Deep Learning for Image-Based Cassava Disease Detection
Pennsylvania State University · University of Pittsburgh · +2 more institutions
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).…
Citation impact
- FWCI
- 60.27
- Percentile
- 100%
- References
- 20
Authors
6- ARAmanda Ramcharan
Pennsylvania State University
- KBKelsee Baranowski
Pennsylvania State University
- PMPeter McCloskey
University of Pittsburgh
- BABabuali Ahmed
International Institute of Tropical Agriculture
- JLJames Legg
International Institute of Tropical Agriculture
Topics & keywords
- Deep learning
- Convolutional neural network
- Transfer of learning
- Plant disease
- Food security
- Streak
- Autoencoder
- Pattern recognition (psychology)