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Deep Learning for Image-Based Cassava Disease Detection

Ramcharan, Amanda ; Baranowski, Kelsee ; McCloskey, Peter ; Ahmed, Babuali ; Legg, James ; Hughes, David P

Frontiers in plant science, 2017-10, Vol.8, p.1852-1852 [Periódico revisado por pares]

Switzerland: Frontiers Media S.A

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  • Título:
    Deep Learning for Image-Based Cassava Disease Detection
  • Autor: Ramcharan, Amanda ; Baranowski, Kelsee ; McCloskey, Peter ; Ahmed, Babuali ; Legg, James ; Hughes, David P
  • Assuntos: cassava disease detection ; convolutional neural networks ; deep learning ; Inception v3 model ; mobile epidemiology ; Plant Science ; transfer learning
  • É parte de: Frontiers in plant science, 2017-10, Vol.8, p.1852-1852
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Edited by: Ashraf El-kereamy, University of California Division of Agriculture and Natural Resources, United States
    Reviewed by: Jan Frederik Kreuze, International Potato Center, Peru; Zhong Yin, University of Shanghai for Science and Technology, China
    This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science
  • Descrição: 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.
  • Editor: Switzerland: Frontiers Media S.A
  • Idioma: Inglês
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