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All Journal TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Atika Arpan
Politeknik Negeri Lampung

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Enhancing rice plant disease detection through transfer learning and image segmentation with YOLOv11 Muhammad Reza Redo I Islami; Sylvia; Atika Arpan; Rizka Permata; Dwi Handoko; Rima Maulini; Dwirgo Sahlinal
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 13 No 1 (2026): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v13i1.1754

Abstract

Rice is a staple food globally, yet its productivity is often threatened by diseases such as blast and brown spot. Traditional diagnostic methods relying on human observation are prone to delays and inaccuracies. This study introduces an automated detection system that utilizes YOLOv11-seg to improve the accuracy and efficiency of rice disease identification. The model integrates object detection and instance segmentation, is trained on over 6,000 annotated images covering six categories (five disease types and healthy leaves), and leverages transfer learning from COCO weights. Experimental results show that the model achieved a bounding box mAP@50 of 0.607 and a segmentation mAP@50 of 0.564, with F1-scores of 0.62 and 0.59, respectively. The highest detection accuracy was recorded for healthy leaves (86%), while segmentation performance declined on visually similar classes such as Brown Spot and Sheath Blight. Overfitting was observed during training, with a 15–20% gap between training and validation metrics. These findings demonstrate the model's potential for real-time field application in precision agriculture. Future improvements should focus on enhancing spatial accuracy and robustness through synthetic data generation and architectural optimization.
Co-Authors Dwi Handoko Dwirgo Sahlinal Muhammad Reza Redo I Islami Rima Maulini Sylvia