Disease Identification in Organic Plants Using Deep Learning

Agriculture in India holds immense importance, particularly due to the country's rapidly growing population and the increasing demand for food. Consequently, boosting crop production has become essential. However, bacterial infections and various organisms significantly contribute to reduced agricultural productivity. One of the most crucial aspects of farming is the analysis of plant diseases, which is vital for maintaining healthy crops. While manual monitoring, inspection, and treatment of plant diseases are challenging and time-consuming tasks, image processing has emerged as a powerful tool for identifying these diseases efficiently. Machine learning techniques have proven effective in classifying plant diseases, involving steps such as dataset construction, image loading, pre-processing, segmentation, feature extraction, training classifiers, and classification. This study leverages the AlexNet convolutional neural network model to detect and classify diseases in tomato leaves. The proposed approach offers a less complex method for identifying various plant diseases while achieving an impressive accuracy rate of 94.09 percent.