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.

http://dx.doi.org/10.3389/fpls.2017.01852

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http://dx.doi.org/10.3389/fpls.2017.01852

Cassava; Food Security; Plant Diseases

Cassava; Food Security; Disease Control; Epidemiology; Deep Learning; Convolutional Neural Networks; Transfer Learning; Mobile Epidemiology; Inception V3 Model

Africa; East Africa

Tanzania

Frontiers in Plant Science