Rice Paddy Disease Detection and Disease Affected Area Segmentation Using Convolutional Neural Networks

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2020
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Abstract
Bangladesh is the fourth largest rice-producing country in the world. Agriculture plays a vital role in the country’s economy. One of the major obstacles in rice production is rice paddy diseases. In this paper, we develop a deep learning-based system to detect rice paddy diseases. In the first step, a rice paddy image dataset is analyzed and preprocessed for classification. To build the classifier, we use the Efficient Net B3 Convolution Neural Network (CNN) model. Next, we train a new model using the segmented data of rice paddy disease-affected areas to detect affected regions using MASK Recurrent Convolutional Neural Network (Mask RCNN). For the classification methods, we obtain an accuracy of nearly ~99%. For segmentation, the loss value of the class, bounding box, and mask are 0.09, 0.29, 0.30. The mean Average Precision(mAP) of the segmentation is around ~89%.
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Electrical and Computer Engineering
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North South University
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