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    Deep learning for image-based cassava disease detection

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    U17ArtRamcharanLearningInthomNodev.pdf (1.986Mb)
    Date
    2017
    Author
    Ramcharan, A.
    Baranowski, K.
    McCloskey, P.
    Ahmed, B.
    Legg, J.P.
    Hughes, D.P.
    Type
    Journal Article
    Target Audience
    Scientists
    Metadata
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    Abstract/Description
    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.
    http://dx.doi.org/10.3389/fpls.2017.01852
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/2337
    Digital Object Identifier (DOI)
    http://dx.doi.org/10.3389/fpls.2017.01852
    IITA Subjects
    Cassava; Food Security; Plant Diseases
    Agrovoc Terms
    Cassava; Food Security; Disease Control; Epidemiology; Deep Learning; Convolutional Neural Networks; Transfer Learning; Mobile Epidemiology; Inception V3 Model
    Regions
    Africa; East Africa
    Countries
    Tanzania
    Journals
    Frontiers in Plant Science
    Collections
    • Journal and Journal Articles4835
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