AUTHOR=Wang Pei , Tan Jiajia , Yang Yuheng , Zhang Tong , Wu Pengxin , Tang Xinglong , Li Hui , He Xiongkui , Chen Xinping TITLE=Efficient and accurate identification of maize rust disease using deep learning model JOURNAL=Frontiers in Plant Science VOLUME=15 YEAR=2025 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1490026 DOI=10.3389/fpls.2024.1490026 ISSN=1664-462X ABSTRACT=

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.