A Novel Framework for Prevention of Rice Plant Disease Using Deep Learning

Agriculture is the backbone of our country. Due to climate change and pollution, yield of rice plant are decreased in the past decades. Farmers are not having sufficient awareness for their plant and cultivation. We proposed a novel disease predication system for rice plant with Normal, Bacterial Leaf, Brown Spot, Leaf Smut, Sheath Blight parameters. We have collected 1000 dataset from different rice plants and implemented with our proposed architecture. We are trying to inculcate deep neural networks and artificial intelligence technologies to investigate the rice plants for disease prediction. In this simulation we analyzed VGG 16, Xception, Inception, ResNet, Hill Climbing ResNet, Modified Hill climbing ResNet, CS ResNet and CS MHC ResNet algorithms. Eventually we got the high accuracy CS MHC ResNet algorithm.