A mobile-based deep learning model for cassava disease diagnosis
Convolutional neural network (CNN) models have the potential to improve plant disease phenotyping where the standard approach is visual diagnostics requiring specialized training. In scenarios where a CNN is deployed on mobile devices, models are presented with new challenges due to lighting and ori...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2019
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/105535 |
| _version_ | 1855515665943756800 |
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| author | Ramcharan, A. McCloskey, P. Baranowski, K. Mbilinyi, N. Mrisho, L. Ndalahwa, M. Legg, James P. Hughes, D.P. |
| author_browse | Baranowski, K. Hughes, D.P. Legg, James P. Mbilinyi, N. McCloskey, P. Mrisho, L. Ndalahwa, M. Ramcharan, A. |
| author_facet | Ramcharan, A. McCloskey, P. Baranowski, K. Mbilinyi, N. Mrisho, L. Ndalahwa, M. Legg, James P. Hughes, D.P. |
| author_sort | Ramcharan, A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Convolutional neural network (CNN) models have the potential to improve plant disease phenotyping where the standard approach is visual diagnostics requiring specialized training. In scenarios where a CNN is deployed on mobile devices, models are presented with new challenges due to lighting and orientation. It is essential for model assessment to be conducted in real world conditions if such models are to be reliably integrated with computer vision products for plant disease phenotyping. We train a CNN object detection model to identify foliar symptoms of diseases in cassava (Manihot esculenta Crantz). We then deploy the model in a mobile app and test its performance on mobile images and video of 720 diseased leaflets in an agricultural field in Tanzania. Within each disease category we test two levels of severity of symptoms-mild and pronounced, to assess the model performance for early detection of symptoms. In both severities we see a decrease in performance for real world images and video as measured with the F-1 score. The F-1 score dropped by 32% for pronounced symptoms in real world images (the closest data to the training data) due to a decrease in model recall. If the potential of mobile CNN models are to be realized our data suggest it is crucial to consider tuning recall in order to achieve the desired performance in real world settings. In addition, the varied performance related to different input data (image or video) is an important consideration for design in real world applications. |
| format | Journal Article |
| id | CGSpace105535 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1055352025-11-11T10:03:48Z A mobile-based deep learning model for cassava disease diagnosis Ramcharan, A. McCloskey, P. Baranowski, K. Mbilinyi, N. Mrisho, L. Ndalahwa, M. Legg, James P. Hughes, D.P. cassava diseases plant diseases diagnosis plant condition tanzania Convolutional neural network (CNN) models have the potential to improve plant disease phenotyping where the standard approach is visual diagnostics requiring specialized training. In scenarios where a CNN is deployed on mobile devices, models are presented with new challenges due to lighting and orientation. It is essential for model assessment to be conducted in real world conditions if such models are to be reliably integrated with computer vision products for plant disease phenotyping. We train a CNN object detection model to identify foliar symptoms of diseases in cassava (Manihot esculenta Crantz). We then deploy the model in a mobile app and test its performance on mobile images and video of 720 diseased leaflets in an agricultural field in Tanzania. Within each disease category we test two levels of severity of symptoms-mild and pronounced, to assess the model performance for early detection of symptoms. In both severities we see a decrease in performance for real world images and video as measured with the F-1 score. The F-1 score dropped by 32% for pronounced symptoms in real world images (the closest data to the training data) due to a decrease in model recall. If the potential of mobile CNN models are to be realized our data suggest it is crucial to consider tuning recall in order to achieve the desired performance in real world settings. In addition, the varied performance related to different input data (image or video) is an important consideration for design in real world applications. 2019-03-20 2019-10-28T13:26:15Z 2019-10-28T13:26:15Z Journal Article https://hdl.handle.net/10568/105535 en Open Access application/pdf Frontiers Media Ramcharan, A., McCloskey, P., Baranowski, K., Mbilinyi, N., Mrisho, L., Ndalahwa, M., ... & Hughes, D.P. (2019). A mobile-based deep learning model for cassava disease diagnosis. Frontiers in Plant Science, 10, 272. |
| spellingShingle | cassava diseases plant diseases diagnosis plant condition tanzania Ramcharan, A. McCloskey, P. Baranowski, K. Mbilinyi, N. Mrisho, L. Ndalahwa, M. Legg, James P. Hughes, D.P. A mobile-based deep learning model for cassava disease diagnosis |
| title | A mobile-based deep learning model for cassava disease diagnosis |
| title_full | A mobile-based deep learning model for cassava disease diagnosis |
| title_fullStr | A mobile-based deep learning model for cassava disease diagnosis |
| title_full_unstemmed | A mobile-based deep learning model for cassava disease diagnosis |
| title_short | A mobile-based deep learning model for cassava disease diagnosis |
| title_sort | mobile based deep learning model for cassava disease diagnosis |
| topic | cassava diseases plant diseases diagnosis plant condition tanzania |
| url | https://hdl.handle.net/10568/105535 |
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