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All Journal Jurnal BETA (Biosistem dan Teknik Pertanian) FLYWHEEL : Jurnal Teknik Mesin Untirta Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Jurnal Sains Teknologi dan Lingkungan (JSTL) Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Agritechno Jurnal Teknologi Informasi dan Multimedia Community Development Journal: Jurnal Pengabdian Masyarakat
Murad, Murad
Program Studi Teknik Pertanian, Fakultas Teknologi Pangan Dan Agroindustri Universitas Mataram
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Transportation

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Non-Destructive Detection of Coffee Bean Defects using Machine Vision and the YOLOv11 Algorithm Hary Kurniawan; Ince Siti Wardatullatifah S; Hanifah Ayu; Surya Abdul Muttalib; Sukmawaty Sukmawaty; Ansar Ansar; Rahmat Sabani; Murad Murad
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 3 (2026): June 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i3.1164-1179

Abstract

Advances in machine vision and deep learning offer a promising solution for automated, non-destructive quality assessment for high-quality coffee. This study evaluated the performance of five YOLOv11 variants (n, s, m, l, and x) for real-time detection of defective coffee beans and identified the most suitable model in terms of detection accuracy and computational efficiency. A conveyor-based machine vision system was developed to acquire top-view images of Robusta coffee beans under controlled illumination. A dataset of 3,500 images was prepared, comprising 3,000 annotated images for training and validation (80:20) and 500 images reserved for blind testing. All defective beans were grouped into a single defect class, and the YOLOv11 variants were evaluated using precision, recall, F1-score, mean Average Precision (mAP) at IoU thresholds of 0.5 and 0.5:0.95, and inference time. All YOLOv11 variants achieved high detection performance, with mAP@0.5 values exceeding 0.98. YOLOv11s showed the best overall balance, achieving the highest recall (0.954), mAP@0.5:0.95 (0.689), and F1-score (0.959), while maintaining low inference time and a compact model size. Larger variants, such as YOLOv11x, achieved slightly higher mAP@0.5 but required substantially greater computational resources, whereas YOLOv11n provided faster inference but lower robustness under stricter localization criteria. Blind testing revealed a performance gap relative to validation results, highlighting remaining challenges in model generalization. Overall, the results confirm the effectiveness of YOLOv11 for coffee bean defect detection and identify YOLOv11s as the most suitable variant for real-time inspection within the defined experimental scope.
Co-Authors Ahyar Ulumuddin Ansar Ansar Ansar Ansar Ansar Ansar Ansar Ansar Baiq Sulasi Muliani Diah Ajeng Setiawati Fakhrul Irfan Khalil Hanifah Ayu Hulfita Ariani Ince Siti Wardatullatifah S Isna Hilda Arini Kurniawan Yuniarto Kurniawan, Hary Rahmat Sabani Sarmini Wati Sirajuddin Haji Abdullah Sukmawaty Sukmawaty Sukmawaty Sukmawaty Sukmawaty Sukmawaty Surya Abdul Muttalib, Surya Abdul Syahroni Hidayat