A FUZZY AND NEURAL DETECTION AND CLASSIFICATION OF PLANT LEAF DISEASE

Disease detection in plants is increasingly crucial in agriculture since it is the key to long-term crop health and production. To guarantee a high-quality harvest, it's important to take swift action in detecting and identifying plant diseases and putting in place suitable management measures. Machine vision and image analysis provide a quick and accurate way for identifying plant leaf diseases. Numerous scientists have dedicated themselves to the study of plant disease segmentation, feature extraction, and disease diagnosis in recent years, and their efforts have yielded some interesting findings. However, due to noise samples, less detection of disease area, and larger dimensionality of features, accurate disease detection with maximum efficiency in minimal time remains a difficult problem to solve. Issues with leaf disease identification are addressed in this study by introducing a novel method based on pre-processing, segmentation, feature extraction, feature selection, and classification.