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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1494688

Rapid and accurate detection of peanut pod appearance quality based on lightweight and improved YOLOv5_ SSE model

Provisionally accepted
Zhixia Liu Zhixia Liu *XIlin Zhong XIlin Zhong Chunyu Wang Chunyu Wang *Guozhen Wu Guozhen Wu *Fengyu He Fengyu He *Jing Wang Jing Wang *Dexu Yang Dexu Yang *
  • College of Engineering, Shenyang Agricultural University, Shenyang, China

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

    This article "Rapid and accurate detection of peanut pod appearance quality based on lightweight and improved YOLOv5_SSE model" focuses on using the optimized deep learning model YOLOv5_SSE to achieve efficient and accurate detection of peanut pod appearance quality. This research is closely related to the development of intelligent agriculture and precision agriculture, and is in line with the focus of Frontiers in Plant Science on the application of new technologies in plant science, especially the exploration of crop quality assessment and intelligent management. Therefore, the content of this article is highly consistent with the professional scope and research direction of the journal.

    Keywords: Appearance quality, YOLOv5, Lightweighting, ShuffleNetV2, Peanut pod

    Received: 25 Sep 2024; Accepted: 27 Jan 2025.

    Copyright: © 2025 Liu, Zhong, Wang, Wu, He, Wang and Yang. 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:
    Zhixia Liu, College of Engineering, Shenyang Agricultural University, Shenyang, China
    Chunyu Wang, College of Engineering, Shenyang Agricultural University, Shenyang, China
    Guozhen Wu, College of Engineering, Shenyang Agricultural University, Shenyang, China
    Fengyu He, College of Engineering, Shenyang Agricultural University, Shenyang, China
    Jing Wang, College of Engineering, Shenyang Agricultural University, Shenyang, China
    Dexu Yang, College of Engineering, Shenyang Agricultural University, Shenyang, 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.