Abstract
Detecting plant diseases is usually difficult without an experts’ knowledge. Therefore, fast and accurate automated diagnostic methods are highly desired in agricultural fields. Several studies on automated plant disease diagnosis have been conducted using machine learning methods. However, with these methods, it can be difficult to detect regions of interest, (ROIs) and to design and implement efficient parameters. In this study, we present a novel plant disease detection system based on convolutional neural networks (CNN). Using only training images, CNN can automatically acquire the requisite features for classification, and achieve high classification performance. We used a total of 800 cucumber leaf images to train CNN using our innovative techniques. Under the 4-fold cross-validation strategy, the proposed CNN-based system (which also extends the training dataset by generating additional images) achieves an average accuracy of 94.9 % in classifying cucumbers into two typical disease classes and a non-diseased class.
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Acknowledgement
This research was partially supported by the Japan Science and Technology Agency (JST) (A-STEP Feasibility Study program, AS262Z00664N, 2014-2015).
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Kawasaki, Y., Uga, H., Kagiwada, S., Iyatomi, H. (2015). Basic Study of Automated Diagnosis of Viral Plant Diseases Using Convolutional Neural Networks. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_59
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DOI: https://doi.org/10.1007/978-3-319-27863-6_59
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