Deep Learning for Image-Based Cassava Disease Detection.

Abstract

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.

Plant phenotyping relevance

画像から植物病害・害虫被害という植物状態を推定する深層学習手法を開発・評価しており、フェノタイピング手法が中心です。

Code and data availability

The paper describes cassava disease image datasets and Inception v3 transfer-learning models, but no public deposit of the datasets, code, or trained model checkpoints is provided. The supplementary material link contains only example figures and tables (e.g., Figures S1–S6, Tables S1–S2), not the underlying 2,756/15,0

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