Broiler Live Weight Estimation through Image Processing and YOLO-based Deep Learning

Hamza Kuzu; Ali Aybek; Hayrettin Karadöl; Adem Tekerek

doi:10.15832/ankutbd.1761039

Broiler Live Weight Estimation through Image Processing and YOLO-based Deep Learning

Abstract

This study aims to estimate the live weight of broilers using image processing and deep learning techniques. The proposed method is designed to optimize management processes in broiler production systems, reduce labor requirements and operational costs, minimize animal stress caused by direct human contact, and serve as an effective alternative to traditional manual weighing practices. The study was conducted at a commercial broiler farm located in Kahramanmaraş, in the Mediterranean region of Türkiye. An automatically controlled measurement enclosure was constructed to capture broiler images with minimal human intervention. Digital cameras mounted at the top of the enclosure recorded images of broilers that spontaneously entered the enclosure. By using these images, live weight estimation was carried out in two stages: the first stage involved morphological image processing in the MATLAB environment, while the second stage focused on deep learning-based modeling for prediction. During the image processing stage, multiple regression analysis was performed using the actual weights obtained through manual weighing and the estimated weights derived from image-based measurements. The analysis resulted in an adjusted R² value of 0.97 and a standard error of ± 131 g (P<0.01). The mean absolute error (MAE) was calculated as 84.4 g, while the mean relative error (MRE) was found to be 7.6%. In the deep learning stage, the YOLOv8 model was trained for 150 and 500 epochs. Notable improvements in both accuracy and generalization capability were observed after 500 epochs. Under these conditions, the model achieved a high mean Average Precision (mAP) of 0.969, with substantial increases in precision, recall, and F1-score across all 17 predefined broiler live weight classes. Furthermore, regression-based performance indicators were approximated from the class-based predictions to enable a quantitative assessment of weight estimation accuracy. Based on this indirect evaluation, the proposed model achieved an estimated MAE of 37.0 g and MRE of 4.43%. Overall, the findings suggest that the proposed framework has strong potential for adaptation to live weight prediction in other livestock species.

Keywords

Project Number

2020/9-26D

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Details

Primary Language

English

Subjects

Precision Agriculture Technologies, Poultry Farming and Treatment

Journal Section

Research Article

Authors

Hamza Kuzu ^*
0000-0001-8585-4467
Türkiye

Ali Aybek
0000-0003-3036-8204
Türkiye

Hayrettin Karadöl
0000-0002-5062-0887
Türkiye

Adem Tekerek
0000-0002-0880-7955
Türkiye

Publication Date

March 24, 2026

Submission Date

August 8, 2025

Acceptance Date

November 21, 2025

Published in Issue

Year 2026 Volume: 32 Number: 2

DOI

https://doi.org/10.15832/ankutbd.1761039

IZ

https://izlik.org/JA36NS74YX

Cite

RIS / Bibtex

APA

Kuzu, H., Aybek, A., Karadöl, H., & Tekerek, A. (2026). Broiler Live Weight Estimation through Image Processing and YOLO-based Deep Learning. Journal of Agricultural Sciences, 32(2), 317-335. https://doi.org/10.15832/ankutbd.1761039

AMA

1.Kuzu H, Aybek A, Karadöl H, Tekerek A. Broiler Live Weight Estimation through Image Processing and YOLO-based Deep Learning. J Agr Sci-Tarim Bili. 2026;32(2):317-335. doi:10.15832/ankutbd.1761039

Chicago

Kuzu, Hamza, Ali Aybek, Hayrettin Karadöl, and Adem Tekerek. 2026. “Broiler Live Weight Estimation through Image Processing and YOLO-Based Deep Learning”. Journal of Agricultural Sciences 32 (2): 317-35. https://doi.org/10.15832/ankutbd.1761039.

EndNote

Kuzu H, Aybek A, Karadöl H, Tekerek A (March 1, 2026) Broiler Live Weight Estimation through Image Processing and YOLO-based Deep Learning. Journal of Agricultural Sciences 32 2 317–335.

IEEE

[1]H. Kuzu, A. Aybek, H. Karadöl, and A. Tekerek, “Broiler Live Weight Estimation through Image Processing and YOLO-based Deep Learning”, J Agr Sci-Tarim Bili, vol. 32, no. 2, pp. 317–335, Mar. 2026, doi: 10.15832/ankutbd.1761039.

ISNAD

Kuzu, Hamza - Aybek, Ali - Karadöl, Hayrettin - Tekerek, Adem. “Broiler Live Weight Estimation through Image Processing and YOLO-Based Deep Learning”. Journal of Agricultural Sciences 32/2 (March 1, 2026): 317-335. https://doi.org/10.15832/ankutbd.1761039.

JAMA

1.Kuzu H, Aybek A, Karadöl H, Tekerek A. Broiler Live Weight Estimation through Image Processing and YOLO-based Deep Learning. J Agr Sci-Tarim Bili. 2026;32:317–335.

MLA

Kuzu, Hamza, et al. “Broiler Live Weight Estimation through Image Processing and YOLO-Based Deep Learning”. Journal of Agricultural Sciences, vol. 32, no. 2, Mar. 2026, pp. 317-35, doi:10.15832/ankutbd.1761039.

Vancouver

1.Hamza Kuzu, Ali Aybek, Hayrettin Karadöl, Adem Tekerek. Broiler Live Weight Estimation through Image Processing and YOLO-based Deep Learning. J Agr Sci-Tarim Bili. 2026 Mar. 1;32(2):317-35. doi:10.15832/ankutbd.1761039