YOLOv8-Based Quality Detection of Bali MSMEs Staple Food

Ni Putu Dita Ariani Sukma Dewi; Jiyestha Aji Dharma Aryasa; I Gede Hendrayana; I Made Ade Prayoga; Sulin Monica Putri

doi:10.38043/tiers.v6i2.7144

Authors

Ni Putu Dita Ariani Sukma Dewi Institut Teknologi dan Kesehatan Bali, Indonesia
Jiyestha Aji Dharma Aryasa Institut Teknologi dan Kesehatan Bali, Indonesia
I Gede Hendrayana Institut Teknologi dan Kesehatan Bali, Indonesia
I Made Ade Prayoga Institut Teknologi dan Kesehatan Bali, Indonesia
Sulin Monica Putri Institut Teknologi dan Kesehatan Bali, Indonesia

DOI:

https://doi.org/10.38043/tiers.v6i2.7144

Keywords:

Staple food quality, YOLOv8, Deep learning, Image detection, MSMEs

Abstract

Ensuring the quality of staple foods such as rice, cooking oil, milk, and meat is crucial for consumer safety and health. In Indonesian Micro, Small and Medium Enterprises (MSMEs), quality assessment often depends on subjective and time-consuming visual inspection. This study develops an automatic quality detection system using YOLOv8, applied to food MSMEs in Bali, to detect 14 quality categories across the four commodities based on image data. The methodology includes dataset collection from MSMEs, image annotation, preprocessing, training YOLOv8s and YOLOv8m models, and evaluating performance using mAP50, accuracy, precision, recall, and F1-score. Results show that YOLOv8m achieved a mAP50 of 96.5%, indicating high detection accuracy. The system, implemented as a web-based application, has strong potential to improve efficiency, ensure consistent product quality, and support Sustainable Development Goals (SDGs) 2, 3, 8, and 9.

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