Approaches to Identify Paddy Plant Diseases Using Deep Learning: A Review

To avoid a falling-off in the volume and production of agrarian products, it is crucial to determine Rice plant illnesses. The application of various ML and image-processing techniques lessens the issues in the agricultural industry. Since Rice feeds more than half of the world's population. Paddy is regarded as one of the most significant plants globally. Like other plants, rice is vulnerable to illnesses that could lower production levels and production of worse quality. Rice illnesses causes crop losses ranging from 20 to 40%. Farmers would need to be aware of the various diseases and be able to physically recognize diseases as early diagnosis of these diseases can favorably affect the yield. Rice Plant diseases may be self-diagnosed, which can assist cultivators to manage their crops more cleverly and enhance yields. Plant disease detection in crops using pictures is a challenging task by nature. This study examined the most pertinent five years paper on identifying various plant leaf diseases. Additionally, we outlined several issues with related plant leaf illnesses found by many machine learning and deep learning algorithm and their solutions. CNN are accurate at classifying and identifying images, many researchers have employed them to identify plant diseases. This Review Paper talked about the route that plant disease detection would go in the future as well as the advantages and drawbacks of employing various ML, DL, and Hybrid models for paddy plant disease detection.