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A novel approach for rice plant diseases classification with deep convolutional neural network

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Agriculture is one of the major revenue-producing fields and a source of livelihood in India. On the largest regions in India, rice is cultivated as an essential food. It is observed that rice crops are strongly affected by diseases, that causes major loses in agriculture sector. Plant pathologists are searching for an accurate and reliable diagnosis method for rice plant disease. The machine learning has been used effectively in various areas of crop remote sensing, particularly in the classification of crop diseases. At present time, deep learning is a hot research topic for crop disease identification. In this research, we developed an efficient rice plant disease detection method based on convolution neural network approach. This paper focuses mainly on three well known rice diseases, namely, leaf smut and brown spot caused by fungus and bacterial leaf blight caused by bacteria. This article proposes an effective approach for recognition and identification of rice plant disease based on size, shape and color of lesions in the leaf image. The proposed model applies Otsu’s global thresholding technique to perform image binarization to remove background noise of the image. The proposed method based on fully connected CNN was trained using 4000 image samples of each diseased leaves and 4000 image samples of healthy rice leaves, to detect the three rice diseases. The analysis of result shows that the proposed fully connected CNN is fast and effective approach, which provides an accuracy of 99.7% on the dataset. This accuracy is significantly higher than that for existing plant disease recognition and classification approaches.

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Correspondence to Santosh Kumar Upadhyay.

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Upadhyay, S.K., Kumar, A. A novel approach for rice plant diseases classification with deep convolutional neural network. Int. j. inf. tecnol. 14, 185–199 (2022).

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