Comparative Analysis of Machine Learning Algorithms for Crop Disease Detection Using Leaf Image Data
Dr. Anoop Kumar Sharma & Dr. James Osei
Published in IJMEER (Vol. 1, Issue 2, July 2026) · Pages 123-134 · ISSN: 3139-6003 · DOI: 10.5281/zenodo.21809155
Abstract
Timely and accurate detection of crop diseases is critical for minimising agricultural yield losses and reducing the overuse of chemical pesticides. This study conducted a comparative performance evaluation of three machine learning approaches — Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random Forest (RF) — for the automatic detection and classification of nine common crop diseases across four crop species using leaf image data drawn from the PlantVillage dataset (54,306 images). The fine-tuned CNN architecture achieved the highest overall classification accuracy of 94.2%, outperforming SVM (87.1%) and RF (82.3%).
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