Deep Neural Network for Disease Detection in Rice Plant Using the Texture and Deep Features

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Abstract
The diseases in plants pose a devastating impact on initiating safety in the production of food and they can lead to a reduction in the quantity and quality of agricultural products. In most cases, plant diseases lead to no grain harvest. Thus, an automatic diagnosis of plant disease is highly recommended for determining agricultural information. Several techniques are devised for plant disease detection wherein deep learning is preferred due to its effective performance. Novel deep learning is presented to spot disease from rice crop images. Here, the rice plant image undergoes pre-processing to remove noise and artifacts contained in the image. Then, the segmentation is performed with Segmentation Network (SegNet) to produce segments. The segments are further adapted for extracting statistical features, convolution neural network (CNN) features and texture features. These features are employed for plant disease detection wherein the deep recurrent neural network (Deep RNN) is utilized. The Deep RNN is trained with the proposed RideSpider Water Wave (RSW) algorithm. The proposed RSW is devised by integrating RWW in Spider monkey optimization. The proposed RWS-based Deep RNN provides superior performance with the highest accuracy of 90.5%, maximal sensitivity of 84.9% and maximal specificity of 95.2%.

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