A Comparative Study of Machine Learning and Deep Learning Approaches for Rice Leaf Disease Detection

Project Abstract

Rice serves as a primary food source for over half of the global population. However, its cultivation is increasingly challenged by diseases such as bacterial blight, blast, and brown spot, which can significantly reduce crop yields. Traditional approaches to diagnosing these diseases tend to be slow, subjective, and often lack accuracy. The research compares and analyzes the ability of Machine Learning (ML) versus Deep Learning (DL) models in image-based classification of rice diseases using a comparable framework and set of experiments. Several machine learning models were assessed: Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), and Logistic Regression (LR), as the learning models based on hand-crafted features extracted from rice leaf images. The deep learning models, including a custom designed Convolutional Neural Network (CNN), ResNet50, and Inception V3, were trained in parallel to learn features directly from the raw image data. It was determined that the learning models utilizing deep learning methods CNN, ResNet50, and Inception V3 produced better accuracy and F1 scores, but RF and SVM also had comparably strong results as models which provided more modeling interpretability. By comparing these methods in a side-by-side analysis, this research highlights the trade-off between prediction accuracy and computational efficiency, and the development of machine learning models directed towards incorporating AI for the purpose of precision agriculture related to rice disease management.

Research Area

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

Project Main Objective

• To evaluate and compare various machine learning and deep learning algorithms in terms of accuracy and performance for classifying rice diseases based on leaf image data

Academic Year

2024/2025

Date Uploaded

Feb 3, 2026

Group Members

KYERE DAVID, AGYEMAN DUAH KELVIN, AGYEMAN DUAH MICHAEL, AKONNOR MARCUS ADU- GYAMFI, TOMETY MAVIS AHOEFA

Program Of Study

BSc Information Technology