Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models
Abstract 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 multiplatfo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-87588-2 |
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| author | Juan Jose Mora Guy Blomme Nancy Safari Sivalingam Elayabalan Ramasamy Selvarajan Michael Gomez Selvaraj |
| author_facet | Juan Jose Mora Guy Blomme Nancy Safari Sivalingam Elayabalan Ramasamy Selvarajan Michael Gomez Selvaraj |
| author_sort | Juan Jose Mora |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-6ce7f478a2bc427fa5d3ba337973adc9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6ce7f478a2bc427fa5d3ba337973adc92025-02-02T12:24:18ZengNature PortfolioScientific Reports2045-23222025-01-0115113010.1038/s41598-025-87588-2Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation modelsJuan Jose Mora0Guy Blomme1Nancy Safari2Sivalingam Elayabalan3Ramasamy Selvarajan4Michael Gomez Selvaraj5Alliance of Bioversity International and International Center for Tropical Agriculture (CIAT)Bioversity International, c/o ILRIBioversity InternationalImayam Institute of Agriculture and Technology (IIAT), Affiliated With Tamil Nadu Agricultural University (TNAU)ICAR-National Research Centre for BananaAlliance of Bioversity International and International Center for Tropical Agriculture (CIAT)Abstract 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.https://doi.org/10.1038/s41598-025-87588-2 |
| spellingShingle | Juan Jose Mora Guy Blomme Nancy Safari Sivalingam Elayabalan Ramasamy Selvarajan Michael Gomez Selvaraj Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-025-87588-2 |
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