Deep Learning for Image-Based Cassava Disease Detection

By Amanda Ramcharan, Kelsee Baranowski, Peter McClowsky, Babuali Ahmed, J. Legg, David P. Hughes. Frontiers in Plant Science, 2017

698 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Plant Science

View Paper: https://www.semanticscholar.org/paper/a11fca45e2758ddb6c2a2f312a732f71e10eb9ca

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Citations in the corpus

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification (2021, methodology): “In the literature of DL techniques applied to plant disease classiﬁcation, the most used public datasets are PlantVillage [53–55] and Kaggle [56]; notably, many authors also collect their own datasets [57–60].”
High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture (2019, background): “With a focus on small-scale farmers in developing countries, an open access mobile application based on deep machine learning to detect foliar symptoms of diseases in cassava using machine learning has been developed and tested in Tanzania [86,87].”
Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, methodology): “[71] used DL algorithms to detect diseases in cassava crops.”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, neutral): “S106 [144] 2017 3,5 Inception V3 98,0% Original Cassava (2.”
Convolutional Neural Networks for the Automatic Identification of Plant Diseases (2019, methodology): “Ramcharan et al. (2017) formed two datasets: an “original cassava dataset” with entire cassava leaves, and a “leaﬂet cassava dataset” where the leaﬂets were manually cropped.”