Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data
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...
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MDPI AG
2025-05-01
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| author | Mateus Alves Gonçalves Maria Samires Martins Castro Eula Regina Carrara Camila Raineri Luciana Navajas Rennó Erica Beatriz Schultz |
| author_facet | Mateus Alves Gonçalves Maria Samires Martins Castro Eula Regina Carrara Camila Raineri Luciana Navajas Rennó Erica Beatriz Schultz |
| author_sort | Mateus Alves Gonçalves |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7ff188791c1d4c1989ac8dda9b45d65b |
| institution | OA Journals |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-7ff188791c1d4c1989ac8dda9b45d65b2025-08-20T01:56:55ZengMDPI AGAnimals2076-26152025-05-011510144910.3390/ani15101449Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging DataMateus Alves Gonçalves0Maria Samires Martins Castro1Eula Regina Carrara2Camila Raineri3Luciana Navajas Rennó4Erica Beatriz Schultz5Department of Animal Science, Federal University of Viçosa, University Campus, PH. Rolfs Ave, Viçosa 36570-900, MG, BrazilDepartment of Animal Science, Federal University of Viçosa, University Campus, PH. Rolfs Ave, Viçosa 36570-900, MG, BrazilDepartment of Animal Science, Federal University of Viçosa, University Campus, PH. Rolfs Ave, Viçosa 36570-900, MG, BrazilSchool of Veterinary Medicine and Animal Science, Federal University of Uberlândia, Uberlândia 38408-144, MG, BrazilDepartment of Animal Science, Federal University of Viçosa, University Campus, PH. Rolfs Ave, Viçosa 36570-900, MG, BrazilDepartment of Animal Science, Federal University of Viçosa, University Campus, PH. Rolfs Ave, Viçosa 36570-900, MG, BrazilThe 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.https://www.mdpi.com/2076-2615/15/10/1449artificial intelligenceprecision livestock farmingsmall ruminants |
| spellingShingle | Mateus Alves Gonçalves Maria Samires Martins Castro Eula Regina Carrara Camila Raineri Luciana Navajas Rennó Erica Beatriz Schultz Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data Animals artificial intelligence precision livestock farming small ruminants |
| title | Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data |
| title_full | Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data |
| title_fullStr | Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data |
| title_full_unstemmed | Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data |
| title_short | Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data |
| title_sort | prediction of weight and body condition score of dairy goats using random forest algorithm and digital imaging data |
| topic | artificial intelligence precision livestock farming small ruminants |
| url | https://www.mdpi.com/2076-2615/15/10/1449 |
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