Development of a Rice Leaf Disease Detection Application Using Python-Based Computer Vision and YOLO
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Fajar Maulana
Yomei Hendra
Guswita Helmi
Radhiatul Husna
Amma Liesvarastranta Haz
Evianita Dewi Fajrianti
Abstract
Rice is one of Indonesia’s primary agricultural commodities and is highly vulnerable to various leaf diseases, including blast, blight, brown spot, and tungro, which can significantly reduce crop productivity. To address this issue, an automated and accurate detection system is needed to assist farmers in identifying rice leaf diseases at an early stage. This study aims to develop a rice leaf disease detection application using computer vision technology based on Python and the YOLO (You Only Look Once) algorithm. The research methodology consisted of several stages: problem identification, data acquisition, data exploration, model development, evaluation, and deployment. The dataset was obtained from Roboflow and comprised five classes: blast, blight, brown spot, healthy, and tungro. The YOLO model was trained using Google Colab with optimized parameters to enhance detection performance. Experimental results demonstrate that the proposed model achieved an accuracy of 95% and a mean Average Precision (mAP) of 95%, indicating strong performance in detecting and classifying rice leaf diseases. The system was implemented as a web-based application using Flask and Bootstrap, allowing users to upload images of rice leaves and obtain real-time detection results. This application enables farmers to identify plant diseases quickly and accurately, facilitating timely and effective intervention to minimize crop losses.
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Copyright (c) 2025 Fajar Maulana, Yomei Hendra, Guswita Helmi, Radhiatul Husna, Amma Liesvarastranta Haz, Evianita Dewi Fajrianti
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