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Using Deep Learning for Image-Based Potato Tuber Disease Detection.
Phytopathology 2018 December 14
Many plant diseases have distinct visual symptoms which can be used to identify and classify them correctly. This paper presents a potato disease classification algorithm which leverages these distinct appearances and the recent advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network training it to classify the tubers into five classes, namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes and diseases, was acquired, classified, and labeled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks. The models were tested over a dataset of images taken using standard low cost RGB sensors, and tagged by experts, demonstrating high classification accuracy. This is the first paper to report the successful implementation of deep convolutional networks, popular in object identification, to the task of disease identification in potato tubers, showing the potential of deep learning techniques in agricultural tasks.
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