International Research Journal of Education and Technology

Sugarcane disease is a major challenge for the sugar industry in India, often leading to significant crop destruction and financial losses. Early detection and treatment of these diseases are crucial, but farmers may lack the expertise to identify them. This study explores the use of machine learning, specifically image processing and deep learning techniques (CNN), as a potential solution to this problem. By training a deep learning model on a dataset of disease-infected sugarcane images, the study successfully develops a model capable of detecting and classifying sugarcane diseases. This research offers a promising approach to assist farmers in detecting and classifying sugarcane diseases using deep learning algorithms.

Key Words:  Sugarcane disease, crop destruction, financial losses, early detection, machine learning, image processing, deep learning, Convolutional Neural Network (CNN), disease classification, agriculture technology, sugar industry in India, disease-infected image dataset, automated disease detection, smart farming, precision agriculture.