Aiming at the three major bottlenecks faced by intelligent agricultural machinery, namely on-board software fragmentation, insufficient hardware-software collaboration, and lack of underlying platforms, this study proposes the agricultural machinery intelligent engine system (AMES). Its design follows three core principles: integrity, reusability, and data-driven. It adopts a layered architecture: integrating a real-time operating system (RTOS), general-purpose on-board software, and service-oriented architecture (SOA) communication middleware to ensure system compatibility and real-time performance, encapsulating the “perception-decision-control” closed-loop capability, providing a multi-domain control model library, a dedicated algorithm library, and a development toolkit to support three core functions: intelligent navigation, intelligent control, and intelligent management. In practical applications, AMES has achieved a pest and disease identification accuracy rate of ≥95% in field inspection robots, improved the overall operational quality by more than 20% in intelligent grain combine harvesters, and achieved a harvesting efficiency of 2 500 stalks per hour with a damage rate of ≤5% in asparagus harvesting robots. These results effectively promote the transformation of the agricultural machinery industry towards “integrated hardware-software services” and help China's intelligent agricultural machinery technology transition from “catching up” to “leading”.

To address the challenges in balancing accuracy and real-time performance for navigation path recognition methods of wheat harvesters, this study introduces a lightweight design based on the DeepLabv3+ model. Specifically, the original backbone network is replaced with MobileNetV3-Large, and the ReLU activation function is substituted with Leaky_ReLU. To further reduce computational load, the three dilated convolutions with different dilation rates within the atrous spatial pyramid pooling (ASPP) module are replaced with depthwise separable convolution (DSC) employing identical dilation rates. To ensure the generalizability of the wheat harvester navigation path recognition research, images of wheat harvest areas were captured under six typical environmental conditions: strong light, low light, back lighting, front lighting, shadows, and field edges. The road information within the collected images was annotated by the Labelme tool, thereby constructing a wheat harvest region data set. In the path extraction stage, key points were acquired from the segmentation mask maps using the horizontal scanning method, and the navigation path was fitted utilizing a piece wise cubic B-spline algorithm. Experimental results demonstrate that the improved DeepLabv3+ model achieved a segmentation accuracy of 98.04% and intersection over union (IoU) ratio of 95.20%, respectively, with a video image processing frame rate of 7.5 frames per second. The average pixel error and average distance error for navigation path recognition were 7.4 pixels and 37 mm, respectively. The wheat harvester operated at a travel speed of approximately 1.5 m/s, and the path recognition time per single frame was only 0.15 seconds. This performance effectively meets the real-time and accuracy requirements for wheat harvester operation. This research provides a theoretical foundation and technical support for enhancing the autonomous navigation capabilities of wheat harvesters.

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.

As a critical component of field management, cultivation and weeding significantly influence the yield and quality of crops. Currently the domestic cultivation and weeding operation mainly relies on mechanical working parts, while chemical and biological technologies are usually applied to weeding operation alone. In the context of realizing modern and sustainable agriculture, efficient mechanical weeding components and their supporting intelligent control technology have gradually become the core direction of the industry's development. Relying on the advantages of flat and continuous farmland, European and American modular farms focus on the development of chemical weed control system with precision variable spraying as the core; while Japan, characterized by intensive cultivation, has developed a variety of advanced weeding-cultivating machine under the conditions of small fields, which occupies the technological commanding heights of the international agricultural equipment market. In this paper, the research progress of cultivation and weeding techniques is systematically described from two dimensions of equipment classification and application, and intelligent control technology. The technical characteristics of four types of inter-row and inter-plant operation equipment, namely, pivoting type, oscillating type, fixed rotary type and shovel type, are summarized. At the same time, the application of mainstream technologies such as image processing, deep learning, sensor fusion and satellite navigation in the field of intelligent control is introduced in detail focusing on the two major aspects of plant sensing and identification, path control and decision-making. Based on the results of domestic and international experiments, this paper further discusses the shortcomings of the existing equipment and control technology in terms of weed removal rate, seedling injury rate, durability and adaptability, and thus looks forward to the future of cultivation and weeding technology, which will continue to take high quality and high efficiency as the core objective, with a particular emphasis on the development and fusion of intelligent precision, greening, integration, and bionic material technology.

In response to the practical issues faced in hilly areas, such as limited per capita arable land, scattered plots, rugged terrain, low efficiency of large agricultural machinery, and the occasional inconvenience of using small machinery, this paper designs a sowing device that combines a groove wheel and a sowing disk based on drone technology. The paper presents the design concept and optimization simulation plan for the combined sowing device, using rice seeds as the research subject. A discrete element method (DEM) is employed to model the rice seeds using basic unit particles. The sowing process of the device is simulated using DEM software EDEM, primarily analyzing the sowing disk structure. It is found that the angle of the sowing disk and its rotational speed significantly affect the distribution of rice seeds on the disk, while the number of blades significantly impacts the distribution of rice seeds in the air. To achieve uniform sowing, the study uses DEM to design an orthogonal experiment with three factors at three levels, focusing on the rotational speed of the sowing disk, the angle of the blades, and the number of blades. The simulation results indicate that the optimal seeding disc rotation speed of 850 r/min, deflection angle of 50° , and number of blades of 5 resulted in a uniformity coefficient of variation of 12.99%. Verification tests were conducted in three sets focusing on flight altitude, sowing amount, and flight speed. The results show that the maximum coefficient of variation for actual distribution is 24.25%, while the minimum is 15.42%. The average coefficients of variation for rice distribution corresponding to flight altitude, sowing amount, and flight speed are 17.83%, 20.90%, and 18.55%, respectively, which generally meet the agronomic requirements for rice aerial seeding. Through the research and analysis of the structural parameters, discrete element simulation, and field tests of the groove wheel and sowing disk combination, the paper identifies the optimal parameters for the operational performance of the sowing device. These parameters lay the groundwork for research on aerial seeding and its practical application in hilly areas.

A large amount of research has been conducted on the data base, quantitative models, intelligent decision-making, and digital scenarios of high water efficiency agriculture both domestically and internationally. Multiple crop high water efficiency universal models and platforms have been developed internationally, but this aspect is still blank in China. This article summarizes and analyzes the research status of management decision-making platforms related to agricultural water use in China. It points out the limitations of the current construction of agricultural water management platforms from the aspects of platform functions and data models, and proposes a preliminary plan and overall architecture for building a “Sky-Air-Ground” integrated high water efficiency intelligent control platform. Addressing the issues of weak digital scenarios and low automation levels in irrigation district information management platforms, this study develops modular integration technology for data, models, algorithms, and simulation engines. It also develops a standardized and regulated intelligent decision-making knowledge base for high-efficiency agriculture, aiming to solve key technologies for the digital mapping, intelligent simulation, and construction of specialized digital scenarios for key production factors in high-efficiency agriculture. The goal is to achieve synchronous simulation operation, virtual-real interaction, and iterative optimization with physical irrigation districts, enabling managers to intelligently simulate, refine, and scientifically manage crop irrigation water needs and optimize regional water resource allocation, thereby promoting intelligent management and sustainable development of agriculture.

To address the challenges of inconsistent salt content and low production efficiency in traditional Chinese ham curing processes—issues arising from the reliance on manual expertise and the lack of automated salting equipment—this study developed an automated three-dimensional profiling and variable-rate salting machine. The system integrates machine vision, dynamic variable control, and 3D contouring technology while adhering to traditional craftsmanship. The overall structure and operational principles of the salting machine are elaborated with detailed analysis and design of key components, including the ham transfer mechanism, vision detection system, and automated salting mechanism. The machine achieves full-process automation in salt quantity calculation, non-uniform path planning, and adaptive salt spraying, effectively resolving the instability caused by irregular ham morphology and significant weight variations. A machine vision-based 3D feature recognition method is proposed, utilizing high-precision cameras to capture morphological characteristics and construct a “three-pick positions” database for key salting regions. Combined with deep learning algorithms, this method generates differentiated salting paths that align with the traditional requirement of “more salt at three-pick positions, less salt in intermuscular areas”. A dynamic weighing-precision salting collaborative system is designed, incorporating gravity sensing and a graded vibratory feeder controlled via PID algorithms to dynamically adjust vibration amplitude, ensuring salt dispensing accuracy within ±5 g. A three-axis linear-module-driven flexible robotic arm integrates real-time trajectory correction, achieving 1 mm positioning accuracy for the salting nozzle. Coupled with a variable-aperture nozzle that dynamically adjusts salting intensity and travel speed (0-0.3 m/s), the system significantly enhances salt layer uniformity on complex curved ham surfaces. Experimental results demonstrate that the machine achieves a production rate of 123 hams/hour within a ham carcass weight range of 6-15 kg, with an average salt application error rate of 7.42%, meeting design requirements in adaptability and precision. This research realizes full automation of the ham salting process and provides critical technical support for the standardization and intelligent transformation of traditional meat processing industries.

This study selected 60 accessions of Camellia sinensis var. assamica from 7 regions in Hainan Province as research materials. It systematically examined 28 phenotypic traits (comprising 19 qualitative traits and 9 quantitative traits) and comprehensively assessed their morphological variation and genetic diversity through diversity analysis, correlation analysis, principal component analysis, and K-means clustering. The findings indicated that the accessions of Camellia sinensis var. assamica in Hainan exhibited rich phenotypic variation with an average diversity index of 4.13 for the 9 quantitative traits and 4.29 for the 19 qualitative traits. Among these traits, the variations in bud length, the length of one bud with two leaves, and leaf base were especially notable. There were intricate correlations among the traits. Leaf length was significantly and positively correlated with the number of leaf veins and bud-leaf trichomes, whereas leaf shape was negatively correlated with leaf tip, leaf tooth density, and sharpness. The cumulative variance contribution rate of the first 9 principal components extracted by principal component analysis was 76.77%, which respectively represented key trait combinations such as leaf morphology, tree structure, and fruit shape. The clustering analysis classified the 60 accessions into 5 phenotypic groups, and the characteristics of each group differed significantly, which were associated with ecological adaptability. The phenotypic database established in this study provided an important morphological foundation for the conservation of Camellia sinensis var. assamica germplasm resources in Hainan, variety breeding, and mechanized production in tea gardens, such as the optimization of pruning equipment and the design of conveyor belts to prevent damage.

With the continuous advancement of facility agriculture, the application of solar greenhouses in northern China is becoming increasingly popular, and double-film solar greenhouses, as an effective agricultural facility, have significantly improved the crop growth environment. This study focuses on double-layered film solar greenhouses in cold and arid northern regions under frigid weather conditions. January 21st and 22nd, the two days with the lowest average temperatures in January 2024, were taken as the research object, and the thermal insulation performance of the greenhouse and the temporal and spatial variation of the internal thermal and humid environment under severe cold weather conditions were discussed through the analysis of its internal thermal and humid environment. The study shows that the double-film solar greenhouse can maintain a relatively stable indoor temperature and humidity in the severe cold weather after heavy snowfall, which greatly improves the growth environment of winter crops. The internal temperature of the double-film solar greenhouse showed three stages, including the heating stage accompanied by the rising sun, the rapid cooling stage after noon, and the slow cooling stage at night. The warming phase accompanied by the rise of the sun gradually decreased from east to west along the east-west length direction, from south to north along the north-south span direction, and gradually decreased from high to low along the vertical height direction. In the slow cooling stage after noon, the temperature gradually decreased from west to east along the east-west length direction, from north to south along the north-south span direction, and gradually decreased from low to high along the vertical height direction. Relative humidity also showed a three-stage periodic variation law, and the specific change was negatively correlated with the temperature variation law. The spatial distribution of soil temperature decreasing from the middle to the east and west along the length direction also showed a decreasing spatial distribution. The span direction decreased from the middle to the north and south, and the temperature of the north wall area was higher than that of the southern front foot. In the vertical direction, the temperature fluctuation decreased with the increase of depth，indicating that the underlying soil had stronger thermal stability. The results show that the double-film solar greenhouse can also keep the planting area not lower than 3.61 ℃ in extreme weather, and the stable cultivation conditions of the ambient temperature of the planting area higher than 5 ℃ in winter. The results of this study further reveal the characteristics of the hot and humid environment of the double-film solar greenhouse under severe cold weather conditions, and provide a scientific basis and data support for the precise regulation of environmental factors in the greenhouse.

China's lotus root industry holds a dominant position in the international market. Given that the rigid chassis of traditional agricultural machinery fails to meet the requirements for stability, trafficability, and adaptability, this study designed a variable triangular track chassis to improve the passability and stability of the harvester in soft and high water content lotus root fields. By analyzing the working conditions of the lotus root field and the force characteristics of the chassis, key parameters such as ground contact pressure of the track, traction force, and power were determined. The overall structure was optimized by using modular design and lightweight aluminum alloy material (ZL303). A three-dimensional model was established using SolidWorks, and finite element analysis was conducted on the drive wheel, variable device, and load-bearing components based on ANSYS Workbench, including stress distribution, fatigue safety margin, and modal characteristics. The simulation results show that when the total mass is 1.1 t and the maximum speed is 15 km/h, the maximum stress of the drive wheel is 44.05 MPa, the fatigue safety factor of key components is all higher than 2.4, and the lowest natural frequency is 323.3 Hz, effectively avoiding resonance with the vibration frequency (50-300 Hz) of auxiliary equipment. In addition, by hydraulically adjusting the track tension (400-2 000 N) and the variable track gauge mechanism, the chassis has a vertical obstacle crossing ability of 300 mm and water depth adaptability of 0.4 m. The whole machine adopts redundant design and modular maintenance system, effectively solving problems such as insufficient system integration, low energy efficiency, and high manufacturing costs in existing technologies. The chassis achieves a load-to-weight ratio of 54.5% when fully loaded, with a center of gravity height controlled below 420 mm. The overturning moment safety factor meets the requirements of GB/T19930-2005 standard, and it reserves 3 sets of power output interfaces and 4 standardized mounting bases, supporting rapid replacement of multi-functional agricultural operation devices, providing a reliable platform for the development of intelligent agricultural machinery equipment.