Detection of multi-class coconut clusters for robotic picking under occlusion conditions - Fu, Yuxing; Zheng, Hongcheng; Wang, Zongbin; Huang, Jinyang; Fu, Wei (International Journal of Agricultural and Biological Engineering, 2025)
With the development of tree-climbing robots and robotic end-effectors, it is possible to develop automated coconutpickingrobots with the help of machine vision technology. Coconuts grow in clusters in the canopy and are easily occluded byleaves. Therefore, the detection of multi-class coconut clusters according to the occlusion condition is necessary for robots todevelop picking strategies. The coconut detection model, named YOLO-Coco, was developed based on the YOLOv7-tinynetwork. It detected coconuts in different conditions such as not-occluded, leaves-occluded, and trunk-occluded fruit. Thedeveloped model used Efficient Channel Attention (ECA) to enhance the feature weights extracted by the backbone network.Re-parameterization Convolution (RepConv) made the model convolution layers deeper and provided more semanticinformation for the detection head. Finally, the Bi-directional Feature Pyramid Network (BiFPN) was used to optimize the headnetwork structure of YOLO-Coco to achieve the balanced fusion of multi-scale features. The results showed that the meanaverage precision (mAP) of YOLO-Coco for detecting multi-class coconut clusters was 93.6%, and the average precision (AP)of not-occluded, leaves-occluded, and trunk-occluded fruit were 90.5%, 93.8%, and 96.4%, respectively. The detectionaccuracy of YOLO-Coco for yellow coconuts was 5.1% higher than that for green coconuts. Compared with seven mainstreamdeep learning networks, YOLO-Coco achieved the highest detection accuracy in detecting multi-class coconut clusters, whilemaintaining advantages in detection speed and model size. The developed model can accurately detect coconuts in complexcanopy environments, providing technical support for the visual system of coconut-picking robots.Keywords: coconut clusters, picking robot, leaves-occluded, multi-class detection, YOLOv7-tinyDOI: 10.25165/j.ijabe.20251801.9031Citation: Fu Y X, Zheng H C, Wang Z B, Huang J Y, Fu W. Detection of multi-class coconut clusters for robotic pickingunder occlusion conditions. Int J Agric & Biol Eng, 2025; 18(1): 267–278.