To address the problems of track slippage, speed fluctuations, attitude instability, and even rollover risks that can occur in combine harvesters operating on undulating slopes, overlapping cross slopes, and soft, wet soil conditions, leading to decreased path tracking accuracy and increased energy consumption, this study proposes an adaptive anti-slip control strategy based on slip-ratio and attitude feedback. The strategy treats the vehicle slip ratio and roll (lateral tilt) angle as the key state variables, integrates positioning data with inertial measurements to obtain these critical states online, and establishes a hierarchical risk-identification mechanism with four levels: “normal-slip risk-attitude risk-coupled high risk”. Thresholds and hysteresis-based exit rules are incorporated to prevent frequent controller switching near critical operating boundaries. Unlike conventional fixed-parameter schemes that focus only on lateral tracking error, this study innovatively integrates longitudinal traction regulation and lateral stability intervention within a unified closed loop. When slip risk is triggered, the controller adaptively reduces the engine target speed to suppress excessive slip. When attitude risk is detected, it simultaneously reduces speed and applies an uphill-direction bias to the preview target point, guiding the vehicle toward a more favorable stabilizing trend. Under the coupled high-risk state where slip and attitude risks coexist, a safety supervisor arbitrates control priorities to realize coordinated control following the principle of “attitude safety first, traction second, tracking last”, thereby balancing safety, tracking accuracy, and fuel economy under complex terrain and load fluctuations. Field comparative experiments were conducted on a tracked combine harvester platform on slopes of approximately 15° -25° with soil moisture content of about 25%-30%. The results show that the proposed method reduces the average path-tracking error from 0.117 1 m to 0.020 8 m and the maximum error from 0.445 1 m to 0.090 5 m, achieves an average slip ratio of approximately 6.12% with a maximum of about 18.00%, and yields an overall fuel-saving improvement of approximately 4.94%. In summary, this study unifies slip-ratio and attitude-risk modeling to drive control-mode switching, and introduces an uphill-biased preview point together with a safety-supervision priority mechanism, providing a practical adaptive anti-slip and stable-operation control solution for hillside and mountainous combine harvesters.

As one of the world leading apple producers, China is facing increasing agricultural labor shortages caused by rising labor costs and demographic shifts. Promoting the automation and intelligent transformation of orchard operations has become a key factor in improving agricultural efficiency. In response to the standardized cultivation patterns of modern apple orchards, this study designs and implements an autonomous navigation system based on the fusion of Global Navigation Satellite System (GNSS) and vision-based navigation. The system is built on an Ackermann-steering chassis and integrates a front-wheel angle detection module and automatic steering control. To enhance the system adaptability in complex orchard environments, a dynamic navigation mode switching strategy is proposed, which relies on key parameters such as point cloud data quality, GNSS positioning accuracy, and path deviation. This enables intelligent switching between GNSS and vision-based navigation modes, allowing the system to meet varying accuracy requirements across different operational areas. In terms of path planning, a full-coverage navigation strategy is developed by combining a shuttle-style inter-row pattern with third-order Bézier curves. Inter-row paths are extracted by filtering, down sampling, and linearly fitting the stereo vision point cloud to determine the navigation centerline. Headland turning paths are generated based on orchard geometry parameters using Bézier curves, optimizing turning maneuvers and reducing unnecessary travel. Field tests demonstrate that the system achieves high-precision path tracking at various travel speeds, with a minimum lateral deviation of 0.052 meters. During headland turning, the average navigation deviations for two-row and single-row crossings are 0.052 meters and 0.066 meters, respectively, meeting the accuracy requirements of practical orchard operations. In conclusion, the proposed navigation system demonstrates strong environmental adaptability and navigation precision, providing solid technical support for the engineering application of automated orchard equipment.

Timely identification of abnormal conditions in broiler chickens through routine inspections is a crucial means to improve the efficiency of intensive poultry farming management. In intensive poultry farming, computer vision-based inspection technologies outperform traditional manual methods in both accuracy and efficiency, showing significant potential for development. However, in large-scale farms, challenges such as multi-scale targets and occlusion pose considerable difficulties for models. To address these issues, this study proposes an improved object detection method, termed GMA-YOLO11n (GSConv and multi-scale attention YOLO11n), based on the YOLO11n (you only look once) framework. Specifically, a GSConv lightweight convolution module is introduced into the Backbone to reduce computational complexity, and a high-resolution feature layer of 160×160 is added through multi-scale feature fusion to enhance the detection of small-scale and densely distributed targets. In addition, a squeeze-and-excitation (SE) channel attention module is incorporated before multi-scale feature inputs to strengthen the representation of key features. Experimental results demonstrate that the proposed model can effectively perform multi-class detection of daily behaviors, including drinking, feeding, and walking, as well as abnormal states of broiler chickens. The model achieves mean average precision values of 93.87% and 90.45% on Dataset I and Dataset Ⅱ , respectively, both outperforming the baseline model, while maintaining an inference speed that satisfies practical video inspection requirements.

To address the issues of low detection accuracy and poor robustness of apple picking robots caused by complex environmental factors such as branch and leaf occlusion and uneven light distribution in orchards, this study proposes an apple fruit detection algorithm that improves the lightweight model of the YOLOv8n network, aiming to enhance the accuracy and reliability of apple detection in complex scenarios. Firstly, an “MPCA” (multi-scale position-channel attention) is added at the end of the backbone network to precisely capture the position information of apples at different scales, thereby enhancing the model's perception of target position features and effectively reducing the interference of complex orchard environments on detection results. Secondly, a “FocusFeature” feature fusion module is introduced in the neck network, which uses multi-scale depth wise separable convolution to achieve information fusion and feature enhancement, improving the model's detection capability. Finally, the loss function CIoU is replaced with WIoUv3 (WiseIoU), dynamically adjusting the weight of samples in the loss calculation based on the matching quality of predicted and ground truth boxes, effectively enhancing the model's localization accuracy. Experiments show that the improved model achieves an accuracy of 92.2%, a recall rate of 86.4%, and mAP@0.5 and mAP@[0.5: 0.95] of 94.4% and 72.4% respectively in complex orchard environments, representing improvements of 3.4%, 6%, 3.9%, and 2.8% compared to the original algorithm. Ablation experiments reveal that WIoUv3, MPCA, and FocusFeature feature fusion module increase mAP@0.5 by 0.3%, 0.9%, and 1.1% respectively, with performance enhancement achieved through the synergy of multiple modules. The model size is 6.69 MB, only 0.74 MB larger thanthe original model, without significantly increasing the computational load. Compared with mainstream algorithms such as YOLOv5n, YOLOv7-tiny, and YOLOv11n, the improved model increases the accuracy by 3.9%, 7.6%, and 5.7% respectively, and mAP@[0.5: 0.95] by 2.5%, 9.9%, and 13%, demonstrating significant advantages in detection accuracy and generalization ability. It is evident that the improved YOLOv8n algorithm proposed in this study significantly enhances the accuracy and robustness of apple detection in complex orchard environments through the collaborative effect of multiple modules, providing an effective technical solution for the practical application of picking robots and playing a crucial role in promoting the intelligent development of apple harvesting.

Abstract: In recent years, the rapid proliferation and expansion of rodent burrows have become one of the major causes of grassland degradation and ecological imbalance. The dense distribution of burrows destroys turf structure, weakens soil stability, reduces vegetation coverage and forage productivity, and seriously threatens the ecological security and sustainable development of animal husbandry in grassland areas. To address the problems of insufficient small-target recognition, inadequate feature extraction, and significant background interference in existing detection algorithms, this study proposes an improved real-time detection model, RT-DETR-ECG (efficient global perception and feature selection), based on the Real-Time Detection Transformer (RT-DETR) framework. The model first introduces the Efficient Vision Mamba (EfficientVIM) module, which achieves efficient fusion of global and local features through state-space modeling and multi-scale feature interaction, thereby enhancing the perception of small targets. Second, it constructs a Convolutional Gated Linear Unit (CGLU) module, which dynamically suppresses complex background noise via the synergy of convolution and gating mechanisms, effectively highlighting the edges and textures of burrows. Finally , it designs a small-object weighted loss function (CGLU-Loss), which introduces center-offset weighting and local-overlap gain terms in the regression stage to strengthen gradient contributions from small-scale samples and improve localization precision. Experiments based on a dataset of 2 397 cross-season UAV images of grassland burrows demonstrate that RT-DETR-ECG outperforms existing models in detection accuracy, real-time performance, and lightweight efficiency. With an IoU threshold of 0.5, the model achieves a mean average precision (mAP@0.5) of 96.3%, 5.1% higher than the original RT-DETR, with a detection speed of 91.8 f/s and a 53.7% reduction in computational load. These results verify that RT-DETR-ECG effectively balances detection accuracy and efficiency, providing a reliable and efficient approach for intelligent grassland rodent burrow detection and ecological monitoring.

To overcome the challenges of slow speed, prolonged time consumption, and high costs in traditional seed vigor detection methods, we propose a non-destructive seed vigor detection approach that integrates laser speckle technology, a STCD algorithm, and a deep learning model, which facilitate the construction of a comprehensive seed vigor detection system encompassing “image acquisition, feature processing, model training, and vigor determination.” An imaging system was developed by utilizing a 632.8 nm He-Ne laser and a CCD camera to capture laser speckle images of pea seeds. After image preprocessing, the individual pea seed area was extracted. Then, two-dimensional wavelet transform was applied to denoise the speckle images. Subsequently, the STCD method was utilized for image data processing. The STCD method is deeply integrated with the image difference improvement strategy to highlight the feature information of the pea seed laser spot image. The weighted average of the first 4 frames is taken as the reference frame. The 3×3 neighborhood local variance weighting mechanism and the forgetting factor α=0.7 are introduced to construct the cumulative difference map. Finally, 1 600 difference images are obtained, with 800 viable seeds and 800 non-viable seeds. Divide the dataset in a ratio of 7∶2∶1. In terms of the model, MobileNetV3 was adopted as the backbone network to construct the lightweight Faster R-CNN model. The parameters were adjusted and the training strategy was optimized until the Accuracy of the validation set was stable. Subsequently, the optimally trained model was employed for target detection, feature extraction, and deep learning on the speckle images of maize seeds. Ultimately, seed classification and regression were accomplished, enabling efficient and accurate detection of seed viability. The results indicate that the laser speckle images of seeds processed by STCD exhibit distinct feature information, with a maximum signal-to-noise ratio of 35.8 dB for the differential images. After training with the lightweight Faster R-CNN model, the classification accuracy for viable seeds is 92.25% and the accuracy for non-viable seeds is 93.25%. The model achieves an AUC value of 98% in the ROC curve, an F1-score of 92.63%, a false positive rate (FPR) of 4.75%. The recognition time for a single image is 0.031 5 seconds, which is 0.049 3 seconds shorter than that of the original model. The detection speed is 2.5 times faster than the original model, and the convergence epoch is 25, reduced by more than half compared to the original model, demonstrating high training efficiency. These findings confirm that the proposed method enables rapid and non-destructive detection of seed viability.

Aiming at the problems of low tracking accuracy and poor scene adaptability of the existing differential tracked chassis path tracking controller, this study uses a small light and simple tracked cabbage harvester as a test platform to innovatively design an improved particle swarm optimization (IPSO) with fore-look distance adaptive fuzzy pure tracking controller (IPSO-FUZZY-PP). First, the study analyzes the operating characteristics of the cabbage harvester's pulling roller, and the cabbage's demand for navigation accuracy is determined. Then, the differential kinematics model of the crawler harvester is constructed to clarify the relationship between the track speed on both sides and the driving and steering states. The lateral deviation and heading deviation are used as the input of the fuzzy controller, and the PWM duty cycle difference of the two-sided motor is used as the output. The IPSO algorithm is used to dynamically optimize the fore-look distance. The simulation results show that the convergence speed of the controller is 60% higher than that of the traditional particle swarm optimization algorithm, which can effectively avoid the local optimal solution. In the cement pavement test (driving speed of 0.5 m/s), the maximum tracking deviation of the controller is 0.035 m, and the average absolute deviation is 0.017 m. Compared with the traditional pure tracking controller, the accuracy is improved by 34.6%, the rise time is shortened from 1.71 s to 0.76 s, and the response speed is improved by 55.6%. In the field test (driving speed 0.3, 0.5, 0.8 m/s), the maximum tracking deviation is not more than 0.031 m, 0.037 m, 0.041 m, respectively, and the average absolute deviation is controlled within 0.010 m, 0.015 m, 0.018 m, respectively. Compared with the traditional pure tracking controller, the accuracy is improved. The controller proposed in this study can dynamically adapt to the narrow row spacing and multi-speed conditions of cabbage harvest, meet the accuracy requirements of cabbage harvest navigation, and provide technical support for the precise alignment of unmanned cabbage harvest.

In response to the problems of low energy utilization efficiency and the uncertain relationship between the drying rate and air volume regulation in traditional heat pump drying systems, this study proposes an intelligent heat pump drying system with a regenerator, which consists of a heat pump cycle subsystem and an air circulation subsystem. To enhance the drying rate and energy utilization efficiency of the system, models for the heat pump cycle, moisture production of materials, air temperature and humidity in the drying chamber, intelligent regulation of air volume ratio, and heat exchange in the air regenerator were constructed using MATLAB/Simulink. Based on the principle of energy conservation and with the constraint that the moisture production of materials is equal to the moisture removal rate, a method for regulating the air circulation temperature and humidity was established to achieve intelligent dynamic regulation of the ratio of return air to fresh air. Taking mushrooms as the drying object, the effects of temperature, humidity, and drying air velocity on drying characteristics and the ratio of return air to fresh air were investigated. The results show that when the ambient temperature increased from 15 ℃ to 35 ℃, the inlet temperature of the drying chamber also rose. To maintain the optimal drying efficiency of the system, the ratio of return air to fresh air gradually decreased from 7∶3 to 6∶4. When the ambient relative humidity decreased from 80% to 40%, the moisture production rate of mushrooms gradually increased, the return air volume increased, and the ratio of return air to fresh air gradually increased from 6∶4 to 7∶3. When the drying air velocity increased from 1 m/s to 3 m/s, the convective heat transfer was enhanced, the moisture production rate of mushrooms accelerated, and the optimal ratio of return air to fresh air increased synchronously from 5∶5 and approaches 7∶3. The comparison results show that reducing the ratio of return air to fresh air under high-temperature conditions and increasing it under low-humidity and high-velocity conditions are beneficial to maintaining a high-temperature and low-humidity environment in the air duct and promoting the evaporation of mushroom moisture. The research results can ensure that the heat pump drying system improves energy utilization efficiency while maintaining a high-temperature and low-humidity environment in the air duct to guarantee the optimal drying rate, providing theoretical and technical support for the intelligence and application of the heat pump drying system.

Cleaning is a critical process in the mechanized harvesting of grains and legumes, with the cleaning device in combine harvesters being the key component determining harvest quality. As agricultural mechanization advances and legume cultivation expands, the mismatch between grain combine harvesters and legume harvesting has become increasingly prominent. This results in low cleaning quality and significant losses, urgently requiring research into efficient, high-quality, low-loss cleaning technologies and devices suitable for legume harvesting. By reviewing global research progress on combine harvester cleaning systems and key component developments, which include sieve structure, motion patterns, and fan design, this study systematically examines research methodologies and findings related to airflow dynamics and material particle movement within cleaning mechanisms. It summarizes the application of intelligent technologies for process monitoring and adaptive adjustment in combine harvester cleaning systems, providing an analytical overview of grain combine cleaning technology research. By contrasting differences in seed characteristics and impurity types between legumes and grains, this section discusses the unique challenges in legume harvesting, which include susceptibility to damage, high cleaning losses, and high moisture content in separated materials, and analyzes the applicability of grain combine cleaning technology to legume harvesting. It summarizes current cleaning technologies and devices suitable for legumes. Finally, it highlights existing issues with current combined cleaning devices, including unstable cleaning performance, low adaptability, and inadequate intelligence levels. Based on these challenges, it outlines future development trends for bean combine cleaning devices. The research findings provide valuable references for enhancing the suitability of combined cleaning systems for bean harvesting.

To address the issues of high labor dependence, low operational efficiency, and high labor intensity in the collection and recycling of waste black fungus sticks, a self-propelled recycling machine was designed based on the characteristics of waste black fungus sticks remaining in the field and the agronomic requirements for recycling. Through the design and analysis of key components such as the needle roller, guide plate, and conveying device, the automation of the collection and recycling process of waste black fungus sticks was realized. In order to optimize the operational performance of the recycling machine, an orthogonal experiment with three factors and three levels was designed and conducted, taking the roller speed, machine forward speed, and roller installation height as the test factors, and the collection rate and breakage rate of waste black fungus sticks as the test indicators. The influence of each interaction factor on the picking performance indicators was analyzed. The results showed that when the forward speed was 0.28 m/s, the roller speed was 46 r/min, and the installation height was 41.3 cm, the collection rate of the recycling machine reached 96.4% and the breakage rate was 3.77%, which met the requirements of field collection operations for waste black fungus sticks. The developed self-propelled waste black fungus stick recycling machine realizes the automation and mechanization of the picking and transfer links in the recycling process of waste black fungus sticks, reduces labor demand and processing costs, and improves the efficiency of recycling and utilization of waste black fungus sticks. Moreover, the designed needle-roller mechanism can realize two different picking actions-piercing and pulling back-during the collection process. While ensuring the success rate of waste black fungus sticks collection, it also reduces the damage rate of waste black fungus sticks to a certain extent, and provides a reference for the research on realizing the automation of low-damage collection of field waste black fungus sticks.