An Automated System for Detecting Rice Crop Disease using CNN Inception V3 Transfer Learning Algorithm

Identification of the sickness might be a significant test in the field. Agriculturists are always suffering from huge losses due to disease in crops. Timely diagnosis of these diseases could reduce the loss and improve agriculture. Plant diseases in rice crops is a crucial research topic because it provides profits in monitoring wide fields of crops. The proposed CNN Inception V3 Learning might be an answer for the identification and grouping of rice crop leaf sicknesses. At the point when a user transfers an unhealthy leaf, the framework utilizes profound learning calculations to recognize and group the sicknesses on the leaf. The framework utilizes convolution neural organization strategies through the commencement V3 learning calculation. The proposed system can classify three classes of diseases present in rice crops.