Using Transfer 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. Image
recognition offers both a cost effective and scalable technology for disease
detection. New transfer learning methods 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.
