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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1474207
This article is part of the Research Topic AI, Sensors and Robotics in Plant Phenotyping and Precision Agriculture, Volume III View all 15 articles

Efficient and Accurate Tobacco Leaf Maturity Detection: An Improved YOLOv10 Model with DCNv3 and Efficient Local Attention Integration

Provisionally accepted
Yi Shi Yi Shi 1*Hong Wang Hong Wang 2*Shunhao Qing Shunhao Qing 1Jianjun Liu Jianjun Liu 3*Long Zhao Long Zhao 4*Hui Wang Hui Wang 2*Feng Zhang Feng Zhang 2*Qiongmin Cheng Qiongmin Cheng 2*Fei Wang Fei Wang 1*
  • 1 College of Agricultural Equipment Engineering, Henan University of Science and Technology,, Luoyang, China
  • 2 Henan Province tobacco company, Luoyang company, Luoyang, Henan Province, China
  • 3 Henan Province tobacco company, Zhengzhou, China
  • 4 College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, China

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

    The precise determination of tobacco leaf maturity is pivotal for safeguarding the taste and quality of tobacco products, augmenting the financial gains of tobacco growers, and propelling the industry's sustainable progression. This research addresses the inherent subjectivity and variability in conventional maturity evaluation techniques reliant on human expertise by introducing an innovative YOLOv10-based method for tobacco leaf maturity detection. This technique facilitates a rapid and noninvasive assessment of leaf maturity, significantly elevating the accuracy and efficiency of tobacco leaf quality evaluation. In our study, we have advanced the YOLOv10 framework by integrating DCNv3 with C2f to construct an enhanced neck network, designated as C2f-DCNv3. This integration is designed to augment the model's capability for feature integration, particularly concerning the morphological and edge characteristics of tobacco leaves. Furthermore, the incorporation of the Efficient Local Attention (ELA) mechanism at multiple stages of the model has substantially enhanced the efficiency and fidelity of feature extraction. The empirical results underscore the model's pronounced enhancement in performance across all maturity classifications. Notably, the overall precision (P) has been elevated from 0.939 to 0.973, the recall rate (R) has improved from 0.968 to 0.984, the mean average precision at 50% intersection over union (mAP50) has advanced from 0.984 to 0.994, and the mean average precision across the 50% to 95% intersection over union range (mAP50-95) has risen from 0.962 to 0.973. This research presents the tobacco industry with a novel rapid detection instrument for tobacco leaf maturity, endowed with substantial practical utility and broad prospects for application. Future research endeavors will be directed towards further optimization of the model's architecture to bolster its generalizability and to explore its implementation within the realm of actual tobacco cultivation and processing.

    Keywords: Tobacco Leaf Maturity, YOLOv10, DCNv3, Efficient Local Attention, Targeted detection

    Received: 01 Aug 2024; Accepted: 05 Dec 2024.

    Copyright: © 2024 Shi, Wang, Qing, Liu, Zhao, Wang, Zhang, Cheng and Wang. 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:
    Yi Shi, College of Agricultural Equipment Engineering, Henan University of Science and Technology,, Luoyang, China
    Hong Wang, Henan Province tobacco company, Luoyang company, Luoyang, Henan Province, China
    Jianjun Liu, Henan Province tobacco company, Zhengzhou, China
    Long Zhao, College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, China
    Hui Wang, Henan Province tobacco company, Luoyang company, Luoyang, Henan Province, China
    Feng Zhang, Henan Province tobacco company, Luoyang company, Luoyang, Henan Province, China
    Qiongmin Cheng, Henan Province tobacco company, Luoyang company, Luoyang, Henan Province, China
    Fei Wang, College of Agricultural Equipment Engineering, Henan University of Science and Technology,, Luoyang, 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.