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Rice Disease Detection and Classification Using Deep Neural Network Algorithm
S. Ramesh,
Published in Springer
Volume: 106
Pages: 555 - 566
In this paper, deep neural networks were proposed to find the crop disease for the normal image, brown spot, blast, sheath rot and bacterial blight. Dataset consists of 209 images. In the image preprocessing, RGB images are converted into HSV to remove the background portion using hue and saturation part. The image segmentation by k-means clustering, various colour and texture features are extracted. The classification is done with existing KNN algorithm. The accuracy obtained is 88% bacterial blight, 82% blast, 88% brown spot, 87% sheath rot and 86% normal images. To improve the accuracy our proposed DNN is implemented. The accuracy obtained for DNN is 93% bacterial blight, 89% blast, 93% brown spot, 92% sheath rot and 96% normal images. © Springer Nature Singapore Pte Ltd. 2020.
About the journal
JournalData powered by TypesetLecture Notes in Networks and Systems
PublisherData powered by TypesetSpringer