Artificial Intelligence Based Sugarcane Leaf Disease Prediction System for Smart Farming

In response to the challenges posed by sugarcane diseases, this research introduces a Sugarcane Disease Prediction System, employing deep learning through a Convolutional Neural Network (CNN) using TensorFlow and Keras. The CNN classifies sugarcane images into Bacterial Blight, Healthy, or Red Rot categories. The model is trained on a diverse dataset, employing data augmentation for enhanced generalization. Integrated into a Flask web application, the model enables users to upload sugarcane images, receiving disease predictions and confidence scores. This user-friendly tool aids farmers and researchers in early disease detection, contributing to precision agriculture. The system's efficacy is showcased through evaluations and visualizations of metrics, providing an automated solution for sugarcane disease prediction.