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Citrus fruit detection based on an improved YOLOv5 under natural orchard conditions.

Tang, Yu; Huang, Wenxuan; Tan, Zhiping; Chen, Weilin; Wei, Sheng; Zhuang, Jiajun; Hou, Chaojun; Ren, Jinchang

Authors

Yu Tang

Wenxuan Huang

Zhiping Tan

Weilin Chen

Sheng Wei

Jiajun Zhuang

Chaojun Hou



Abstract

Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions, where some specific information of citrus might be lost due to the resultant complex occlusion. Traditional deep learning models might result in lower detection accuracy and detection speed when facing occluded targets. To solve this problem, an improved deep learning algorithm based on YOLOv5, named IYOLOv5, was proposed for accurate detection of citrus fruits. An innovative Res-CSPDarknet network was firstly employed to both enhance feature extraction performance and minimize feature loss within the backbone network, which aims to reduce the miss detection rate. Subsequently, the BiFPN module was adopted as the new neck net to enhance the function for extracting deep semantic features. A coordinate attention mechanism module was then introduced into the network's detection layer. The performance of the proposed model was evaluated on a home-made citrus dataset containing 2000 optical images. The results show that the proposed IYOLOv5 achieved the highest mean average precision (93.5%) and F1-score (95.6%), compared to the traditional deep learning models including Faster R-CNN, CenterNet, YOLOv3, YOLOv5, and YOLOv7. In particular, the proposed IYOLOv5 obtained a decrease of missed detection rate (at least 13.1%) on the specific task of detecting heavily occluded citrus, compared to other models. Therefore, the proposed method could be potentially used as part of the vision system of a picking robot to identify the citrus fruits accurately.

Citation

TANG, Y., HUANG, W., TAN, Z., CHEN, W., WEI, S., ZHUANG, J., HOU, C. and REN, J. 2025. Citrus fruit detection based on an improved YOLOv5 under natural orchard conditions. International journal of agricultural and biological engineering [online], 18(3), pages 176-185. Available from: https://doi.org/10.25165/j.ijabe.20251803.8935

Journal Article Type Article
Acceptance Date May 26, 2025
Online Publication Date Jun 30, 2025
Publication Date Jun 30, 2025
Deposit Date Jul 25, 2025
Publicly Available Date Jul 25, 2025
Journal International journal of agricultural and biological engineering
Electronic ISSN 2873-2944
Publisher International Scholars Journals Publishing Corporation
Peer Reviewed Peer Reviewed
Volume 18
Issue 3
Pages 176-185
DOI https://doi.org/10.25165/j.ijabe.20251803.8935
Keywords Occluded citrus fruits detection; Improved YOLOv5; Coordinate attention mechanism; Object detection
Public URL https://rgu-repository.worktribe.com/output/2935242
Publisher URL https://www.ijabe.org/index.php/ijabe/article/view/8935

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