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

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

[Objective] 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, which 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 in this research. [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 was simplified to a two-wheel differential rotation robot model when designing the mathematical 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. 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. 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 joint simulation test used MATLAB-Simulink and the experiments based on the self-developed electric multi-functional chassis were conducted. [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 is simple and efficient, and the tracking and obstacle avoidance effects are significantly improved, the control method can only handle static obstacles on the reference path at present, and subsequent research will focus on dynamic obstacles.

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