Identification of Paddy Disease Using Image Processing

Rice is cultivated almost allover the world. In spite of the usage of several pesticides, a variety of diseases and pests reduce rice yield. Pesticide use reduces crop quality while increasing crop production. Thus, one of the key project in the field of computer science and agriculture department is the identification of diseases from captures of rice. This research study uses CNN to describe and classify various rice plant diseases. Processing techniques for images such as changing the size., rescaling, restoring equilibrium, and transforming into colors are carried out for recovering vital disease-related features. The approaches are highly helpful for farmers to quickly take the necessary corrective actions to protect their crops from diseases. In order to examine the current situation regarding the agricultural decision support system, this study has explored a number of research articles on the identification of rice plant diseases. This research study analyzes the datasets to obtain the required deep learning framework. The proposed model differentiates healthy leaves and disease-affected leaves.