Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI
Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and qu...
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/159717 |
| _version_ | 1855534777569902592 |
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| author | Gomez, Daniela Selvaraj, Michael Gomez Casas, Jorge Mathiyazhagan, Kavino Rodriguez, Michael Assefa, Teshale Mlaki, Anna Nyakunga, Goodluck Kato, Fred Mukankusi, Clare Girma, Ellena Mosquera, Gloria Arredondo, Victoria Espitia, Ernesto |
| author_browse | Arredondo, Victoria Assefa, Teshale Casas, Jorge Espitia, Ernesto Girma, Ellena Gomez, Daniela Kato, Fred Mathiyazhagan, Kavino Mlaki, Anna Mosquera, Gloria Mukankusi, Clare Nyakunga, Goodluck Rodriguez, Michael Selvaraj, Michael Gomez |
| author_facet | Gomez, Daniela Selvaraj, Michael Gomez Casas, Jorge Mathiyazhagan, Kavino Rodriguez, Michael Assefa, Teshale Mlaki, Anna Nyakunga, Goodluck Kato, Fred Mukankusi, Clare Girma, Ellena Mosquera, Gloria Arredondo, Victoria Espitia, Ernesto |
| author_sort | Gomez, Daniela |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers’ ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLONAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality. |
| format | Journal Article |
| id | CGSpace159717 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1597172025-11-11T19:01:53Z Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI Gomez, Daniela Selvaraj, Michael Gomez Casas, Jorge Mathiyazhagan, Kavino Rodriguez, Michael Assefa, Teshale Mlaki, Anna Nyakunga, Goodluck Kato, Fred Mukankusi, Clare Girma, Ellena Mosquera, Gloria Arredondo, Victoria Espitia, Ernesto common beans artificial intelligence blight plant viruses anthracnosis rusts plant breeding biotechnology Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers’ ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLONAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality. 2024-07-06 2024-11-14T08:38:35Z 2024-11-14T08:38:35Z Journal Article https://hdl.handle.net/10568/159717 en Open Access application/pdf Springer Gomez, D.; Selvaraj, M.G.; Casas, J.; Mathiyazhagan, K.; Rodriguez, M.; Assefa, T.; Mlaki, A.; Nyakunga, G.; Kato, F.; Mukankusi, C.; Girma, E.; Mosquera, G.; Arredondo, V.; Espitia, E. (2024) Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI. Scientific Reports 14: 15596. ISSN: 2045-2322 |
| spellingShingle | common beans artificial intelligence blight plant viruses anthracnosis rusts plant breeding biotechnology Gomez, Daniela Selvaraj, Michael Gomez Casas, Jorge Mathiyazhagan, Kavino Rodriguez, Michael Assefa, Teshale Mlaki, Anna Nyakunga, Goodluck Kato, Fred Mukankusi, Clare Girma, Ellena Mosquera, Gloria Arredondo, Victoria Espitia, Ernesto Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI |
| title | Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI |
| title_full | Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI |
| title_fullStr | Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI |
| title_full_unstemmed | Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI |
| title_short | Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI |
| title_sort | advancing common bean phaseolus vulgaris l disease detection with yolo driven deep learning to enhance agricultural ai |
| topic | common beans artificial intelligence blight plant viruses anthracnosis rusts plant breeding biotechnology |
| url | https://hdl.handle.net/10568/159717 |
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