Improving lameness detection in cows: A machine learning algorithm application
ABSTRACT: The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high-frequency sensor data from 6 farms to predic...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Dairy Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0022030224011457 |
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| author | Elma Dervić Caspar Matzhold Christa Egger-Danner Franz Steininger Peter Klimek |
| author_facet | Elma Dervić Caspar Matzhold Christa Egger-Danner Franz Steininger Peter Klimek |
| author_sort | Elma Dervić |
| collection | DOAJ |
| description | ABSTRACT: The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high-frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 wk. A Random Forest classifier, using input features selected by the Boruta algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 wk and when using information from the last 3 wk, combined with a balanced accuracy of 79%. Removing sensor data results has a tendency to reduce the precision for predictions, especially when using information from the last 1, 2, or 3 wk. Moving to a larger dataset (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high-resolution data from other systems, such as automated milking systems. |
| format | Article |
| id | doaj-art-2413d00f1eed49399d60fcf24645ea80 |
| institution | OA Journals |
| issn | 0022-0302 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Dairy Science |
| spelling | doaj-art-2413d00f1eed49399d60fcf24645ea802025-08-20T02:07:00ZengElsevierJournal of Dairy Science0022-03022024-12-0110712115501156210.3168/jds.2024-24730Improving lameness detection in cows: A machine learning algorithm applicationElma Dervić0Caspar Matzhold1Christa Egger-Danner2Franz Steininger3Peter Klimek4Complexity Science Hub Vienna, 1080 Vienna, Austria; Supply Chain Intelligence Institute Austria, 1080 Vienna, Austria; Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, 1090 Vienna, AustriaMedical University of Vienna, Section for Science of Complex Systems, CeMSIIS, 1090 Vienna, Austria; ZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaComplexity Science Hub Vienna, 1080 Vienna, Austria; Supply Chain Intelligence Institute Austria, 1080 Vienna, Austria; Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, 1090 Vienna, Austria; Corresponding authorABSTRACT: The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high-frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 wk. A Random Forest classifier, using input features selected by the Boruta algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 wk and when using information from the last 3 wk, combined with a balanced accuracy of 79%. Removing sensor data results has a tendency to reduce the precision for predictions, especially when using information from the last 1, 2, or 3 wk. Moving to a larger dataset (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high-resolution data from other systems, such as automated milking systems.http://www.sciencedirect.com/science/article/pii/S0022030224011457data integrationdisease predictionmachine learningprecision livestock farminglameness |
| spellingShingle | Elma Dervić Caspar Matzhold Christa Egger-Danner Franz Steininger Peter Klimek Improving lameness detection in cows: A machine learning algorithm application Journal of Dairy Science data integration disease prediction machine learning precision livestock farming lameness |
| title | Improving lameness detection in cows: A machine learning algorithm application |
| title_full | Improving lameness detection in cows: A machine learning algorithm application |
| title_fullStr | Improving lameness detection in cows: A machine learning algorithm application |
| title_full_unstemmed | Improving lameness detection in cows: A machine learning algorithm application |
| title_short | Improving lameness detection in cows: A machine learning algorithm application |
| title_sort | improving lameness detection in cows a machine learning algorithm application |
| topic | data integration disease prediction machine learning precision livestock farming lameness |
| url | http://www.sciencedirect.com/science/article/pii/S0022030224011457 |
| work_keys_str_mv | AT elmadervic improvinglamenessdetectionincowsamachinelearningalgorithmapplication AT casparmatzhold improvinglamenessdetectionincowsamachinelearningalgorithmapplication AT christaeggerdanner improvinglamenessdetectionincowsamachinelearningalgorithmapplication AT franzsteininger improvinglamenessdetectionincowsamachinelearningalgorithmapplication AT peterklimek improvinglamenessdetectionincowsamachinelearningalgorithmapplication |