Accurately identifying cherry-tomato picking points and obtaining their coordinate locations is critical to the success of cherry-tomato picking robots. However, previous methods for semantic segmentation alone or combining object detection with traditional image processing have struggled to accurately determine the cherry-tomato picking point due to challenges such as leaves as well as targets that are too small. In this study, we propose a YOLOv8n-DDA-SAM model that adds a semantic segmentation branch to target detection to achieve the desired detection and compute the picking point. To be specific, YOLOv8n is used as the initial model, and a dynamic snake convolutional layer (DySnakeConv) that is more suitable for the detection of the stems of cherry-tomato is used in neck of the model. In addition, the dynamic large convolutional kernel attention mechanism adopted in backbone and the use of ADown convolution resulted in a better fusion of the stem features with the neck features and a certain decrease in the number of model parameters without loss of accuracy. Combined with semantic branch SAM, the mask of picking points is effectively obtained and then the accurate picking point is obtained by simple shape-centering calculation. As suggested by the experimental results, the proposed YOLOv8n-DDA-SAM model is significantly improved from previous models not only in detecting stems but also in obtaining stem’s masks. In the mAP@0.5 and F1-score, the YOLOv8n-DDA-SAM achieved 85.90% and 86.13% respectively. Compared with the original YOLOv8n, YOLOv7, RT-DETR-l and YOLOv9c, the mAP@0.5 has improved by 24.7%, 21.85%, 19.76%, 15.99% respectively. F1-score has increased by 16.34%, 12.11%, 10.09%, 8.07% respectively, and the number of parameters is only 6.37M. In the semantic segmentation branch, not only does it not need to produce relevant datasets, but also improved its mIOU by 11.43%, 6.94%, 5.53%, 4.22% and mAP@0.5 by 12.33%, 7.49%, 6.4%, 5.99% compared to Deeplabv3+, Mask2former, DDRNet and SAN respectively. In summary, the model can well satisfy the requirements of high-precision detection and provides a strategy for the detection system of the cherry-tomato.

In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps.

Environmental mapping and robot navigation are the basis for realizing robot automation in modern agricultural production. This study proposes a new autonomous mapping and navigation method for gardening scene robots. First, a new LiDAR slam-based semantic mapping algorithm is proposed to enable the robots to analyze structural information from point cloud images and generate roadmaps from them. Secondly, a general robot navigation framework is proposed to enable the robot to generate the shortest global path according to the road map, and consider the local terrain information to find the optimal local path to achieve safe and efficient trajectory tracking; this method is equipped in apple orchards. The LiDAR was evaluated on a differential drive robotic platform. Experimental results show that this method can effectively process orchard environmental information. Compared with vnf and pointnet++, the semantic information extraction efficiency and time are greatly improved. The map feature extraction time can be reduced to 0.1681 s, and its MIoU is 0.812. The resulting global path planning achieved a 100% success rate, with an average run time of 4ms. At the same time, the local path planning algorithm can effectively generate safe and smooth trajectories to execute the global path, with an average running time of 36 ms.

With the rapid development of the turtle breeding industry in China, the demand for automated turtle sorting is increasing. The automatic sorting of Chinese softshell turtles mainly consists of three parts: visual recognition, weight prediction, and individual sorting. This paper focuses on two aspects, i.e., visual recognition and weight prediction, and a novel method for the object detection and weight prediction of Chinese softshell turtles is proposed. In the individual sorting process, computer vision technology is used to estimate the weight of Chinese softshell turtles and classify them by weight. For the visual recognition of the body parts of Chinese softshell turtles, a color space model is proposed in this paper to separate the turtles from the background effectively. By applying multiple linear regression analysis for modeling, the relationship between the weight and morphological parameters of Chinese softshell turtles is obtained, which can be used to estimate the weight of turtles well. An improved deep learning object detection network is used to extract the features of the plastron and carapace of the Chinese softshell turtles, achieving excellent detection results. The mAP of the improved network reached 96.23%, which can meet the requirements for the accurate identification of the body parts of Chinese softshell turtles.

Digital modelling stands as a pivotal step in the realm of Digital Twinning. The future trend of Digital Twinning involves automated exploration and environmental modelling in complex scenes. In our study, we propose an innovative solution for robot odometry, path planning, and exploration in unknown outdoor environments, with a focus on Digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation, with emphasis on full coverage of unknown maps based on feasible boundaries of interest. The approach allows for dynamic changes to expected targets and behaviours. The evaluation is conducted on a robotic platform with a lightweight 3D LiDAR sensor model. The robustness of different types of odometry is compared, and the impact of parameters on motion planning is explored. The consistency and efficiency of exploring completely unknown areas are assessed in both indoor and outdoor scenarios. The experiment shows that the method proposed in this article can complete autonomous exploration and environmental modelling tasks in complex indoor and outdoor scenes. Finally, the study concludes by summarizing the reasons for exploration failures and outlining future focuses in this domain.