GC-1233 An AI-Powered Convolutional Neural Network System for Multi-Class Image Classification of Rice Plant Leaf Diseases

GC-1233 An AI-Powered Convolutional Neural Network System for Multi-Class Image Classification of Rice Plant Leaf Diseases

https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php

This project focuses on building an intelligent system that can automatically identify common diseases found on rice leaves by analyzing simple images. Using a deep convolutional neural network, the model learns to recognize visual patterns associated with three major diseases: Bacterial Blight, Brown Spot, and Leaf Smut. These diseases often show subtle differences in color, texture, and leaf damage, and the model is trained to distinguish them accurately. The goal of this work is to show how artificial intelligence can support modern agriculture by helping farmers detect problems early, even without expert knowledge. By processing images through careful preprocessing, augmentation, and feature extraction, the system becomes capable of making reliable predictions from real-world leaf photographs. This reduces manual dependency, improves diagnostic speed, and promotes healthier crop management.