Efficient and accurate identification of maize rust disease using deep learning model

文献类型： 外文期刊

第一作者： Wang, Pei

作者： Wang, Pei;Tan, Jiajia;Wu, Pengxin;Li, Hui;Wang, Pei;Yang, Yuheng;Zhang, Tong;Chen, Xinping;Yang, Yuheng;Tang, Xinglong;He, Xiongkui

作者机构：

关键词： maize; southern rust; common rust; SimAM; small target detection

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 15 卷

页码：

收录情况： SCI

摘要： Common corn rust and southern corn rust, two typical maize diseases during growth stages, require accurate differentiation to understand their occurrence patterns and pathogenic risks. To address this, a specialized Maize-Rust model integrating a SimAM module in the YOLOv8s backbone and a BiFPN for scale fusion, along with a DWConv for streamlined detection, was developed. The model achieved an accuracy of 94.6%, average accuracy of 91.6%, recall rate of 85.4%, and F1 value of 0.823, outperforming Faster-RCNN and SSD models by 16.35% and 12.49% in classification accuracy, respectively, and detecting a single rust image at 16.18 frames per second. Deployed on mobile phones, the model enables real-time data collection and analysis, supporting effective detection and management of large-scale outbreaks of rust in the field.

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