Yu Tang
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
Wenxuan Huang
Zhiping Tan
Weilin Chen
Sheng Wei
Jiajun Zhuang
Chaojun Hou
Professor Jinchang Ren j.ren@rgu.ac.uk
Professor of Computing Science
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 |
Files
TANG 2025 Citrus fruit detection based (VOR)
(6.8 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2025 International Journal of Agricultural and Biological Engineering.
You might also like
FusDreamer: label-efficient remote sensing world model for multimodal data classification.
(2025)
Journal Article
MDDNet: multilevel difference-enhanced denoise network for unsupervised change detection in SAR images.
(2025)
Presentation / Conference Contribution
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search