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All Journal Journal of Information Technology and Computer Science
Nadia Nafista
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparative Analysis of Random Forest, Support Vector Machine, and K-Nearest Neighbor with Image Feature Extraction for Rice Leaf Disease Detection Nadia Nafista; Wawu Tri Ambodo; Analicia; Ulfa Siti Nuraini
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112856

Abstract

Plant diseases and pest infestations have caused a decline in global food production of up to 40%, including in Indonesia, making an efficient and accurate disease detection system essential to support food security. This study proposes a supervised learning approach to detect rice leaf diseases based on image processing. Leaf images are processed through the stages of image segmentation, normalization, Gaussian blur, Canny edge detection, visualization of diseased areas, and hybrid feature extraction. Supervised learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were trained and compared. Test results show that Random Forest delivers the best performance with an accuracy of 97%, outperforming SVM and KNN. These findings indicate that the proposed approach can serve as an effective and reliable solution for the automatic detection of rice leaf diseases.
Co-Authors Analicia Ulfa Siti Nuraini Wawu Tri Ambodo