Body Weight Estimation in Holstein × Zebu Crossbred Heifers: Comparative Analysis of XGBoost and LightGBM Algorithms.
Saved in:
| Title: | Body Weight Estimation in Holstein × Zebu Crossbred Heifers: Comparative Analysis of XGBoost and LightGBM Algorithms. |
|---|---|
| Authors: | Herrera‐Camacho, Jose1 (AUTHOR), Tırınk, Cem2 (AUTHOR) cem.tirink@gmail.com, Parra‐Cortés, Rosa Inés3 (AUTHOR), Bayyurt, Lütfi4 (AUTHOR), Uskenov, Rashit5 (AUTHOR), Omarova, Karlygash6 (AUTHOR), Makhanbetova, Aizhan6 (AUTHOR), Chekirov, Kadyrbai7 (AUTHOR), Chay‐Canul, Alfonso Juventino8 (AUTHOR) |
| Source: | Veterinary Medicine & Science. Jul2025, Vol. 11 Issue 4, p1-10. 10p. |
| Document Type: | Article |
| Subjects: | Machine learning, Milk yield, Body weight, Heifers, Rural geography |
| Author-Supplied Keywords: | body weight prediction crossbred heifer LightGBM machine learning XGBoost |
| Abstract: | This study evaluates the effectiveness of XGBoost and LightGBM algorithms for estimating the live weight of Holstein×Zebu crossbred heifers. The study compares the performance of both algorithms using a wide range of biometric measurements and tests various hyperparameter settings. The research results show that the XGBoost algorithm provides almost perfect agreement with an R2 value of 0.999 on the training set and high performance with an R2 value of 0.986 on the test set. The LightGBM algorithm also achieved effective results with R2 values of 0.986 and 0.981 on both training and test sets. The machine learning algorithms used in the current study stand out as having the potential to provide a practical and economical solution for live weight estimation in livestock enterprises and especially for herd management applications in rural areas through input variables such as body measurements, milk yield, etc. However, the obtained results in the current study reveal the potential of machine learning algorithms for live weight estimation in the livestock sector and indicate that advanced research is needed for the optimisation of these algorithms. [ABSTRACT FROM AUTHOR] |
| Copyright of Veterinary Medicine & Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Author Affiliations: | 1Universidad Michoacana de San Nicolás de Hidalgo, Morelia Michoacán,, Mexico 2Department of Animal Science, Igdir University, Faculty of Agriculture, Iğdır, Türkiye 3Universidad de Ciencias Aplicadas y Ambientales U.D.C.A, Área de Ciencias Agropecuarias, Grupo de Investigación en Ciencia Animal, Bogotá, Colombia 4Faculty of Agriculture, Department of Animal Science, Tokat Gaziosmanpaşa University, Tokat, Türkiye 5Agronomic Faculty, Saken Seifullin Kazakh Agrotechnical University, Astana, Kazakhstan 6Faculty of Veterinary and Livestock Technology, Saken Seifullin Kazakh Agrotechnical University, Astana, Kazakhstan 7Kyrgyz‐Turkish Manas University, Bishkek, Kyrgyz Republic 8División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villahermosa Tabasco,, México |
| Full Text Word Count: | 7136 |
| ISSN: | 2053-1095 |
| DOI: | 10.1002/vms3.70422 |
| Accession Number: | 186810057 |
| Database: | Veterinary Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | This study evaluates the effectiveness of XGBoost and LightGBM algorithms for estimating the live weight of Holstein×Zebu crossbred heifers. The study compares the performance of both algorithms using a wide range of biometric measurements and tests various hyperparameter settings. The research results show that the XGBoost algorithm provides almost perfect agreement with an R2 value of 0.999 on the training set and high performance with an R2 value of 0.986 on the test set. The LightGBM algorithm also achieved effective results with R2 values of 0.986 and 0.981 on both training and test sets. The machine learning algorithms used in the current study stand out as having the potential to provide a practical and economical solution for live weight estimation in livestock enterprises and especially for herd management applications in rural areas through input variables such as body measurements, milk yield, etc. However, the obtained results in the current study reveal the potential of machine learning algorithms for live weight estimation in the livestock sector and indicate that advanced research is needed for the optimisation of these algorithms. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20531095 |
| DOI: | 10.1002/vms3.70422 |