Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models
Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georef...
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| Format: | Journal Article |
| Language: | Inglés |
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NATURE PORTFOLIO
2025
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| Online Access: | https://hdl.handle.net/10568/179959 |
| _version_ | 1855517826730688512 |
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| author | Mora, Juan Jose Blomme, Guy Safari, Nancy Elayabalan, Sivalingam Selvarajan, Ramasamy Selvaraj, Michael Gomez |
| author_browse | Blomme, Guy Elayabalan, Sivalingam Mora, Juan Jose Safari, Nancy Selvaraj, Michael Gomez Selvarajan, Ramasamy |
| author_facet | Mora, Juan Jose Blomme, Guy Safari, Nancy Elayabalan, Sivalingam Selvarajan, Ramasamy Selvaraj, Michael Gomez |
| author_sort | Mora, Juan Jose |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georeferenced surveillance system designed to enhance the detection and management of banana wilt diseases. We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. Our results demonstrate the superior performance of YOLOv9 in detecting healthy, Fusarium Wilt and Xanthomonas Wilt diseased plants in aerial images, achieving high mAP@50, precision and recall metrics ranging from 55 to 86%. In terms of ground level images, we organized the dataset based on disease occurrence in Africa, Latin America, India, Asia and Australia. For this platform, YOLOv8 outperforms the rest and achieves mAP@50, precision and recall between 65 and 99% depending on the plant part and region. Additionally, we incorporated Explainable AI techniques, such as Gradient-weighted Class Activation Mapping, to enhance model transparency and trustworthiness. Human in the Loop Artificial Intelligence was also utilized to enhance the ground level model’s predictions. |
| format | Journal Article |
| id | CGSpace179959 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | NATURE PORTFOLIO |
| publisherStr | NATURE PORTFOLIO |
| record_format | dspace |
| spelling | CGSpace1799592026-01-17T02:03:12Z Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models Mora, Juan Jose Blomme, Guy Safari, Nancy Elayabalan, Sivalingam Selvarajan, Ramasamy Selvaraj, Michael Gomez bananas disease control banana xanthomonas wilt fusarium Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georeferenced surveillance system designed to enhance the detection and management of banana wilt diseases. We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. Our results demonstrate the superior performance of YOLOv9 in detecting healthy, Fusarium Wilt and Xanthomonas Wilt diseased plants in aerial images, achieving high mAP@50, precision and recall metrics ranging from 55 to 86%. In terms of ground level images, we organized the dataset based on disease occurrence in Africa, Latin America, India, Asia and Australia. For this platform, YOLOv8 outperforms the rest and achieves mAP@50, precision and recall between 65 and 99% depending on the plant part and region. Additionally, we incorporated Explainable AI techniques, such as Gradient-weighted Class Activation Mapping, to enhance model transparency and trustworthiness. Human in the Loop Artificial Intelligence was also utilized to enhance the ground level model’s predictions. 2025-01-28 2026-01-16T08:24:47Z 2026-01-16T08:24:47Z Journal Article https://hdl.handle.net/10568/179959 en Open Access application/pdf NATURE PORTFOLIO Mora, J.J.; Blomme, G.; Safari, N.; Elayabalan, S.; Selvarajan, R.; Selvaraj, M.G. (2025) Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models. Scientific Reports 15(1): 3491. ISSN: 2045-2322 |
| spellingShingle | bananas disease control banana xanthomonas wilt fusarium Mora, Juan Jose Blomme, Guy Safari, Nancy Elayabalan, Sivalingam Selvarajan, Ramasamy Selvaraj, Michael Gomez Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models |
| title | Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models |
| title_full | Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models |
| title_fullStr | Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models |
| title_full_unstemmed | Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models |
| title_short | Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models |
| title_sort | digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop ai and yolo foundation models |
| topic | bananas disease control banana xanthomonas wilt fusarium |
| url | https://hdl.handle.net/10568/179959 |
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