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.
Highlights
Cassava (Manihot esculenta Crantz) is the most widely grown root crop in the world and a major source of calories for roughly two out of every five Africans (Nweke et al, 2002)
The results of this study show that image recognition with transfer learning from the convolutional neural network Inception v3 is a powerful method for high accuracy automated cassava disease detection
Transfer learning is capable of applying common machine learning methods by retraining the vectors produced by the trained model on new class data
Summary
Cassava (Manihot esculenta Crantz) is the most widely grown root crop in the world and a major source of calories for roughly two out of every five Africans (Nweke et al, 2002). In 2014, over 145 million tonnes of cassava were harvested on 17 million hectares of land on the African continent (FAOSTAT, 2017). It is considered a food security crop for smallholder farms, especially in lowincome, food-deficit areas (Bellotti et al, 1999) as it provides sufficient yields in low soil fertility conditions and where there are irregular rainfall patterns (De Bruijn and Fresco, 1989). Smallholder farmers, representing 85% of the world’s farms, face numerous risks to their agricultural production such as climate change, market shocks, and pest and disease outbreaks (Nagayet, 2005). An exotic species introduced to Africa from South America in the 16th century, initially had few pest and disease constraints on the continent. In particular cassava mosaic disease (CMD) and cassava brown streak disease (CBSD), have a longer history on
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
