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METHODS article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1472230

A New Maturity Recognition Algorithm for Xinhui Citrus Based on Improved YOLOv8

Provisionally accepted
Fuqin Deng Fuqin Deng 1Jianle Chen Jianle Chen 1Lanhui Fu Lanhui Fu 1*Nannan Li Nannan Li 2Weibiao Chen Weibiao Chen 1Jialong Luo Jialong Luo 3Weilai Qiao Weilai Qiao 1Jianfeng Hou Jianfeng Hou 1
  • 1 School of Electronics and Information Engineering, Wuyi University, Jiangmen, Guangdong Province, China
  • 2 Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao, Macao, SAR China
  • 3 School of Mechanical and Automation Engineering, The Wuyi University, Jiangmen, China

The final, formatted version of the article will be published soon.

    Current object detection algorithms lack accuracy in detecting citrus maturity color, and feature extraction needs improvement. In automated harvesting, accurate maturity detection reduces waste caused by incorrect evaluations. To address this issue, this study proposes an improved YOLOv8-based method for detecting Xinhui citrus maturity. GhostConv was introduced to replace the ordinary convolution in the Head of YOLOv8, reducing the number of parameters in the model and enhancing detection accuracy. The CARAFE (Content-Aware Reassembly of Features) upsampling operator was used to replace the conventional upsampling operation, retaining more details through feature reorganization and expansion. Additionally, the MCA (Multidimensional Collaborative Attention) mechanism was introduced to focus on capturing the local feature interactions between feature mapping channels, enabling the model to more accurately extract detailed features, thus further improving the accuracy of citrus color identification. Experimental results show that the precision, recall, and average precision of the improved YOLOv8 on the test set are 88.6%, 93.1%, and 93.4%, respectively. Compared to the original model, the improved YOLOv8 achieved increases of 16.5%, 20.2%, and 14.7%, respectively, and the parameter volume was reduced by 0.57%. This paper aims to improve the model for detecting Xinhui citrus maturity in complex orchards, supporting automated fruitpicking systems.

    Keywords: object detection, Maturity detection, XinHui citrus, YOLOv8, CARAFE lightweight operator, multi-dimensional collaborative attention mechanism (MCA), GhostConv

    Received: 29 Jul 2024; Accepted: 02 Jan 2025.

    Copyright: © 2025 Deng, Chen, Fu, Li, Chen, Luo, Qiao and Hou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lanhui Fu, School of Electronics and Information Engineering, Wuyi University, Jiangmen, Guangdong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.