Buffalo Price Estimation Using YOLOv8 And Image Thresholding

Amelia Amelia; Wawan  Firgiawan; Sulfayanti Sulfayanti

doi:10.52436/1.jutif.2026.7.1.4749

Authors

Amelia Informatics, Universitas Sulawesi Barat, Indonesia
Wawan Firgiawan Informatics, Universitas Sulawesi Barat, Indonesia
Sulfayanti Informatics, Universitas Sulawesi Barat, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.1.4749

Keywords:

Buffalo detection, Buffalo price estimation, Image thresholding, YOLOv8

Abstract

The skin color pattern of buffaloes can determine their market price, especially for traditional ceremonial purposes that involve buffaloes. Currently, the pricing of buffaloes is still done subjectively by sellers or buyers, resulting in inconsistencies in price determination. This study proposes the development of a system to estimate the price of buffaloes based on their type and the percentage of light and dark skin, specifically for the Saleko buffalo type. The algorithm used to recognize buffalo types is YOLOv8, which was trained to detect four classes: Lotongboko, Saleko, Bonga, and Other types. The model was trained over 100 epochs using the Adam optimizer and hyperparameters. A thresholding method was applied to identify the percentage of black and white on the Saleko buffalo images that were successfully detected by YOLOv8. If the light skin percentage exceeds 80%, the buffalo is estimated to be worth 800 million rupiah. Otherwise, the Saleko buffalo is estimated at 300 million rupiah. The YOLOv8 training achieved a highest mAP value of 97.8%, with steadily decreasing loss and increasing metrics at each iteration, indicating a successful training process with strong detection performance. The price estimation model achieved an accuracy of 76.3% based on 55 tested images. Estimation errors were caused by low image resolution and poor lighting quality. This study provides insights into the application of technology for buffalo price estimation through digital image processing.

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