Detection of multi-class coconut clusters for robotic picking under occlusion conditions
Abstract
robots with the help of machine vision technology. Coconuts grow in clusters in the canopy and are easily occluded by
leaves. Therefore, the detection of multi-class coconut clusters according to the occlusion condition is necessary for robots to
develop picking strategies. The coconut detection model, named YOLO-Coco, was developed based on the YOLOv7-tiny
network. It detected coconuts in different conditions such as not-occluded, leaves-occluded, and trunk-occluded fruit. The
developed 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 semantic
information for the detection head. Finally, the Bi-directional Feature Pyramid Network (BiFPN) was used to optimize the head
network structure of YOLO-Coco to achieve the balanced fusion of multi-scale features. The results showed that the mean
average 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 detection
accuracy of YOLO-Coco for yellow coconuts was 5.1% higher than that for green coconuts. Compared with seven mainstream
deep learning networks, YOLO-Coco achieved the highest detection accuracy in detecting multi-class coconut clusters, while
maintaining advantages in detection speed and model size. The developed model can accurately detect coconuts in complex
canopy environments, providing technical support for the visual system of coconut-picking robots.
Keywords: coconut clusters, picking robot, leaves-occluded, multi-class detection, YOLOv7-tiny
DOI: 10.25165/j.ijabe.20251801.9031
Citation: Fu Y X, Zheng H C, Wang Z B, Huang J Y, Fu W. Detection of multi-class coconut clusters for robotic picking
under occlusion conditions. Int J Agric & Biol Eng, 2025; 18(1): 267–278.
Keywords
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