文献类型： 外文期刊
作者： Wang, Pei 1 ; Tan, Jiajia 1 ; Yang, Yuheng 2 ; Zhang, Tong 2 ; Wu, Pengxin 1 ; Tang, Xinglong 4 ; Li, Hui 1 ; He, Xiongkui 5 ; Chen, Xinping 2 ;
作者机构： 1.Southwest Univ, Coll Engn & Technol, Key Lab Agr Equipment Hilly & Mt Areas, Chongqing, Peoples R China
2.Southwest Univ, Interdisciplinary Res Ctr Agr Green Dev Yangtze Ri, Chongqing, Peoples R China
3.Southwest Univ, Coll Plant Protect, Chongqing, Peoples R China
4.Chongqing Acad Agr Sci, Inst Agr Machinery, Chongqing, Peoples R China
5.China Agr Univ, Coll Sci, Ctr Chem Applicat Technol, Beijing, Peoples R China
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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