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Arisqi, Wais
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Application of Rice Plant Image Processing for Disease Identification and Classification Using YOLOv11 Arisqi, Wais
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 3 No. 3: February 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v3i3.111124

Abstract

Rice is one of the main food commodities in Indonesia and plays an important role in meeting the food needs of the population. However, rice productivity is often reduced due to various leaf diseases, such as Bacterial Leaf Blight, Blast, and Brown Spot. This study aims to develop an artificial intelligence-based rice leaf disease detection system using the YOLOv11 algorithm implemented on an Android application. The method used in this study includes collecting a rice leaf image dataset consisting of three disease classes, namely Bacterial Leaf Blight, Blast, and Brown Spot. The dataset was then annotated and used to train the YOLOv11 model. After the training process was completed, the model was evaluated using Precision, Recall, mAP50, mAP50-95, and F1-Score metrics. The trained model was subsequently converted into TensorFlow Lite format to enable real-time detection on Android devices. The experimental results showed that the model achieved a Precision of 75.4%, Recall of 71.4%, mAP50 of 77.4%, mAP50-95 of 42.1%, and an F1-Score of 73%. Based on class-wise testing using 100 test images for each disease class, the model obtained accuracies of 88% for Bacterial Leaf Blight, 74% for Blast, and 65% for Brown Spot. The implementation of the model on the Android application successfully displayed detection results in the form of bounding boxes, disease labels, and confidence scores in real time. The YOLOv11 algorithm is capable of detecting rice leaf diseases with satisfactory performance and can be effectively implemented on Android devices using TensorFlow Lite.
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