Red Rot Disease Prediction in Sugarcane Using the Deep Learning Approach

The disadvantages of traditional models for disease prediction in sugarcane crops include high expenses for obtaining the data input required to run the model, a lack of geographical data, or a bad dataset. We blend the convolutional neural networks (CNN) model for Red Rot illnesses prediction and classification in our article and also discuss the sugarcane issue. The sugarcane red rot infection is explored in this work. The diseased plant is initially captured on camera via secondary sources. The best characteristics of CNN’s deep learning techniques are used to extract features from the illness and classify it. The study predicts the Red Rot Sugarcane Disease with an accuracy of 93%.