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Journal Article 2019-03-20

Autor: Ramcharan, A. McCloskey, P. Baranowski, K. Mbilinyi, N. Mrisho, L. Ndalahwa, M. Legg, J.P. Hughes, D.P.

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 ...

Enlace original: https://cgspace.cgiar.org/handle/10568/105535

Investigación en Agricultura
Enlace permanente

Ramcharan, A. ,  McCloskey, P. ,  Baranowski, K. ,  Mbilinyi, N. ,  Mrisho, L. ,  Ndalahwa, M. ,  Legg, J.P. ,  Hughes, D.P. ,  [A mobile-based deep learning model for cassava disease diagnosis] ,  A mobile-based deep learning model for cassava disease diagnosis
Journal Article 2017

Autor: Ramcharan, A. Baranowski, K. McCloskey, P. Ahmed, B. Legg, J.P. Hughes, D.P.

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...

Enlace original: https://cgspace.cgiar.org/handle/10568/89938

Investigación en Agricultura
Enlace permanente

Ramcharan, A. ,  Baranowski, K. ,  McCloskey, P. ,  Ahmed, B. ,  Legg, J.P. ,  Hughes, D.P. ,  [Deep learning for image-based cassava disease detection] ,  Deep learning for image-based cassava disease detection

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