Leaf Disease Detection using Machine Learning: A Review and Comparative Analysis

Abstract

Early detection of plant diseases are crucial for maintaining agricultural yield and food security. Traditional methods often rely on expert knowledge and visual inspection, which can be subjective, time- consuming, and resource-intensive. Machine learning (ML) offers a promising alternative for automated and accurate leaf disease detection. This paper reviews the current state of the art in applying ML for this task. We explore various ML techniques, including traditional classifiers and deep learning approaches, discuss their strengths and limitations, and analyze their performance on public datasets. Additionally, we present a comparative study of different ML models for leaf disease detection on a curated dataset of tomato leaf images. Finally, we highlight the challenges and future directions of research in this field.

DOI - https://doi.org/10.65525/SVUP.9788199963351.2026.17-21