[Significance] Grain production in China spans multiple stages and involves numerous heterogeneous factors, including agronomic inputs, natural resources, environmental conditions, and socio-economic variables. However, the associated data generated throughout the entire production process—ranging from cultivation planning to harvest evaluation—remains highly fragmented, unstructured, and semantically diverse. This complexity data, combined with the lack of integrated core algorithms to support decision-making, has severely limited the potential of big data to drive innovation in grain production. Knowledge graph (KG) technology, by offering structured, semantically-rich representations of complex data, provides a promising approach to address these challenges. KGs enable the integration of multi-source and heterogeneous data, enhance semantic mining and reasoning capabilities, and offer intelligent, knowledge-driven support for sustainable grain production. [Progress] This paper systematically reviewed the current state of research and application of knowledge graphs in the domain of grain production big data. A comprehensive KG-driven framework was proposed based on a hybrid paradigm combining data-driven modeling and domain knowledge guidance. The framework was designed to support the entire grain production lifecycle and addressed three primary dimensions of data complexity: structural diversity, relational heterogeneity, and semantic ambiguity. The key techniques of constructing multimodal knowledge map and temporal reasoning for grain production were described. First, an agricultural ontology system for grain production was designed, incorporating domain-specific concepts, hierarchical relationships, and attribute constraints. This ontology provided the semantic foundation for knowledge modeling and alignment. Second, multimodal named entity recognition (NER) techniques were employed to extract entities such as crops, varieties, weather conditions, operations, and equipment from structured and unstructured data sources, including satellite imagery, agronomic reports, IoT sensor data, and historical statistics. Advanced deep learning models, such as BERT and vision-language transformers, were used to enhance recognition accuracy across text and image modalities. Third, the system implemented multimodal entity linking and disambiguation, which connected identical or semantically similar entities across different data sources by leveraging graph embeddings, semantic similarity measures, and rule-based matching. Finally, temporal reasoning modules were constructed using temporal KGs and logical rules to support dynamic inference over time-sensitive knowledge, such as crop growth stages, climate variations, and policy interventions. The proposed KG-driven system enabled the development of intelligent applications across multiple stages of grain production. In the pre-production stage, knowledge graphs supported decision-making in resource allocation, crop variety selection, and planting schedule optimization based on past data patterns and predictive inference. During the in-production stage, the system facilitated precision operations—such as real-time fertilization and irrigation—by reasoning over current field status, real-time sensor inputs, and historical trends. In the post-production stage, it enabled yield assessment and economic evaluation through integration of production outcomes, environmental factors, and policy constraints. Conclusions and Prospects Knowledge graph technologies offer a scalable and semantically-enhanced approach for unlocking the full potential of grain production big data. By integrating heterogeneous data sources, representing domain knowledge explicitly, and supporting intelligent reasoning, KGs can provide visualization, explainability, and decision support across various spatial scales, including national, provincial, county-level, and large-scale farm contexts. These technologies are of great scientific and practical significance in supporting China's national food security strategy and advancing the goals of storing grain in the land and storing grain in technology. Future directions include the construction of cross-domain agricultural knowledge fusion systems, dynamic ontology evolution mechanisms, and federated KG platforms for multi-region data collaboration under data privacy constraints.

[Objective] China is the world's large tobacco producer, where tobacco significantly contributes to the national economy. Among all production stages, leaf picking process requires the most labor. Currently, tobacco picking in China remains predominantly manual, characterized by low mechanization, high labor demand, constrained picking period, and intensive labor intensity requirement. As agricultural modernization progresses, mechanized tobacco picking has become increasingly essential. However, existing foreign tobacco harvesters are oversized and cause substantial leaf damage, making them unsuitable for domestic conditions. A rotating envelope picking mechanism designed is proposed to minimize leaf damage and loss during picking. [Methods] A tobacco plant model was developed based on morphological characteristics and assessed mechanical properties of tobacco leaves using a digital push-pull force gauge to measure tensile and bending characteristics. Force measurements for leaf separation in various directions revealed minimal force requirements when separating leaves from top to bottom. Based on these findings, a rotating envelope comb-type picking mechanism was designed, featuring both a transmission mechanism and a picking wheel. During operation, the picking wheel rotates around the tobacco stem, employing inertial combing from top to bottom for efficient leaf separation. Analysis of interactions between the picking mechanism and tobacco leaves identified combing speed as the parameter with greatest impact on picking efficiency. The mechanism's structural parameters affecting the picking wheel's movement trajectory were examined, and an improved particle swarm optimization algorithm was applied using MATLAB to refine these parameters. Additionally, Abaqus finite element simulation software was utilized to optimize the wheel structure's mechanical combing process. Dynamic simulation tests using Adams software modeled the mechanism's process of enveloping the tobacco stem and separating leaves, validating suction efficiency and determining optimal envelope range and speed parameters at various traveling speeds. To evaluate the picking effect and effectiveness of the tobacco leaf picking mechanism designed in this study, a field experiment was conducted in Sanxiang town, Yiyang county, Henan province. The performance of the picking mechanism was analyzed based on two critical evaluation criteria: the rate of tobacco leaf damage and the leakage rate. [Results and Discussions] By optimizing the mechanism's structural parameters using MATLAB, horizontal movement was reduced by 50.66%, and the movement trajectory was aligned vertically with the tobacco leaves, significantly reducing the risk of collision during the picking process. Finite element analysis identified the diameter of the picking rod as the key structural parameter influencing picking performance. Following extensive simulations, the optimal picking rod diameter was determined to be 15 mm, offering an ideal balance between structural strength and functional performance. The optimal envelope circle diameter for the mechanism was established at 70 mm. Aluminum alloy was selected as the material for the picking rod due to its lightweight nature, high strength-to-weight ratio, and excellent corrosion resistance. Dynamics analysis further revealed that the combing speed should not exceed 2.5 m/s to minimize leaf damage. The ideal rotational speed range for the picking mechanism was determined to be between 120 and 210 r/min, balancing operational efficiency with leaf preservation. These findings provide crucial guidance for refining the design and enhancing the practical performance of the picking mechanism. Field tests confirmed that the mechanism significantly improved operational performance, achieving a leakage rate below 7% and a damage rate below 10%, meeting the requirements for efficient tobacco picking. It was observed that excessive leaf leakage primarily occurred when leaves were steeply inclined, which hindered effective stem envelopment by the picking mechanism. Consequently, the mechanism proved particularly effective for picking centrally positioned leaves, while drooping leaves resulted in higher leakage and damage rates. The primary cause of leaf damage was found to be mechanical contact between the picking mechanism and the leaves during operation. Notably, while increasing striking speed reduced leakage, it simultaneously led to a higher damage rate. Compared to existing picking mechanism, this newly developed mechanism was more compact and supports layered leaf picking, making it especially well-suited for integration into small- and medium-sized picking machinery. [Conclusions] This study presents an effective and practical solution for tobacco leaf picking mechanization, specifically addressing the critical challenges of leaf damage and leakage. The proposed solution not only improves picking quality but also features a significantly simplified mechanical structure. By combining innovative technology with optimized design, this approach minimizes impact on delicate leaves, reduces leakage, and ensures higher yields with minimal human intervention. Analysis and testing demonstrate this mechanized solution's potential to significantly reduce production losses, offering both economic and operational benefits for the tobacco industry.

[Objective] The frequent occurrence of land use conflicts, such as the occupation of arable land by urban construction land expansion, non-grain use of arable land, and the shrinking of ecological space, poses multiple pressures on the Shandong section of the Yellow River in terms of economic development, arable land protection, and ecological conservation. Accurately identifying and predicting future trends of land-use conflicts in the Shandong section of the Yellow River under various scenarios will provide a reference for the governance of land use conflicts, rational land resource utilization, and optimization of the national land spatial pattern in this region. [Methods] The data used mainly including land use data, elevation data, basic geographic information data, meteorological data, protected area data, and socio-economic data. Drawing from the concept of ecological risk assessment, an "External Pressure + Vulnerability-Stability" model was constructed. Indicators such as area-weighted average patch fractal dimension, landscape value of land use types, and patch density were used to quantify and characterize land use conflicts in the Shandong section of the Yellow River from 2000 to 2020. Subsequently, the CA-Markov model was employed to establish cellular automata transition rules, with a 10-year simulation period using a default 5×5 cellular filter matrix, projecting 2030 land use conflict patterns under natural development, cultivated land protection, and ecological conservation scenarios. [Results and Discussions] From 2000 to 2020, significant changes in land use were observed in the Shandong section of the Yellow River, mainly characterized by rapid expansion of urban construction land and a reduction in grassland and arable land. Urban construction land increased by 4 346 km², with its proportion rising from 13.50% in 2000 to 18.67% in 2020. During the study period, the level of land use conflict showed a mitigating trend, with the average land use conflict index decreasing from 0.567 in 2000 to 0.522. Medium conflict has been the dominant type of land use conflict in the Shandong section of the Yellow River, followed by low conflict, while high conflict accounted for the smallest proportion. This indicate that land use conflicts in the region were generally controllable. The spatial pattern of land use conflicts in the Shandong section of the Yellow River remained relatively stable. Low conflicts were mainly distributed in areas with high concentration of arable land and water bodies, as well as in urban built-up areas. Medium conflicts were most widespread, especially in the transitional zones between arable land and rural settlements, and between arable land and forest land. The proportion of high conflict decreased from 19.34% in 2000 to 8.61% in 2020, mainly clustering in the transitional zones between urban construction land and other land types, the land type interlacing belt in the Central Shandong Hills, and along the Yellow River. The multi-scenario land use simulation results for 2030 showed significant differences in land use changes under different scenarios. Under the natural development scenario, the level of land use conflict was expected to deteriorate, with the most severe conflict situation. While both arable land protection and ecological conservation scenarios demonstrate partial conflict mitigation, the expansion of arable land occurs at the expense of ecological spaces, potentially compromising regional ecological security. In contrast, under the ecological conservation scenario, by prioritizing ecological protection and highlighting the protection of the ecological environment, the expansion of urban construction land and the reclamation of arable land, which cause ecological damage, were effectively curbed. Notably, this scenario exhibited the lowest proportion of high conflicts and demonstrated superior conflict mitigation effectiveness. [Conclusions] Land use conflicts in the Shandong section of the Yellow River have been somewhat mitigated, with the main form of conflict being the rapid expansion of urban construction land encroaching on arable land and ecological land. The ecological conservation scenario effectively balances the relationship between arable land protection, ecological security, and urbanization development, and is an optimal strategy for alleviating land use conflicts in the Shandong section of the Yellow River.

[Objective] This study aims to solve the current problem of the intelligent pruning robot's insufficient recognition accuracy of fruit tree branches and inaccurate pruning point localization in complex field environments. To address this, a deep learning method based on the fusion of images and point clouds was proposed, enabling non-contact segmentation of dormant high-spindle apple tree branches and phenotypic parameter measurement. And complete the automatic identification and accurate localization of pruning points. [Methods] In this study, localized RGB-D data were gathered from apple trees using a Realsense D435i camera, a device capable of effective depth measurements within a range of 0.3~3.0 m. Data collection occurred between early and mid-January 2024, from 9:00 AM to 4:00 PM daily. During this period, the weather remained sunny, ensuring optimal conditions for high-quality data acquisition. To maintain consistency, the camera was mounted on a stand at a distance of 0.4~0.5 m from the main stem of the apple trees. After collecting the data, researchers manually labeled trunks and branches using Labelme software. They also employed the OpenCV library to enhance image data, which helped prevent overfitting during model training. To improve segmentation accuracy for tree trunks and branches in RGB images, the research team introduced an enhanced U-Net model. This model utilized VGG16 (Visual Geometry Group 16) as its backbone feature extraction network and incorporated the Convolutional Block Attention Module (CBAM) at the up-sampling stage. Based on the segmentation results, a multimodal data processing flow was established. Initially, the segmented branch mask maps were obtained from skeleton lines extracted using OpenCV's algorithm. The first-level branch connection points were identified based on their positions relative to the trunk. Subsequently, potential pruning points were searched for within local neighborhoods through coordinate translation. An edge detection algorithm was applied to locate the nearest edge pixels to these potential pruning points. By extending the diameter line of the branch pixel points on the images and combining this with depth information, the actual diameter of the branches could be estimated. Additionally, the branch spacing was calculated using the differences in vertical coordinates of potential pruning points in the pixel coordinate system, alongside depth information. Meanwhile, the trunk point cloud data were acquired by merging the trunk mask map with the depth map. Preprocessing of the point cloud enabled the estimation of the average trunk diameter in the local view through cylindrical fitting using the Randomized Sampling Consistency (RANSAC) algorithm. Finally, an intelligent pruning decision-making algorithm was developed through investigation of orchardists' pruning experience, analysis of relevant literature, and integration of phenotypic parameter acquisition methods, thus achieving accurate prediction of apple tree pruning points. [Results and Discussion] The improved U-Net model in this study achieved a mean pixel accuracy (mPA) of 95.52% for branch segmentation, representing a 2.74% improvement over the original architecture. Corresponding increases were observed in mean intersection over union (mIoU) and precision metrics. Comparative evaluations against DeepLabV3+, PSPNet, and the baseline U-Net were conducted under both backlight and front-light illumination conditions. The enhanced model demonstrated superior segmentation performance and robustness across all tested scenarios. Ablation experiments indicated that replacing the original feature extractor with VGG16 yielded a 1.52% mPA improvement, accompanied by simultaneous gains in mIoU and precision. The integration of the Convolutional Block Attention Module (CBAM) at the up sampling stage further augmented the model's capacity to resolve fine branch structures. Phenotypic parameter estimation using segmented branch masks combined with depth maps showed strong correlations with manual measurements. Specifically, the coefficient of determination (R²) values for primary branch diameter, branch spacing, and trunk diameter were 0.96, 0.95, and 0.91, respectively. The mean absolute errors (MAE) were recorded as 1.33, 13.96, and 5.11 mm, surpassing the accuracy of visual assessments by human pruning operators. The intelligent pruning decision system achieved an 87.88% correct identification rate for pruning points, with an average processing time of 4.2 s per viewpoint. These results validated the proposed methodology's practical feasibility and operational efficiency in real-world agricultural applications. [Conclusion] In summary, an efficient and accurate method was proposed for identifying pruning points on apple trees based on the fusion of image and point cloud data through deep learning. This comprehensive approach provides significant support for the application of intelligent pruning robots in modern agriculture, further advancing the shift towards smarter and more efficient agricultural production. The findings demonstrate that this method not only offers high feasibility but also exhibits outstanding efficiency and accuracy in practical applications, laying a solid foundation for future agricultural automation.

Objective In modern agriculture, the rapid and accurate detection of chillies at different maturity stages is a critical step for determining the optimal harvesting time and achieving intelligent sorting of field-grown chillies. However, existing target detection models face challenges in efficiency and accuracy when applied to the task of detecting chilli maturity, which limits their widespread use and effectiveness in practical applications. To address these challenges, a new algorithm, Chilli-YOLO, was proposed for achieving efficient and precise detection of chilli maturity in complex environments. Methods This research focused on field-grown chillis cultivated at the national precision agriculture base in Changping district, Beijing, China. A comprehensive image dataset was collected, capturing chillis under diverse and realistic agricultural conditions, including varying lighting conditions, camera angles, and background complexities. These images were then meticulously categorized into four distinct maturity stages: Immature, transitional, mature, and dried. To ensure data quality and robustness, the initial image pool underwent a rigorous process of manual screening, followed by precise annotation using bounding boxes to delineate individual chillis. Furthermore, data augmentation techniques were employed to expand the dataset and enhance the model's generalization capabilities. To develop an accurate and efficient chili maturity detection system, the YOLOv10s object detection network was chosen as the foundational architecture. The model's performance was further enhanced through strategic optimizations targeting the backbone network. Specifically, standard convolutional layers were replaced with Ghost convolutions. This technique generated more feature maps from fewer parameters, resulting in significant computational savings and improved processing speed without compromising feature extraction quality. Additionally, the C2f module was substituted with the more computationally efficient GhostConv module, further reducing redundancy and enhancing the model's overall efficiency. To improve the model's ability to discern subtle visual cues indicative of maturity, particularly in challenging scenarios involving occlusion, uneven lighting, or complex backgrounds, the Partial Self-Attention (PSA) module within YOLOv10s was replaced with the Second-Order Channel Attention (SOCA) mechanism. SOCA leverages higher-order feature correlations to more effectively capture fine-grained characteristics of the chillis. This enabled the model to focus on relevant feature channels and effectively identify subtle maturity-related features, even when faced with significant visual noise and interference. Finally, to refine the precision of target localization and minimize bounding box errors, the Extended Intersection over Union (XIoU) loss function was integrated into the model training process. XIoU enhances the traditional IoU loss by considering factors such as the aspect ratio difference and the normalized distance between the predicted and ground truth bounding boxes. By optimizing for these factors, the model achieved significantly improved localization accuracy, resulting in a more precise delineation of chillis in the images and contributing to the overall enhancement of the detection performance. The combined implementation of these improvements aimed to construct an effective approach to correctly classify the maturity level of chillis within the challenging and complex environment of a real-world farm. Results and Discussion The experimental results on the custom-built chilli maturity detection dataset showed that the Chilli-YOLO model performed excellently across multiple evaluation metrics. The model achieved an accuracy of 90.7%, a recall rate of 82.4%, and a mean average precision (mAP) of 88.9%. Additionally, the model's computational load, parameter count, model size, and inference time were 18.3 GFLOPs, 6.37 M, 12.6 M, and 7.3 ms, respectively. Compared to the baseline model, Chilli-YOLO improved accuracy by 2.6 percent point, recall by 2.8 percent point and mAP by 2.8 percent point. At the same time, the model's computational load decreased by 6.2 GFLOPs, the parameter count decreased by 1.67 M, model size reduced by 3.9 M. These results indicated that Chilli-YOLO strikes a good balance between accuracy and efficiency, making it capable of fast and precise detection of chilli maturity in complex agricultural environments. Moreover, compared to earlier versions of the YOLO model, Chilli-YOLO showed improvements in accuracy of 2.7, 4.8, and 5 percent point over YOLOv5s, YOLOv8n, and YOLOv9s, respectively. Recall rates were higher by 1.1, 0.3, and 2.3 percent point, and mAP increased by 1.2, 1.7, and 2.3 percent point, respectively. In terms of parameter count, model size, and inference time, Chilli-YOLO outperformed YOLOv5. This avoided the issue of YOLOv8n's lower accuracy, which was unable to meet the precise detection needs of complex outdoor environments. When compared to the traditional two-stage network Faster RCNN, Chilli-YOLO showed significant improvements across all evaluation metrics. Additionally, compared to the one-stage network SSD, Chilli-YOLO achieved substantial gains in accuracy, recall, and mAP, with increases of 16.6%, 12.1%, and 16.8%, respectively. Chilli-YOLO also demonstrated remarkable improvements in memory usage, model size, and inference time. These results highlighted the superior overall performance of the Chilli-YOLO model in terms of both memory consumption and detection accuracy, confirming its advantages for chilli maturity detection. Conclusion The proposed Chilli-YOLO model optimizes the network structure and loss functions, not only significantly improving detection accuracy but also effectively reducing computational overhead, making it better suited for resource-constrained agricultural production environments. This model provides a reliable technical reference for intelligent harvesting of chillies in agricultural production environments, especially in resource-constrained settings. By improving both performance and efficiency, Chilli-YOLO represents a significant step forward in the field of agricultural automation and precision farming.

[Objective] Sichuan province, recognized as a strategic core region for China's food security, exhibits spatiotemporal dynamics in grain production that have significant implications for regional resource allocation and national food strategies. Existing studies have primarily focused on the spatial dimension of grain production, treating temporal and spatial characteristics separately. This approach neglected the non-stationary effects of time and failed to integrate the evolutionary patterns of time and space in a coherent manner. Consequently, the interrelationship between the temporal evolution and spatial distribution of grain production was not fully elucidated. Therefore, this study proposed a spatiotemporal integrated analysis framework along with a method for extracting spatiotemporal features. The objective was to elucidate the spatial pattern of grain production and its temporal variations in Sichuan province, thereby providing a scientific basis for regional food security management. [Methods] The study was based on county-level panel data from Sichuan province spanning the years 2000 to 2019. Multiple spatiotemporal analysis techniques were employed to comprehensively examine the evolution of grain production and to identify its driving mechanisms. Initially, standard deviation ellipse analysis and the centroid migration trajectory model were applied to assess the spatial distribution of major grain-producing areas and their temporal migration trends. This analysis enabled the identification of spatial agglomeration patterns and the direction of change in grain production. Subsequently, a three-dimensional spatiotemporal framework was constructed based on the space-time cube model. This framework integrated both temporal and spatial information. Hotspot analysis and the local Moran's I statistic were then utilized to systematically identify the distribution of cold and hot spots as well as spatial clustering patterns in county-level grain output. This approach revealed the spatiotemporal hotspots, clustering characteristics, and the evolving trends of grain production over time. Finally, a spatiotemporal geographically weighted regression model was employed to quantitatively assess the influence of various factors on grain production. These factors included natural elements (such as topography, climate, and soil properties), agricultural factors (such as the total sown area, mechanization level, and irrigation conditions), economic factors (such as per capita gross domestic product and rural per capita disposable income), and human factors (such as rural population and nighttime light intensity). The analysis elucidated the spatial heterogeneity and evolution of the principal driving forces affecting grain production in the province. [Results and Discussions] The results indicated that a high-yield core area was established on the eastern Sichuan plain, with the spatial distribution exhibiting a pronounced northeast-southwest orientation. The production centroid consistently remained near Lezhi County, although it experienced significant shifts during the periods 2000–2001 and 2009–2010. In contrast, the grain production levels in the western Sichuan plateau and the central hilly regions were relatively low. Over the past two decades, the province demonstrated seven distinct patterns in the distribution of cold and hot spots and three clustering patterns in grain production. Specifically, grain output on the Chengdu Plain continuously increased, the decline in production on the western plateau decelerated, and production in the central region consistently decreased. Approximately 64.77% of the province exhibited potential for increased production, particularly in the western region, where improvements in natural conditions and the gradual enhancement of agricultural infrastructure contributed to significant yield growth potential. Conversely, roughly 16.93% of the areas, characterized by complex topography and limited resources, faced potential yield reductions due to resource scarcity and restrictive cultivation conditions. The analysis further revealed that agricultural factors served as the dominant determinants influencing the spatiotemporal characteristics of grain production. In this regard, the total sown area and the area of cultivated land acted as positive contributors. Natural factors, including slope, soil pH, and annual sunshine duration, exerted negative effects. Although human and economic factors had relatively minor influences, indicators such as population density and nighttime light intensity also played a moderating role in regional grain production. The maintenance of agricultural land area proved crucial in safeguarding and enhancing grain yields, while improvements in natural resource conditions further bolstered production capacity. These findings underscored the inherent spatiotemporal disparities in grain production within Sichuan province and revealed the impact of agricultural resource allocation, environmental conditions, and policy support on the heterogeneity of spatial production patterns. [Conclusions] The proposed spatiotemporal integrated analysis framework provided a novel perspective for elucidating the dynamic evolution and driving mechanisms of grain production in Sichuan province. The findings demonstrated that the grain production pattern exhibited complex characteristics, including regional concentration, dynamic spatiotemporal evolution, and the interplay of multiple factors. Based on these results, future policies should emphasize the construction of high-standard farmland, the promotion of precision agriculture technologies, and the rational adjustment of agricultural resource allocation. Such measures are intended to enhance agricultural production efficiency and to improve the regional eco-agricultural system. Ultimately, these recommendations aim to furnish both theoretical support and practical guidance for the establishment of a stable and efficient grain production system and for advancing the development of Sichuan as a key granary.

[Objective] Selection of rice varieties requires consideration of several factors, such as yield, fertility, disease resistance and resistance to downfall. There are many rice varieties in the world, and different rice varieties have different traits. When users select rice varieties, they need to spend a lot of time to retrieve information about different rice varieties and make a selection, which increases the workload to some extent. In order to meet the user's rice variety selection needs, help users quickly access to the rice varieties they need, improve efficiency, and further promote the informatization and intelligence of rice breeding work, the bi-intentional modeling and knowledge graph diffusion model, an advanced method was proposed. [Methods] The research work was mainly carried out at two levels: data and methodology. At the data level, considering the current lack of relevant data support for rice variety selection and breeding recommendation, a certain amount of recommendation dataset was constructed. The rice variety selection recommendation dataset consisted of two parts: interaction data and knowledge graph. For the interaction data, the rice varieties that had been planted in the region were collected on a region-by-region basis, and then a batch of users was simulated and generated from the region. The corresponding rice varieties were assigned to the generated users according to the random sampling method to construct the user-item interaction data. For the knowledge graph, detailed text descriptions of rice varieties were first collected, and then information was extracted from them to construct data in ternary format from multiple varietal characteristics, such as selection unit, varietal category, disease resistance, and cold tolerance. At the methodological level, a model of Bi-intentional Modeling and Knowledge Graph Diffusion (BMKGD) was proposed. The intent factor in the interaction behavior and the denoising process of the knowledge graph were both taken into account by the BMKGD model. Intentions were usually considered from two perspectives: individual independence and conformity. A dual intent space was chosen to be built by the model to represent both perspectives. For the problem of noisy data in the knowledge graph, denoising was carried out by combining the idea of the diffusion model. Random noise was introduced to destroy the original structure when the knowledge graph was initialized, and the original structure was restored through iterative learning. The denoising was completed in this process. After that, cross-view contrastive learning was carried out in both views. [Results and Discussions] The results demonstrated that the method proposed in this paper achieved optimal performance in the rice variety selection dataset, with Recall and NDCG values improved by 2.9% and 3.7% compared to the suboptimal model. The performance improvement validated the effectiveness of the method to some extent, indicating that the BMKGD model was more suitable for rice variety recommendation. The Recall value of the BMKGD model on the rice variety selection dataset was 0.327 6, meeting the basic requirements of the recommendation system. It indicated that the method proposed in this paper could be used in the actual rice variety selection and breeding work to reduce the workload of users in the process of information retrieval and assist users in making decisions. In contrast, traditional knowledge-aware recommendation models did not perform well on the rice variety selection and breeding dataset, underperforming even compared to models without knowledge graph integration. The analysis revealed that the collaborative signals in the interaction data played a major role, while the quality of the constructed knowledge graph still had some room for improvement. The module variants with key components removed all exhibited a decrease in performance compared to the original model, which validated the effectiveness of the modules. The performance degradation of the model variants with each component removed varied, indicating that different components played different roles. The performance drop of the model variant with the cross-view contrastive learning module removed was small, indicating that there was some room for improvement in the module to fully utilize the collaborative relationship between the two views. [Conclusions] The BMKGD model proposed in this paper achieves good performance on the rice variety selection dataset and accomplishes the recommendation task well. It shows that the model can be used to support the rice variety selection and breeding work and help users to select suitable rice varieties. In addition, a certain amount of rice selection and breeding dataset is constructed, which provides data support for the subsequent rice variety recommendation work. Improvements in modeling methods also provide ideas for subsequent work. The research results can be applied to the work of rice variety selection and breeding to reduce the user's workload in information retrieval, and can also provide technical support for scientific breeding.

[Objective] With the rapid development of remote sensing technology, remote sensing images have become a crucial data source for fields such as surface observation, environmental monitoring, and natural disaster prediction. However, the acquisition of remote sensing images is often affected by atmospheric conditions, particularly weather phenomena like haze and cloud cover, which degrade image quality and pose challenges to subsequent analysis and processing tasks. The presence of haze significantly reduces the contrast, color, and clarity of remote-sensing images, thereby impairing the extraction and identification of ground features. Consequently, effectively removing haze from remote-sensing images has become a focal point of interest for academia and industry. Haze removal is especially critical in agriculture, environmental protection, and urban planning, where high-quality remote sensing data is essential for monitoring crop growth, assessing soil quality, and predicting natural disasters. In recent years, the rise of deep learning has brought new possibilities for haze removal in remote-sensing images. The introduction of attention mechanisms, in particular, has allowed models to better capture and utilize important features within images, significantly improving dehazing performance. However, despite these advancements, traditional channel attention mechanisms typically rely on global average pooling to aggregate feature information. While this approach simplifies computational complexity, it is less effective when dealing with images that exhibit significant local variations and are sensitive to outliers. Additionally, remote sensing images often cover vast areas with diverse terrains, complex landforms, and dramatic spectral variations, making haze patterns more complex and uneven. Developing more efficient and adaptive dehazing methods that can fully account for local and global features in remote sensing images is a key direction for the future development of dehazing technology. [Method] Therefore, to address this issue, this paper proposes a Hybrid Attention-Based Generative Adversarial Network (HAB-GAN), which integrates an Efficient Channel Attention (ECA) module and a Spatial Attention Block (SAB). By merging feature extraction from both channel and spatial dimensions, the model effectively enhances its ability to identify and recover hazy areas in remote sensing images. In HAB-GAN, the Efficient Channel Attention (ECA) module captures local cross-channel interactions, addressing the shortcomings of traditional global average pooling in terms of insufficient sensitivity to local detail information. The ECA module uses a global average pooling strategy without dimensionality reduction, automatically adapting to the characteristics of each channel without introducing extra parameters, thereby enhancing the inter-channel dependencies. ECA employs a one-dimensional convolution operation, which uses a learnable kernel size to adaptively determine the range of channel interactions. This design effectively avoids the over-smoothing of global features common in traditional pooling layers, allowing the model to more precisely extract local details while maintaining low computational complexity. The SAB module introduces a weighted mechanism on the spatial dimension by constructing a spatial attention map to enhance the model's ability to identify hazy areas in the image. This module extracts feature maps through convolution operations and applies attention weighting in both horizontal and vertical directions, highlighting regions with severe haze, allowing the model to better capture spatial information in the image, thereby enhancing dehazing performance. The generator of HAB-GAN combines residual network structures with hybrid attention modules. It first extracts initial features from input images through convolutional layers and then passes these features through several residual blocks. The residual blocks effectively mitigate the vanishing gradient problem in deep neural networks and maintain feature consistency and continuity by passing input features directly to deeper network layers through skip connections. Each residual block incorporates ECA and SAB modules, enabling precise feature learning through weighted processing in both channel and spatial dimensions. After extracting effective features, the generator generates dehazed images through convolution operations. The discriminator adopts a standard convolutional neural network architecture, focusing on extracting local detail features from the images generated by the generator. It consists of multiple convolutional layers, batch normalization layers, and Leaky ReLU activation functions. By extracting local features layer by layer and down-sampling, the discriminator progressively reduces the spatial resolution of the images, evaluating their realism at both global and local levels. The generator and discriminator are jointly optimized through adversarial training, where the generator aims to produce increasingly realistic dehazed images, and the discriminator continually improves its ability to distinguish between real and generated images, thereby enhancing the learning effectiveness and image quality of the generator. [Results and Discussions] To validate the effectiveness of HAB-GAN, extensive experiments were conducted on the RESISC45 dataset. The experimental results demonstrate that compared to existing dehazing models, HAB-GAN excels in key evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Specifically, compared to SpA GAN, HAB-GAN improves PSNR by 2.64 dB and SSIM by 0.012 2; compared to HyA-GAN, PSNR improves by 1.14dB and SSIM by 0.001 9. Additionally, to assess the generalization capability of HAB-GAN, further experiments were conducted on the RICE2 dataset to verify its performance in cloud removal tasks. The results show that HAB-GAN also performs exceptionally well in cloud removal tasks, with PSNR improving by 3.59 dB and SSIM improving by 0.040 2. Compared to HyA-GAN, PSNR and SSIM increased by 1.85 dB and 0.012 4, respectively. To further explore the impact of different modules on the model's performance, ablation experiments were designed, gradually removing the ECA module, the SAB module, and the entire hybrid attention module. The experimental results show that removing the ECA module reduces PSNR by 2.64 dB and SSIM by 0.012 2; removing the SAB module reduces PSNR by 2.96 dB and SSIM by 0.008 7; and removing the entire hybrid attention module reduces PSNR and SSIM by 3.87 dB and 0.033 4, respectively. [Conclusions] This demonstrates that the proposed HAB-GAN model not only performs excellently in dehazing and declouding tasks but also significantly enhances the clarity and detail recovery of dehazed images through the synergistic effect of the Efficient Channel Attention (ECA) module and the Spatial Attention (SAB) module. Additionally, its strong performance across different remote sensing datasets further validates its effectiveness and generalization ability, showcasing broad application potential. Particularly in fields such as agriculture, environmental monitoring, and disaster prediction, where high-quality remote sensing data is crucial, HAB-GAN is poised to become a valuable tool for improving data reliability and supporting more accurate decision-making and analysis.

[Objective] The accuracy of identifying litchi pests is crucial for implementing effective control strategies and promoting sustainable agricultural development. However, the current detection of litchi pests is characterized by a high percentage of small targets, which makes target detection models challenging in terms of accuracy and parameter count, thus limiting their application in real-world production environments. To improve the identification efficiency of litchi pests, this study proposed a lightweight target detection model, YOLO-LP (YOLO-Litchi Pests), based on YOLOv10n. The model aimed to enhance the detection accuracy of small litchi pest targets in multiple scenarios by optimizing the network structure and loss function, while also reducing the number of parameters and computational costs. [Methods] Two classes of litchi insect pests (cocoon and gall) images were collected as datasets for modeling in natural scenarios (sunny, cloudy, post-rain) and laboratory environments. The original data were expanded through random scaling, random panning, random brightness adjustments, random contrast variations, and Gaussian blurring to balance the category samples and enhance the robustness of the model, generating a richer dataset named the CG dataset (cocoon and gall dataset). The YOLO-LP model was constructed after the following three improvements. Specifically, (1) the C2f module of the backbone network (Backbone) in YOLOv10n was optimized and the C2f_GLSA module was constructed using the Global-to-Local Spatial Aggregation (GLSA) module to focus on small targets and enhance the differentiation between the targets and the backgrounds, while simultaneously reducing the number of parameters and computation. (2) A frequency-aware feature fusion module (FreqFusion) was introduced into the neck network (Neck) of YOLOv10n and a frequency-aware path aggregation network (FreqPANet) was designed to reduce the complexity of the model and address the problem of fuzzy and shifted target boundaries. (3) The SCYLLA-IoU (SIoU) loss function replaced the Complete-IoU (CIoU) loss function from the baseline model to optimize the target localization accuracy and accelerate the convergence of the training process. [Results and Discussions] YOLO-LP achieved 90.9%, 62.2%, and 59.5% for AP₅₀, AP_50:95, and AP-Small_50:95 in the CG dataset, respectively, and 1.9%, 1.0%, and 1.2% higher than the baseline model. The number of parameters and the computational costs were reduced by 13% and 17%, respectively. These results suggested that YOLO-LP had a high accuracy and lightweight design. Comparison experiments with different attention mechanisms validated the effectiveness of the GLSA module. After the GLSA module was added to the baseline model, AP₅₀, AP_50:95, and AP-Small_50:95 achieved the highest performance in the CG dataset, reaching 90.4%, 62.0%, and 59.5%, respectively. Experiment results comparing different loss functions showed that the SIoU loss function provided better fitting and convergence speed in the CG dataset. Ablation test results revealed that the validity of each model improvement and the detection performance of any combination of the three improvements was significantly better than the baseline model in the YOLO-LP model. The performance of the models was optimal when all three improvements were applied simultaneously. Compared to several mainstream models, YOLO-LP exhibited the best overall performance, with a model size of only 5.1 MB, 1.97 million parameters (Params), and a computational volume of 5.4 GFLOPs. Compared to the baseline model, the detection of the YOLO-LP performance was significantly improved across four multiple scenarios. In the sunny day scenario, AP₅₀, AP_50:95, and AP-Small_50:95 increased by 1.9%, 1.0 %, and 2.0 %, respectively. In the cloudy day scenario, AP₅₀, AP_50:95, and AP-Small_50:95 increased by 2.5%, 1.3%, and 1.3%, respectively. In the post-rain scenario, AP₅₀, AP_50:95, and AP-Small_50:95 increased by 2.0%, 2.4%, and 2.4%, respectively. In the laboratory scenario, only AP₅₀ increased by 0.7% over the baseline model. These findings indicated that YOLO-LP achieved higher accuracy and robustness in multi-scenario small target detection of litchi pests. [Conclusions] The proposed YOLO-LP model could improve detection accuracy and effectively reduce the number of parameters and computational costs. It performed well in small target detection of litchi pests and diseases and demonstrated strong robustness across different scenarios. These improvements made the model more suitable for deployment on resource-constrained mobile and edge devices. The model provided a valuable technical reference for small target detection of litchi pests in various scenarios.

[Objective] The purpose of this study is to solve the problem of accuracy and efficiency in the detection of tree planting sites (tree pits) in Inner Mongolia of China's 'Three North Project'. The traditional manual field investigation method of the tree planting sites is not only inefficient but also error-prone, and the low-altitude unmanned aerial vehicle (UAV) has become the best choice to solve these problems. To this end, the research team proposed an accurate recognition and detection model of tree planting sites based on YOLOv10-MHSA. [Methods] In this study, a long-endurance multi-purpose vertical take-off and landing fixed-wing UAV was used to collect images of tree planting sites. The UAV was equipped with a 26 million pixel camera with high spatial resolution, which was suitable for high-precision mapping in the field. The aerial photography was carried out from 11:00 to 12:00 on August 1, 2024. The weather was sunny, the wind force was 3, the flight height was set to 150 m (ground resolution was about 2.56 cm), the course overlap rate was 75 %, the side overlap rate was 65 %, and the flight speed was 20 m/s. After the image acquisition was completed, the aerial images were stitched using Metashape software (v2.1.0) to generate a digital orthophoto map (DOM) covering about 2 000 mu (880 m×1 470 m) of tree planting sites, and it was cut through a 640-pixel sliding window into 3 102 high-definition RGB images of 640×640 size for subsequent detection and analysis. In order to prevent overfitting in the process of network training, the research team expanded and divided the original data set. By increasing the amount of model training data, introducing different attention mechanisms and optimizing loss functions, the quality and efficiency of model training are improved. A more effective EIOU loss function was introduced, which was divided into three parts: IOU loss, distance loss and azimuth loss, which directly minimized the width and height difference between the target frame and Anchor, resulting in faster convergence speed and better positioning results. In addition, the Focal-EIOU loss function was introduced to optimize the sample imbalance problem in the bounding box regression task, which further improves the convergence speed and positioning accuracy of the model. [Results and Discussions] After the introduction of the multi-head self-attention mechanism (MHSA), the model was improved by 1.4% and 1.7% on the two evaluation criteria of AP@0.5 and AP@0.5:0.95, respectively, and the accuracy and recall rate were also improved. It showed that MHSA could better help the model to extract the feature information of the target and improve the detection accuracy in complex background. Although the processing speed of the model decreases slightly after adding the attention mechanism, the overall decrease was not large, and it could still meet the requirements of real-time detection. On the optimization of the loss function, the experiment compared the four loss functions of CIOU, SIOU, EIOU and Focal-EIOU. The results showed that the Focal-EIOU loss function was improved, and the precision and recall rates were also significantly improved. This showed that the Focal-EIOU loss function could accelerate the convergence speed of the model and improve the positioning accuracy when dealing with the sample imbalance problem in small target detection. Although the processing speed of the model was slightly reduced, it still meet the requirements of real-time detection. Finally, an improved model, YOLOv10-MHSA, was proposed, which introduces MHSA attention mechanism, small target detection layer and Focal-EIOU loss function. The results of ablation experiments showed that AP@0.5 and AP@0.5:0.95 were increased by 2.1% and 0.9%, respectively, after adding only small target detection layer on the basis of YOLOv10n, and the accuracy and recall rate were also significantly improved. When the MHSA and Focal-EIOU loss functions were further added, the model detection effect was significantly improved. Compared with the baseline model YOLOv10n, the AP@0.5, AP@0.5:0.95, P-value and R-value were improved by 6.6%, 9.8%, 4.4% and 5.1%, respectively. Although the FPS was reduced to 109, the detection performance of the improved model was significantly better than that of the original model in various complex scenes, especially for small target detection in densely distributed and occluded scenes. [Conclusions] In summary, this study effectively improved the YOLOv10n model by introducing MHSA and the optimized loss function (Focal-EIOU), which significantly improved the accuracy and efficiency of tree planting site detection in the 'Three North Project' in Inner Mongolia. The experimental results show that MHSA can enhance the ability of the model to extract local and global information of the target in complex background, and effectively reduce the phenomenon of missed detection and false detection. The Focal-EIOU loss function accelerates the convergence speed of the model and improves the positioning accuracy by optimizing the sample imbalance problem in the bounding box regression task. Although the model processing speed has declined, it still meets the real-time detection requirements and provides strong technical support for the scientific afforestation of the 'Three North Project'.

[Objective] Winter wheat yield is crucial for national food security and the standard of living of the population. Existing crop yield prediction models often show low accuracy under disaster-prone climatic conditions. This study proposed an improved hierarchical linear model (IHLM) based on a drought weather index reduction rate, aiming to enhance the accuracy of crop yield estimation under drought conditions. [Methods] The study constructed a winter wheat yield prediction hierarchical linear model (HLM) using the maximum Enhanced Vegetation Index-2 (EVI2max), meteorological data (precipitation, radiation, and temperature from March to May), and observed winter wheat yield data from 160 agricultural survey stations in Shandong province (2018–2021). To validate the model's accuracy, 70% of the data from Shandong province was randomly selected for model construction, and the remaining data was used to validate the accuracy of the yield model. Based on the basic HLM model, the study proposed a method to build an improved hierarchical linear model (IHLM). It considered the variation in meteorological factors as a key obstacle affecting crop growth and improved the model by calculating the relative meteorological factors. The calculation of relative meteorological factors helped reduce the impact of inter-annual differences in meteorological data. The accuracy of the improved HLM model was compared with that of the Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models. The HLM model provided more intuitive interpretation, especially suitable for processing hierarchical data, which helped capture the variability of winter wheat yield data under drought conditions. Therefore, a drought weather index reduction rate model from the agricultural insurance industry was introduced to further optimize the HLM model, resulting in the construction of the IHLM model. The IHLM model was designed to improve crop yield prediction accuracy under drought conditions. Since the precipitation differences between Henan and Shandong Provinces were small, to test the transferability of the IHLM model, Henan province sample data was processed in the same way as in Shandong, and the IHLM model was applied to Henan province to evaluate its performance under different geographical conditions. [Results and Discussions] The accuracy of the HLM model, improved based on relative meteorological factors (rMF), was higher than that of RF, SVR, and XGBoost. The validation accuracy showed a Pearson correlation coefficient (r) of 0.76, a Root Mean Squared Error (RMSE) of 0.60 t/hm², and a Normalized RMSE (nRMSE) of 11.21%. In the drought conditions dataset, the model was further improved by incorporating the relationship between the winter wheat drought weather index and the reduction rate of winter wheat yield. After the improvement, the RMSE decreased by 0.48 t/hm², and the nRMSE decreased by 28.64 percentage points, significantly enhancing the accuracy of the IHLM model under drought conditions. The IHLM model also demonstrated good applicability when transferred to Henan Province. This study used meteorological data from March to May and remote sensing data from EVI2max, which are easy to obtain, highly transferable, and offer good accuracy and resolution. Additionally, this research provided an approach to improve model stability under extreme weather conditions, showing a significant improvement in the model's performance. [Conclusions] The IHLM model developed in this study improved the accuracy and stability of crop yield predictions, especially under drought conditions. Compared to RF, SVR, and XGBoost models, the IHLM model was more suitable for predicting winter wheat yield. This research can be widely applied in the agricultural insurance field, playing a significant role in the design of agricultural insurance products, rate setting, and risk management. It enables more accurate predictions of winter wheat yield under drought conditions, with results that are closer to actual outcomes.

[Significance] The explosive development of agricultural big data has accelerated agricultural production into a new era of digitalization and intelligentialize. Agricultural big data is the core element to promote agricultural modernization and the foundation of intelligent agriculture. As a new productive forces, big data enhances the comprehensive intelligent management decision-making during the whole process of grain production. But it facing the problems such as the indistinct management mechanism of grain production big data resources, the lack of the full-chain decision-making algorithm system and big data platform for the whole process and full elements of grain production. [Progress] The big data platform for grain production is a comprehensive service platform that uses modern information technologies such as big data, Internet of Things, remote sensing and cloud computing, and provides intelligent decision-making support for the whole process of grain production based on intelligent algorithms for data collection, processing, analysis and monitoring related to grain production. This paper reviews the progress and challenges in grain production big data, monitoring and decision-making algorithms, as well as big data platforms in China and worldwide. With the development of the Internet of Things and high-resolution multi-modal remote sensing technology, the massive agricultural big data generated by the "Space-Air-Ground" Integrated Agricultural Monitoring System, has laid an important foundation for smart agriculture and promoted the shift of smart agriculture from model-driven to data-driven. However, there are still some problems in field management decision-making, such as the requirements for high spatio-temporal resolution and timeliness of the information are difficult to meet, and the algorithm migration and localization methods based on big data need to be studied. In addition, the agricultural machinery operation and spatio-temporal scheduling algorithm based on remote sensing and Internet of Things monitoring information to determine the appropriate operation time window and operation prescription, needs to be further developed, especially the cross-regional scheduling algorithm of agricultural machinery for summer harvest in China. Aiming at the problems that the monitoring and decision-making algorithms of grain production are not bi-connected, and the integration of agricultural machinery and information perception is insufficient, a framework of the grain production big data intelligent platform is proposed based on digital twins. The platform is based on multi-source heterogeneous grain production big data, incorporates with the full-chain standardized algorithms including data acquisition, information extraction, knowledge map construction, intelligent decision-making, full-chain collaboration of agricultural machinery operations, involving the typical application scenarios such as irrigation, fertilization, pests and diseases, drought and flood disaster emergency response, by digital twins. [Conclusions and Prospects] The emphasis should be the requirements of monitoring at macro-level management and intelligent production at micro-level farm, fully integrating big data technology with artificial intelligence, digital twin, cloud-edge computing, and other emerging technologies. The suggestions and trend for development of grain production big data platform are summarized in three aspects. (1) Creating an open, symbiotic grain production big data platform, with core characteristics such as open interface for crop and environmental sensors, maturity grading and cloud-native packaging mechanism of the core algorithms, highly efficient response to data and decision services. (2) Focusing on the typical application scenarios of grain production, take the exploration of technology integration and bi-directional connectivity as the base, and the intelligent service as the soul of the development path for the big data platform research. (3) the data-algorithm-service self-organizing regulation mechanism, the integration of decision-making information with the intelligent equipment operation, and the standardized, compatible and open service capabilities, can form the new quality productivity forces ensuring food safety, and green efficiency grain production.

[Significance] Estrus monitoring and identification in cows is a crucial aspect of breeding management in beef and dairy cattle farming. Innovations in precise sensing and intelligent identification methods and technologies for estrus in cows are essential not only for scientific breeding, precise management, and smart breeding on a population level but also play a key supportive role in health management, productivity enhancement, and animal welfare improvement at the individual level. The aims are to provide a reference for scientific management and the study of modern production technologies in the beef and dairy cattle industry, as well as theoretical methodologies for the research and development of key technologies in precision livestock farming in China. [Progress] Based on describing the typical characteristics of normal and abnormal estrus in cows, this paper systematically categorizes and summarizes the recent research progress, development trends, and methodological approaches in estrus monitoring and identification technologies, focusing on the monitoring and diagnosis of key physiological signs and behavioral characteristics during the estrus period. Firstly, it outlines the digital monitoring technologies for three critical physiological parameters—body temperature, rumination, and activity levels—and their applications in cow estrus monitoring and identification. It analyzes the intrinsic reasons for performance bottlenecks in estrus monitoring models based on body temperature, compares the reliability issues faced by activity-based estrus monitoring, and addresses the difficulties in balancing model generalization and robustness design. Secondly, it examines the estrus sensing and identification technologies based on three typical behaviors: feeding, vocalization, and sexual desire. It highlights the latest applications of new artificial intelligence technologies, such as computer vision and deep learning, in estrus monitoring and points out the critical role of these technologies in improving the accuracy and timeliness of monitoring. Finally, it focuses on multi-factor fusion technologies for estrus perception and identification, summarizing how different researchers combine various physiological and behavioral parameters using diverse monitoring devices and algorithms to enhance accuracy in estrus monitoring. It emphasizes that multi-factor fusion methods can improve detection rates and the precision of identification results, being more reliable and applicable than single-factor methods. The importance and potential of multi-modal information fusion in enhancing monitoring accuracy and adaptability are underlined. The current shortcomings of multi-factor information fusion methods are analyzed, such as the potential impact on animal welfare from parameter acquisition methods, the singularity of model algorithms used for representing multi-factor information fusion, and inadequacies in research on multi-factor feature extraction models and estrus identification decision algorithms. [Conclusions and Prospects] From the perspectives of system practicality, stability, environmental adaptability, cost-effectiveness, and ease of operation, several key issues are discussed that need to be addressed in the further research of precise sensing and intelligent identification technologies for cow estrus within the context of high-quality development in digital livestock farming. These include improving monitoring accuracy under weak estrus conditions, overcoming technical challenges of audio extraction and voiceprint construction amidst complex background noise, enhancing the adaptability of computer vision monitoring technologies, and establishing comprehensive monitoring and identification models through multi-modal information fusion. It specifically discusses the numerous challenges posed by these issues to current technological research and explains that future research needs to focus not only on improving the timeliness and accuracy of monitoring technologies but also on balancing system cost-effectiveness and ease of use to achieve a transition from the concept of smart farming to its practical implementation.

[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.