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Animals : an open access journal from MDPI2022; 12(10); doi: 10.3390/ani12101234

Body Weight Prediction from Linear Measurements of Icelandic Foals: A Machine Learning Approach.

Abstract: Knowledge of the body weight of horses permits breeders to provide appropriate feeding and care regimen and allows veterinarians to monitor the animals' health. It is not always possible to perform an accurate measurement of the body weight of horses using horse weighbridges, and therefore, new body weight formulas based on biometric measurements are required. The objective of this study is to develop and validate models for estimating body weight in Icelandic foals using machine learning methods. The study was conducted using 312 data records of body measurements on 24 Icelandic foals (12 colts and 12 fillies) from birth to 404 days of age. The best performing model was the polynomial model that included features such as heart girth, body circumference and cannon bone circumference. The mean percentage error for this model was 4.1% based on cross-validation and 3.8% for a holdout dataset. The body weight of Icelandic foals can also be estimated using a less complex model taking a single trait defined as the square of heart girth multiplied by body circumference. The mean percentage error for this model was up to 5% both for the training and the holdout datasets. The study results suggest that machine learning methods can be considered a useful tool for designing models for the estimation of body weight in horses.
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Publication Date: 2022-05-11 PubMed ID: 35625080PubMed Central: PMC9137917DOI: 10.3390/ani12101234Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The study aims to predict the body weight of Icelandic horses using machine learning techniques, allowing for improved horse care and stable management. A best-performing model was determined that used various body measurements for this prediction, offering breeders and veterinarians an alternative to weighbridges which are not always feasible.

Objective and Methodology

  • The objective of this study was to create and validate machine learning models that can estimate the body weight of Icelandic foals.
  • The researchers utilized 312 data records of body measurements of 24 foals, comprising of 12 colts and 12 fillies, collected from birth to 404 days of age.

Analyzing Measurements and Models

  • The primary features used in the models included the foals’ heart girth, body circumference, and cannon bone circumference.
  • The most successful model was a polynomial one that considered these parameters. Based on cross-validation, the average percentage error of this model was only 4.1% and was 3.8% for a subset of the data set aside for testing.
  • A simpler, less complex model was also developed which only considered a single trait, defined as the square of heart girth multiplied by body circumference.
  • This simpler model, while not as accurate as the polynomial model, still performed decently with a percentage error of up to 5% for both the training and testing datasets.

Implications of the Results

  • The results of this study suggest that machine learning methods can be effectively used to design models that estimate the body weight of horses.
  • These models can supplement or even replace the use of horse weighbridges, which are not always accessible or reliable.
  • This modern approach provides an effective way for breeders to ensure their horses are receiving appropriate feeding and care.
  • Veterinarians can also leverage these models to monitor the health of horses, making necessary interventions when the trends in the weight measurements suggest potential health issues.

Cite This Article

APA
Satoła A, Łuszczyński J, Petrych W, Satoła K. (2022). Body Weight Prediction from Linear Measurements of Icelandic Foals: A Machine Learning Approach. Animals (Basel), 12(10). https://doi.org/10.3390/ani12101234

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 12
Issue: 10

Researcher Affiliations

Satoła, Alicja
  • Department of Genetics, Animal Breeding and Ethology, Faculty of Animal Science, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Krakow, Poland.
Łuszczyński, Jarosław
  • Department of Genetics, Animal Breeding and Ethology, Faculty of Animal Science, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Krakow, Poland.
Petrych, Weronika
  • Punktur Icelandic Horses, Debowy Gaj 47, 59-600 Lwówek Śląski, Poland.
Satoła, Krzysztof
  • Independent Researcher, www.satola.net.

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

This article has been cited 2 times.
  1. Salamanca-Carreño A, Vélez-Terranova M, Parés-Casanova PM, Toalombo-Vargas PA, Rangel-Pachón DE, Castillo-Pérez AF. Assessment of Body Morphometry to Classify Two Colombian Creole Pigs Using Statistical and Machine Learning Methods. Life (Basel) 2025 Apr 24;15(5).
    doi: 10.3390/life15050693pubmed: 40430122google scholar: lookup
  2. Zhu X, Li J, Gao J, Lan J, Li M, Deng J, Peng W, Feng Y, Li B, Pang H, Liu J, Kou J, Wang Y. Predicting Body Weight from Birth to Old Age in Giant Pandas Using Machine Learning. Animals (Basel) 2024 Dec 20;14(24).
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