Identification and Classification of Rice Plant Disease Using Hybrid Transfer Learning

Abstract

The Rice crop is considered one of the most widely grown crops in Asia and it is susceptible to various types of illnesses at different stages of production. Food safety and production can be affected by rice plant diseases, as well as a significant decline in the quality and quantity of agricultural goods. Plant diseases can potentially prevent grain harvesting entirely in severe circumstances. As a result, automation of identification and diagnosis of plant disease is widely needed in the agriculture field. Many approaches for doing this problem have been offered with deep learning rising as the preferred method because of its excellent achievement. In this proposed research, we used Hybrid deep CNN transfer learning with rice plant images or the classification and identification of various rice diseases, we employed Transfer Learning to generate our deep learning model using Rice_Leaf_Dataset from a secondary source. The proposed model is 90.8% accurate, Experiments show that the proposed approach is viable, and it can be used to detect plant diseases efficiently and outperformed.