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New perspectives on plant disease characterization based on deep learning

Sue Han Lee 1 Hervé Goëau 1 Pierre Bonnet 1 Alexis Joly 2
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The control of plant diseases is a major challenge to ensure global food security and sustainable agriculture. Several recent studies have proposed to improve existing procedures for early detection of plant diseases through modern automatic image recognition systems based on deep learning. In this article, we study these methods in detail, especially those based on convolutional neural networks. We first examine whether it is more relevant to fine-tune a pre-trained model on a plant identification task rather than a general object recognition task. In particular, we show, through visualization techniques, that the characteristics learned differ according to the approach adopted and that they do not necessarily focus on the part affected by the disease. Therefore, we introduce a more intuitive method that considers diseases independently of crops, and we show that it is more effective than the classic crop-disease pair approach, especially when dealing with disease involving crops that are not illustrated in the training database. This finding therefore encourages future research to rethink the current de facto paradigm of crop disease categorization.
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Soumis le : lundi 9 mars 2020 - 09:35:19
Dernière modification le : vendredi 20 mars 2020 - 15:13:32

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Sue Han Lee, Hervé Goëau, Pierre Bonnet, Alexis Joly. New perspectives on plant disease characterization based on deep learning. Computers and Electronics in Agriculture, Elsevier, 2020, 170, pp.105220. ⟨10.1016/j.compag.2020.105220⟩. ⟨hal-02470280⟩

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