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Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data.

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Titel: Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data.
Autoren: Gonçalves, Mateus Alves, Castro, Maria Samires Martins, Carrara, Eula Regina, Raineri, Camila, Rennó, Luciana Navajas, Schultz, Erica Beatriz
Quelle: Animals (2076-2615); May2025, Vol. 15 Issue 10, p1449, 7p
Schlagwörter: MACHINE learning, PEARSON correlation (Statistics), GOATS, RANDOM forest algorithms, ARTIFICIAL intelligence
Abstract: Simple Summary: On dairy goat farms, monitoring weight and body condition is essential for proper animal management. Currently, these measurements are recorded manually, which can be time-consuming and inconsistent. This study explores the use of digital images and artificial intelligence to estimate weight and body condition in dairy goats. By analyzing images of 154 dairy goats, we tested whether machine learning models could accurately predict these traits. The results showed that digital imaging is a reliable method for estimating body weight, providing a faster and more cost-effective alternative to traditional techniques. However, the approach was less effective in classifying body condition, suggesting that further improvements are needed. This research highlights the potential of digital tools to enhance farm efficiency, reducing labor while improving data accuracy for better decision-making in animal care. The aim of study was to evaluate the use of digital images to predict body weight (BW) and classify the body condition score (BCS) of dairy goats. A total of 154 female Saanen and Alpine goats were used to obtain eight body measurements features from digital images: withers height (WH), rump height (RH), body length (BL), chest depth (D), paw height (PH), chest width (CW), rump width (RW), rump length (RL). All animals were weighed using manual scales, and their BCS was evaluated on a scale of 1 to 5. For classification purposes, the BCS was grouped into three categories: low (1–2), moderate (2–3), and high (>3). Pearson's correlation analysis and the Random Forest algorithm were performed. It was possible to predict BW using image features with an R2 of 0.87, with D (22.14%), CW (18.93%) and BL (15.47%) being the most important variables. For the BCS, the classification accuracy was 0.4054 with the CW (20.38%) the most important variable followed by RH and RL with 15.78% and 12.63%, respectively. It was concluded that digital image features can be used to obtain precise estimates of body weight, but it is necessary to increase data variability to improve the BCS classification of dairy goats. [ABSTRACT FROM AUTHOR]
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Datenbank: Biomedical Index
Beschreibung
Abstract:Simple Summary: On dairy goat farms, monitoring weight and body condition is essential for proper animal management. Currently, these measurements are recorded manually, which can be time-consuming and inconsistent. This study explores the use of digital images and artificial intelligence to estimate weight and body condition in dairy goats. By analyzing images of 154 dairy goats, we tested whether machine learning models could accurately predict these traits. The results showed that digital imaging is a reliable method for estimating body weight, providing a faster and more cost-effective alternative to traditional techniques. However, the approach was less effective in classifying body condition, suggesting that further improvements are needed. This research highlights the potential of digital tools to enhance farm efficiency, reducing labor while improving data accuracy for better decision-making in animal care. The aim of study was to evaluate the use of digital images to predict body weight (BW) and classify the body condition score (BCS) of dairy goats. A total of 154 female Saanen and Alpine goats were used to obtain eight body measurements features from digital images: withers height (WH), rump height (RH), body length (BL), chest depth (D), paw height (PH), chest width (CW), rump width (RW), rump length (RL). All animals were weighed using manual scales, and their BCS was evaluated on a scale of 1 to 5. For classification purposes, the BCS was grouped into three categories: low (1–2), moderate (2–3), and high (>3). Pearson's correlation analysis and the Random Forest algorithm were performed. It was possible to predict BW using image features with an R<sup>2</sup> of 0.87, with D (22.14%), CW (18.93%) and BL (15.47%) being the most important variables. For the BCS, the classification accuracy was 0.4054 with the CW (20.38%) the most important variable followed by RH and RL with 15.78% and 12.63%, respectively. It was concluded that digital image features can be used to obtain precise estimates of body weight, but it is necessary to increase data variability to improve the BCS classification of dairy goats. [ABSTRACT FROM AUTHOR]
ISSN:20762615
DOI:10.3390/ani15101449