Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics
The accurate prediction of body mass (BM) in cattle is crucial for herd monitoring, assessing biological efficiency, and optimizing nutritional management. This study evaluated BM prediction models using morphological data from 465 lactating Holstein cows, including the dorsal length (DL), thoracic...
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MDPI AG
2025-04-01
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| author | Franck Morais de Oliveira Patrícia Ferreira Ponciano Ferraz Gabriel Araújo e Silva Ferraz Marcos Neves Pereira Matteo Barbari Giuseppe Rossi |
| author_facet | Franck Morais de Oliveira Patrícia Ferreira Ponciano Ferraz Gabriel Araújo e Silva Ferraz Marcos Neves Pereira Matteo Barbari Giuseppe Rossi |
| author_sort | Franck Morais de Oliveira |
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| description | The accurate prediction of body mass (BM) in cattle is crucial for herd monitoring, assessing biological efficiency, and optimizing nutritional management. This study evaluated BM prediction models using morphological data from 465 lactating Holstein cows, including the dorsal length (DL), thoracic width (TW), abdominal width (AW), rump width (RW), hip height (HH), body depth (BD), thoracic perimeter (TP), and abdominal perimeter (AP). Spearman’s correlation analysis identified TP (r = 0.89), AP (r = 0.88), and RW (r = 0.80) as the strongest predictors. Simple and multiple linear regression models, artificial neural networks (ANNs), and Support Vector Regression (SVR) were tested. The dataset was split into 90% for training (419 samples), 5% for validation (23 samples), and 5% for testing (23 samples). The best simple model, using only TP, achieved an R<sup>2</sup> of 0.7763 and an RMSE of 43.69 kg. A multiple regression model with TP, AP, and RW improved performance (R<sup>2</sup> = 0.9067, RMSE = 28.00 kg). The ANN outperformed all of the models (R<sup>2</sup> = 0.9125, RMSE = 25.86 kg), and was followed by SVR (R<sup>2</sup> = 0.9046, RMSE = 27.41 kg). As an indication of the evaluation of the results obtained, it is observed that, although regression models are effective, the ANNs and SVR provide greater accuracy, reinforcing their potential for herd management. However, simpler models remain viable alternatives for practical on-farm application. |
| format | Article |
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| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Animals |
| spelling | doaj-art-0bdadbf61df24c4ba6a35ff9b81051af2025-08-20T02:09:18ZengMDPI AGAnimals2076-26152025-04-01157105410.3390/ani15071054Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological CharacteristicsFranck Morais de Oliveira0Patrícia Ferreira Ponciano Ferraz1Gabriel Araújo e Silva Ferraz2Marcos Neves Pereira3Matteo Barbari4Giuseppe Rossi5Department of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), Lavras 37203-202, BrazilDepartment of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), Lavras 37203-202, BrazilDepartment of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), Lavras 37203-202, BrazilDepartment of Animal Science, Federal University of Lavras (UFLA), Lavras 37203-202, BrazilDepartment of Agriculture, Food, Environment and Forestry, University of Florence, Via San Bonaventura, 13-50145 Florence, ItalyDepartment of Agriculture, Food, Environment and Forestry, University of Florence, Via San Bonaventura, 13-50145 Florence, ItalyThe accurate prediction of body mass (BM) in cattle is crucial for herd monitoring, assessing biological efficiency, and optimizing nutritional management. This study evaluated BM prediction models using morphological data from 465 lactating Holstein cows, including the dorsal length (DL), thoracic width (TW), abdominal width (AW), rump width (RW), hip height (HH), body depth (BD), thoracic perimeter (TP), and abdominal perimeter (AP). Spearman’s correlation analysis identified TP (r = 0.89), AP (r = 0.88), and RW (r = 0.80) as the strongest predictors. Simple and multiple linear regression models, artificial neural networks (ANNs), and Support Vector Regression (SVR) were tested. The dataset was split into 90% for training (419 samples), 5% for validation (23 samples), and 5% for testing (23 samples). The best simple model, using only TP, achieved an R<sup>2</sup> of 0.7763 and an RMSE of 43.69 kg. A multiple regression model with TP, AP, and RW improved performance (R<sup>2</sup> = 0.9067, RMSE = 28.00 kg). The ANN outperformed all of the models (R<sup>2</sup> = 0.9125, RMSE = 25.86 kg), and was followed by SVR (R<sup>2</sup> = 0.9046, RMSE = 27.41 kg). As an indication of the evaluation of the results obtained, it is observed that, although regression models are effective, the ANNs and SVR provide greater accuracy, reinforcing their potential for herd management. However, simpler models remain viable alternatives for practical on-farm application.https://www.mdpi.com/2076-2615/15/7/1054digital livestockprecision livestockdairy cattleartificial intelligenceneural networks |
| spellingShingle | Franck Morais de Oliveira Patrícia Ferreira Ponciano Ferraz Gabriel Araújo e Silva Ferraz Marcos Neves Pereira Matteo Barbari Giuseppe Rossi Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics Animals digital livestock precision livestock dairy cattle artificial intelligence neural networks |
| title | Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics |
| title_full | Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics |
| title_fullStr | Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics |
| title_full_unstemmed | Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics |
| title_short | Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics |
| title_sort | prediction of body mass of dairy cattle using machine learning algorithms applied to morphological characteristics |
| topic | digital livestock precision livestock dairy cattle artificial intelligence neural networks |
| url | https://www.mdpi.com/2076-2615/15/7/1054 |
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