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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Alif Bahktiar Akbar
Universitas Muhammadiyah Lamongan
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Implementation of Yolo26 Nano Model on Riceguard AI System for Rice Leaf Disease Detection Gilang Septia Firmansyah; Alif Bahktiar Akbar; Mufti Ari Bianto; Khairul Umam
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 6 No. 1 (2026): October 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v6i1.2772

Abstract

RiceGuard AI is a new tool for diagnosing leaf diseases affecting rice plants. Early diagnosis of these diseases is crucial for maintaining rice productivity and global food security. This newly developed tool is designed to assist in diagnosing rice diseases by employing computer-vision techniques to automatically detect the condition of rice leaves. The Ultralytics YOLO26 Nano model was selected for this task because it adopts a novel "edge-first" architecture, enabling high processing speeds by eliminating the Non-Maximum Suppression (NMS) step during processing. To achieve this, the model was trained for 50 epochs, attaining a mean Average Precision (mAP) of 0.651 and an inference speed of 7.1 ms per image on an NVIDIA GTX 1650 GPU. The model achieved high accuracy for the leaf scald (99.4%) and healthy leaf (96.7%) categories but struggled to detect the narrow brown spot class (30.1%) due to visual ambiguities associated with it. YOLO26 Nano provides an accurate and efficient solution for detecting these rice conditions, enabling farmers to perform image-based diagnoses in the field for the early detection of leaf scald or healthy leaves, thereby facilitating prompt action. The model is integrated into a modern Flask web application featuring a "Glassmorphism" design style.
Co-Authors Gilang Septia Firmansyah Khairul Umam Mufti Ari Bianto