[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.

[Significance] This paper addresses the issues of inconsistent acquisition standards, incomplete data collection, and unclear governance mechanisms in China's agricultural production data. It explores the existing governance models for agricultural production big data, clarifying the technical path for the value realization of data elements through the integration and innovative application of key big data governance technologies and tools in scenarios. This provides a reference for achieving high-quality agricultural production driven by data. [Progress] From the perspective of agricultural production big data governance, it explores 17 types of big data governance technologies and tools in 6 major processes: data acquisition and processing, data storage and exchange, data management, data analysis, large model, and data security guarantee. It deeply studies the application methods of big data governance technologies in agricultural production. The remote sensing, unmanned aerial vehicle, Internet of Things, and terminal data acquisition and processing systems are basically mature, the data storage and exchange system is developing rapidly, the data management technology is in the initial stage, the data analysis technology is widely applied, the large model technology system is initially formed, and the data security guarantee system is gradually applied. The above technologies are well applied in scenarios through tools and middleware such as data matching, computing power matching, network adaptation, model matching, scenario matching, and business configuration. It analyzes the data governance throughout the entire chain of agricultural production, including pre-production, production, and post-production, as well as service cases for different types of agricultural parks, research institutes and universities, production entities, and farmers. It shows that good data governance can provide sufficient planning and input analysis before production, helping planting entities to plan reasonably; in production, it can provide data-based guidance for key scenarios such as agricultural machinery operations and agricultural technical services to fully assist the decision-making process of the production process; and based on massive data, it can achieve good results in yield assessment and production benefit evaluation. It introduces the governance experience in national-level industrial parks, provincial-level agricultural science and technology parks, and some single-product entities, and investigates the technologies, practices, and tools of agricultural production big data governance at home and abroad, indicating that it is necessary to break through the business chain and service model of agricultural production across regions, themes, and scenarios. [Conclusions and Prospects] This paper puts forward insights on the future development direction of agricultural production big data governance, including promoting the formulation and implementation of standards for agricultural production big data governance, building a general resource pool for agricultural production big data governance, expanding diversified application scenarios for agricultural production big data governance, adapting to the new paradigm of agricultural production big data governance driven by large models and massive data, and strengthening the security and privacy protection of agricultural production big data.

[Significance] ​The efficient and precise identification of rice growth stages through remote sensing technology holds critical significance for varietal breeding optimization and production management enhancement. Remote sensing, characterized by high spatial-temporal resolution and automated monitoring capabilities, provides transformative solutions for large-scale dynamic phenology monitoring, offering essential technical support to address climate change impacts and food security challenges in complex agroecosystems where precise monitoring of growth stage transitions enables yield prediction and stress-resilient cultivation management.​ [Progress] In recent years, the technical system for monitoring rice growth stages has achieved systematic breakthroughs in the perception layer, decision-making layer, and execution layer, forming a technological ecosystem covering the entire chain of "data acquisition-feature analysis-intelligent decision-making-precise operation". At the perception layer, a "space-air-ground" three-dimensional monitoring network has been constructed: high-altitude satellites (Sentinel-2, Landsat) realize regional-scale phenological dynamic tracking through wide-spectrum multi-temporal observations; low-altitude unmanned aerial vehicle (UAVs) equipped with hyperspectral and light detection and ranging (LiDAR) sensors analyze the heterogeneity of canopy three-dimensional structure; near-ground sensor networks real-timely capture leaf-scale photosynthetic efficiency and nitrogen metabolism parameters. Radiometric calibration and temporal interpolation algorithms eliminate the spatio-temporal heterogeneity of multi-source data, forming continuous and stable monitoring capabilities. Innovations in technical methods show three integration trends: first, multimodal data collaboration mechanisms break through the physical characteristic barriers between optical and radar data. Second, deep integration of mechanistic models and data-driven approaches embeds the PROpriétés SPECTrales-Scattering by arbitrary inclined leaves (PROSAIL) radiative transfer model into the long short-term memory (LSTM) network architecture. Third, cross-scale feature analysis technology breaks through by constructing organ-population association models based on dynamic attention mechanisms, realizing multi-granularity mapping between panicle texture features and canopy leaf area index (LAI) fluctuations. The current technical system has completed three-dimensional leaps: From discrete manual observations to full-cycle continuous perception, with monitoring frequency upgraded from weekly to hourly; from empirical threshold-based judgment to mechanism-data hybrid-driven, the cross-regional generalization ability of the model can be significantly improved; from independent link operations to full-chain collaboration of "perception-decision-execution", constructing a digital management closed-loop covering rice sowing to harvest, providing core technical support for smart farm construction. [Conclusions and Prospects] Current technologies face three-tiered challenges: Data heterogeneity, feature limitations and algorithmic constraints. Future research should focus on three aspects: 1) Multi-source data assimilation systems to reconcile spatiotemporal heterogeneity through UAV-assisted satellite calibration and GAN-based cloud-contaminated data reconstruction; 2) Cross-scale physiological-spectral models integrating 3D canopy architecture with adaptive soil-adjusted indices to overcome spectral saturation; 3) Mechanism-data hybrid paradigms embedding thermal-time models into LSTM networks for environmental adaptation, developing lightweight CNNs with multi-scale attention for occlusion-resistant panicle detection, and implementing transfer learning for cross-regional model generalization. The convergence of multi-source remote sensing, intelligent algorithms, and physiological mechanisms will establish a full-cycle dynamic monitoring system based on agricultural big data.

[Objective]Asparagus officinalis L. is a perennial plant with a long harvesting cycle and fast growth rate. The harvesting period of tender stems is relatively concentrated, and the shelf life of tender stems is very short. Therefore, the harvested asparagus needs to be classified according to the specifications of asparagus in a short time and then packaged and sold. However, at this stage, the classification of asparagus specifications basically depends on manual work, and it is difficult for asparagus of different specifications to rely on sensory grading, which requires a lot of money and labor. To save labor costs, an algorithm based on asparagus stem diameter classification was developed using deep learning and computer vision technology. This method selected YOLOv11 as the baseline model and makes several improvements, aiming to study a lightweight model for accurate grading of post-harvest asparagus. [Methods] This dataset was obtained by cell phone photography of post-harvest asparagus using fixed camera positions. In order to improve the generalization ability of the model, the training set was augmented with data by increasing contrast, mirroring, and adjusting brightness. The data-enhanced training set includes a total of 2 160 images for training the model. And the test set and validation set include 90 and 540 images respectively for inference and validation of the model. In order to enhance the performance of the improved model, the following four improvements were made to the baseline model, respectively. First, the efficient channel attention (ECA) module was added to the twelfth layer of the YOLOv11 backbone network. The ECA enhanced asparagus stem diameter feature extraction by dynamically adjusting channel weights in the convolutional neural network and improved the recognition accuracy of the improved model. Second, the bi-directional feature pyramid network (BiFPN) module was integrated into the neck network. This module modified the original feature fusion method to automatically emphasize key asparagus features and improved the grading accuracy through multi-scale feature fusion. What's more, BiFPN dynamically adjusted the importance of each layer to reduce redundant computations. Next, the slim-neck module was applied to optimize the neck network. The slim-neck Module consisted of GSConv and VOVGSCSP. The GSConv module replaced the traditional convolutional. And the VOVGSCSP module replaced the C2k3 module. This optimization reduced computational costs and model size while improving the recognition accuracy. Finally, the original YOLOv11 detection head was replaced with an EfficientDet Head. EfficientDet Head had the advantages of light weight and high accuracy. This head co-training with BiFPN to enhance the effect of multi-scale fusion and improve the performance of the model. [Results and Discussions] In order to verify the validity of the individual modules introduced in the improved YOLOv11 model and the superiority of the performance of the improved model, ablation experiments and comparison experiments were conducted respectively. The results of the comparison test between different attentional mechanisms added to the baseline model showed that the ECA module had better performance than other attentional mechanisms in the post-harvest asparagus grading task. The YOLOv11-ECA had higher recognition accuracy and smaller model size, so the selection of the ECA module had a certain degree of reliability. Ablation experiments demonstrated that the improved YOLOv11 achieved 96.8% precision (P), 96.9% recall (R), and 92.5% mean average precision (mAP), with 4.6 GFLOPs, 1.67 × 10⁶ parameters, and a 3.6 MB model. The results of the asparagus grading test indicated that the localization frames of the improved model were more accurate and had a higher and higher confidence level. Compared with the original YOLOv11 model, the improved YOLOv11 model increased the precision, recall, and mean average precision by 2.6, 1.4, and 2.2 percentage points, respectively. And the floating-point operation, parameter quantity, and model size were reduced by 1.7 G, 9.1 × 10⁵, and 2.2 MB, respectively. Moreover, various improvements to the model could increase the accuracy of the model while ensuring that the model was light weight. In addition, the results of the comparative tests showed that the performance of the improved YOLOv11 model was better than those of SSD, YOLOv5s, YOLOv8n, YOLOv11, and YOLOv12. Overall, the improved YOLOv11 had the best overall performance, but still had some shortcomings. In terms of the real-time performance of the model, the inference speed of the improved model was not optimal, and the inference speed of the improved YOLOv11 was inferior to that of YOLOv5s and YOLOv8n. On this basis, to evaluate the inference speed of improved YOLOv11 and YOLOv11 used the aggregate test. The results of the Wilcoxon signed-rank test showed that the improved YOLOv11 had a significant improvement in inference speed compared to the original YOLOv11 model. [Conclusions] The improved YOLOv11 model demonstrated better recognition, lower parameters and floating-point operations, and smaller model size in the asparagus grading task. The improved YOLOv11 provided a theoretical foundation for intelligent post-harvest asparagus grading. Deploying the improved YOLOv11 model on asparagus grading equipment enables fast and accurate grading of post-harvest asparagus.

[Significance] AI for Science (AI4S), as an emerging paradigm of deep integration between artificial intelligence (AI) and scientific research, has triggered profound transformations in research methodologies. By accelerating scientific discovery through AI technologies, it promotes the transition of scientific research from traditional experience- and intuition-driven approaches to data- and AI-co-driven methodologies. This shift has led to innovative breakthroughs across numerous scientific domains and presents new opportunities for the transformation of agricultural research. With its powerful capabilities in data processing, intelligent analysis, and pattern recognition, AI can break through the cognitive limitations of field scientists and is gradually becoming an indispensable tool in modern agricultural scientific research, injecting new impetus into the intelligent, efficient, and collaborative development of agricultural scientific research. [Progress] This paper systematically reviews the current advancements in AI4S and its impact on agricultural research. It is show that AI4S has led to a wave of countries around the world vying for the commanding heights of a new round of scientific and technological strategies. Developed countries such as those in Europe and America have laid out the frontier fields of AI4S and introduced relevant policies. Meanwhile, some top universities and research institutions are accelerating related research, and technology giants are actively cultivating related industries to promote the application and layout of AI technology in scientific research. In recent years, AI4S has witnessed remarkable development, showing great potential in multiple disciplinary fields and has been widely applied in data mining, model construction, and result prediction. In the field of agricultural scientific research, AI4S has played an important role in accelerating multi-disciplinary integration, promoting the improvement of the scientific research efficiency, facilitating the breakthrough of complex problems, driving the transformation of the scientific research paradigm, and upgrading scientific research infrastructure. The continuous progress of information technology and synthetic biology has made the interdisciplinary integration of agriculture and multiple disciplines increasingly closer The deep integration of AI and agricultural scientific research not only improves the application level of AI in the agricultural field but also drives the transformation of traditional agricultural scientific research models towards intelligence, data-driven, and collaborative directions, providing new possibilities for agricultural scientific and technological innovation. The new agricultural digital infrastructure is characterized by intelligent data collection, edge computing power deployment, high-throughput network transmission, and distributed storage architecture, aiming to break through the bottlenecks of traditional agricultural scientific research facilities in terms of real-time performance, collaboration, and scalability. Taking emerging disciplines such as Agrinformatics and climate-focused Agriculture-Forestry-AI (AgFoAI) as examples, they focus on using AI technology to analyze agricultural data, construct crop growth models, and climate change models, etc., to promote the development and innovation of agricultural scientific research. [Conclusions and Prospects] With its robust capabilities in data processing, intelligent analysis, and pattern recognition, AI is increasingly becoming an indispensable tool in modern agricultural scientific research. To address emerging demands, core domains, and research processes in agricultural research, the concept of agricultural intelligent research is proposed, characterized by human-machine collaboration and interdisciplinary integration. This paradigm employs advanced data analytics, pattern recognition, and predictive modeling to perform in-depth mining and precise interpretation of multidimensional, full-lifecycle, large-scale agricultural datasets. By comprehensively unraveling the intrinsic complexities and latent patterns of research subjects, it autonomously generates novel, scientifically grounded, and high-value research insights, thereby driving agricultural research toward greater intelligence, precision, and efficiency. The framework's core components encompass big science infrastructure (supporting large-scale collaborative research), big data resources (integrating heterogeneous agricultural datasets), advanced AI model algorithms (enabling complex simulations and predictions), and collaborative platforms (facilitating cross-disciplinary and cross-institutional synergy). Finally, in response to challenges related to data resources, model capabilities, research ecosystems, and talent development, actionable pathways and concrete recommendations are outlined from the perspectives of top-level strategic planning, critical technical ecosystems, collaborative innovation ecosystems, disciplinary system construction, and interdisciplinary talent cultivation, aiming to establish a new AI4S-oriented agricultural research framework.

[Objective] Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production. This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature. [Methods] A dynamic prediction model for crop canopy temperature was developed, based on Long Short-Term Memory (LSTM), Variational Mode Decomposition (VMD), and the Rime Ice Morphology-based Optimization Algorithm (RIME) optimization algorithm, named RIME-VMD-RIME-LSTM (RIME²-VMD-LSTM). Firstly, crop canopy temperature data were collected by an inspection robot suspended on a cableway. Secondly, through the performance of multiple pre-test experiments, VMD-LSTM was selected as the base model. To reduce cross-interference between different frequency components of VMD, the K-means clustering algorithm was applied to cluster the sample entropy of each component, reconstructing them into new components. Finally, the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM, enhancing the model's prediction accuracy. [Results and Discussions] The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) (0.360 1 and 0.254 3°C, respectively) in modeling different noise environments than the comparator model. Furthermore, the R² value reached a maximum of 0.994 7. [Conclusions] Therefore, this model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.

[Objective] Beef cattle breeding stands as a pivotal element in contemporary animal husbandry, with precise individual identification serving as the cornerstone for the advancement of automated technologies, including intelligent weight measurement, body condition scoring, body conformation assessment, and behavior monitoring. However, the actual breeding environment is fraught with challenges such as soiled conditions, intricate backgrounds, and the constant movement of animals, which contribute to the high variability of cattle face data features. Additionally, the effects of inconsistent lighting and diverse shooting angles can lead to blurred key features, increasing the risk of misjudgment during the detection process. In light of these challenges, an improved model named YOLO-PCW, built upon the YOLOv11 algorithm, was introduced to enhance the detection performance while preserving a lightweight structure to address the complexities of precise cattle face recognition in challenging breeding environments. [Methods] The research leveraged the cow fusion dataset (CFD), a comprehensive collection of real-world cattle face images captured under variable lighting conditions, from multiple angles, and against complex backgrounds, for the purpose of model training and validation. Concurrently, a custom cow monitor dataset (CMD) was created from video footage obtained through the Zhaoyuan Qingyangbao Breeding Farm's monitoring system, providing a robust basis for evaluating the model's generalization capabilities. The YOLOv11 architecture served as the foundational framework for implementing the following performance improvements. The partial convolution (PConv) was seamlessly integrated into the C3K2 module within the YOLOv11 head network. Utilizing the sparse convolutional properties of PConv on the feature maps, the convolutional structure was meticulously optimized, reduceing computational redundancy and memory access while preserving the model's accuracy—rendering it highly suitbale for real-time applications. Additionally, the convolutional block attention module (CBAM) was incorporated to enhance feature map processing through adaptive channel-wise and spatial attentions. This refinement enabled precise extraction of target regions by mitigating background interference, allowing the model to focus on critical anatomical features such as the eyes, mouth, and nose. Furthermore, the weighted intersection over union (WIoU) loss function was adopted to replace the CIoU, optimizing the weighted strategy for bounding box regression errors. This innovation reduced the adverse effects of large or outlier gradients in extreme samples, enabling the model to prioritize average-quality samples for refinement. The resulting improvment in key region localization accuracy bolstered the model's generalization capability and overall performance, establishing a state-of-the-art cattle face recognition framework. [Results and Discussion] The YOLO-PCW model achieved a remarkable accuracy rate (P) of 96.4%, a recall rate (R) of 96.7%, and a mean average precision (mAP) of 98.7%. With a parameter count of 2.3 M and a computational load of 5.6 GFLOPs, the YOLO-PCW not only improved accuracy, recall, and mean average precision by 3.6, 5, and 4.4 percentage point respectively, but also achieved a significant reduction in floating-point computational load and parameter size, down to 88.9% and 88.5% of the original model, respectively. Ablation studies revealed that the CBAM module enhanced precision from 92.8% to 95.2%. The WIoU loss function optimized target positioning accuracy, achieving a precision of 93.8%. The PConv module contributed to a substantial reduction in computational load from 6.3 GFLOPs to 5.5 GFLOPs, thereby significantly lightening the model's computational burden. The synergistic collaboration of these multiple components provided robust support for enhancing the performance of the cattle face recognition model. Comparative experiments demonstrated that the YOLO-PCW model, when benchmarked against algorithms such as Faster-RCNN, SSD, YOLOv5, YOLOv7-tiny, and YOLOv8 under identical conditions, exhibited the most outstanding performance, effectively balancing recognition accuracy with computational efficiency and achieving optimal utilization of computational resources. [Conclusions] The improved YOLO-PCW model, with its lightweight architecture and optimized attention mechanism, could successfully improve detection accuracy while simplify deployment. It is capable of delivering precise cattle face recognition in real-world breeding environments, offering an efficient and practical solution for individual identification in applications such as animal welfare breeding, intelligent ranch management, smart ranch construction, and animal health monitoring.

[Objective] Fish pose estimation (FPE) provides fish physiological information, facilitating health monitoring in aquaculture. It aids decision-making in areas such as fish behavior recognition. When fish are injured or deficient, they often display abnormal behaviors and noticeable changes in the positioning of their body parts. Moreover, the unpredictable posture and orientation of fish during swimming, combined with the rapid swimming speed of fish, restrict the current scope of research in FPE. In this research, a FPE model named HPFPE is presented, designed to capture the swimming posture of fish and accurately detect their key points. [Methods] On the one hand, this model incorporated the CBAM module into the HRNet framework. The attention module enhanced accuracy without adding computational complexity, while effectively capturing a broader range of contextual information. On the other hand, the model incorporated dilated convolution to increase the receptive field, allowing it to capture more spatial context. [Results and Discussions] Experiments showed that compared with the baseline method, the average precision (AP) of HPFPE based on different backbones and input sizes on the oplegnathus punctatus datasets had increased by 0.62, 1.35, 1.76, and 1.28, respectively, while the average recall (AR) had also increased by 0.85, 1.5, 1.4, and 1, respectively. Additionally, HPFPE outperformed other mainstream methods, including DeepPose, CPM, SCNet, and Lite-HRNet. Furthermore, when compared to other methods using the ornamental fish data, HPFPE achieved the highest AP and AR values of 52.96, and 59.50, respectively. Conclusions The proposed HPFPE can accurately estimate fish posture and assess their swimming patterns, serving as a valuable reference for applications such as fish behavior recognition.

[Objective] Vegetables are a vital component of the human diet, serving not only as a cornerstone of nutritional well-being but also as a significant source of income for agricultural producers. The price volatility of vegetables has profound implications for both farmers and consumers. Fluctuating prices directly impact farmers' earnings and pose challenges to market stability and consumer purchasing behaviors. These fluctuations are driven by a multitude of complex and interrelated factors, including supply and demand, seasonal cycles, climatic conditions, logistical efficiency, government policies, consumer preferences, and suppliers' trading strategies. As a result, vegetable prices tend to exhibit nonlinear and non-stationary patterns, which significantly complicate efforts to produce accurate price forecasts. Addressing these forecasting challenges holds considerable practical and theoretical value, as improved prediction models can support more stable agricultural markets, secure farmers' incomes, reduce cost-of-living volatility for consumers, and inform more precise and effective government regulatory strategies. [Methods] The study investigated the application of neural network-based time series forecasting models for the prediction of vegetable prices. In particular, a selection of state-of-the-art neural network architectures was evaluated for their effectiveness in modeling the complex dynamics of vegetable pricing. The selected models for the research included PatchTST and iTransformer, both of which were built upon the Transformer architecture, as well as SOFTS and TiDE, which leveraged multi-layer perceptron (MLP) structures. In addition, Time-LLM, a model based on a large language model architecture, was incorporated to assess its adaptability to temporal data characterized by irregularity and noise. To enhance the predictive performance and robustness of these models, an automatic hyperparameter optimization algorithm was employed. This algorithm systematically adjusted key hyperparameters such as learning rate, batch size, early stopping, and random seed. It utilized probabilistic modeling techniques to construct performance-informed distributions for guiding the selection of more effective hyperparameter configurations. Through iterative updates informed by prior evaluation data, the optimization algorithm increased the search efficiency in high-dimensional parameter spaces, while simultaneously minimizing computational costs. The training and validation process allocated 80 percent of the data to the training set and 20 percent to the validation set, and employed the mean absolute error (MAE) as the primary loss function. In addition to the neural network models, the study incorporated a traditional statistical model, the autoregressive integrated moving average (ARIMA), as a baseline model for performance comparison. The predictive accuracy of all models was assessed using three widely recognized error metrics: MAE, mean absolute percentage error (MAPE), and mean squared error (MSE). The model that achieved the most favorable performance across these metrics was selected for final vegetable price forecasting. [Results and Discussions] The experimental design of the study focused on four high-demand, commonly consumed vegetables: carrots, white radishes, eggplants, and iceberg lettuce. Both daily and weekly price forecasting tasks were conducted for each type of vegetable. The empirical results demonstrated that the neural network-based time series models provided strong fitting capabilities and produced accurate forecasts for vegetable prices. The integration of automatic hyperparameter tuning significantly improved the performance of these models. In particular, after tuning, the MSE for daily price prediction decreased by at least 76.3% for carrots, 94.7% for white radishes, and 74.8% for eggplants. Similarly, for weekly price predictions, the MSE reductions were at least 85.6%, 93.6%, and 64.0%, respectively, for the same three vegetables. These findings confirm the substantial contribution of the hyperparameter optimization process to enhancing model effectiveness. Further analysis revealed that neural network models performed better on vegetables with relatively stable price trends, indicating that the underlying consistency in data patterns benefited predictive modeling. On the other hand, Time-LLM exhibited stronger performance in weekly price forecasts involving more erratic and volatile price movements. It's robustness in handling time series data with high degrees of randomness suggests that model architecture selection should be closely aligned with the specific characteristics of the target data. Ultimately, the study identified the best-performing model for each vegetable and each prediction frequency. The results demonstrated the generalizability of the proposed approach, as well as its effectiveness across diverse datasets. By aligning model architecture with data attributes and integrating targeted hyperparameter optimization, the research achieved reliable and accurate forecasts. [Conclusions] The study verified the utility of neural network-based time series models for forecasting vegetable prices. The integration of automatic hyperparameter optimization techniques notably improved predictive accuracy, thereby enhancing the practical utility of these models in real-world agricultural settings. The findings provide technical support for intelligent agricultural price forecasting and serve as a methodological reference for predicting prices of other agricultural commodities. Future research may further improve model performance by integrating multi-source heterogeneous data. In addition, the application potential of more advanced deep learning models can be further explored in the field of price prediction.

[Significance] Plant active small molecules play a key role in regulating plant growth and resisting environmental stress. Accurate detection of these molecules is of great significance for achieving precise management of agriculture and promoting the development of smart agriculture. Various detection methods have been used for the detection of plant active small molecules, among which electrochemical sensors have attracted much attention due to their sensitivity, portability, and low cost. [Progress] By reviewing relevant literature, this article deeply analyzes the research status of electrochemical sensors in the field of detecting plant active small molecules, and provides a detailed analysis of the sensing principles, signal amplification strategies, and application potential of each sensor. The development trend of sensors from in vitro to in vivo and in situ detection, the important role of nanomaterials in the sensing process, and the combination of sensors with flexible electronics and artificial intelligence technology are discussed. [Conclusion] This article summarizes the technical challenges currently faced by electrochemical sensors in the field of detecting plant active small molecules and analyzes the next development direction, including improving sensing performance, optimizing electrolyte materials, and integrating sensors with microelectronics and artificial intelligence technology, providing a reference for the technical research and application of plant small molecule electrochemical sensors.

Significance The intelligent transformation of agricultural product supply chains is an essential solution to the challenges faced by traditional supply chains, such as information asymmetry, high logistics costs, and difficulties in quality traceability. This transformation also serves as a vital pathway to modernize agriculture and enhance industrial competitiveness. By integrating technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI), intelligent supply chains facilitate precise production and processing, efficient logistics distribution, and transparent quality supervision. As a result, they improve circulation efficiency, ensure product safety, increase farmers' incomes, and promote sustainable agricultural development. Furthermore, in light of global shifts in agricultural trade, this transformation bolsters the international competitiveness of China's agricultural products and propels the agricultural industrial chain toward higher value-added segments. This paper systematically examines the conceptual framework, technological applications, and future trends of intelligent supply chains, aiming to provide a theoretical foundation for industry practices and insights for policymaking and technological innovation. Progress In the production phase, IoT and remote sensing technologies enable real-time monitoring of crop growth conditions, including soil moisture, temperature, and pest infestation, facilitating precision irrigation, fertilization, and pest management. Big data analysis, coupled with AI algorithms, helps in predicting crop yields, optimizing resource allocation, and minimizing waste. Additionally, AI-driven smart pest control systems can dynamically adjust pesticide application, reducing chemical usage and environmental impact. The processing stage leverages advanced technologies for efficient sorting, grading, cleaning, and packaging. Computer vision and hyperspectral imaging technologies enhance the sorting efficiency and quality inspection of agricultural products, ensuring only high-quality products proceed to the next stage. Novel cleaning techniques, such as ultrasonic and nanobubble cleaning, effectively remove surface contaminants and reduce microbial loads without compromising product quality. Moreover, AI-integrated systems optimize processing lines, reducing downtime and enhancing overall throughput. Warehousing employs IoT sensors to monitor environmental conditions like temperature, humidity, and gas concentrations, ensuring optimal storage conditions for diverse agricultural products. AI algorithms predict inventory demand, optimizing stock levels to minimize waste and maximize freshness. Robotics and automation in warehouses improve picking, packing, and palletizing efficiency, reducing labor costs and enhancing accuracy. The transportation sector focuses on cold chain innovations to maintain product quality during transit. IoT-enabled temperature-controlled containers and AI-driven scheduling systems ensure timely and efficient delivery. Additionally, the integration of blockchain technology provides immutable records of product handling and conditions, enhancing transparency and trust. The adoption of new energy vehicles, such as electric and hydrogen-powered trucks, further reduces carbon footprints and operating costs. In the distribution and sales stages, big data analytics optimize delivery routes, reducing transportation time and costs. AI-powered demand forecasting enables precise inventory management, minimizing stockouts and excess inventories. Moreover, AI and machine learning algorithms personalize marketing efforts, improving customer engagement and satisfaction. Blockchain technology ensures product authenticity and traceability, enhancing consumer trust. Conclusions and Prospects As technological advancements and societal demands continue to evolve, the intelligent transformation of agricultural product supply chains has become increasingly urgent. Future development should prioritize unmanned operations to alleviate labor shortages and enhance product quality and safety. Establishing information-sharing platforms and implementing refined management practices will be crucial for optimizing resource allocation, improving operational efficiency, and enhancing international competitiveness. Additionally, aligning with the "dual-carbon" strategy by promoting clean energy adoption, optimizing transportation methods, and advocating for sustainable packaging will drive the supply chain toward greater sustainability. However, the application of emerging technologies in agricultural supply chains faces challenges such as data governance, technical adaptability, and standardization. Addressing these issues requires policy guidance, technological innovation, and cross-disciplinary collaboration. By overcoming these challenges, the comprehensive intelligent upgrade of agricultural product supply chains can be achieved, ultimately contributing to the modernization and sustainable development of the agricultural sector.

Objective In the context of modern agricultural practices, regulating the indoor microclimate of Venlo-type greenhouses during the summer months through mechanical ventilation remains a significant challenge. This is primarily due to the nonlinear dynamics and strong coupling characteristics inherent in greenhouse environmental systems, where variables such as temperature, humidity, and CO₂ concentration interact in complex, interdependent ways. Traditional control strategies, which often rely heavily on empirical knowledge and manual intervention, are insufficient to cope with such dynamic conditions. These approaches tend to result in imprecise control, substantial time delays in response to environmental fluctuations, and unnecessarily high energy consumption during ventilation operations. Therefore, the study combines computational fluid dynamics (CFD) model with a multi-objective particle swarm optimization (MOPSO) algorithm to establish a joint optimization framework, aiming to address the issues of significant time delays and excessive operational energy consumption caused by vague environmental control strategies in this scenario. Methods To build a reliable simulation and optimization framework, environmental parameters such as temperature, wind speed, and CO₂ concentration were continuously collected via a network of environmental monitoring sensors installed at various positions inside the greenhouse. These real-time data served as validation benchmarks for the CFD model and supported the verification of mesh independence. In the CFD model construction, the internal structure of the Venlo-type greenhouse was precisely reconstructed, and appropriate boundary conditions were set based on empirical data. The airflow dynamics and thermal field were simulated using a finite volume-based solver. Four grid resolutions were evaluated for grid independence by comparing the variations in output metrics. The controllable parameters in the model included fan outlet wind speed and cooling pad condensation temperature. These parameters were systematically varied within predefined ranges. To evaluate the greenhouse environmental quality and energy consumption under different control conditions, three custom-defined objective functions were proposed: temperature suitability, CO₂ uniformity, and fan operating energy consumption. The MOPSO algorithm was then applied to conduct iterative optimization over the defined parameter space. At each step, the objective functions were recalculated based on CFD outputs, and Pareto-optimal solutions were identified using non-dominated sorting. After iterative optimization using the algorithm, the conflicting objectives of environmental deviation and energy consumption were balanced, leading to the optimal range for the greenhouse environmental control strategy in this scenario. Results and Discussions The experimental results showed that the environmental field simulation accuracy of the CFD model was high, with an average relative error of 5.7%. In the grid independence test, three grid types—coarse, medium, and fine—were selected. The variations in the grid divisions were 1.7% and 0.6%, respectively. After considering both computational accuracy and efficiency, the medium grid division standard was adopted for subsequent simulations. The optimization strategy proposed in this study allows for closed-loop evaluation of the environment. The algorithm set the population size to 100 particles, and within the specified range of fan outlet wind speed and cooling pad condensation temperature, each particle iterates 5 times for optimization. The position updated in each iteration was used to calculate the values of the three objective evaluation functions, followed by non-dominated comparison and adaptation of the solutions, until the optimization was complete. In the Pareto surface fitted by the output results, the fan outlet wind speed ranges from 2.8 to 5.4 m/s, and the inlet temperature ranges from 295.3 to 299.7 K. Since the evaluation functions under the environmental control strategy were all in an ideal, non-dominated state, two sets of boundary control conditions were randomly selected for simulation: operating Condition A [296 K, 3.5 m/s] and operating Condition B [299 K, 5 m/s]. Post-processing contour plots showed that both operating conditions achieve good environmental optimization uniformity. The approximate ranges for each parameter were: temperature from 300.3 to 303.9 K, wind speed from 0.7 to 2.3 m/s, and CO₂ concentration from 2.43 × 10^-5 to 3.56 × 10^-5 kmol/m³. Based on environmental uniformity optimization, operating Condition A focused on adjusting the suitable temperature for crops by lowering the cooling pad condensation temperature, but there was a relative stagnation of CO₂. Operating Condition B, by increasing the fan outlet wind speed, focused on regulating CO₂ flow and diffusion, but the gradient change of airflow near the two side walls was relatively abrupt. Conclusions This study complements the research on the systematic adjustment of greenhouse environmental parameters, while its closed-loop iterative features also enhance the simulation efficiency. The simulation results show that by arbitrarily combining the optimal solution set within the theoretical range of the strategy output, optimization of the targeted objectives can be achieved by appropriately discarding other secondary objectives, providing a reference for regulating the uniformity and economy of mechanical ventilation in greenhouses. Subsequent research can further quantify the coupling effects and weight settings of each objective function to improve the overall optimization of the functions.

[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] 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] 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'.

[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.