An Analysis of Identification of Plant Leaf Diseases and Classification Using Machine Learning and Computer Vision

Agriculture is a significant source of revenue for Indians. Farmers can grow a wide range of crops. However, crop growth is hampered by illnesses. Plant pests are one of the reasons for the damage to plant crops. Different diseases affect different plants. The proposed work discussed a detailed investigation of disease recognition and classification. Implementing reliable methods with high recognition rates for diagnosing plant diseases is still on hold. Supervised learning techniques are mostly employed for the identification of diseases in plants. Unsupervised learning is an alternative, though. This suggested illness detection system includes image acquisition, pre-processing, segmenting the images, extracting features, and classification. As a result, image processing and feature extraction technologies have mainly been used to detect plant illnesses. This paper was classified based on two important concepts: leaf feature identification and classification. A detailed in-depth disclosure of disease identification and classification efficiency is depicted here, considering and evaluating certain well-defined suggested techniques by various authors from the last few decades. This paper also focuses on the state-of-the-art methodologies that came into the picture the in the present day in the boom of AI. Finally, examine and categorize the obstacles and potential future improvements in this domain.