To determine the optimal operating parameters of the single-longitudinal-axial-flow threshing device for wheat harvesters under high feed rate conditions and lay a theoretical and methodological foundation for the subsequent development of a specialist system for threshing device parameter optimization, this study constructed a discrete element model (DEM) of wheat plants at harvest stage in EDEM software based on the Hertz–Mindlin with bonding contact model, combined with the discrete element parameters of wheat plants calibrated by a texturometer. Simulation experiments were designed and conducted to optimize the operating parameters of the threshing device, and a methodology that employs simulation experiments to obtain optimal parameter combinations under different operating environments was proposed, which was applied to the development of a specialist system for threshing device parameter optimization. Taking cylinder speed, cylinder inclination and threshing gap as experimental factors, and unthreshed rate, separation loss rate and breakage rate as evaluation indexes, single-factor and Box–Behnken simulation experiments were carried out. Mathematical models were established to reveal the correlation between evaluation indexes and influencing parameters, and then the optimal parameter combination was solved. The results showed that the threshing device achieved the optimal comprehensive operating performance at a cylinder speed of 850 r/min, a cylinder inclination of 6° and a threshing gap of 20 mm, with an unthreshed rate of 1.575%, a separation loss rate of 0.158% and a breakage rate of 0.509%. Bench validation tests indicated that the relative errors between simulated and measured indexes were all less than 5%, verifying the reliability of the established DEM. Compared with bench tests, the simulation experiment method in this study ensured high accuracy while significantly reducing the time and labor costs of tests. The obtained optimal operating parameters and the proposed simulation optimization methodology provide core data support and technical reference for the development of a specialist system for parameter optimization of the threshing device of wheat harvesters.

This study addresses the problem of inconsistent and unstable sowing depth in conventional wheat planters. A novel measurement and control strategy based on mulch quantity regulation is proposed for a dual-axis rotary planter. A mathematical model of sowing depth is established and a corresponding adjustment unit is designed. An adaptive fuzzy PID controller is developed and compared with a conventional PID controller. The adaptive fuzzy PID controller achieves a rise time of 0.19 s and a stabilization time of 0.26 s. Compared with the conventional PID controller, these values represent reductions of approximately 26.2% and 14.9%, respectively. Simulation results confirm the controller's effectiveness and adaptability. The system is then implemented on a dual-axis rotary-tillage wheat planter and validated through field experiments. The proposed method enables precise and stable control of sowing depth, improving both the quality and consistency of wheat planting operations.

Flower picking is a labor intensive process heavily in the floriculture industry, and it remains predominantly manual. With the increasing shortage of agricultural labor and the continuous rise in labor costs, the sustainable development of the flower industry is facing severe challenges. Therefore, the using of robotic systems to picking flower plants has become an inevitable trend in the development of modern agriculture. This paper presents a systematic review of recent advances in flower picking robot, with a particular focus on system architecture and core enabling technologies. The core technologies are visual perception and target recognition, motion control and path planning, and flexible, non-destructive end effector design. The research status and limitations of these key technologies are analyzed, with special attention to their applications in both open field and controlled facility environments. Current challenges include low recognition accuracy under complex lighting and occlusion, insufficient end effector adaptability to diverse varieties, and low overall picking efficiency. Future directions include multiple modal sensing integrated with artificial intelligence, bionic and soft robotic end effectors, and multiple robot collaborative system. Finally, based on the perspectives of design and application, the review provides future research directions toward industrialization, with a particular focus on key enabling technologies of these technologies.

With the rapid advancement of agricultural intelligence, the scarcity of publicly available point cloud datasets and insufficiency semantic segmentation performance for complex farm scene models have become critical bottlenecks restricting improvements in scene perception capabilities for intelligent agricultural machinery. This study used unmanned aerial vehicle technology to collect point cloud data from multiple types of farms, constructed a high-quality farm scene point cloud dataset, and proposed a novel deep learning-based semantic segmentation model. This model significantly improved segmentation performance through a dual feature enhancement strategy that combines local feature refinement and global context correlation enhancement, enabling effectively segmentation of complex farm objects. Compared with other models, in typical farm scenes, this model demonstrated the best semantic segmentation performance for tree and man-made construction categories, especially for man-made constructions, where all evaluation metrics exceeded 0.8. In atypical farm scenes, it outperformed all others with a 19.6 % improvement over the deep layer aggregation model. The segmentation accuracy for key categories exceeded 0.84. In mixed farm scenes, the overall performance of this model reached 0.5, and it ranked among the top 2 across all core metrics for semantic segmentation of all categories. The farm scene point cloud dataset constructed in this study fills a gap in high-quality point cloud data for specific farm scenes. The proposed semantic segmentation model, by virtue of its excellent performance in complex farm scenes, is expected to enhance the scene perception capabilities of intelligent agricultural machinery.

To address the recognition and localization challenges stemming from densely clustered, adhering flowers and complex occlusions in greenhouse tomatoes, along with the constrained computational resources of embedded devices, this study proposes a lightweight instance segmentation model, LT-Net, and a multimodal 3D localization method specifically designed for tomato pollination robots. First, developed as an improvement over YOLOv8n-seg, the model integrates a Large Selective Kernel (LSK) network, Omni-Dimensional Dynamic Convolution (ODConv), and the SENetV2 attention mechanism to enhance its representation capability for fine-grained features while minimizing computational complexity. Additionally, an Inner-IoU loss function is incorporated to alleviate positioning sensitivity during the regression process of tiny targets. Second, to mitigate the centroid offset caused by dense flower clusters, a mask segmentation strategy based on an adaptive watershed algorithm is designed to achieve independent centroid extraction for individual flower targets. Concurrently, monocular depth estimation is fused with hardware-based ranging information for scale recovery, achieving a precise coordinate mapping from 2D image space to 3D physical space. Experimental results demonstrate that the enhanced LT-Net model achieves an mAP50 of 93.8% with only 1.16 M parameters and 5.7 G FLOPs. In the segmentation task, the mIoU and F1-score reach 0.8779 and 81.68%, respectively, showing favorable lightweight performance while maintaining high segmentation accuracy. With the introduction of the adaptive watershed splitting, the average deviation of individual flower centroids in densely clustered and adhering scenes is restricted to 33.21 px, effectively suppressing abnormal localization fluctuations. Greenhouse field trials verify that when deployed on the embedded terminal, the system achieves a target detection accuracy of 94.2% for tomato stamens and a pollination point localization success rate of 90.1%. The spatial axial root-mean-square error ZRMSE is 6.43 cm. This study provides an efficient, lightweight, and deployable visual perception solution for automated greenhouse tomato pollination.

IntroductionThe industrial cultivation of tomato seedlings requires a high degree ofuniformity and consistency in grading. However, traditional grading methods based on phenotypictraits such as leaf area and canopy width are susceptible to environmental conditions, therebylimiting the accuracy and efficiency of grading. Since cumulative internode length is relativelystable and closely correlated with seedling vigor, this study aims to develop an accurate methodfor measuring and grading the cumulative internode length of tomato seedlings.MethodsA tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework. First, deformable convolution was introducedinto the backbone to enhance feature extraction for curved stems and occluded regions. Second,the original loss function was replaced with SIoU to improve the alignment between predictedregions and the actual stem structure. Third, a Haar wavelet downsampling module was embeddedinto the backbone to preserve high-frequency detail information and reduce information loss underocclusion conditions. An intelligent grading system was further developed to verify the practicalapplicability of the proposed method.ResultsExperimental results showed that DSH_YOLO achieved a Precision of 96.1%, aRecall of 94.3%, and an mAP@0.5 of 92.1%. Compared with the baseline YOLOv8 model, theproposed method substantially improved segmentation performance for cumulative internoderegions in tomato seedlings. In prototype validation, the intelligent grading system achieved anaverage grading success rate of 87.50%, with an average cumulative internode length error of 8.0mm.DiscussionThe results indicate that DSH_YOLO and the grading system can meet therequirements for large-scale grading and detection of tomato seedlings, demonstrating highdetection accuracy and success rates. This approach can provide insights for grading other types ofseedlings during their growth stages and offer support for seedling production and thedevelopment of intelligent agricultural equipment.

Given that the natural pollination of tomatoes is limited by the closed environment of facility agriculture, and the problems of low efficiency, high cost, or side effects associated with traditional artificial, insect, and hormone pollination methods, this study proposes an intelligent vibration pollination robot solution. Firstly, we designed the YOLO-MAF algorithm for detecting the flowering period of tomato flowers, which integrates the MPDIoU loss function module, the AFPN feature fusion module, and the efficient multi-scale attention (EMA) module to improve the accuracy of flowering period detection in complex environments; Secondly, the multimodal geometric feature fusion detection algorithm (MGFF) is used to achieve precise spatial pixel positioning of the flowers to be pollinated; Finally, the camera coordinate system is mapped to the robotic arm coordinate system through the hand-eye calibration algorithm, driving the end effector to complete precise vibration pollination. Experimental results show that the YOLO-MAF algorithm achieves a precision rate of 90.2 %, a recall rate of 85.4 %, an mAP@0.5 of 91.4 %, and a GPU inference time of 8.9 ms; the MGFF algorithm achieves the highest positioning accuracy of 91 %. By encapsulating the algorithm and integrating it with a vibrating pollination robot, validation tests have shown an average pollination success rate of 73.58 % and an average pollination time of 30.486 s. This technology can provide efficient and low-cost automated pollination technology support for facility agriculture, helping to improve the quality and efficiency of the tomato industry.

Reliable estimation of winter wheat yield is critical to food system stability and farmland management. Integrating multi-spectral remote sensing data with agronomic parameters represents a primary strategy for improving yield estimation accuracy. However, existing research often overlooks parameters reflecting crop population structure and fails to account for dynamic shifts in the contributions of multidimensional agronomic variables across growth stages, thereby limiting prediction accuracy and model stability. To address these limitations, a winter wheat yield estimation model was developed. This model integrates multi-source and multi-temporal data, incorporates stem tiller density, a key population structure parameter, and accounts for dynamic variation across growth stages. Unmanned aerial vehicle multi-spectral images were collected at four key growth stages: jointing (stem elongation with detectable nodes), booting (flag leaf sheath swelling preceding heading), heading (spike emergence) and filling (grain filling with dry matter accumulation). Three growth indicators, stem tiller density, leaf area index and above-ground biomass, were measured. Two comprehensive growth indicators were derived using the coefficient of variation and the CRITIC weighting methods, respectively (CGICV and CGICR). Correlation and feature importance analyses were used to identify sensitive vegetation indices (VIs), which were subsequently integrated with the comprehensive growth indicators. Single-stage, multi-source feature fusion and multi-temporal yield estimation models were established using the Kernel Extreme Learning Machine and its optimised algorithm using the Crested Porcupine Optimizer. The results showed the following: (1) among the individual growth stages, features from the filling stage achieved the highest prediction accuracy; (2) the fusion of multi-source features (VIs + CGICR) enhanced the prediction accuracy of the model, achieving a validation set R2 of 0.884 and a relative prediction deviation of 2.916 at the filling stage; and (3) the multi-temporal model further improved predictive performance, with the validation R2 reaching 0.920, indicating that information from different growth stages contributed complementarily to yield prediction and improved overall model performance. By contrast, the model exhibited relatively weak predictive capability at the early growth stages and was better-suited to early risk identification. Meanwhile, its generalisation ability under cross-regional and inter-annual conditions still requires further validation. Overall, integrating multi-source and multi-temporal data can enhance the precision and stability of predicting winter wheat yield, thereby facilitating precision agriculture management.

Widespread infestation of pests and pathogens during winter wheat's heading stage poses significant risks to yield loss. In this study, an assessment model of health degree (HD) of winter wheat under field conditions was established by using unmanned aerial vehicle remote sensing (UAV RS) imagery. Firstly, non-photosynthetic features were identified from the UAV RS imagery based on different machine learning methods, including Minimum Distance (MD), Maximum Likelihood Estimation (MLE), and Support Vector Machine (SVM). Classification results indicated that MD demonstrates the best performance, according to the values of Overall Accuracy (0.898), Kappa Coefficient (0.863), and Precision (0.856). Therefore, the inversion model between the proportion of pixels classified as non-photosynthetic features and the corresponding ground truth of the incidence of non-photosynthetic features was established. Coefficient of determination (R2), RMSE (root mean square error), and RRMSE (Relative RMSE) of the inversion model are 0.73, 4.86%, and 19.81%, respectively, demonstrating strong correlation and high accuracy. Subsequently, an assessment model for HD of the wheat field was generated based on the predicted incidence of the non-photosynthetic features, and the conclusion was reached that HD1 (pre-symptoms of the infestation of pests and pathogens) dominated in the wheat field, with the proportion of area as 56.16%, while HD4 and HD5 (severe infestation of pests and pathogens) were negligible, with proportions of area of 2.29% and 17.75%. Finally, the assessment model of HD was used to simulate the precision OSMP (One-Spray-Multiple-Protection), and the agricultural chemical could be reduced to 69.11% of the conventional OSMP operation, which provides theoretical and methodological support for the reduction of agricultural chemicals in the domain of precision agriculture.

IntroductionLeaf water content is a key physiological indicator of plant growth and health status. Constructing leaf water content estimation models based on spectroscopy is an effective method for monitoring plant physiological conditions.MethodsTo improve the accuracy of leaf water content estimation and develop models applicable to different plants, this study collected 1,680 groups of hyperspectral and water content data from peach tree leaves. Estimation models were established using two methods: “constructing vegetation indices” and “selecting characteristic wavelengths.” The accuracy and number of wavelengths used in each model were systematically evaluated. The optimal model was used to predict the water content of each pixel in the hyperspectral images, achieving visualization of leaf water distribution. Additionally, 244 groups of hyperspectral and water content data from apple tree and lettuce leaves were collected to validate the generalization ability of the optimal model.ResultsResults showed that the optimal models established using the two methods were the linear regression model based on the vegetation index NISDI (3 wavelengths, RP2 = 0.9636, RMSEP=0.0356), and the CARS-RF model (12 wavelengths, RP2 = 0.9861, RMSEP=0.0219). Although the accuracy of the two models was similar, the latter used four times more wavelengths than the former, so the former was chosen as the optimal model. Using the optimal model to estimate the water content of apple tree leaves, the RP2 and RMSEP were 0.9504 and 0.1226, respectively. For lettuce containing only leaf tissue, the RP2 and RMSEP were 0.8211 and 0.1771, respectively.DiscussionThese results indicate that the model has some generalization ability and can accurately estimate the water content of leaves of woody plants in the same family, with some performance degradation across different growth forms. The study results achieved accurate estimation of leaf water content for three types of plants and also provided a reference for establishing plant leaf water content estimation models with generalization ability.

Multi-machine collaboration in agricultural machinery is a key focus in current research, with task allocation being an indispensable component. However, the current optimization objectives for task allocation in agricultural machinery are mostly confined to travel distance or time, aiming to balance task distribution. These methods are not suitable for emerging electric agricultural machinery, especially when operating in hilly areas. To address these limitations, this study proposed a task allocation method optimized for energy consumption, specifically for weeding robots in hilly orchards. Initially, drones were employed to obtain the Digital Surface Model (DSM) and orthophotos of the orchard test area. After processing the data through vegetation filtering, DEM construction, and slope analysis, slope information of the surface was derived. An electronic map of the orchard reflecting this slope information was then generated. Subsequently, the task allocation problem for weeding robots in hilly orchards was defined. A mathematical model was then established with energy consumption as the optimization objective. Finally, a Golden Kepler Optimization Algorithm (GKOA) was developed and tested through simulations using real data from the test area. The results indicated that, compared to Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), and Kepler Optimization Algorithm (KOA), GKOA reduced the optimal solution cost by 10.3%, 8.2%, 7.0%, and 4.5%, respectively. This task allocation method was able to achieve the optimal task allocation plan with lower travel energy consumption costs and a higher balance in task distribution, whether for all plots in the orchard or nested plots.

The existing discrete element model of wheat plants lacks the glume, which hinders the simulation of the entire threshing process. To address this issue, this paper takes wheat at the harvest stage as the research object and constructs a complete discrete element model of wheat plants with glumes based on the Hertz-Mindlin with bonding model in the EDEM simulation software. The parameter calibration of wheat glumes discrete element model is studied through collision bounce experiments, slope experiments, and accumulation experiments. The results show that the coefficient of restitution, coefficient of static friction, and coefficient of rolling friction between glume and steel are 0.488, 0.625, and 0.048, respectively, and the coefficient of restitution, coefficient of static friction, and coefficient of rolling friction between glume and glume are 0.232, 0.966, and 0.059, respectively. The relative errors between the simulation results and the measured values are less than 5%, and the calibration parameters are effective. Based on the structural parameters of the self-developed experiment-bed of tangential axial-flow grain threshing device, a three-dimensional model of the wheat threshing device is established to simulate the whole threshing process of the complete wheat plant, and the bench-scale experiments are carried out with the non-threshing rate as the performance index. The results indicate that the model can completely simulate the separation process of glume and grain and the movement law of different grains, and the relative error of non-threshing rate between the simulation experiments and bench-scale experiments is 4.36%. This further demonstrates that the proposed model can provide a reference for the wheat threshing process research and device performance optimization design.

Simultaneous Localization and Mapping (SLAM) has emerged as one of the foundational technologies enabling mobile robots to achieve autonomous navigation, garnering significant attention in recent years. To address the limitations inherent in traditional SLAM systems when operating within dynamic environments, this paper proposes a new SLAM system named GNV2-SLAM based on ORB-SLAM2, offering an innovative solution for the scenario of cowshed inspection. This innovative system incorporates a lightweight object detection network called GNV2 based on YOLOv8. Additionally, it employs GhostNetv2 as backbone network. The CBAM attention mechanism and SCDown downsampling module were introduced to reduce the model complexity while ensuring detection accuracy. Experimental results indicate that the GNV2 network achieves excellent model compression effects while maintaining high performance: mAP@0.5 increased by 1.04%, reaching a total of 95.19%; model parameters were decreased by 41.95%, computational cost reduced by 36.71%, and the model size shrunk by 40.44%. Moreover, the GNV2-SLAM system incorporates point and line feature extraction techniques, effectively mitigate issues reduced feature point extraction caused by excessive dynamic targets or blurred images. Testing on the TUM dataset demonstrate that GNV2-SLAM significantly outperforms the traditional ORB-SLAM2 system in terms of positioning accuracy and robustness within dynamic environments. Specifically, there was a remarkable reduction of 96.13% in root mean square error (RMSE) for absolute trajectory error (ATE), alongside decreases of 88.36% and 86.19% for translation and rotation drift in relative pose error (RPE), respectively. In terms of tracking evaluation, GNV2-SLAM successfully completes the tracking processing of a single frame image within 30 ms, demonstrating expressive real-time performance and competitiveness. Following the deployment of this system on inspection robots and subsequent experimental trials conducted in the cowshed environment, the results indicate that when the robot operates at speeds of 0.4 m/s and 0.6 m/s, the pose trajectory output by GNV2-SLAM is more consistent with the robot's actual movement trajectory. This study systematically validated the system's significant advantages in target recognition and positioning accuracy through experimental verification, thereby providing a new technical solution for the comprehensive automation of cattle barn inspection tasks.

Wheat spike detection holds significant importance for agricultural production as it enhances the efficiency of crop management and the precision of operations. This study aims to improve the accuracy and efficiency of wheat spike detection, enabling efficient crop monitoring under resource-constrained conditions. To this end, a wheat spike dataset encompassing multiple growth stages was constructed, leveraging the advantages of MobileNet and ShuffleNet to design a novel network module, SeCUIB. Building on this foundation, a new wheat spike detection network, LGWheatNet, was proposed by integrating a lightweight downsampling module (DWDown), spatial pyramid pooling (SPPF), and a lightweight detection head (LightDetect). The experimental results demonstrate that LGWheatNet excels in key performance metrics, including Precision, Recall, and Mean Average Precision (mAP50 and mAP50-95). Specifically, the model achieved a Precision of 0.956, a Recall of 0.921, an mAP50 of 0.967, and an mAP50-95 of 0.747, surpassing several YOLO models as well as EfficientDet and RetinaNet. Furthermore, LGWheatNet demonstrated superior resource efficiency with a parameter count of only 1,698,529 and GFLOPs of 5.0, significantly lower than those of competing models. Additionally, when combined with the Slicing Aided Hyper Inference strategy, LGWheatNet further improved the detection accuracy of wheat spikes, especially for small-scale targets and edge regions, when processing large-scale high-resolution images. This strategy significantly enhanced both inference efficiency and accuracy, making it particularly suitable for image analysis from drone-captured data. In wheat spike counting experiments, LGWheatNet also delivered exceptional performance, particularly in predictions during the filling and maturity stages, outperforming other models by a substantial margin. This study not only provides an efficient and reliable solution for wheat spike detection but also introduces innovative methods for lightweight object detection tasks in resource-constrained environments.

Transplanting represents a vital technique in contemporary vegetable cultivation; mechanized transplanting entails a complex process characterized by interactions among machine, soil, and plants, which ultimately constrains the quality of mechanized transplanting. Currently, due to the unclear mechanism of machinesoil-plant interaction, it is difficult to solve the problem of transplanting verticality. To this end, this study established a machine-soil-pot seedling coupled simulation model through EDEM-Recurdyn co-simulation to analyze the disturbance of the soil and pot seedling by the transplanter during the transplanting process. Simulation experiment results indicate that the trajectory eigenvalue of the transplanting point and the opening angle of the duckbill are the main factors affecting transplanting effects. Through regression modelling, it is predicted that when the eigenvalue lambda is 1.1, the duckbill opening angle gamma is 33.3 degrees, and the opening time t is 0.4 s, the transplanting effects are optimal, corresponding to the soil backflow ratio of 0.44, the soil disturbance ratio of 1.29, and the verticality of 79.83 degrees. By comparing the results of the simulation with field experiments, the average errors of soil backflow ratio, soil disturbance ratio, and verticality were 2.14 %, 2.46 %, and 1.86 %, respectively. The experiment results show that the simulation model can effectively reflect the physical changes during the transplanting process and can provide a reference for the optimization of the transplanting device design.

Rapid economic growth since the turn of the century has often been accompanied by significant challenges, including fossil fuel depletion, environmental degradation, and energy security concerns. Urgent measures are essential to promote environmentally friendly advancements and adopt sustainable energy solutions. Biomass energy, an important component of renewable energy, stands out as the sole renewable energy source containing carbon and has attracted significant attention from governments and the scientific community worldwide. Attention to biomass conversion technologies and their practical applications has gradually increased. This paper provides an in-depth analysis of the utilization of biomass and its wastes, and systematically introduces the progress of the application of biomass conversion technologies, including biochemical and thermochemical conversion, to provide readers with a clear picture of the technological development. By meticulously summarizing the current status of the application of different products produced by these technologies, it provides a valuable reference for researchers and practitioners in the field of biomass energy, aiming to meet the challenges of clean energy production and biomass waste management, and to mitigate the adverse impacts of human activities on the environment. In addition, this paper explores the application of machine learning in the field of biomass conversion, especially its potential in optimizing the biomass conversion process, improving the accuracy of energy yield prediction, and enhancing process control. Despite challenges such as data quality and model interpretability, developments in machine learning, particularly advances in feature engineering and interpretable AI, promise to address these issues. This study contributes positively to advancing biomass energy technologies.

With the aging of the rural population,standardized and intelligent orchard production has be-come a key development direction for modem orchards.Orchard production currently faces several challenges,including labor shortages,low mechanization levels,and inefficient resource utilization.In-telligent orchard technologies offer potential solutions to these problems by improving productivity,re-ducing costs,and minimizing resource waste.This study systematically reviews the four key technologi-cal areas of intelligent orchards:information perception,intelligent decision-making,precision opera-tions,and intelligent management,and analyzes their current status,future directions,and applications in modem orchards based on recent research and developments at home and abroad.First,information perception technology forms the foundation of intelligent orchard production.By integrating various sensors,drones,and Internet of Things(IoT),orchards can achieve real-time,multidimensional monitor-ing of their environments,crop growth,and operational equipment.Environmental perception technolo-gies cover factors such as climate,soil moisture,and temperature.These data are collected using tools like LiDAR,remote sensing,and soil sensors,helping orchard managers better understand the microen-vironment of the orchard.Crop perception technology monitors the health,growth,and pest infestation status of trees using hyperspectral imaging,infrared technology,and other advanced sensors.This en-ables early interventions to prevent losses in yield or quality.Additionally,operational equipment per-ception technology provides real-time monitoring of the status and performance of agricultural machin-ery,supporting autonomous navigation and precision operations by providing crucial data for optimiz-ing equipment use and ensuring efficient orchard management.By fusing multiple sources of informa-tion,intelligent orchards can monitor and manage their operations across the full lifecycle of the or-chard,from planting to harvest.Secondly,intelligent decision-making systems are essential for achiev-ing smart orchard production.By analyzing and processing the collected data,these systems can opti-mize various orchard production processes such as irrigation,fertilization,flower thinning,pesticide ap-plication,and harvesting.For example,intelligent irrigation systems analyze soil moisture levels and meteorological data to determine the best times and quantities for irrigation,ensuring efficient use of water resources.Fertilization and pesticide application systems adjust the timing and dosage based on the specific growth needs of the trees,promoting healthy growth while reducing the use of fertilizers and pesticides,thus minimizing environmental pollution.Additionally,smart harvesting systems use fruit maturity detection to schedule harvests efficiently,improving productivity while reducing fruit damage.Precision operations are a vital component of smart orchard production.Autonomous naviga-tion technologies allow agricultural machinery to operate autonomously in the complex environments of orchards.Using LiDAR,vision-based navigation,and obstacle avoidance algorithms,machinery can complete tasks safely and efficiently.Precision operations are enhanced by real-time sensor data,which enables machinery to adjust its parameters automatically to ensure accuracy and quality.For example,precision fertilization and pesticide application systems adjust the amount applied based on the actual needs of each tree,improving resource utilization efficiency and production outcomes.In terms of sys-tem integration,intelligent orchards rely on cloud platforms to achieve unmanned and automated man-agement.Orchard inspection robots collect real-time data on tree growth and pest status,which is up-loaded to the cloud for analysis by intelligent decision-making systems.Orchard management robots carry out tasks such as fertilization,flower thinning,and pruning based on the instructions from the in-telligent decision systems,executing complex operations automatically.Harvesting robots,equipped with visual recognition technology and deep learning algorithms,can assess fruit maturity and perform harvesting tasks efficiently.In multi-machine collaborative operation systems,several robots in the or-chard are coordinated through cloud platforms to work together,improving overall efficiency.For exam-ple,during harvest seasons,harvesting robots and transport robots collaborate to ensure that picked fruits are swiftly transported to designated locations,reducing spoilage and enhancing workflow effi-ciency.Finally,this study looks forward to the future development direction of smart orchard technolo-gy and provides specific research ideas.Future smart orchard technology will place greater emphasis on multi-source information fusion,autonomous operation of agricultural machinery,and intelligent man-agement throughout the entire process.By deeply integrating perception data from different sources,or-chard managers can more accurately grasp the production dynamics of the orchard,further improving the scientificity of decision-making.The autonomous operation technology of agricultural machinery will continue to improve,achieving autonomous navigation and operation in more complex environ-ments.The fully intelligent management system will optimize the production process,reduce operating costs,improve the overall production efficiency and fruit quality of the orchard through technologies such as big data analysis and cloud computing,in order to provide reference and guidance for the devel-opment of key technologies in standardized orchards.In conclusion,the future of intelligent orchards lies in the continuous improvement and integration of these technologies.Through the development of more advanced sensing technologies,intelligent decision-making systems,and autonomous machinery,orchards will become more efficient,sustainable,and productive,helping farmers to manage their re-sources better while meeting the demands of modern agriculture.This will not only enhance orchard productivity and fruit quality but also contribute to the overall sustainability and competitiveness of the agricultural sector.

To investigate the interaction mechanism between agricultural tillage machinery and soil, this study established a precise simulation model by integrating physical and numerical experiments using typical yellow cinnamon soil collected from western Henan Province, China. The discrete element parameters for soils with varying moisture contents were calibrated based on the Hertz–Mindlin (no slip) contact model. Through Plackett–Burman screening, steepest ascent optimization, and Box–Behnken response surface methodology, a predictive model correlating moisture content, parameters, and repose angle was developed, yielding the optimal contact parameter combination: interparticle static friction coefficient (0.6), soil–65Mn static friction coefficient (0.69), and interparticle rolling friction coefficient (0.358). For the Bonding model, orthogonal experiments coupled with NSGA-II multi-objective optimization determined the optimal cohesive parameters targeting maximum load (673.845 N) and displacement (9.765 mm): normal stiffness per unit area (8.8 × 107 N/m3), tangential stiffness per unit area (6.85 × 107 N/m3), critical normal stress (6 × 104 Pa), critical tangential stress (3.15 × 104 Pa), and bonding radius (5.2 mm). Field validation using rotary tillers and power harrows demonstrated less than 6% deviation in soil fragmentation rates between simulations and actual operations, confirming parameter reliability and providing theoretical foundations for constructing soil-tillage machinery interaction models.