Wheat Leaf Disease Detection And Solution Using Deep Learning Algorithms

Abstract

In the field of agricultural science, the automated detection of wheat leaf diseases is essential for the preservation of crop health and the guarantee of optimal agricultural productivity. The necessity of sophisticated technology, such as deep convolutional neural networks (CNNs), for accurate and efficient disease classification is underscored by the labor-intensive and error-prone nature of traditional manual methods. Our dataset consists of 2,856 original wheat leaf images that have been augmented to increase diversity and include a variety of disease manifestations. EfficientNet demonstrated robust capabilities in the identification and classification of wheat leaf diseases, achieving the maximum accuracy of 96% when evaluating the performance of CNN models. Intricate disease patterns were effectively captured by DenseNet, which followed closely with 93%. The competence of ResNet50 and VGG16 in disease detection tasks was demonstrated by their accuracies of 93% and 92%, respectively, while VGG19 performed exceptionally well at 94%. This investigation underscores the transformative potential of AI-driven solutions to improve agricultural sustainability and productivity by means of precise disease identification and management.