Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models

Sugarcane is a crucial agricultural crop, providing 75% of the world's sugar production. Like all plant species, any disease that affects sugarcane can significantly impact yield and planning. Traditional manual methods for diagnosing diseases in sugarcane leaves are slow, inefficient, and often lack accuracy. In this study, we present a deep learning-based approach for the robust detection of diseases in sugarcane leaves. Specifically, we trained and evaluated all models from the EfficientNetv1 and EfficientNetv2 architectures, which are among the most notable convolutional neural network (CNN) architectures, using the publicly available Sugarcane Leaf Dataset. This dataset includes 11 disease classes and a total of 6748 images. Additionally, we compared these models with other popular CNN models. Our findings reveal that there is no direct correlation between model complexity, depth, and accuracy for the 11-class sugarcane dataset. Among the 13 models tested, EfficientNet-b6 and InceptionV4 achieved the highest accuracy rates of 93.39% and 93.10%, respectively. These results have a big impact on the ways managers can manage diseases and the agricultural processes of sugarcane production. A deep learning-based disease detection system facilitating the diagnostic process can, in turn, result in a more accurate and faster identification of diseases. That may enable farmers and agricultural managers to make timely and informed decisions, cutting down crop loss and enhancing the whole yield. These highlight the potential of deep learning in developing fast, accurate, and automatic disease diagnosis systems, which can significantly improve disease management and increase the yield of sugarcane.