Identification and Categorization of Paddy Leaf Diseases Using Deep Learning for Efficient Agricultural Practices

Plant diseases can be quickly detected to lessen the harm they cause to crops. Convolutional neural networks are widely used in deep learning, specifically for problems related to pattern recognition and machine vision. This paper aims to provide a comprehensive overview of the numerous image processing and machine learning strategies for rice plant disease diagnosis. It does this by examining the various algorithms and machine learning techniques used to diagnose illnesses in rice plants using photographs of diseased rice plants. This study discusses several methods and offers a concise synopsis of important concepts in the fields of machine learning and image processing in relation to the classification and identification of illnesses in rice plants. This study examines and contrasts various algorithmic models and methods for classifying and identifying diseases that impact rice plants. It includes scientific results that over the last five years have been published in international review publications. In addition, this paper presents an overview and analysis of various methods used to identify agricultural illnesses, leaf health, and the image processing techniques used for the same. The framework mentioned here, can be used to more effectively detect and diagnose various rice crop diseases, which can cause the agriculture industry to experience large losses owing to rice plant diseases. These frameworks are efficient enough to safely automate repeated processes, generating enough data for additional study.