Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR’s potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Optimized design and experiment of the three-arm transplanting mechanism for rice potted seedlings

To better meet the requirements of mechanized transplanting of pepper plug seedlings, this study explores the seedling picking mechanism of a fully automatic pepper transplanting machine. It introduces a novel “eagle beak” type trajectory for seedling picking and designs a probe-type mechanism for pepper plug seedling retrieval. We establish a kinematic theoretical model and delineate the composition and operational principles of this probe-type mechanism. Additionally, we develop an auxiliary optimization software tailored based on Visual Basic 6.0 visual programming software for this mechanism. It employs a blend of manual fine-tuning and a “parameter guidance” optimization algorithm, enabling the determination of 11 optimal target parameters. Our comparative analysis between the theoretical model, optimization software, and high-speed camera experiments reveals a strong correlation in the motion trajectories, and the maximum error of the pose angle is 1.2°. To validate the mechanism’s design, we conducted a seedling retrieval experiment. In this test, the success rates of the seedling harvesting mechanism at speeds of 30, 40, and 50 r/min were 96.4%, 94.3%, and 91.4%, respectively, thus demonstrating its practical feasibility.

Super rice contains a variety of advantageous characteristics. However, current rice seedling transplanting machines fail to achieve the necessary trajectory and distance required for super rice mechanized transplanting. To address this issue, this study introduces a differential-speed rotary mechanism for transplanting super rice pot seedlings. The developed mechanism operates using a non-uniform speed differential gear train, which enables the transplanting arm components to mimic the specific trajectory and posture necessary for transplanting super rice pot seedlings. The kinematic model of the differential-speed rotary super rice pot seedling transplanting mechanism (PSTM) was established, and optimization design software was developed. This software facilitated the determination of a set of mechanism parameters optimized for super rice pot seedling transplantation. The results obtained from virtual simulations were found to be in alignment with those from the optimization software, thereby verifying the accuracy of the theoretical analysis and simulation. A testing bench for the rice PSTM was also developed and used for pot seedling pickup experiments. The bench tests demonstrated that the designed super rice PSTM yielded a seedling pickup success rate of 97% and a seedling injury rate of 1.8% when operating at an efficiency of 200 times/min.

Introduction: The rapid urbanization of rural regions, along with an aging population, has resulted in a substantial manpower scarcity for agricultural output, necessitating the urgent development of highly intelligent and accurate agricultural equipment technologies.Methods: This research introduces YOLOv8-PSS, an enhanced lightweight obstacle detection model, to increase the effectiveness and safety of unmanned agricultural robots in intricate field situations. This YOLOv8-based model incorporates a depth camera to precisely identify and locate impediments in the way of autonomous agricultural equipment. Firstly, this work integrates partial convolution (PConv) into the C2f module of the backbone network to improve inference performance and minimize computing load. PConv significantly reduces processing load during convolution operations, enhancing the model's real-time detection performance. Second, a Slim-neck lightweight neck network is introduced, replacing the original neck network's conventional convolution with GSConv, to further improve detection efficiency and accuracy. This adjustment preserves accuracy while reducing the complexity of the model. After optimization, the bounding box loss function is finally upgraded to Shape-IoU (Shape Intersection over Union), which improves both model accuracy and generalization.Results: The experimental results demonstrate that the improved YOLOv8_PSS model achieves a precision of 85.3%, a recall of 88.4%, and an average accuracy of 90.6%. Compared to the original base network, it reduces the number of parameters by 55.8%, decreases the model size by 59.5%, and lowers computational cost by 51.2%. When compared with other algorithms, such as Faster RCNN, SSD, YOLOv3-tiny, and YOLOv5, the improved model strikes an optimal balance between parameter count, computational efficiency, detection speed, and accuracy, yielding superior results. In positioning accuracy tests, the, average and maximum errors in the measured distances between the camera and typical obstacles (within a range of 2-15 meters) were 2.73% and 4.44%, respectively.Discussion: The model performed effectively under real-world conditions, providing robust technical support for future research on autonomous obstacle avoidance in unmanned agricultural machinery.