Detection of Rice Plant Disease Using Deep Learning Techniques

Abstract

Deep learning has recently grown a lot of interest as a way to create a fast, efficient, and reliable image identification and categorization system. India, being one of the world’s most important rice producers and consumers, relies heavily on rice to propel its economy and provide its food needs. In the crop protective device, early and precise diagnosis of plant diseases is critical. Traditionally, identification was done either through visual inspection or laboratory testing. It is critical to identify any disease early and perform the necessary treatment to the damaged plants in order to guarantee the rice plants’ healthy and proper growth. Because disease detection by hand takes a long time and requires a lot of effort, having an automated system is unavoidable. A rice plant disease identification method depends on deep learning methodologies are presented in this research. Leaf smut, bacterial leaf blight, sheat blight, and brown spot diseases are four of the most frequent rice plant diseases identified in this study. The rice plant disease is identified and recognized using deep learning algorithms. This method of early detection of rice diseases could be utilized as a preventative tool as well as an early detection. The proposed approach provides enhanced accuracy of 99.45% and it is compared with the existing state-of-the-art approaches.