Deep learning for image-based cassava disease detection
Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. I...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2017
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/89938 |
| _version_ | 1855529031835844608 |
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| author | Ramcharan, A. Baranowski, K. McCloskey, P. Ahmed, B. Legg, James P. Hughes, D.P. |
| author_browse | Ahmed, B. Baranowski, K. Hughes, D.P. Legg, James P. McCloskey, P. Ramcharan, A. |
| author_facet | Ramcharan, A. Baranowski, K. McCloskey, P. Ahmed, B. Legg, James P. Hughes, D.P. |
| author_sort | Ramcharan, A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved
control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava
disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection. |
| format | Journal Article |
| id | CGSpace89938 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace899382025-11-11T10:32:49Z Deep learning for image-based cassava disease detection Ramcharan, A. Baranowski, K. McCloskey, P. Ahmed, B. Legg, James P. Hughes, D.P. cassava food security disease control epidemiology deep learning convolutional neural networks transfer learning mobile epidemiology Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection. 2017 2018-01-08T10:37:55Z 2018-01-08T10:37:55Z Journal Article https://hdl.handle.net/10568/89938 en Open Access application/pdf Frontiers Media Ramcharan, A., Baranowski, K., McCloskey, P., Ahamed, B., Legg, J. & Hughes, D.P. (2017). Deep learning for image-based cassava disease detection. Frontiers in Plant Science, 8, 1-7. |
| spellingShingle | cassava food security disease control epidemiology deep learning convolutional neural networks transfer learning mobile epidemiology Ramcharan, A. Baranowski, K. McCloskey, P. Ahmed, B. Legg, James P. Hughes, D.P. Deep learning for image-based cassava disease detection |
| title | Deep learning for image-based cassava disease detection |
| title_full | Deep learning for image-based cassava disease detection |
| title_fullStr | Deep learning for image-based cassava disease detection |
| title_full_unstemmed | Deep learning for image-based cassava disease detection |
| title_short | Deep learning for image-based cassava disease detection |
| title_sort | deep learning for image based cassava disease detection |
| topic | cassava food security disease control epidemiology deep learning convolutional neural networks transfer learning mobile epidemiology |
| url | https://hdl.handle.net/10568/89938 |
| work_keys_str_mv | AT ramcharana deeplearningforimagebasedcassavadiseasedetection AT baranowskik deeplearningforimagebasedcassavadiseasedetection AT mccloskeyp deeplearningforimagebasedcassavadiseasedetection AT ahmedb deeplearningforimagebasedcassavadiseasedetection AT leggjamesp deeplearningforimagebasedcassavadiseasedetection AT hughesdp deeplearningforimagebasedcassavadiseasedetection |