Identification of Diseases for Tomato Leaves Using AlexNet

Plants are the key source of human energy generation and have nutritional, therapeutic, and other benefits. Plant diseases cause a significant loss in crop productivity, and manually inspecting for plant diseases is a labor-intensive and ineffective approach. To overcome this problem automated plant disease detection systems have been developed using many approaches rely on machine learning and image processing to address the indicated issue. The ability of plant illnesses to alter the color and texture of leaves is exploited to build techniques for detecting plant diseases. In this discipline, deep learning models like VGG and ResNET are often applied. However, because they are primarily focused on disease classification on a specific crop or dataset, the majority of these models are not scalable. The purpose of this work is to present an enhanced approach for detecting leaf diseases. The suggested system is built with Alexnet and trained and tested on a variety of tomato leaf diseases. This model achieves 94.9% accuracy for classification and validation. In future this model is implemented by increasing number of diseased classes as well as other plant disease.