In order to improve the efficiency of fine segmentation and obstacle removal in the road of tea plantation in hilly areas, a lightweight and high-precision DR-YOLO instance segmentation algorithm is proposed to realize environment awareness. Firstly, the road data of tea gardens in hilly areas were collected under different road conditions and light conditions, and data sets were generated. YOLOv8n-seg, which has the highest operating efficiency, was selected as the basic model. The MSDA-CBAM and DR-Neck feature fusion network were added to the YOLOv8-seg model to improve the feature extraction capability of the network and the feature fusion capability and efficiency of the model. Experimental results show that, compared with the YOLOv8-seg model, the DR-YOLO model proposed in this study has 2.0% improvement in AP@0.5 and 1.1% improvement in Precision. In this study, the DR-YOLO model is pruned and quantitatively compressed, which greatly improves the model inference speed with little reduction in AP. After deploying on Jetson, compared with the YOLOv8n-seg model, the Precision of DR-YOLO is increased by 0.6%, the AP@0.5 is increased by 1.6%, and the inference time is reduced by 17.1%, which can effectively improve the level of agricultural intelligent automation and realize the efficient operation of the instance segmentation model at the edge.

Visual simultaneous localization and mapping (VSLAM) is a foundational technology that enables robots to achieve fully autonomous locomotion, exploration, inspection, and more within complex environments. Its applicability also extends significantly to agricultural settings. While numerous impressive VSLAM systems have emerged, a majority of them rely on static world assumptions. This reliance constrains their use in real dynamic scenarios and leads to increased instability when applied to agricultural contexts. To address the problem of detecting and eliminating slow dynamic objects in outdoor forest and tea garden agricultural scenarios, this paper presents a dynamic VSLAM innovation called MOLO-SLAM (mask ORB label optimization SLAM). MOLO-SLAM merges the ORBSLAM2 framework with the Mask-RCNN instance segmentation network, utilizing masks and bounding boxes to enhance the accuracy and cleanliness of 3D point clouds. Additionally, we used the BundleFusion reconstruction algorithm for 3D mesh model reconstruction. By comparing our algorithm with various dynamic VSLAM algorithms on the TUM and KITTI datasets, the results demonstrate significant improvements, with enhancements of up to 97.72%, 98.51%, and 28.07% relative to the original ORBSLAM2 on the three datasets. This showcases the outstanding advantages of our algorithm.

The cutter of the hand-held tea picker is the key cutting component in the efficient tea harvesting process. In order to solve the problems of large cutting resistance and uneven incision during tea picking, this study fully applied the bionics principle to combine the excellent cutting performance of Aeolesthes induta Newman’s mandibles with the tea cutter, which extracted and fitted the tooth profile structure curve of the upper edge of the Aeolesthes induta Newman’s mandibles. The trapezoidal teeth on the reciprocating cutter of ordinary hand-held tea-picking harvesters were optimized by the fitted curve, and a new tea cutter with the shape of Aeolesthes induta Newman teeth was obtained, which included four kinds of bionic tea-harvesting cutters. The multi-body system software ADAMS 2020 and finite element analysis software ANSYS 2024R1 were used to compare the kinematics, statics and explicit dynamics of cutting properties of the four bionic cutters and common cutters with ordinary trapezoidal teeth and saw teeth. The simulation results showed that the maximum equivalent elastic strain and the maximum cutting force during the cutting operation were reduced by 36.7% and 42.89%, respectively, for the cutting teeth of the bionic tea-harvesting cutter #4 compared with that of the cutter with ordinary trapezoidal teeth. The bionic tea-harvesting cutter designed in this study has better cutting performance than the cutter with traditional cutting teeth, which can effectively reduce the cutting force and improve the flatness and cutting quality of the cutting surface.