Disease Identification in Organic Plants

Abstract

Agriculture today plays a critical role in India, owing to the country's rapid population increase and extensive interest in food. As a result, increased crop output is required. Bacterial infections and organisms are significant contributors to decreased collecting productivity. Plant disease analysis is one of the most significant and fundamental activities in cultivation. Plant disease detection methods are usually effective in preventing it. Observing manually, analyzing, and taking cures for plant diseases is a highly challenging duty. Using image processing requires a significant amount of effort as well as planning time to identify plant diseases. Plant diseases can be classified using machine learning techniques, including dataset construction, image loading, pre-processing, segmenting, feature extraction, training classifiers, and classification. This study uses the AlexNet convolutional neural network model to detect and classify illnesses in tomato leaves. The proposed method identifies distinct diseases in plants with less complexity and the best accuracy of 94.09 percent.