April 15, 2025, served as the remote sensing source, comprising 13 bands resampled to a spatial resolution of 10 m. The dataset was divided into training (70%) and validation (30%) subsets, with LAI values ranging from 1.646 to 7.505 The proposed method combined the PROSAIL radiative transfer model with a conditional GAN framework. First, PROSAIL was employed to simulate canopy reflectance and corresponding biophysical parameters, including chlorophyll content (C_ab), carotenoid content (C_ar), brown pigment content (C_brown), equivalent water thickness (C_w), dry matter content (C_m), LAI, and leaf inclination distribution (LIDFa). A multi-layer perceptron (MLP) surrogate model was then trained to approximate the forward mapping of PROSAIL, enabling differentiability for integration with deep learning architectures. The cGAN generator received random noise and physical parameters as conditional inputs to produce corresponding canopy reflectance, while the discriminator jointly evaluated authenticity and physical consistency. During adversarial training, physical constraints were incorporated into the generator's loss function to ensure biophysical realism. The generated samples were subsequently filtered based on parameter ranges and discriminator confidence scores. Kernel density overlap between real and generated LAI distributions was used to quantify their statistical consistency. Finally, the enhanced dataset was used to train random forest (RF) and extreme gradient boosting (XGBoost) regression models for the LAI inversion. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE), and compared with three baselines: 1) field-measured modeling, 2) the PROSAIL lookup table (LUT) method, and 3) cGAN-only augmentation. [Results and Discussions] The surrogate MLP model accurately reproduced PROSAIL-simulated spectra, achieving R²=0.817, RMSE=0.008 5, and MAE=0.005 5, confirming its feasibility as a differentiable physical proxy. The cGAN-based augmentation achieved an LAI distribution overlap of 0.806 with the measured samples, whereas the PROSAIL-cGAN improved the overlap to 0.827, demonstrating enhanced physical realism and sample diversity. Model comparisons revealed substantial differences in performance. The LUT-based inversion yielded only R²=0.353 0 and RMSE=1.284 0, reflecting its limited adaptability to spectral heterogeneity. Direct regression using field data improved accuracy (R²=0.680 1 for XGBoost and 0.648 8for RF). Incorporating cGAN-generated samples further enhanced model accuracy (R²=0.745 0 for RF and 0.739 0 for XGBoost). The PROSAIL-cGAN–enhanced RF model achieved the best overall performance, with R²=0.848 8, RMSE=0.540 9, and MAE=0.293 7 The sample-size sensitivity analysis demonstrated that as the number of field samples increased from 27 to 106, R² improved from 0.546 2 to 0.848 8 and RMSE decreased from 1.024 3 to 0.540 9. When the sample size exceeded 79, model performance stabilized, indicating strong robustness. Spatial mapping results showed that LAI values were higher in the central and northern regions (4–7) and lower in the southern mountainous areas (1.5–4), consistent with variations in soil fertility and field management practices. These findings validate the model's applicability for regional-scale monitoring of crop growth. [Conclusions] This study developed a physics-constrained PROSAIL-cGAN spectral sample augmentation method for winter wheat LAI inversion. By integrating a radiative transfer model, a conditional generative network, and a differentiable surrogate, the method effectively generated physically consistent and diverse spectral–parameter samples under small-sample conditions. The PROSAIL-cGAN–based RF model achieved a relatively high inversion accuracy, outperforming traditional LUT and field-only approaches. The proposed method successfully mitigated small-sample limitations, ensured physical interpretability, and improved model generalization. It provides a robust framework for the remote sensing inversion of crop canopy parameters, supporting precision agriculture and dynamic monitoring of crop growth. Future work will focus on optimizing sample generation strategies, integrating multi-temporal satellite data and additional physiological parameters, and coupling with deep or semi-supervised learning techniques to further enhance scalability and applicability across crops and regions.

[Objective] Soybean stands as one of the most crucial global crops, serving as a vital source of plant-based protein and vegetable oil while playing an indispensable role in sustainable agricultural systems and global food security. Accurate and timely mapping of soybean cultivation areas is essential for agricultural monitoring, policy-making, and precision farming. However, existing remote sensing methods for soybean identification—such as threshold-based approaches, traditional machine learning, and deep learning—often face challenges related to model complexity, computational efficiency, and interpretability. These limitations collectively highlight the pressing need for a methodological solution that maintains classification accuracy while simultaneously offering computational efficiency, operational simplicity, and interpretable results—a balance crucial for effective agricultural monitoring and policy-making. To address these limitations, a lightweight and interpretable soybean mapping framework is proposed based on Sentinel-2 imagery and a binary logistic regression model. [Methods] Six representative agricultural regions within the primary U.S. soybean production belt were selected to capture the diversity of cultivation practices and environmental conditions across this major production area. The analysis utilized the complete growing season (April-October) Sentinel-2 satellite imagery from 2021 to 2023. The USDA's Cropland Data Layer served as reference data for model training and validation, benefiting from its extensive ground verification and statistical rigor. All Sentinel-2 imagery underwent rigorous preprocessing including atmospheric correction using the SEN2COR processor, cloud and shadow masking with the Scene Classification Layer, and spatial subsetting to the regions of interest. The Jeffries-Matusita distance was employed as a quantitative metric to objectively identify the optimal temporal window for soybean discrimination.. This statistical measure evaluated the separability between soybean and other major crops across the growing season, with calculations performed on 10 d composite periods to ensure data quality and temporal consistency. The analysis revealed that late July to mid-September (Day of Year 210–260) provided maximum spectral separability, corresponding to the soybean's critical reproductive stages (pod setting and filling) when its spectral signature becomes most distinct from other crops, particularly in short-wave infrared regions sensitive to canopy structure and water content. Within this optimally identified window, a binary logistic regression model was implemented that treated soybean identification as a probabilistic classification problem.. The model was trained using spectral features from the optimal period through maximum likelihood estimation, creating a computationally efficient framework that required optimization of only a limited number of parameters while maintaining physical interpretability through explicit feature coefficients. [Results and Discussion] The comprehensive evaluation showed that the integrated approach balanced classification performance and operational practicality optimally. The temporal optimization identified late July to mid-September as the peak discriminative period, which matches soybean's reproductive phenological stages (when its canopy spectral characteristics differ most from other crops). This finding was consistent across three study years and multiple regions, verifying the robustness of the data-driven window selection. The binary logistic regression model, trained on features from this optimal period, performed excellently: in the 2022 model construction region, it achieved 0.90 overall accuracy and 0.79 Kappa coefficient. When applied to independent validation regions in the same year, it maintained strong performance (0.88 overall accuracy, 0.76 Kappa) without region-specific parameter adjustments, demonstrating outstanding spatial transferability.Temporal validation further confirmed the model's robustness: across the 2021 to 2023 study period, it maintained consistent performance across all regions, with an average accuracy of 0.87 and Kappa of 0.76. This inter-annual stability is notable—despite potential variations in annual weather, management practices, and planting schedules—and highlights the advantage of basing the model on a stable phenological period rather than fixed calendar dates.The model's lightweight architecture offered practical benefits: compared with complex ensemble or deep learning methods, it only requires optimizing a limited number of parameters. This parsimonious structure enhances computational efficiency, enabling rapid training and deployment over large areas while reducing reliance on extensive labeled datasets—a key advantage in regions lacking sufficient ground truth data. Beyond accuracy and efficiency, the model exhibited exceptional interpretability via its probabilistic framework and transparent feature weighting. Coefficient analysis provided quantifiable insights into feature contributions, revealing that short-wave infrared bands and specific vegetation indices had the highest discriminative power during the optimal temporal window. [Conclusion] This study presents an effective soybean mapping approach that balances accuracy with operational practicality through the strategic combination of temporal optimization and binary logistic regression. The method offers a viable solution for operational agricultural monitoring, especially in resource-constrained environments. Future work can enhance the robustness of the model across multiple regional conditions through cross-regional validation in different climate zones and cropping systems, or by integrating transfer learning with domain adaptation methods. This will improve its potential for global-scale application. Concurrently, integrating additional data, methodologies, and models to achieve end-to-end feature learning should be considered.

[Significance] Hyperspectral imaging (HSI) is an advanced sensing technique that simultaneously acquires high-resolution spatial data and continuous spectral information, enabling non-destructive, real-time evaluation of both external and internal fruit quality attributes. Despite its widespread application in agricultural product assessment, comprehensive reviews specifically addressing fruit quality evaluation using HSI are limited. This paper presents a comprehensive review of recent advancements in the application of HSI technology for fruit quality detection. [Progress] This paper provides a comprehensive review from three key dimensions: scenario adaptability, technological evolution trends, and industrial implementation bottlenecks, with a further analysis of the research outlook in HSI applications for fruit quality assessment. Specifically, by employing non-destructive and rapid spectral imaging techniques, HSI has markedly enhanced the accuracy of assessing various quality parameters, including external appearance, surface defects, internal quality (such as sugar content, acidity, and moisture), and ripeness. Furthermore, significant progress has been achieved in utilizing HSI for disease detection, variety classification, and origin traceability, thereby providing robust technical support for fruit quality control and supply chain management. In addition, bibliometric analysis was utilized to identify key research areas and emerging trends in the application of HSI technology for fruit quality assessment. [Conclusions and Prospects] Future research should focus on optimizing spectral dimensionality reduction techniques to enhance both the efficiency and accuracy of models. Transfer learning and incremental learning approaches should also be explored to improve the models' ability to generalize across various scenarios and fruit types. In parallel, developing lightweight system hardware and strengthening edge processing capabilities will be essential for enabling the practical deployment of HSI technology in real-world applications. Integrating lightweight deep learning networks and acceleration modules will support real-time inference, enhancing processing speed and facilitating faster data analysis. It is also crucial to establish standardized systems and protocols to promote the sharing of research findings and ensure broader application across different industries. Additionally, incorporating multimodal technologies, such as thermal imaging, gas sensors, and visual data, will improve the accuracy and robustness of detection platforms. This integration will allow for more precise and comprehensive assessments of fruit quality, further advancing the digitalization and intelligent application of HSI technology. By addressing these challenges, future advancements will not only enhance the performance of models but also support large-scale, real-time deployment, providing significant benefits to agriculture and commercial sectors.

[Significance] Opto-intelligent agriculture represents an emerging paradigm that deeply integrates optical sensing and intelligent decision-making within agricultural systems, aiming to transform production from experience-based management to data-driven precision cultivation. The core of this paradigm lies in exploiting the dual role of light: As an information carrier, it enables non-destructive sensing of crop physiological states through hyperspectral imaging, fluorescence, and other optical sensors; As a regulatory factor, it allows feedback-based manipulation of the light environment to precisely regulate crop growth.This establishes a closed-loop framework of "perception-decision-execution", which substantially enhances water and fertilizer use efficiency, enables early warning of pests and diseases, and supports quality-oriented production. Nevertheless, the transition of this technology from laboratory research to large-scale field application remains challenged by unstable signals under complex environments, weak model generalization, high equipment costs, a shortage of interdisciplinary talent, and insufficient policy support and promotion mechanisms. Therefore, this paper systematically reviews the technological architecture, practical achievements, and intrinsic limitations of opto-intelligent agriculture, with the objective of providing theoretical guidance and practical directions for future development. [Progress] Opto-intelligent agriculture is evolving from isolated technological advances toward full-chain integration, characterized by significant progress in optical sensing, intelligent decision-making, and precision execution. At the optical sensing level, technological approaches have expanded from traditional spectral imaging to multi-scale, synergistic sensing networks. Hyperspectral imaging captures subtle spectral variations during the early stages of crop stress, chlorophyll fluorescence imaging enables ultra-early diagnosis of both biotic and abiotic stresses, LiDAR provides accurate three-dimensional phenotypic data, and emerging quantum-dot sensors have enhanced detection sensitivity down to the molecular scale. In terms of intelligent decision-making, recent advances focus on the deep integration of mechanistic and data-driven models, which compensates for the limited adaptability of purely mechanistic models while improving the interpretability of purely data-based ones. Through multi-source data fusion, the system jointly analyzes optical, environmental, and soil parameters to generate globally optimal strategies that balance yield, quality, and resource efficiency. At the execution stage, systems have developed into real-time feedback control loops. Dynamic light-spectrum LED systems and intelligent variable-spray drones transform decision outputs into precise actions, while continuous monitoring enables adaptive self-optimization. This mature technological chain has delivered measurable outcomes across the agricultural value chain. Integrated solutions demonstrate even greater potential. Collectively, the achievements signify the transition of opto-intelligent agriculture from conceptual exploration to practical implementation. [Conclusions and Prospects] By synergizing optical perception with intelligent decision-making, opto-intelligent agriculture is driving a fundamental transformation in agricultural production. To achieve the transition from merely usable to genuinely effective, a comprehensive advancement framework integrating technology, equipment, talent, and policy must be established. Technologically, efforts should focus on enhancing sensing stability under open-field conditions, developing lightweight and interpretable models, and promoting the domestic development of core components. From a talent perspective, interdisciplinary education and agricultural technology training must be strengthened. From a policy standpoint, improving subsidy mechanisms, digital infrastructure, and innovation-oriented dissemination systems will be essential. Looking forward, through integration with emerging technologies such as 6G communication and digital twin systems, opto-intelligent agriculture is poised to become a cornerstone for ensuring both food security and ecological sustainability.

【Objective】 Conventional steering systems for autonomous tractors often suffer from insufficient precision and robustness, making it difficult to meet the demands of high-accuracy trajectory tracking. Although single-motor steer-by-wire (SBW) systems proposed in previous studies can improve tracking precision, they are limited by restricted output torque and poor fault tolerance, especially under complex conditions such as low-speed and heavy-load operations. To address these issues, the aim is to develop a high-performance trajectory tracking control method based on a dual-motor SBW system, thereby improving tracking accuracy, robustness, and operational safety across a variety of working conditions. 【Methods】 A hierarchical control architecture was constructed in this study. At the upper level, a trajectory tracking controller was designed. Based on a two-wheel bicycle dynamic model of the tractor, a linear quadratic regulator (LQR) controller was developed to solve the optimal control problem and compute the desired front-wheel steering angle for trajectory tracking. At the lower level, a steering execution controller was developed for the dual-motor SBW system. First, a mathematical model incorporating both electrical and mechanical components of the system was established. To address nonlinearities and uncertainties, a sliding mode controller (SMC) was designed for each motor to achieve robust control. To mitigate the inherent chattering issue of SMC, a disturbance observer (DOB) was incorporated. The DOB continuously estimated the total disturbance in real time, including both external interferences and internal parameter variations, and provided feed-forward compensation. This compensation enabled a reduction in the switching gain of the SMC, thereby effectively suppressing chattering while maintaining robust performance. For dual-motor coordination, an independent control strategy was adopted, leveraging the identical parameters of both motors. Under this scheme, the two motors received the same steering angle command and operated in parallel, maintaining structural simplicity while enhancing system fault tolerance. The overall control strategy was structured as a hierarchical system, comprising an upper-level LQR trajectory tracking module and a lower-level steering execution module based on SMC+DOB. 【Results and Discussion】 To validate the effectiveness of the proposed method, both simulations and field experiments were conducted. Simulations were performed on a Matlab–Carsim co-simulation platform, specifically evaluating steering angle tracking performance and the disturbance rejection capability of the DOB. The results showed that, compared to the SMC controller without DOB, the SMC+DOB controller reduced the front-wheel steering angle tracking error by 13.12% while significantly suppressing chattering. Field experiments were carried out on a T954 tractor platform to evaluate the control strategy under realistic operating conditions. A representative working path, consisting of both straight and curved segments, was used, and tests were performed at two speeds: low speed (10 km/h) and high speed (30 km/h). To benchmark performance, three control strategies were compared: LQR+SMC, MPC+SMC+DOB, and the proposed LQR+SMC+DOB, enabling a comprehensive assessment of tracking accuracy, robustness, and real-time control effectiveness. The field experimental results show that: 1) All SBW-based methods significantly outperformed traditional steering in terms of lateral tracking error and heading error. 2) Under low-speed conditions, the proposed LQR+SMC+DOB method reduced the average lateral tracking error, heading error, sideslip angle, and front-wheel steering angle error by 19.9%, 40.2%, 6.5%, and 14.3%, respectively, compared to LQR+SMC. Under high-speed conditions, the corresponding reductions were 23.7%, 52.2%, 4.5%, and 27.3%. 3) Compared with MPC+SMC+DOB, the proposed LQR+SMC+DOB achieved similar trajectory tracking performance while significantly reducing the computation time per iteration. The above experimental results indicate that the adopted dual-motor SBW system effectively enhanced the steering output torque and system fault tolerance, thereby improving trajectory tracking accuracy and thus providing a hardware foundation for resolving the power and safety challenges in tractor heavy-load operations, and the designed SMC+DOB controller combined strong robustness with smooth control action, effectively suppressing external disturbances and markedly reducing chattering, thereby improving the accuracy and stability of steering execution. 【Conclusion】 This research successfully proposed and validated a high-precision trajectory tracking control strategy for autonomous tractors. By integrating an upper-level LQR trajectory tracking controller with a lower-level dual-motor SMC+DOB steering controller, a robust and efficient hierarchical control framework was established. This control strategy consistently exhibited excellent trajectory tracking performance and real-time capability across different speeds, offering a reliable technological solution for high-performance operations of autonomous tractors. In comparison with the MPC control strategy, the control strategy proposed in the study demonstrated exceptionally high computational efficiency, rendering it highly suitable for real-time control applications.

[Objective] Real-time and accurate quality monitoring of fruits and vegetables during cold chain logistics is of great importance for ensuring supply chain quality and reducing economic losses. However, traditional detection methods generally suffer from several core deficiencies, such as being offline, relying on unimodal information, and being unable to capture dynamic evolution. To overcome these challenges, an online freshness detection system is proposed and implemented for fruits and vegetables based on temporal multi-source information fusion. This system was designed to achieve precise online detection of fruit and vegetable freshness, providing an effective technical solution for the refined management and early spoilage warning within the cold chain supply chain, thereby significantly reducing economic losses. [Methods] A complete system was constructed composed of a lower-computer data acquisition node, an IoT cloud platform, and an upper-computer Qt client. The lower-computer synchronously collected environmental temporal sensing data (temperature, humidity, CO₂, ethylene) and visual temporal images of indicator tags via a self-designed portable acquisition node. A novel co-attention-based convolutional recurrent network (Co-ACRN) deep learning model was proposed for deeply mining the complex correlations between the two heterogeneous time-series data streams. This model innovatively employed a "co-attention + self-attention" dual mechanism. Firstly, in the early fusion stage, a co-attention module intelligently aligned and deeply integrated visual and sensor feature sequences by constructing a cross-modal affinity matrix. Subsequently, the fused sequence was fed into a long short-term memory (LSTM) network to encode temporal cumulative effects. Finally, a self-attention module performed a global contextual review on the LSTM output to capture long-range temporal dependencies. In the specific implementation, visual features were extracted by a lightweight convolutional neural network (CNN) with two convolutional-pooling layers; the co-attention calculated weights by generating context-aware intermediate features; and the self-attention adopted the standard scaled dot-product attention mechanism. For application deployment, the model was efficiently deployed to the QT client in the open neural network exchange (ONNX) format, achieving real-time, edge-side inference. [Results and Discussions] Experimental results showed that the proposed Co-ACRN model achieved an overall accuracy of 96.93% on the test set in the three-class mango freshness detection task, with its performance significantly surpassing that of various mainstream baselines and advanced temporal multimodal fusion models, such as modality-invariant and specific-representations for multimodal sentiment analysis (MISA), recurrent attended variation embedding network (RAVEN), multimodal transformer (MulT), and heterogeneous hierarchical message passing network (HHMPN). To verify the rationale of the model design, two sets of ablation experiments were conducted. The input-based ablation study decisively proved that the combination of "time-series information + multimodal information" is a necessary prerequisite for accurate detection, as any model relying on unimodal or static information exhibited significant performance bottlenecks. The architecture-based ablation study further confirmed the superiority of the proposed "dual-attention" system; compared to a backbone network without any attention mechanism, its accuracy was improved by more than five percentage points, and the recall rate for the critical "spoiled" category was as high as 99.16%. An in-depth analysis of the confusion matrix revealed that the vast majority of the model's errors occurred between adjacent categories with the most similar physical states, with no serious cross-category misclassifications, demonstrating its strong robustness. After deployment on the client side, the system's single diagnosis time was less than 2 s, verifying the solution's combination of high accuracy and real-time performance. [Conclusions] The developed online detection system and Co-ACRN model successfully enabled the real-time, accurate, and non-destructive intelligent detection of fruit and vegetable freshness. The research findings indicate that by combining advanced co-attention and self-attention mechanisms, the fusion challenges of complex multimodal temporal data can be effectively solved. In summary, this study provides a complete solution that combines theoretical innovation with engineering practicality for the online and intelligent detection of distributed fruit and vegetable freshness, and paves new paths for the development of this field in both theory and practice.

[Objective] Tea diseases significantly affect both the yield and quality of tea throughout the year. To address the issue of inadequate segmentation finesse in the current tea spot segmentation models, a novel diagnosis was proposed of the severity of tea spots, designated as MDC-UNet3+. [Methods] This model composed the Multi-scale Feature Fusion Module (MSFFM), dual multi-scale attention (DMSA), and conditional random fields (CRF) to enhance segmentation accuracy on the base framework of UNet3+. MSFFM was incorporated into the backbone network of UNet3+ to obtain feature information across multiple receptive fields of diseased spots, thereby reducing the loss of features within the encoder. DMSA was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue. This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale. Furthermore, the segmented mask image was subjected to CRF to enhance the optimization of the segmentation results. [Results and Discussions] It has been established that the enhanced model achieves a mean pixel accuracy (mPA) of 94.92%, accompanied by a mean Intersection over Union (mIoU) ratio of 90.9%. When compared to the mPA and mIoU of UNet3+, this model showed improvements of 1.85 and 2.12 percentage points, respectively. These results illustrate a more effective segmentation performance than that achieved by other classical semantic segmentation models. [Conclusions] The methodology presented herein provides valuable data support for automated disease detection and precise medication, consequently reducing the losses associated with tea diseases.

[Objective] With the rapid advancement of large language models (LLMs), intelligent question-answering (QA) systems have attracted widespread attention in specialized fields such as agriculture, medicine, and finance. However, existing systems often struggle with queries containing numerous technical terms and complex semantic expressions, frequently leading to unclear semantic comprehension, insufficient retrieval coverage, and low answer accuracy, thereby limiting their practical application. To address these challenges, a retrieval-augmented generation approach named ReG-RAG (Rewrite and Graph-enhanced Retrieval-Augmented Generation) is proposed, which integrates query rewriting and knowledge-graph enhancement to improve the accuracy and interpretability of complex QA tasks by optimizing query semantics and incorporating structured knowledge. Rapeseed cultivation was selected as a case study to demonstrate the framework's effectiveness and generalizability in a specialized domain. [Methods] The ReG-RAG framework comprised three hierarchical layers: query rewriting, dual-channel knowledge retrieval, and knowledge-aware generation. In the query rewriting layer, the system utilized the T5 model to semantically normalize the original query using a predefined set of prompts designed to standardize terminology and clarify user intent. The instruction set, covering five categories of rapeseed knowledge (varieties, breeding, cultivation management, pest and disease control, and nutrient regulation), was constructed from domain literature and expert contributions to enhance query clarity and matching accuracy. In the retrieval layer, the system performed text-vector retrieval and knowledge-graph subgraph retrieval in parallel. The text channel retrieved the top-k (k = 3) most relevant documents via cosine similarity, while the knowledge-graph channel extracted entities and relations using an LLM and, together with a structured metadata annotation system for graph construction, obtained relevant neighborhood subgraphs. The study adopted LightRAG's document segmentation and entity extraction methods to ensure knowledge integrity and traceability. In the generation layer, an attention-routing mechanism was designed to integrate textual and graph-based retrieval results into a multi-source knowledge base. The generation model then produced the final answers, with dynamic weight allocation emphasizing the most relevant knowledge nodes to ensure contextual coherence and factual accuracy. [Results and Discussions] Experiments were conducted on a rapeseed knowledge base (319 QA pairs) and the public WikiEval dataset, with comparative methods including RAG-Fusion, Decomposition, Step Back, HyDE, and Multi-Query. The results demonstrated that ReG-RAG outperformed all baseline methods across multiple evaluation metrics. On the rapeseed dataset, ReG-RAG achieved context precision of 0.904 2, context recall of 0.842 1, faithfulness of 0.988 6, and answer relevance of 0.986 9, significantly exceeding existing approaches. On the WikiEval dataset, it attained corresponding scores of 0.862 0, 0.838 7, 0.969 4, and 0.942 1, reflecting improvements of approximately 2.6%–4.7% over the best-performing baseline methods. Case analyses revealed that ReG-RAG produced logically consistent and complete answers by integrating graph-based reasoning for pest and disease control queries, whereas traditional methods often provided fragmented information. In variety-improvement tasks, responses from RAG-Fusion and LightRAG suffered from information gaps, while ReG-RAG, aided by query rewriting and knowledge Fusion, generated more professional and coherent answers. These findings indicated that ReG-RAG offered notable advantages in resolving query ambiguity, improving knowledge coverage, and enhancing answer faithfulness. [Conclusions] The proposed ReG-RAG framework effectively addressed semantic ambiguity, inadequate retrieval, and generation inaccuracies in domain-specific QA by integrating query rewriting and knowledge-graph enhancement. Experimental validation demonstrated that the method significantly outperformed mainstream baseline approaches on both specialized and general evaluation datasets, exhibiting strong adaptability and generalizability. Limitations of this study included the relatively small data scale and the need for further optimization in inference efficiency and deployment costs. Future work will extend the framework to additional crops such as wheat and rice, expand knowledge coverage, and investigate model compression and edge-deployment strategies to enhance practical applicability. Further optimization of the query-rewriting module's architecture and training procedure will also be pursued to strengthen robustness and generalization in complex semantic scenarios.

[Significance] With the deepening of intelligent agriculture and precision agriculture, the agricultural production mode is gradually transforming from traditional manual experience based operations to a modern model driven by data, intelligent decision-making, and autonomous execution. In this context, improving agricultural operation efficiency and achieving large-scale continuous and seamless operation coverage have become key requirements for promoting the modernization of agriculture. The multi robot full coverage operation technology, with its significant advantages in operation efficiency, system robustness, scalability, and resource utilization efficiency, provides practical and feasible intelligent solutions for key links such as sowing, plant protection, and harvesting in large-scale farmland. This technology, through the collaborative work of multiple robot systems, can not only effectively reduce the repetition rate of tasks and avoid omissions, but also achieve efficient and accurate continuous operations in complex and dynamic agricultural environments, greatly improving the automation and intelligence level of agricultural production. [Progress] Started from the global perspective of systems engineering, an integrated closed-loop technology framework of "perception decision execution" is constructed. It systematically sorts out and deeply analyzes the technological development status and research methods of each key link in the full coverage operation of agricultural multi robots. At the level of perception and recognition, focus on exploring the application of multi-source information fusion and collaborative perception technology. By integrating multi-source sensor data, multi-level fusion of data level, feature level, and decision level is achieved, and a refined global environment model is constructed to provide accurate crop status, obstacle distribution, and terrain information for the robot system. Especially in the field of multi robot collaborative perception, research has covered advanced models such as distributed SLAM and ground to ground collaboration. Through information sharing and complementary perspectives, the system's perception ability and modeling accuracy in wide area, unstructured agricultural environments have been improved. At the decision-making and planning level, three key aspects are analyzed: task allocation, global path planning, and local path adjustment. Task allocation has evolved from traditional deterministic methods to market mechanisms, heuristic algorithms, and intelligent methods that integrate reinforcement learning and graph neural networks to address the challenges of dynamic and complex resource constraints in agricultural scenarios. The global path planning system analyzed the characteristics of geometric decomposition, grid method, global planning, and learning methods in terms of path redundancy, computational efficiency, and terrain adaptability. Local path planning emphasizes the combination of real-time perception in dynamic environments, using methods such as graph search, sampling optimization, model predictive control, and end-to-end reinforcement learning to achieve real-time obstacle avoidance and trajectory smoothing. At the control execution level, the focus is on model-based trajectory tracking and control technology, aiming to accurately convert planned paths into robot motion. Traditional control methods such as PID, LQR, sliding mode control, etc. are continuously optimized to cope with terrain undulations and system disturbances. In recent years, intelligent methods such as fuzzy control, neural network control, reinforcement learning, and multi machine collaborative strategies have been gradually applied, further improving the control accuracy and collaborative operation capability of the system in dynamic environments. [Conclusions and Prospects] The closed-loop technical framework is systematically constructed for agricultural multi robot full coverage operations, and in-depth analysis of key modules are conducted, providing some understanding and suggestions, and providing theoretical references and technical paths for related research. However, the technology still faces many challenges, including perceptual uncertainty, dynamic changes in tasks, vast and irregular work areas, unpredictable dynamic obstacles, communication and collaboration barriers, and energy endurance issues. In the future, this field will further strengthen the integration with artificial intelligence, the Internet of Things, edge computing and other technologies, focusing on promoting the following directions, including the development of intelligent dynamic task allocation mechanism; Optimize global and local path planning algorithms to enhance their efficiency and adaptability in large-scale complex scenarios; Enhance the real-time perception and response capability of the system to dynamic environments; Promote software hardware collaboration and intelligent system integration to achieve efficient communication and integrated task management; Develop high-efficiency power systems and intelligent energy consumption strategies to ensure long-term continuous operation capability. Through these efforts, agricultural multi robot systems will gradually achieve higher levels of precision, automation, and intelligence, providing key technological support for the transformation of modern agriculture.

[Objective] Acquiring valid data is a critical part for establishing accurate greenhouse prediction models. However, simple averaging and weighted averaging are commonly used to process multi-sensor data in current research, but these methods are often ineffective against sensor noise interference. Additionally, greenhouse temperature and humidity exhibit strong coupling characteristics that necessitate coordinated control strategies. Prevailing studies predominantly train separate models for temperature and humidity prediction, which risks generating physically inconsistent results (e.g., simultaneous high temperature and high humidity), using it as the basis for control may lack reliability. Furthermore, the multi-dimensional environmental factor data in greenhouses has the characteristics of large volume and high computational cost. In the training process of the traditional LSTM model, parameters are manually adjusted based on human experience. When dealing with high-dimensional data, the model's convergence is slow and it is prone to getting stuck in local optima. [Methods] To address multi-point data fusion challenges, the traditional Kalman filtering algorithm was improved by dynamically adjusting the process noise covariance (Q) and observation noise covariance (R), while adaptively assigning weights to multiple sensors based on the innovation. This adaptation was achieved by monitoring the innovation sequence—the difference between observed and predicted measurements. Furthermore, the algorithm utilized the innovation covariance to assign adaptive weights to multiple sensors. Sensors with consistently smaller innovations which indicated higher reliability, were assigned greater weights. This mechanism enabled the system to swiftly identify and mitigate the impact of abnormal sensor readings, thereby ensureing robust and accurate fusion of multi-sensor data and providing a reliable foundation for subsequent model training. To address the strong coupling between temperature and humidity and their collaborative control requirements, a multi-output LSTM-attention model was developed for joint temperature-humidity prediction within a unified architecture. This model employed an attention mechanisms to adaptively weight critical environmental factors, thereby resolving physical constraint violations inherent in univariate forecasting approaches. The multi-dimensional nature of greenhouse environmental data often lead to high computational costs during model training. In traditional practices, the hyperparameters of LSTM models were often manually tuned based on experience, a process that was not only inefficient but also prone to suboptimal convergence and local optima traps, especially with high-dimensional data. To overcome this limitation, the grey wolf optimizer (GWO) was integrated to automatically perform hyperparameter optimization search to automatically and efficiently search for the optimal combination of key hyperparameters, such as the number of hidden units, learning rate, and dropout rate. [Results and Discussions] The adaptive Kalman filtering algorithm proposed in this paper achieved mean absolute deviations (MAD) of 1.59℃ and 8.64% for multi-point temperature and humidity fusion, respectively. Compared to the traditional Kalman filter algorithm, these represented reductions of 1.24% and 8.57%. The algorithm enabled swift identification of abnormal sensors and effectively mitigated their impact. When utilizing the fusion results of this algorithm as the model training dataset, the R² values for temperature and humidity predictions reached 98.2% and 99.3%, respectively. This constituted an increase of 4.7 and 4.3 percentage points compared to results obtained using the Kalman filter, demonstrating that the algorithm provided a highly reliable data foundation for model training. Furthermore, the GWO-LSTM-Attention model trained on this data yielded root mean square errors (RMSE) of 0.776 8 and 2.056 4 for temperature and humidity prediction, respectively. Compared to the LSTM and LSTM-Attention time-series prediction models, the temperature RMSE was reduced by 15.6% and 6.6%, while the humidity RMSE saw reductions of 29.2% and 5.7%. This reflects the role of the GWO algorithm in enhancing model generalization capability and convergence efficiency. [Conclusions] The proposed adaptive Kalman fusion algorithm effectively integrates multi-sensor data, demonstrating robustness in handling sensor noise, outliers, and non-stationary environmental fluctuations. For predicting multiple greenhouse environmental factors, the developed GWO-LSTM-attention model provides reliable forecasts across diverse time horizons. This research can provide a highly accurate prediction tool for greenhouse environment control. The combined prediction results could directly support the coordinated control of ventilation and irrigation equipment in the future, thereby reducing energy consumption.

[Objective] The precise quantification of rice seeds within individual cavities of seedling trays constitutes a critical operational parameter for optimizing seeding efficiency and fine-tuning the performance of air-vibration precision seeders. Achieving high accuracy in this task directly impacts resource utilization, seedling uniformity, and ultimately crop yield. However, the operational environment presents significant challenges, including complex backgrounds, seed overlap, variations in lighting and seed orientation, and the inherent difficulty of distinguishing individual seeds within dense clusters. These factors often lead to suboptimal performance in existing automated detection systems, manifesting as low detection accuracy and an inability to achieve robust, precise instance segmentation of individual rice seeds. To address these persistent limitations and advance the state-of-the-art in precision seeding monitoring, a novel, integrated framework for rice seed instance segmentation is proposed. The core innovation lies in the synergistic combination of a cross-modal grounding generation network (CGG) with a pretrained model, designed to leverage complementary information from visual and textual domains. [Methods] The proposed methodology fundamentally aimed to bridge the gap between visual perception and semantic understanding within the specific context of rice seed detection. The CGG-pretrained model framework achieved this through deep joint alignment of visual features extracted from seedling tray images and textual features derived from contextual knowledge. This cross-modal grounding enabled collaborative learning, where the visual processing stream (handled object localization and pixel-level segmentation) was continuously informed and refined by the semantic understanding stream (interpreting context and relationships). Specifically, the visual backbone network processes input imagery to generate feature maps, while the pretrained language model component, which utilized contextual embeddings, generated semantically rich textual representations. The CGG module acted as the fusion engine, establishing explicit correspondences between specific regions in the image (potential seeds or clusters) and relevant semantic concepts or descriptors provided by the pretrained model. This bidirectional interaction significantly enhanced the model's ability to disambiguate overlapping seeds, resolved occlusions, and accurately delineated individual seed boundaries under challenging conditions. Key technical innovations validated through rigorous ablation studies include: (1) The strategic use of the BLIP model for generating high-quality pseudo-labels from unlabeled or weakly labeled image data, facilitating more effective semi-supervised learning and reducing annotation burden; and (2) the application of BERT-based word embed to capture deep semantic relationships and contextual nuances within textual descriptors related to seeds and seeding environments. Crucially, the ablation experiments demonstrated a pronounced synergistic effect when these two core improvements were combined, resulting in a segmentation accuracy improvement exceeding 3 percentage points compared to the baseline model lacked these integrations. [Results and Discussions] Comprehensive experimental evaluation demonstrated the superior performance of the proposed CGG model against established benchmarks. Under the standard intersection over union (IoU) threshold of 0.5, the model achieved a mean average precision (mAP) of 90.7% for bounding box detection (denoted as mAP50^bb for detection) and an outstanding 91.4% mAP for instance segmentation (denoted as mAP50^seg for segmentation). These results represented a statistically significant improvement over leading contemporary models, including MaskR-CNN and Mask2Former, which highlighted the efficacy of the cross-modal grounding approach in accurately localizing and segmenting individual rice seeds. Further validation within realistic seeding trial scenarios, which involved direct comparison with meticulous manual annotations, confirmed the model's practical robustness. The CGG model attained the highest accuracy in two critical operational metrics: (1) Precision in segmenting individual seed instances (single-seed segmentation accuracy), and (2) accuracy in determining the exact seed count per cavity, and it achieved an average accuracy of 88% for per-cavity quantification. Moreover, the model exhibited superior performance in minimizing estimation errors for cavity seed counts, which was evidenced by its significantly lower error metrics: a root mean square error (RMSE = 16.8 seeds), a mean absolute error (MAE = 13.7 seeds), and a mean absolute percentage error (MAPE = 2.46%). These error values were markedly lower than those recorded by the comparison models, which underscored the CGG model's enhanced reliability in practical counting tasks. The discussion contextualized these results and attributed the performance gains to the model's ability to leverage semantic context to resolve ambiguities inherent in visual-only approaches, particularly in dense and overlapping seed scenarios common in precision seeding trays. [Conclusions] The developed CGG-pretrained model integration presents a significant advancement in automated monitoring for precision rice seeding. The model successfully addresses the core challenges of low detection accuracy and imprecise instance segmentation in complex environments. Its high accuracy in both individual seed segmentation and per-cavity seed count quantification, coupled with low error rates, demonstrates strong potential for practical deployment. Importantly, the model enables real-time detection of rice seeds during the image analysis stage. This functionality provides a quantifiable, data-driven basis for making immediate operational decisions, most notably enabling the targeted precision reseeding of empty or under-seeded cavities identified during the seeding process. By ensuring optimal seed placement and density from the outset, the technology contributes directly to improved resource efficiency (reducing seed waste), enhanced seedling uniformity, and potentially higher crop yields. Consequently, this research offers valuable and robust technical support for the ongoing advancement of smart agriculture systems and the development of next-generation intelligent seeding machinery, paving the way for more automated, efficient, and sustainable rice production. Future work will focus on further optimizing inference speed for higher-throughput seeding lines and exploring generalization to other crop types and seeding mechanisms.

[Objective] The vegetable supply chain is characterized by multiple production entities, diverse product varieties, and complex circulation processes, which often result in low data accuracy, label forgery, data tampering, and difficulties in cross-enterprise collaboration in traditional traceability systems. Furthermore, the rapid development of quantum computing poses significant threats to existing cryptographic foundations, by enabling efficient factorization or discrete logarithm attacks. This study aimed to design and implement a vegetable supply chain anti-counterfeiting and traceability system that integrates the Internet of Things (IoT), blockchain technology, and a post-quantum enhanced elliptic curve integrated encryption scheme (PQ-ECIES). The system seeks to enhance the trustworthiness, privacy protection, and collaborative efficiency of supply chain data management, while maintaining practical performance for IoT devices and high-frequency data uploading scenarios. [Methods] The proposed system was constructed on an IoT framework incorporating nine categories of devices. A registration and admission mechanism was developed to establish a trusted mapping between "device–enterprise–data", effectively preventing unauthorized entities from uploading forged data. At the data layer, collected information was divided into public and private categories: public data were uploaded directly to the blockchain, while private data were encrypted using PQ-ECIES before being stored on-chain. Smart contracts automated processes such as data classification, permission verification, and encrypted data querying, thus reducing human intervention and ensuring compliance. PQ-ECIES was designed by combining elliptic curve cryptography (ECC) and the Kyber algorithm from lattice-based post-quantum cryptography. A dual-key mechanism was employed to generate session keys, where an ECC-derived shared secret was combined with a Kyber-derived shared secret through SHA3-256 hashing, followed by key derivation for encryption and authentication. This design provided resilience against Shor's algorithm and other quantum attacks while maintaining efficiency compatible with IoT devices. The blockchain system was implemented using Hyperledger Fabric 1.4.4, with seven organizational nodes and the Raft consensus mechanism. Performance testing included evaluations of data collection accuracy, on-chain latency, query latency, and encryption performance across RSA, advanced encryption standard (AES), and PQ-ECIES. [Results and Discussions] The IoT-based data collection achieved significantly higher accuracy than manual input, particularly in large-scale sample scenarios such as pesticide residue testing. The average latency for data uploading to the blockchain was 2 879 ms, while data query latency averaged 122 ms, both of which met the practical requirements of vegetable supply chain applications. In cryptographic performance testing, PQ-ECIES achieved encryption and decryption of 128 B plaintext in approximately 10–30 ms, outperforming RSA (50–80 ms) and only slightly slower than AES (<10 ms). This result indicates that PQ-ECIES achieved an optimal trade-off between efficiency and security, offering asymmetric encryption benefits such as key distribution and identity verification, along with strong post-quantum resistance. Simulation under quantum attack models confirmed that traditional ECC and AES could be compromised within hours using Shor's and Grover's algorithms, whereas PQ-ECIES maintained resilience due to the lattice-based hardness assumptions of Kyber. From a system-level perspective, three major contributions were identified. First, trustworthiness was enhanced by binding IoT devices to enterprises through Bluetooth-based verification and blockchain's immutable ledger, ensuring data authenticity at the source. Second, privacy protection was achieved by adopting graded visibility: consumers accessed only public data such as testing results and logistics status, while regulators could decrypt private information (e.g., production location and batch details) via authorized keys, balancing transparency with confidentiality. Third, collaboration across enterprises was improved through the consortium blockchain structure and Fabric channel mechanisms, which eliminated information silos and enabled selective data sharing in real time, reducing inter-organizational access time from weeks to minutes. [Conclusions] This study proposed and implemented a vegetable supply chain traceability system that integrates IoT, blockchain, and PQ-ECIES. By deploying nine categories of IoT devices, establishing trusted device–enterprise mappings, and incorporating blockchain's decentralized and tamper-proof ledger, the system ensured reliable data collection and storage. The integration of PQ-ECIES provided dual cryptographic protection, balancing efficiency with long-term quantum security. Experimental validation confirmed that IoT-based collection significantly improved accuracy, blockchain integration achieved acceptable on-chain and query latency, and PQ-ECIES outperformed RSA while offering post-quantum resistance not available in AES. Beyond technical performance, the system enhanced trust, privacy, and collaboration across the vegetable supply chain, effectively addressing common issues of data forgery, tampering, and cross-enterprise coordination.Overall, the proposed framework demonstrates high potential for real-world deployment in agricultural supply chains, offering a secure, efficient, and future-proof solution to ensure authenticity, reliability, and transparency in vegetable traceability. The study also provides a reference model for extending post-quantum blockchain-based traceability to other agri-food sectors facing similar challenges.

[Objective] Extracting valuable knowledge from vast amounts of dispersed, heterogeneous, and unstructured agricultural big data, correlating and structuring it, and enhancing large models to form intelligent question-answering systems enables the effective delivery of services to all in agriculture. This approach can rapidly advance the scientific and precision-based development of agricultural production. Existing agricultural Q&A systems lack enough semantic understanding of complex symptoms, while general-purpose large language models (LLM) produce factual hallucinations due to incomplete training data coverage. It aims to address the issues of insufficient scale and low quality in the construction of knowledge bases in the agricultural field [Methods] First pest and disease data were collected along for five typical crops: wheat, rice, corn, potatoes, and cotton. Using manual verification methods, outliers were precisely identified and removed, ultimately yielding 87 901 unstructured data entries. Then, a few-shot learning model was employed to extract entities defined in the pattern layer, and these entities were aligned with the semantic vectors of Bert and LLM prompt engineering, ultimately yielding a triplet knowledge base of 916 239 entries for knowledge retrieval. A knowledge retrieval-augmented LLM approach for intelligent Q&A on crop pests and diseases was proposed, specifically the adaptive hybrid retrieval-augmented generation (AHR-RAG) approach. Firstly, an overlapping mechanism was introduced during fixed-length segmentation to mitigate semantic fragmentation. Simultaneously, vector semantic similarity was used to match highly related text blocks to the topic for optimization and storage. Then, single-hop and multi-hop retrieval were designed based on the complexity of the problem. Single-hop retrieval used the BM25 algorithm to match information extracted from the query with document content in the Elasticsearch index, feeding the results into the LLM to enhance answer generation. Multi-hop retrieval first converted user queries into structured conditions and semantic vector representations. Results retrieved from different knowledge bases were then fused using reciprocal rank fusion (RRF) and fed into the LLM. [Results and Discussions] The proposed method was experimentally compared with multiple baseline approaches, including different query types and complexity queries. The results demonstrated that the proposed method achieved accuracy and F₁ improvements of 0.193 and 0.170, respectively, on the Qwen1.5-7B-Chat model. Compared to the improved methods Self-RAG and Adaptive-RAG, AHR-RAG maintained low response times while achieving F₁ improvements of 0.05 and 0.021, respectively, with an accuracy as high as 0.896. For multi-type question-answering tasks, compared to the Naive-RAG method that relied solely on prior knowledge, our AHR-RAG approach achieved accuracy improvements of 0.231, 0.123, and 0.157 for comparison, judgment and selection query types, respectively. For parsing complex semantics, AHR-RAG also demonstrated significant advantages. In single-hop queries, its accuracy reached 0.921, representing a 0.29 improvement over Adaptive-RAG. In multi-hop query scenarios, its accuracy reached at 0.748, achieving gains of 0.082 and 0.059 over Self-RAG and Adaptive-RAG respectively. In retrieval-augmented generation, AHR-RAG achieved a 0.013 increase in accuracy and a 0.09 improvement in F₁ by optimizing prompt strategies, compared to directly feeding retrieval results to the model's output. [Conclusions] This research methodology demonstrates strong adaptability to diverse query types and excels at reasoning complex queries such as multi-hop searches. It delivers significant advantages in answer generation accuracy, relevance, and comprehensiveness, producing responses with enhanced logical coherence and richer content. Future work will explore integrating multimodal knowledge bases.

[Objective] Planthoppers suck the sap from the phloem of rice plants, causing malnutrition and slow growth of the plants, resulting in large-scale yield reduction. Therefore, timely and effective monitoring of planthopper pests and analysis of their occurrence degree are of vital importance for the prevention of rice diseases. The traditional detection of planthopper pests mainly relies on manual methods for diagnosis and identification. However, due to the tiny size of planthopper pests, on-site manual investigation is not only time-consuming and labor-intensive but also greatly influenced by human subjectivity, making it easy to misjudge. In response to the above issues, the intelligent light traps can be used to assist in the work. When using intelligent light traps to detect dense and occluded low-resolution and small-sized planthopper pests, problems such as low accuracy, false detection, and missed detection are prone to occur. For this purpose, based on YOLOv11x, the research proposes a light-trapping small-sized rice pest detection method by combining spatial depth transform convolution and multi-scale attention mechanism. [Methods] The image data in this research were collected by multiple light-induced pest monitoring devices installed in the experimental rice fields. The images included two types of planthopper pests, the brown planthopper and the white-backed planthopper. The image sizes were both 5 472 pixels ×3 648 pixels, totaling 998 images. The original dataset was divided into a training set and a validation set in a 4:1 ratio. To enhance the learning efficiency of the model during training, two data augmentation operations, horizontal flipping and vertical flipping, were performed on the images in the training set. A total of 2 388 images in the training set were obtained for model training, and 200 images in the validation set were used for model inference validation. To improve the model performance, first of all, the C3k2 module in the original YOLOv11x network was improved by using the Efficient Multi-Scale Attention (EMA) attention mechanism to enhance the perception of the model and the fusion ability of small-volume pest features in dense and occlusions. Secondly, the Space-to-Depth-Convolution (SPD-Conv) convolution was used to replace the Conv common convolution module in the original model, further improving the extraction accuracy of the model for low-resolution and small-volume pest features and reducing the number of parameters. In addition, a P2 detection layer was added to the original network and the P5 detection layer was removed, thereby enhancing the model's detection performance for small targets in a targeted manner. Finally, by introducing the dynamic non-monotonic focusing mechanism loss function Wise-Intersection over Union (WIoU)v3, the positioning ability of the model was enhanced, thereby reducing the false detection rate and missed detection rate. [Results and Discussions] The test results show that the Precision (P), Recall(R), mean Average Precision at IoU equals 0.50(mAP₅₀) and the mean Average Precision at IoU thresholded from 0.50 to 0.95 with a step size of 0.05 (mAP_50-95) of the improved model on the self-built planthopper pest dataset dataset_Planthopper reached 77.5%, 73.5%, 80.8%, and 44.9% respectively. Compared with the baseline model YOLOv11x, it has increased by 4.8, 3.5, 5.5 and 4.7 percent point, respectively. The number of parameters has been reduced from 56 M to 40 M, a reduction of 29%. Compared with the current mainstream object detection models YOLOv5x, YOLO8x, YOLOv10x, YOLOv11x, YOLOv12x, Salience DETR-R50, Relation DETR-R50, RT-DETR-x, the mAP₅₀ of the improved model was 6.8, 7.8, 8.6, 5.5, 5.6, 8.7, 6.9 and 6.9 percentage points higher, respectively, and it had the best comprehensive performance. In practical applications, it could assist in achieving precise monitoring of farmland pests and scientific prevention and control decisions, thereby reducing the use of chemical pesticides and promoting the intelligent development of agriculture. Although this method has achieved significant improvements in multiple indicators, it still had certain limitations. Firstly, the species of planthoppers were numerous and their forms were diverse. The current models mainly targeted some typical species, and their generalization ability needed to be further verified. Secondly, due to the limitations of the data collection environment, there was still room for improvement in the performance of the model under extreme lighting changes and extremely occluded scenarios. Finally, although the number of parameters had decreased, the real-time detection speed still needed to be optimized to meet the requirements of some low-power edge devices. [Conclusions] The improved YOLOv11x model effectively enhances the performance of detecting low-resolution and small-sized planthopper pests under dense and occluded insect conditions, and reduces the probability of missed detection and false detection. Future research can focus on expanding the generalization, robustness and lightweighting of more types of rice planthopper pest models in more complex situations.

Significance Cropland abandonment in hilly and mountainous regions is a pronounced manifestation of land‐use marginalization, with profound implications for both regional food security and ecosystem service provision. In southern China, this issue is particularly acute due to a confluence of factors including early and rapid urbanization, rugged topographic relief, complex multi‐cropping systems, and substantial rural‐to‐urban labor migration, which have driven widespread abandonment of steep, fragmented terraces. This trend presents a profound dual dilemma: on one hand, the cessation of cultivation diminishes local grain production capacity, amplifies pressure on existing cropland, and threatens national food supplies. On the other hand, the secondary succession of spontaneous vegetation on these deserted parcels offers significant carbon sequestration potential and contributes to biodiversity recovery. Yet, accurately mapping these spatio-temporal patterns is severely hampered by persistent cloud cover and the landscape's complexity. This leaves decision-makers without the timely, high‐resolution maps needed to track abandonment dynamics, uncover their socioeconomic and environmental drivers, and craft land-use policies that holistically balance agricultural output, carbon storage, and landscape resilience. Progress Drawing from literature published since 2014, this paper systematically reviews remote sensing‐based methods for cropland abandonment, revealing a clear developmental trajectory. Methodologically, the approaches have evolved along two parallel paths. First, the monitoring paradigm has shifted from early "state comparison" methods, such as post-classification comparison of discrete multi-temporal images, to modern "process tracking" approaches. These leverage dense time series, utilizing phenology‐aware algorithms such as LandTrendr and BFAST to identify abrupt or gradual breaks in the vegetation trajectory, thus capturing the dynamics of abandonment and distinguishing it from short-term fallows. Second, the identification algorithm has progressed from traditional machine learning classifiers and Object-Based Image Analysis (OBIA), which depend on hand‐crafted features, towards sophisticated deep learning frameworks capable of automatically learning complex spatio-temporal signatures. Concurrently, data pre-processing techniques have advanced significantly, with harmonic analysis, Savitzky-Golay filtering, and the integration of Synthetic Aperture Radar (SAR) data now routinely applied to reconstruct continuous, high-quality time series. Furthermore, this review provides a critical synthesis of common methodological issues, focusing on the spatio-temporal representativeness bias in ground validation samples and the multiple sources of uncertainty stemming from cloud cover, mixed pixels, and phenological variability. Conclusions and Prospects Despite considerable advances, persistent challenges continue to limit operational monitoring. Looking forward, the field must evolve from descriptive mapping toward a truly predictive and decision‐ready framework. This transformation hinges on five interlinked frontiers. First, it requires forging the seamless integration of diverse data streams: fusing optical imagery, radar backscatter, and terrain models within cloud computing environments to yield uninterrupted, high‐resolution time series that capture both abrupt and gradual land‐use changes. Second, it necessitates the establishment of an extensive, stratified ground‐truth network; by systematically sampling high-risk, transitional, and reference plots and collecting synchronized measurements, researchers can iteratively recalibrate classification models and improve their resilience across the region's landscape mosaic. Third, on the algorithmic frontier, hybrid approaches that embed expert‐defined phenological rules within deep learning architectures offer a promising path to robustly disentangle permanent abandonment from temporary fallows and to quantify a continuous "abandonment intensity". Fourth, the deployment of fully automated and reproducible processing pipelines on cloud platforms like Google Earth Engine will democratize access to near-real‐time monitoring and enhance reproducibility through open-source workflows. Finally, anchoring detection within dynamic simulation frameworks (e.g., agent‐based models) driven by historical trajectories and key drivers will allow for the projection of future abandonment of "hotspots". Layering these projections with multi‐criteria risk assessments will yield spatially explicit risk maps to guide precision interventions—such as targeted recultivation subsidies or ecological restoration efforts—enabling sustainable land stewardship that simultaneously safeguards food security and enhances ecosystem resilience.

[Significance] The agricultural low-altitude economy (ALE) has rapidly emerged as a transformative force in rural modernization, catalyzed by advancements in intelligent technologies and gradual improvements in low-altitude airspace governance. It encompasses a wide array of applications where aerial platforms—particularly unmanned aerial vehicles (UAVs)—play a pivotal role in reshaping agricultural production, environmental monitoring, and rural management systems. Systematically analyzing the development trajectory and implementation pathways of ALE holds considerable significance for promoting cross-sector integration, accelerating policy and technological innovation, and enabling the widespread adoption of intelligent solutions across agriculture-related industries. [Progress] This review draws upon an extensive body of literature and applies keyword clustering analysis to systematically explore the development of the agricultural ALE. Based on 13 570 peer-reviewed articles retrieved from the Web of Science database between 2000 and 2024, the study reveals a rapid growth trajectory—particularly since 2011—driven by technological breakthroughs in UAVs, sensors, and intelligent analytics. Five representative application domains were identified: smart farming, animal husbandry, forestry, fisheries, and rural governance. Through the integration of bibliometric tools and structured keyword combinations, the study captures both the evolution of research focus and the expansion of technical capabilities. The results demonstrate that ALE has progressed from early-stage feasibility validation toward large-scale, multi-functional applications. At the same time, it has catalyzed the development of an emerging industrial framework encompassing equipment manufacturing, aerial service provision, operational support systems, and talent development. These trends highlight the growing maturity and strategic relevance of ALE as a technological enabler for modern agriculture. In the context of smart farming, low-altitude technologies are extensively utilized for precision sowing, variable-rate fertilization, real-time crop health monitoring, and pest and disease detection. UAV-based remote sensing facilitates the creation of high-resolution field maps and spatially explicit data layers that support data-driven and site-specific decision-making in modern agricultural management. In smart livestock systems, drones are employed for livestock monitoring, early disease detection, and fence-line inspections, particularly in large, remote pasture areas with limited ground accessibility. Smart forestry applications include forest fire early warning, forest inventory updates, and dynamic monitoring of ecological changes, enabled by low-altitude hyperspectral, LiDAR, and thermal imaging technologies. For smart fisheries, UAVs and amphibious drones support water quality sensing, pond surveillance, and feeding behavior analysis, thereby enhancing aquaculture productivity, animal welfare, and environmental sustainability. In the broader context of smart rural governance, ALE technologies assist with infrastructure inspections, land use monitoring, rural logistics coordination, and even public security surveillance, contributing to comprehensive, intelligent rural revitalization. Alongside scenario-based applications, this study also summarizes the current structure of the ALE industry chain, which is preliminarily composed of four key components: manufacturing, flight operations, supporting services, and integrated service platforms. In the manufacturing segment, the development and production of specialized agricultural UAVs, multi-rotor drones, and fixed-wing VTOL aircraft are advancing rapidly, enabling adaptation to various terrains and crop types. The flight operations segment is expanding with increasing market participation, offering aerial spraying, broadcasting, surveying, and inspection services with improved timeliness and precision. Supporting services include airspace coordination, meteorological forecasting, equipment maintenance, and safety supervision. Additionally, comprehensive service systems have emerged, integrating digital platform management, third-party evaluation, data analysis, and agricultural technical advisory services. Talent development and training form a crucial pillar of ALE's development. A growing number of vocational institutions and drone enterprises are providing structured training programs for agricultural drone pilots, technicians, and system operators. These programs aim to enhance operational capabilities, ensure safety compliance, and foster human capital that can support the sustainable growth of ALE applications across rural regions. [Conclusions and Prospects] ALE is transitioning from isolated, technology-specific applications to an integrated, platform-oriented ecosystem that blends equipment, data, services, and governance. To fully realize its potential, several strategic directions should be pursued. First, there is a pressing need to refine regulatory frameworks to accommodate the unique operational characteristics of rural low-altitude airspace, including dynamic zoning policies, UAV registration standards, and risk management protocols. Second, sustained investment in core technologies—such as autonomous flight control, multi-modal sensing, and AI-based analytics—is essential for overcoming current technical limitations and unlocking new capabilities. Third, ALE's infrastructure backbone must be reinforced through the deployment of distributed drone ports, mobile ground control stations, and secure data networks, particularly in underserved regions. Additionally, establishing a national-scale training and certification framework is critical for ensuring an adequate supply of skilled professionals, while innovative funding models such as public-private partnerships and scenario-based insurance can enhance accessibility and scalability. Pilot projects and demonstration zones should also be expanded to validate and refine ALE systems under diverse agroecological and socio-economic conditions. In summary, ALE offers strategic leverage for advancing China's goals in smart agriculture, green development, and rural revitalization. With cross-sector collaboration and proactive policy support, ALE can evolve into a resilient and inclusive ecosystem that fosters agricultural transformation, boosts productivity, and enhances environmental sustainability.

[Objective] The adoption of smart agricultural technologies has significantly accelerated the development of modern agriculture, emerging as a pivotal driver for green transition within the agricultural sector. By enabling a fundamental shift in agricultural production paradigms, these innovations not only enhance desirable agricultural outputs but also effectively reduce undesirable environmental outputs. This dual effect substantially contributes to the improvement of Agricultural Green Total Factor Productivity (AGTFP). [Progress] This study undertakes a systematic investigation into the pivotal role of smart agriculture technology innovation in driving the green transition within the agricultural sector. The analysis is meticulously structured across three sequential stages: Firstly, it elucidates the intrinsic mechanisms through which technological advancements serve as enablers for this green transition, detailing how innovations in agriculture technology translate into environmental sustainability gains. Secondly, it identifies and categorizes the existing implementation barriers that hinder the widespread adoption of smart agriculture solutions, providing insights into the challenges faced by stakeholders at various levels. Smart agricultural technologies advancement facilitate a paradigm shift, moving from isolated, fragmented data systems to cohesive, interconnected ecological systems that support sustainable growth. The core technological logic underpinning this transition operates through a synergistic "data-algorithm-equipment-ecology" evolutionary trajectory: 1.Data Infrastructure Construction: Establishing comprehensive, multi-source heterogeneous agricultural data acquisition systems that collect, process, and analyze data from diverse sources, forming the foundational data assets necessary for the development and deployment of smart agriculture solutions. 2.Algorithmic Decision-Making: Developing sophisticated AI-driven resource allocation models that leverage big data analytics to enable precision management, optimizing resource use, and enhancing productivity through intelligent decision-making systems. 3.Equipment Modernization: Creating technical closed loops characterized by a virtuous cycle of "equipment upgrading - efficiency enhancement - cost reduction - environmental benefits" through the implementation of automated production management and intelligent harvesting systems that minimize human intervention and maximize output. 4.Ecological Optimization: Building circular systems for waste valorization, carbon footprint tracking, and ecological monitoring that facilitate the recycling of agricultural by-products, reduce greenhouse gas emissions, and promote the restoration and preservation of agricultural ecosystems, ultimately achieving self-regulating, resilient agricultural environments. However, the implementation process faces three categories of barriers: 1.Technological Barriers: High infrastructure costs, inefficient technology commercialization, insufficient digital literacy among farmers, and inadequate full-process automation/intelligence levels constrain transformation depth. 2.Market Barriers: Significant mismatches between supply-side capabilities and demand-side requirements in technology applicability. 3.Institutional Barriers: Firstly, the lack of policy coordination is evident in the "prioritizing construction over operation"approach, leading to many agricultural big data platforms failing to effectively implement green technologies post-completion due to inadequate policies supporting sustained operations and practical applications. Secondly, structural deficiencies in the standard system, particularly the absence of unified classification standards for agricultural information resources, create technical barriers to cross-departmental and cross-regional data sharing. [Conclusions and Prospects] To effectively advance smart agriculture technology in driving the agricultural green transition and systematically address identified barriers, this study proposes the following policy recommendations: 1.To effectively overcome the persistent "last mile" barrier that impedes the widespread adoption of technological innovations among smallholder farmers, it is imperative to actively promote multi-stakeholder participation across the agricultural sector. This involves fostering collaboration among governments, private enterprises, non-profit organizations, and local communities. Additionally, establishing a comprehensive technical service network is crucial, one that seamlessly integrates government guidance to ensure policy alignment, market operations to drive efficiency and sustainability, and social engagement to enhance community buy-in and knowledge dissemination. 2.To significantly boost the efficiency of green resource allocation within the agricultural sector, it is imperative to harness the power of market-oriented reforms. These reforms should serve as the cornerstone for forging a novel supply-demand-driven model that is both dynamic and responsive. By integrating intelligent algorithms and advanced data analytics, this coordinated approach will facilitate the precise matching of essential resources such as land, capital, and cutting-edge green technologies. Such precision will enable a paradigm shift from traditional, scale-oriented production methods to more flexible, demand-driven customization tailored to specific market needs. Ultimately, this transformation will cultivate sustainable endogenous growth momentum, providing a robust foundation for the agricultural sector's green transition and ensuring its long-term viability and competitiveness. 3.Governments ought to take the lead in driving systemic and comprehensive reform of existing policy formulation frameworks, ensuring that agricultural development strategies are aligned with long-term sustainability goals. Additionally, they should strengthen technological support systems for agricultural resource zoning, leveraging advanced tools such as big data analytics and geographic information systems to optimize land use and resource allocation. By doing so, governments can foster a new universal smart agriculture development paradigm that integrates digital innovation with ecological principles. These targeted measures will not only enhance agricultural productivity but also accumulate valuable experience for comprehensively advancing the agricultural green transition and promoting rural ecological revitalization in a systematic manner.

[Objective] Accurate identification of maize tassels is critical for hybrid seed production. Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity, insufficient feature extraction, high computational load, and low detection efficiency. To address these challenges, a real-time field maize tassel detection model, LightTassel-YOLO is proposed, based on an improved YOLOv11n. The model is designed to quickly and accurately identify maize tassels, enabling efficient operation of detasseling drones and reducing the impact of manual intervention. [Methods] In this study, data was continuously collected during the tasseling stage of maize from 2023 to 2024 using unmanned aerial vehicles (UAVs), establishing a large-scale, high-quality maize tassel dataset that covered different maize tasseling stages, multiple varieties, varying altitudes, and diversed meteorological conditions. First, EfficientViT was applied as the backbone network to enhance the ability to perceive information across multi-scale features. Second, the C2PSA-CPCA (Convolutional block with Parallel Spatial Attention with Channel prior convolutional attention) module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps, effectively enhancing the network's capability to extract target features while reducing computational complexity. Finally, the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction, thereby improving the model's detection accuracy. [Results and Discussions] The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection. The final model achieved an accuracy of 92.6%, a recall of 89.1%, and an AP@0.5 of 94.7%, representing improvements of 2.5, 3.8, and 4.0 percentage points over the baseline model YOLOv11n, respectively. The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs. In addition, LightTassel-YOLO was compared with mainstream object detection algorithms such as Faster R-CNN, SSD, and multiple versions of the YOLO series. The results demonstrated that the proposed method outperforms these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios. [Conclusions] The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.

[Significance] The low-altitude economy in orchards represents a key emerging direction in the integrated development of new quality productive forces in agriculture. As a burgeoning industry driving the high-quality development of the fruit sector, it relies on the integration of advanced equipment manufacturing, the application of smart agriculture technologies, and the expansion of consumer-centric ecosystems. Together, these elements contribute to building a full-cycle industrial chain encompassing orchard production, management, and services. This fosters the coordinated development of the entire low-altitude value chain and supports the formation of a closed-loop industrial ecosystem. This paper systematically reviews the key technological pathways and development trends of the orchard low-altitude economy across three dimensions: upstream equipment manufacturing, midstream operational processes, and downstream service systems. The aim is to provide strategic reference for technological innovation and industrial planning in related fields. [Progress] In the upstream segment, research and industrial development are increasingly focused on lightweight, multifunctional aerial platforms tailored to the complex terrain of mountainous orchards. By utilizing carbon fiber composites, high energy-density batteries, and hybrid power systems, these platforms achieve significant reductions in weight and improvements in flight endurance. The integration of Artificial Intelligence (AI) computing chips, Light Detection and Ranging(LiDAR), and multispectral sensors equips drones with advanced capabilities for precise fruit tree recognition, obstacle avoidance in complex landscapes, and multimodal environmental perception. With centimeter-level Real-time kinematic(RTK) positioning and multi-sensor fusion flight control algorithms, operational safety and autonomy have been greatly enhanced. Furthermore, low-altitude infrastructure, such as distributed takeoff and landing points and mobile battery-swapping stations, based on integrated 5G-A and BeiDou navigation communication systems, is being systematically deployed. This provides strong support for continuous unmanned operations in hilly and mountainous orchards. In the upstream segment, current research and industrial development efforts are centered on lightweight, multi-functional aerial platforms designed to operate in the complex terrain of mountainous orchards. By incorporating carbon fiber composite materials, high energy-density batteries, and hybrid power systems, these platforms achieve significant reductions in overall weight while enhancing flight endurance. The integration of AI processing chips, LiDAR, and multispectral sensors enables drones to perform precise fruit tree identification, navigate complex topographies, and perceive the environment through multimodal sensing. Coupled with centimeter-level RTK positioning and advanced multi-sensor fusion flight control algorithms, the safety and autonomy of aerial operations have been significantly improved. In addition, the deployment of low-altitude infrastructure, such as distributed takeoff and landing stations and mobile battery-swapping cabins, based on 5G-A and BeiDou-integrated communication systems, is rapidly taking shape. These developments provide robust support for continuous, unmanned operations across hilly and mountainous orchard regions. At the downstream service level, the orchard low-altitude economy has evolved beyond single-equipment sales into a diversified service ecosystem. This emerging model centers on pilot training, drone insurance, equipment leasing, and the integration of orchard tourism, forming a new type of business landscape. On one hand, standardized pilot training programs and operational quality evaluation systems have enhanced both talent development and safety assurance. On the other hand, risk control models developed by insurers based on operational data, along with "rent-to-own" financing schemes, have effectively lowered entry barriers for farmers. Moreover, the rise of integrated low-altitude agri-tourism models is steadily boosting the brand value of fruit products and generating new income streams through cultural and tourism-related activities. [Conclusions and Prospects] As a vital carrier of new quality productive forces in agriculture, the orchard low-altitude economy has established a comprehensive industrial chain encompassing equipment manufacturing, operational systems, and service platforms. This integrated structure is driving the transformation of orchard management toward greater intelligence, precision, and sustainability. Despite current challenges such as limited equipment endurance and underdeveloped service systems, the sector is expected to achieve continuous breakthroughs through the development of high-payload aerial platforms, the integration of data-driven operational systems, the construction of diversified service ecosystems, and the refinement of relevant policies and standards. With the gradual opening of low-altitude airspace and the rapid iteration of core technologies, the orchard low-altitude economy is poised to become a key driver of agricultural modernization and rural revitalization.

[Objective] With the accelerated development of large-scale and intelligent aquaculture, accurate estimation of the body mass of individual Chinese mitten crabs is critical for tasks such as precise feeding, disease prevention, and optimization of harvest decisions. Traditional methods of manually catching and weighing crabs are time-consuming, labor-intensive, and can cause stress or injury to the crabs, while also failing to provide real-time monitoring. To address the challenges posed by turbid water conditions in aquaculture, which lead to poor image quality and difficulty in feature extraction, a method is proposed for estimating Chinese mitten crab quality that combines binocular vision with deep learning–based keypoint detection. This approach achieves high-precision detection of anatomical keypoints on the crab, providing new technical support for precision aquaculture and intelligent management. [Methods] Based on a lightweight YOLOv11 framework, in its C3K2 module, MBConv depthwise-separable convolutions were incorporated to significantly reduce computational complexity and improve feature extraction efficiency. An EffectiveSE channel attention mechanism was introduced to adaptively emphasize important channel-wise features. To further enhance cross-scale information fusion, a spatial dynamic feature fusion module (SDFM) was added. The SDFM adaptively and weightedly fused local spatial attention with global channel attention, enabling detailed extraction of crab shell edges and anatomical keypoints. The improved YOLOv11-ES model could simultaneously output the crab's bounding box, the positions of four anatomical keypoints, and the crab's sex classification in a single forward pass. In the 3D reconstruction stage, calibrated stereo camera parameters were used, and a sparse keypoint matching strategy guided by the crab's sex and spatial geometric constraints was employed. High-confidence keypoint pairs were selected from the left and right views, and the true 3D coordinates of the crab's carapace length and width were computed by triangulation. Finally, the obtained carapace length, width, and sex label data were fed into a two-layer back-propagation (BP) neural network to perform a regression prediction of the individual crab's mass. [Results and Discussion] To validate the effectiveness and robustness of the proposed method, a dataset of Chinese mitten crab images with annotated keypoints was constructed under varying water turbidity and lighting conditions, and both ablation and comparative experiments were conducted. The YOLOv11-ES achieved a mean Average Precision at intersection over union (IOU) threshold of 0.5 (mAP@50) of 97.2% on the test set, which is 4.4 percentage point higher than the original YOLOv11 model. The keypoint detection component reached an mAP@50 of 96.7%, which is 3.6 percentage point higher than that of the original YOLOv11 model. In comparative experiments, YOLOv11-ES also demonstrated significant advantages over other models in the same series. Moreover, in a full-system evaluation using images of 30 individual crabs, the mean absolute percentage error (MAPE) for carapace width measurements was only 2.68%, and for carapace length it was 1.48%. The Pearson correlation coefficients between the measured and manually obtained true values for both carapace length and width exceeded 0.977, indicating high accuracy in the 3D reconstruction and minimal measurement error. Experiments analyzing the influence of image quality on measurement accuracy showed that when the underwater image quality measure (UIQM) reached at least 1.5, the combined MAPE of carapace length and width errors could be kept below 5%. When UIQM reached at least 2.2, the MAPE dropped to about 1.9%. These results confirmed the robustness of the method against variations in water turbidity and lighting conditions. For mass regression prediction, the BP network trained on carapace length, width, and sex features achieved a mean absolute error (MAE) of 2.39 g and a MAPE of 7.1% on an independent test set, demonstrating high-precision estimation of individual crab mass. [Conclusions] The proposed method, which combines an improved YOLOv11 object detection network, binocular sparse keypoint matching, and a two-layer BP regression network, enabled high-precision, low-error, real-time, non-contact estimation of Chinese mitten crab mass in complex turbid aquatic environments. This approach featured a lightweight model, high computational efficiency, excellent measurement accuracy, and strong adaptability to varying environmental conditions. It provided key technical parameters for intelligent Chinese mitten crab farming. In the future, this approach could be extended to other aquaculture species and complex farming scenarios. Combined with transfer learning and online adaptive calibration techniques, its generalization capability could be further improved and integrated with intelligent monitoring platforms to achieve large-scale, all-weather underwater crab quality estimation, contributing to the sustainable development of smart aquaculture.

[Objective] The detection of corn borer infestations is essential for improving maize yield and quality, as corn borer pests pose a significant threat to global maize production. In traditional agricultural practices, identifying corn borer infestations relies on manual field inspections or trapping tools, which are labor-intensive, time-consuming, and difficult to implement over large areas. These methods are further limited by their susceptibility to human error and inability to meet the demands of modern precision agriculture. To address these challenges, a method for detecting corn borer infestations using low-altitude, close-range imagery captured by unmanned aerial vehicles (UAVs) and a model are investigated, you only look once enhanced small object network (YOLO-ESN). By focusing on detecting boreholes rather than insect bodies, this approach overcomes the difficulties of detecting corn borers, which are nocturnal and often concealed within plant tissues, thereby enhancing the applicability of field-based detection and aligning with practical field conditions. [Methods] Based on the you only look once version 11 (YOLOv11) object detection algorithm, a model—YOLO-ESN was introduced, for corn borer infestation detection. The YOLO-ESN model has been optimized through multiple modifications. In the Backbone, an enhanced lightweight attention (ELA) mechanism was incorporated to increase sensitivity and improve the extraction of small visual features, such as boreholes, by modeling spatial dependencies in horizontal and vertical directions using one-dimensional convolutions. In the Neck, a C3k2-Spatial and channel reconstruction convolution (C3k2-SCConv) module was introduced to reduce the number of model parameters while improving feature fusion efficiency through spatial and channel reconstruction, suppressing redundant information. In the Head, a small-object detection branch, termed the P2 detection head, was added, enabling YOLO-ESN to directly utilize shallow, high-resolution features from early network layers to enhance the detection of fine-grained targets like boreholes. Additionally, a combined loss function of normalized wasserstein distance (NWD) and efficient intersection over union (EIoU) was employed to optimize bounding box regression accuracy, addressing gradient vanishing issues for small targets and improving target localization stability and robustness. A decision tree algorithm was applied to classify infestation severity levels based on borehole detection results, and heatmaps were generated to visualize the spatial distribution of corn borer infestations across the field. [Results and Discussions] Multiple experiments were conducted using a constructed dataset of corn borer infestation images. The results demonstrated that YOLO-ESN achieved an mAP@50 of 88.6% and an mAP@50:95 of 40.5%, representing an improvement of 7.6 and 4.9 percent points, respectively, compared to the original YOLOv11 model. The total number of parameters in YOLO-ESN was reduced by 11.52%, contributing to a lighter model suitable for UAV deployment. Ablation studies evaluated individual contributions: incorporating the ELA mechanism alone improved mAP@50 by 0.3 percent points, and the parameters are reduced by 10.57%; replacing the C3k2 module with C3k2-SCConv reduced parameters by 2.5% while increasing mAP@50 by 0.9 percent points; adding the P2 detection head enhanced mAP@50 and mAP@50:95 by 4.1 and 1.2 percent points, respectively; and introducing the NWD+EIoU loss function improved mAP@50 and mAP@50:95 by 1.9 and 1.2 percent points, respectively. Comparative experiments demonstrate that YOLO-ESN outperforms a range of mainstream object detection models, including Faster R-CNN, SSD, YOLOv8, YOLOv11, and YOLOv12. YOLO-ESN achieves an mAP@50 and an mAP@50:95, surpassing Faster R-CNN by 14.9 and 9.7 percentage points, respectively, and SSD by 17.8 and 11.4 percentage points, respectively. With a compact parameter size of 8.37 M, YOLO-ESN delivers excellent detection accuracy and generalization, striking a strong balance between performance and efficiency. Although its inference speed (32.48 FPS) was slightly slower than YOLOv12 (75.44 FPS), it offered a superior trade-off between accuracy and efficiency. These results validated YOLO-ESN as a lightweight, high-performing solution for small object detection tasks, such as dense small targets in remote sensing images. The decision tree algorithm classified infestation severity with high accuracy, achieving F₁-Scores of 0.906, 0.803, and 0.842 for mild, moderate, and severe infestations, respectively. Heatmaps generated from borehole detection results enabled spatial visualization of infestation severity, providing a scientific basis for quantitative monitoring and targeted pesticide application in field infestations. [Conclusions] The results show that the YOLO-ESN model has more advantages in overall detection accuracy and running speed. While improving the lightweight degree and deployment efficiency of the model, it also shows better recognition ability in small target detection, and can accurately locate the wormhole area on the corn leaf, effectively improving the bounding box regression accuracy and feature extraction efficiency. Compared with the traditional insect recognition method, the use of wormholes as detection objects is more in line with the actual field situation, effectively avoiding the problems of insect occlusion and strong concealment, and improving the availability of field image data and algorithm robustness. The heat map generated by the model detection results can also effectively display the distribution changes of insect pests in farmland, providing a scientific basis for precision pesticide spraying and farmland management. Overall, this study provides an effective solution for the intelligent detection of corn borer pests, has strong versatility and promotion prospects, and can provide strong technical support for precision agriculture and smart farmland management.

[Objective] Timely detection and early warning of livestock health issues are critical for green and efficient management within large-scale cattle farms. Traditional manual inspections are time-consuming, labor-intensive, and prone to missed or erroneous detections. Robotic inspections offer significant advantages including all-weather operation, high precision, high efficiency, and low cost. However, existing path planning approaches predominantly focus on dynamic obstacle avoidance and fixed target point inspection path, often failing to address two key challenges in dynamic large-scale farm environments: global traversal of individual large livestock (e.g., beef cattle, dairy cows) and accessibility of local areas compromised by dynamic obstacles. This study aims to overcome the limitations of existing robotic inspection systems in large-scale cattle farms, specifically addressing the lack of comprehensive inspection capability for dynamic individuals, excessive path redundancy, and insufficient proactive obstacle avoidance capability. [Methods] A global-local optimization algorithm was proposed for large-scale cattle farm intelligent inspection path planning, which integrated the traveling salesman problem (TSP), A* and dynamic window approach (DWA), and solved the problems of global multi-objective individual traversal, path redundancy and local passability with proactive obstacle avoidance in dynamic cattle farm scenarios. ​​For global traversal optimization, a global path planning algorithm was introduced which combined ​​improved TSP​​ and ​​optimized A*. Specifically, the ​​inspection status list​​ tracking breeding sheds and individual cattle was maintained to enhance the TSP's Nearest Neighbor Algorithm, dynamically updating targets to avoid re-visits. A ​​dynamic priority mechanism​​ optimized multi-objective inspection, determining the optimal visitation sequence across barns and dynamic paths within barns. Optimize the data structure of the A* algorithm, and introduce a diagonal distance heuristic function to replace Manhattan distance, which more accurately reflected the movement cost in eight directions. Simplify the path obtained by the A* algorithm through greedy strategy, and used Bresenham's line algorithm to check whether there were obstacles in the straight line field of view. If there were no obstacles, redundant inflection points were removed to construct an efficient moving path between sheds. ​​For local passability Optimization,​​ an enhanced DWA-based local path was proposed for planning algorithm. The dynamic safety threshold of obstacle size was introduced to improve the DWA. When the inspection robot judged that the size of the obstacle in the local accessible area was too large and the robot was difficult to pass, it would actively avoid or detour in advance to ensure the safe avoidance of large obstacles in narrow passages. The improved DWA also increases the ​​task progress potential field​​, drives the robot to move to the breeding shed to be visited with the attractive force field model, balances the local obstacle avoidance and global inspection efficiency, and realized the real-time judgment of local area passability caused by dynamic obstacles and proactived obstacle avoidance in advance. [Results and Discussions] The optimized A* algorithm's data structures significantly improved search efficiency. The diagonal distance heuristic and greedy strategy substantially enhanced path smoothness. Compared to the traditional A*, the improved A* achieved average reductions of 90.06% in planning time, 85.13% in path turns, and 1.83% in path length. The global inspection algorithm performance was validated in a simulated dynamic cattle farm environment built in Matlab using discrete grid mapping and cattle movement models. The global inspection algorithm combining improved TSP and optimized A* achieved 100% average coverage of individual cattle. Inspection path length and time were reduced by 17.99% and 20.85%, respectively, compared to the classic ant colony optimization (ACO) algorithm, demonstrating superior efficiency in dynamic multi-objective inspection scenarios. The improved DWA successfully enabled proactive judgment of local path passability based on obstacle size. By adjusting the robot's linear velocity, angular velocity, and attitude angle in real time, the algorithm achieved robust proactive obstacle avoidance. The inspection robot will reduce the linear velocity in advance when encountering obstacles, and realize proactive obstacle avoidance by adjusting the attitude angle. Simulation experiments confirmed that robots equipped with the improved DWA effectively navigated around unknown static and dynamic obstacles while maintaining global path-tracking capability. [Conclusions] This study proposes a dynamic multi-objective inspection path planning algorithm for large-scale cattle farms, integrating an improved TSP, optimized A*, and enhanced DWA. The global inspection algorithm combining improved TSP and optimized A*, utilizing dynamic inspection status lists and path optimization techniques, achieved global inspection coverage of individual cattle, significantly improving inspection quality and efficiency. The local inspection algorithm based on improved DWA, incorporating obstacle size dynamic safety threshold and task progress, achieved real-time judgment of local passability and proactive obstacle avoidance, ensuring safe robot navigation in complex environments. The global-local co-optimization framework demonstrated adaptability to the dynamic farm environment, enabling the timely completion of individual traversal tasks, and providing a robust solution for intelligent inspection in large-scale cattle operations. Future work involves integrating the proposed path planning algorithm with simultaneous localization and mapping (SLAM), cattle identification, distance detection systems on inspection robot platforms, and conducting extensive field tests within operational cattle farms. Exploring multi-robot collaborative inspection frameworks and incorporating the Vision-and-Language Navigation model to enhance environmental perception and anomaly-handling capabilities are promising directions for adapting to the complexities of even larger-scale farming scenarios.

[Objective] Bee pollination plays a crucial role in plant reproduction and crop yield, making its identification and monitoring highly significant for agricultural production. This study aimed to scientifically evaluate pollination efficiency, accurately detect the pollination status of flowers, and provide reliable data to guide flower and fruit thinning in orchards. Ultimately, it supports the scientific management of bee colonies and enhances agricultural efficiency. However, practical detection of bee pollination poses various challenges, including the small size of bee targets, their low pixel occupancy in images, and the complexity of floral backgrounds. To address these issues, the study proposed a lightweight recognition model capable of effectively overcoming these obstacles, thereby advancing the practical application of bee pollination detection technology in smart agriculture. [Methods] A specialized bee pollination dataset was constructed comprising three flower types: strawberry, blueberry, and chrysanthemum. Videos capturing the pollination process were recorded using high-resolution cameras and subjected to frame sampling to extract representative images. These initial images underwent manual screening to ensure quality and relevance. To address challenges such as limited data diversity and class imbalance, a comprehensive data augmentation strategy was employed. Techniques including rotation, flipping, brightness adjustment, and mosaic augmentation were applied, significantly expanding the dataset's size and variability. The enhanced dataset was subsequently split into training and validation sets at an 8:2 ratio to ensure robust model evaluation. The base detection model was built upon an improved YOLOv10n architecture. The conventional C2f module in the backbone was replaced with a novel Cross Stage Partial network_multi-scale edge information enhance (CSP_MSEE) module, which synergizes the cross-stage partial connections from cross stage partial network (CSPNet) with a multi-scale edge enhancement strategy. This design greatly improved feature extraction, particularly in scenarios involving fine-grained structures and small-scale targets like bees. For the neck, the researchers implemented a hybrid-scale feature pyramid network (HS-FPN), incorporating a channel attention (CA) mechanism and a Dimension Matching (DM) module to refine and align multi-scale features. These features were further integrated through a selective feature fusion (SFF) module, enabling the effective combination of low-level texture details and high-level semantic representations. The detection head was replaced with the lightweight shared detail enhanced convolutional detection head (LSDECD), an enhanced version of the Lightweight shared convolutional detection head (LSCD) detection head. It incorporated detail enhancement convolution (DEConv) from DEA-Net to improve the extraction of fine-grained bee features. Additionally, the standard convolution_groupnorm (Conv_GN) layers were replaced with detail enhancement convolution_ groupnorm (DEConv_GN), significantly reducing model parameters and enhancing the model's sensitivity to subtle bee behaviors. This lightweight yet accurate model design made it highly suitable for real-time deployment on resource-constrained edge devices in agricultural environments. [Results and Discussions] Experimental results on the three bee pollination datasets—strawberry, blueberry, and chrysanthemum—demonstrated the effectiveness of the proposed improvements over the baseline YOLOv10n model. The enhanced model achieved significant reductions in computational overhead, lowering the computational complexity by 3.1 GFLOPs and the number of parameters by 1.3 M. These reductions contribute to improved efficiency, making the model more suitable for deployment on edge devices with limited processing capabilities, such as mobile platforms or embedded systems used in agricultural monitoring. In terms of detection performance, the improved model showed consistent gains across all three datasets. Specifically, the recall rates reached 82.6% for strawberry, 84.0% for blueberry, and 84.8% for chrysanthemum flowers. Corresponding mAP50 (mean Average Precision at IoU threshold of 0.5) scores were 89.3%, 89.5%, and 88.0%, respectively. Compared to the original YOLOv10n model, these results marked respective improvements of 2.1% in recall and 1.7% in mAP50 on the strawberry dataset, 2.0% and 2.6% on the blueberry dataset, and 2.1% and 2.2% on the chrysanthemum dataset. [Conclusions] The proposed YOLOv10n-CHL lightweight bee pollination detection model, through coordinated enhancements at multiple architectural levels, achieved notable improvements in both detection accuracy and computational efficiency across multiple bee pollination datasets. The model significantly improved the detection performance for small objects while substantially reducing computational overhead, facilitating its deployment on edge computing platforms such as drones and embedded systems. This research provides a solid technical foundation for the precise monitoring of bee pollination behavior and the advancement of smart agriculture. Nevertheless, the model's adaptability to extreme lighting and complex weather conditions remains an area for improvement. Future work will focus on enhancing the model's robustness in these scenarios to support its broader application in real-world agricultural environments.

[Objective] Trajectory tracking and obstacle avoidance control are important components of autonomous driving chassis, but most current studies treat these two issues as two independent tasks. This will cause the chassis to stop trajectory tracking when facing an obstacle, and then implement trajectory tracking again after completing obstacle avoidance. If the distance from the reference path after obstacle avoidance is too far, the subsequent tracking performance will be affected. There are also some studies on trajectory tracking and obstacle avoidance at the same time, but these studies are either not smooth enough and prone to chatter, or the control system is too complex. Therefore, a simple algorithm is proposed that can simultaneously implement trajectory tracking and obstacle avoidance control of the chassis. This method can achieve the chassis avoiding obstacles in the reference path while tracking the trajectory, and can quickly converge to the reference trajectory after avoiding. [Methods] First, the kinematic model and kinematic error model of the chassis were designed. Since skid-steering was adopted, the kinematic model of the chassis needs to be specially processed when designing the mathematical model, and it was simplified to a two-wheel differential rotation robot model. Secondly, the Takagi-Sugeno (T-S) fuzzy controller of the chassis was designed. Since the error model of the chassis was designed in advance, the T-S fuzzy model of the chassis could be designed. Based on the T-S model, a T-S fuzzy controller was designed using the parallel distributed compensation (PDC) algorithm. The linear quadratic regulator (LQR) controller was used as the state feedback controller of each fuzzy subsystem in the T-S fuzzy controller to form a global T-S fuzzy controller, which could realize the trajectory tracking function of the chassis when there were no obstacles. Secondly, the obstacle avoidance controller of the chassis was designed. A new \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller was designed in the global open-loop system to generate the reference trajectory to avoid obstacles. The implementation method was that when the system detects an obstacle in the environment, the \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller starts working, and generates a new path by judging the distance between the obstacle and the chassis, so that the chassis could avoid the obstacle. When the chassis bypassed the obstacle, the \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller stopped working. The \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller had two gain matrices, Q and R . How to select them determined the control performance of the \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller. Usually, these two parameters were fixed parameters summarized by designers through trial and error, but they were often only suitable for certain fixed driving conditions and were difficult to adapt to the scene where obstacles suddenly appeared in the path. Therefore, in order to better realize the obstacle avoidance function, a fuzzy controller was designed to adjust the gain matrices Q and R of the \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller in real time. Then, in order to realize trajectory tracking and obstacle avoidance controlled at the same time, a fuzzy fusion controller was designed to combine the two controllers to form the final chassis input, and the Mamdani fuzzy controller was selected to achieve it. Finally, the method was simulated and experimental tested. The simulation test used MATLAB/Simulink joint simulation test, and the experiments was based on the self-developed electric multi-functional chassis. [Results and Discussions] The simulation results showed that when there were no obstacles, the control method could achieve stable trajectory tracking in the reference path composed of straight lines and curves. When there were obstacles, the vehicle could avoid them smoothly and quickly converge to the reference trajectory. When facing obstacles, the designed fuzzy logic \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller could adaptively change the controller gain matrix according to the vehicle's speed and the distance between the current obstacles to achieve rapid convergence. The experimental results showed that when there were no obstacles, the chassis could use the T-S fuzzy controller to achieve stable tracking of the reference trajectory, and the average errors in the lateral and longitudinal directions of the entire tracking process were 0.041 and 0.052 m, respectively. When facing obstacles, the T-S fuzzy controller and the \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller realized the obstacle avoidance and tracking control of the chassis through joint control. The fuzzy controller was used to adjust the gain matrix of the \mathrm{L}\mathrm{Q}{\mathrm{R}}_{\mathrm{o}\mathrm{b}\mathrm{s}} controller in real time, and the tracking error was reduced by 33.9% compared with the controller with a fixed gain matrix. [Conclusions] The control system can simultaneously realize the trajectory tracking and obstacle avoidance control of the chassis, can quickly converge the tracking error to zero, and achieve smooth obstacle avoidance control. Although the control method proposed in this paper is simple and efficient, and can achieve trajectory tracking and obstacle avoidance control at the same time, and the tracking and obstacle avoidance effects are significantly improved, the control method can only handle static obstacles in the reference path at present, and subsequent research will focus on dynamic obstacles.