A Survey on Deep Learning Technique for Sugarcane Disease Detection

Abstract

Abstract: Sugarcane is a vital crop, and its health directly impacts agricultural yields and the sugar industry. To address the challenges associated with disease detection in sugarcane, we propose an approach using deep learning techniques. Our study leverages convolutional neural networks (CNNs) and image analysis to accurately identify and classify various sugarcane diseases. By analyzing high-resolution images of sugarcane leaves and stems, our deep learning algorithm provides remarkable accuracy in disease detection, offering a promising solution for early diagnosis. This research contributes to sustainable agriculture and aids in preserving the economic viability of sugarcane cultivation.