Sugarcane leaf disease classification using deep neural network approach

Abstract

The study demonstrates the effectiveness of DL models, particularly EfficientNet-B7 and DenseNet201, for fast, accurate, and automatic disease detection in sugarcane leaves. These systems offer a significant improvement over traditional manual methods, enabling farmers and agricultural managers to make timely and informed decisions, ultimately reducing crop loss and enhancing overall sugarcane yield. This work highlights the transformative potential of DL in agriculture.