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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1490026
This article is part of the Research Topic Precision Information Identification and Integrated Control: Pest Identification, Crop Health Monitoring, and Field Management View all 8 articles

Efficient and Accurate Identification of Maize Rust Disease Using Deep Learning Model

Provisionally accepted
Pei Wang Pei Wang 1*Jiajia Tan Jiajia Tan 1Yuheng Yang Yuheng Yang 1Tong Zhang Tong Zhang 1Pengxin Wu Pengxin Wu 1Xinglong Tang Xinglong Tang 2Hui Li Hui Li 1*Xiongkui He Xiongkui He 3Xinping Chen Xinping Chen 1
  • 1 Southwest University, Chongqing, China
  • 2 Chongqing Academy of Agricultural Sciences, Chongqing, Chongqing Municipality, China
  • 3 China Agricultural University, Beijing, Beijing Municipality, China

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

    Common corn rust and southern corn rust are two typical diseases that affect maize during growth stages. Accurate differentiation between these rust species is crucial for understanding their occurrence patterns and associated pathogenic risks. To address this, a specialized Maize-Rust model is developed in this study for precise distinguish of these similar phenotypic symptoms. Initially, a SimAM module is integrated into the YOLOv8s backbone network to enhance feature extraction for both rust types. Additionally, a BiFPN is introduced to improve fusion across scales, particularly for detecting small disease spots. To expedite detection, a DWConv is used to streamline the model structure. Through the training and testing of the data set, the accuracy, average accuracy, recall rate and F1 value of the improved model are 94.6%, 91.6%, 85.4% and 0.823, respectively. The classification accuracy of the new model is 16.35% and 12.49% higher than that of Faster-RCNN and SSD models, respectively. The speed of detecting a single rust image is 16.18 frames/s. The proposed model is also deployed on mobile phones to achieve real-time data collection and analysis. It provides an effective support for the accurate detection of large-scale outbreaks of rust in the field.

    Keywords: Maize, Southern rust, common rust, SimAM, Small target detection

    Received: 02 Sep 2024; Accepted: 26 Nov 2024.

    Copyright: © 2024 Wang, Tan, Yang, Zhang, Wu, Tang, Li, He and Chen. 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:
    Pei Wang, Southwest University, Chongqing, China
    Hui Li, Southwest University, Chongqing, 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.