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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1484784
This article is part of the Research Topic Machine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume II View all 3 articles

ASD-YOLO: A lightweight network for coffee fruit ripening detection in complex scenarios

Provisionally accepted
Baofeng Ye Baofeng Ye Xue Renzheng Xue Renzheng *Haiqiang Xu Haiqiang Xu
  • Qiqihar University, Qiqihar, China

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

    Coffee is one of the most popular and widely used drinks worldwide. At present, how to judge the maturity of coffee fruit mainly depends on the visual inspection of human eyes, which is both timeconsuming and labor-intensive. Moreover, the occlusion between leaves and fruits is also one of the challenges. In order to improve the detection efficiency of coffee fruit maturity, this paper proposes an improved detection method based on YOLOV7 to efficiently identify the maturity of coffee fruits, called ASD-YOLO. Firstly, a new dot product attention mechanism (L-Norm Attention) is designed to embed attention into the head structure, which enhances the ability of the model to extract coffee fruit features. In addition, we introduce SPD-Conv into backbone and head to enhance the detection of occluded small objects and low-resolution images. Finally, we replaced upsampling in our model with DySample, which requires less computational resources and is able to achieve image resolution improvements without additional burden. We tested our approach on the coffee dataset provided by Roboflow. The results show that ASD-YOLO has a good detection ability for coffee fruits with dense distribution and mutual occlusion under complex background, with a recall rate of 78.4%, a precision rate of 69.8%, and a mAP rate of 80.1%. Compared with the recall rate, accuracy rate and mAP of YOLOv7 model, these results are increased by 2.0%, 1.1% and 2.1%, respectively.The enhanced model can identify coffee fruits at all stages more efficiently and accurately, and provide technical reference for intelligent coffee fruit harvesting.

    Keywords: object detection, YOLOv7, attention mechanism, Coffee fruit, smart agriculture

    Received: 22 Aug 2024; Accepted: 14 Jan 2025.

    Copyright: © 2025 Ye, Renzheng and Xu. 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: Xue Renzheng, Qiqihar University, Qiqihar, 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.