Efficient and accurate identification of maize rust disease using deep learning model
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...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1490026/full |
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| author | Pei Wang Pei Wang Jiajia Tan Yuheng Yang Yuheng Yang Tong Zhang Pengxin Wu Xinglong Tang Hui Li Xiongkui He Xinping Chen |
| author_facet | Pei Wang Pei Wang Jiajia Tan Yuheng Yang Yuheng Yang Tong Zhang Pengxin Wu Xinglong Tang Hui Li Xiongkui He Xinping Chen |
| author_sort | Pei Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-76bbfe2441034484929bdeec9a627916 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-76bbfe2441034484929bdeec9a6279162025-02-06T04:11:06ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-02-011510.3389/fpls.2024.14900261490026Efficient and accurate identification of maize rust disease using deep learning modelPei Wang0Pei Wang1Jiajia Tan2Yuheng Yang3Yuheng Yang4Tong Zhang5Pengxin Wu6Xinglong Tang7Hui Li8Xiongkui He9Xinping Chen10Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, ChinaInterdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin College of Resources and Environment, Southwest University, Chongqing, ChinaKey Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, ChinaInterdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin College of Resources and Environment, Southwest University, Chongqing, ChinaCollege of Plant Protection, Southwest University, Chongqing, ChinaInterdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin College of Resources and Environment, Southwest University, Chongqing, ChinaKey Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, ChinaChongqing Academy of Agricultural Sciences, Institute of Agricultural Machinery, Chongqing, ChinaKey Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, ChinaCentre for Chemicals Application Technology, College of Science, China Agricultural University, Beijing, ChinaInterdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin College of Resources and Environment, Southwest University, Chongqing, ChinaCommon 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.https://www.frontiersin.org/articles/10.3389/fpls.2024.1490026/fullmaizesouthern rustcommon rustSimAMsmall target detection |
| spellingShingle | Pei Wang Pei Wang Jiajia Tan Yuheng Yang Yuheng Yang Tong Zhang Pengxin Wu Xinglong Tang Hui Li Xiongkui He Xinping Chen Efficient and accurate identification of maize rust disease using deep learning model Frontiers in Plant Science maize southern rust common rust SimAM small target detection |
| title | Efficient and accurate identification of maize rust disease using deep learning model |
| title_full | Efficient and accurate identification of maize rust disease using deep learning model |
| title_fullStr | Efficient and accurate identification of maize rust disease using deep learning model |
| title_full_unstemmed | Efficient and accurate identification of maize rust disease using deep learning model |
| title_short | Efficient and accurate identification of maize rust disease using deep learning model |
| title_sort | efficient and accurate identification of maize rust disease using deep learning model |
| topic | maize southern rust common rust SimAM small target detection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1490026/full |
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