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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1489655
This article is part of the Research Topic Harnessing Machine Learning to Decode Plant-Microbiome Dynamics for Sustainable Agriculture View all 15 articles

Tea Disease Identification Based on ECA Attention Mechanism and ResNet50 Network

Provisionally accepted
Li Lanting Li Lanting *
  • Jiangxi Agricultural University, Nanchang, China

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

    Addressing the challenge of identifying tea plant diseases against the complex background of tea gardens, this study proposes the ECA-ResNet50 model.By optimizing the ResNet50 architecture, adopting a multi-layer small convolution kernel strategy to enhance feature extraction capabilities, and introducing the ECA attention mechanism to focus on key features, the model achieves a 93.06% accuracy rate in tea disease identification, representing a 3.18% improvement over the original model, demonstrating industry-leading performance advantages. This model not only accurately identifies tea diseases in gardens but also possesses excellent generalization capabilities, performing outstandingly on datasets of other plant categories. These results indicate that ECA-ResNet50 can effectively mitigate the interference of complex backgrounds and precisely recognize tea disease targets.

    Keywords: Tea Plant Diseases, ECA attention mechanism, Resnet50, deep learning, Model

    Received: 01 Sep 2024; Accepted: 07 Jan 2025.

    Copyright: © 2025 Lanting. 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: Li Lanting, Jiangxi Agricultural University, Nanchang, 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.