PLANTATION DISEASE DETECTION AND DIAGNOSIS

Abstract

Deep learning, as a branch of AI , has got into the limelight in recent years due to its automatic learning and feature extraction capabilities. It has found widespread applications in various domains, including multimedia processing, voice analysis, and NLP. Moreover, it has emerged as a promising area of research in agricultural plant protection, specifically in tasks like plant disease recognition and weed/bacterial assessment. Leveraging neural network learning for plant disease recognition mitigates the drawbacks associated with manual process of choosing of disease marks attributes and features, processing disease feature selection and extraction more objectively and accelerating research productivity and technological advancements. This research paper encapsulates the recent advancements in DL technology concerning the identification of plantation diseases. The paper highlights current tech and their complexities in utilizing neural networks and optimized image processing techniques for plantation disease detection. We aim for this work to serve as an effective reference for researchers involved in the study of plant. Additionally, we delve into the existing challenges and issues that warrant resolution