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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1503033
This article is part of the Research Topic Optimizing Deep Learning for Effective Plant Species Recognition and Conservation View all 8 articles

YOLOv7-DWS: Tea bud recognition and detection network in multidensity environment via improved YOLOv7

Provisionally accepted
Xiaoming Wang Xiaoming Wang 1Zhenlong Wu Zhenlong Wu 2Guannan Xiao Guannan Xiao 1Cheng Fang Cheng Fang 2*Chongyang Han Chongyang Han 2*
  • 1 Sichuan Provincial Engineering Research Center of Thermoelectric Materials and Devices, Chengdu Polytechnic, Chengdu, Sichuan Province, China
  • 2 South China Agricultural University, Guangzhou, China

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

    Introduction:Accurate detection and recognition of tea bud images can drive advances in intelligent harvesting machinery for tea gardens and technology for tea bud pests and diseases. In order to realise the recognition and grading of tea buds in a complex multi-density tea garden environment. Methods:This paper proposes an improved YOLOv7 object detection algorithm, called YOLOv7-DWS, which focuses on improving the accuracy of tea recognition. First, we make a series of improvements to the YOLOv7 algorithm, including decouple head to replace the head of YOLOv7, to enhance the feature extraction ability of the model and optimize the class decision logic. The problem of simultaneous detection and classification of one-bud-one-leaf and one-bud-two-leaves of tea was solved. Secondly, a new loss function WiseIoU is proposed for the loss function in YOLOv7, which improves the accuracy of the model. Finally, we evaluate different attention mechanisms to enhance the model's focus on key features. Results and discussion: The experimental results show that the improved YOLOv7 algorithm has significantly improved over the original algorithm in all evaluation indexes, especially in the Tea (+6.2%) and mAP@0.5 (+7.7%). From the results, the algorithm in this paper helps to provide a new perspective and possibility for the field of tea image recognition.

    Keywords: Tea buds, Images recognition, Multi-density, object detection, YOLOv7, deep learning

    Received: 27 Sep 2024; Accepted: 09 Dec 2024.

    Copyright: © 2024 Wang, Wu, Xiao, Fang and Han. 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:
    Cheng Fang, South China Agricultural University, Guangzhou, China
    Chongyang Han, South China Agricultural University, Guangzhou, 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.