Machine learning approaches for the prediction of lameness in dairy cows
Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learnin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | Animal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1751731121002342 |
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| author | S. Shahinfar M. Khansefid M. Haile-Mariam J.E. Pryce |
| author_facet | S. Shahinfar M. Khansefid M. Haile-Mariam J.E. Pryce |
| author_sort | S. Shahinfar |
| collection | DOAJ |
| description | Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming. |
| first_indexed | 2024-12-19T03:51:30Z |
| format | Article |
| id | doaj.art-4b6459488cac4416b0d58dcc2864e71f |
| institution | Directory Open Access Journal |
| issn | 1751-7311 |
| language | English |
| last_indexed | 2024-12-19T03:51:30Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Animal |
| spelling | doaj.art-4b6459488cac4416b0d58dcc2864e71f2022-12-21T20:36:56ZengElsevierAnimal1751-73112021-11-011511100391Machine learning approaches for the prediction of lameness in dairy cowsS. Shahinfar0M. Khansefid1M. Haile-Mariam2J.E. Pryce3Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; Corresponding author.Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, AustraliaAgriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, AustraliaAgriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, AustraliaLameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming.http://www.sciencedirect.com/science/article/pii/S1751731121002342Animal welfareLogistic regressionNaïve bayesNeural networksPrecision dairy farming |
| spellingShingle | S. Shahinfar M. Khansefid M. Haile-Mariam J.E. Pryce Machine learning approaches for the prediction of lameness in dairy cows Animal Animal welfare Logistic regression Naïve bayes Neural networks Precision dairy farming |
| title | Machine learning approaches for the prediction of lameness in dairy cows |
| title_full | Machine learning approaches for the prediction of lameness in dairy cows |
| title_fullStr | Machine learning approaches for the prediction of lameness in dairy cows |
| title_full_unstemmed | Machine learning approaches for the prediction of lameness in dairy cows |
| title_short | Machine learning approaches for the prediction of lameness in dairy cows |
| title_sort | machine learning approaches for the prediction of lameness in dairy cows |
| topic | Animal welfare Logistic regression Naïve bayes Neural networks Precision dairy farming |
| url | http://www.sciencedirect.com/science/article/pii/S1751731121002342 |
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