Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Bovine Respiratory Disease Complex*/diagnosis ; Social Behavior*; Animals ; Cattle ; Machine Learning ; Behavior, Animal/physiology ; Weaning ; Feeding Behavior ; Early Diagnosis ; Movement ; Female

Previous research shows that feeding and activity behaviours in combination with machine learning algorithms has the potential to predict the onset of bovine respiratory disease (BRD). This study used 229 novel and previously researched feeding, movement, and social behavioural features with machine learning classification algorithms to predict BRD events in pre-weaned calves. Data for 172 group housed calves were collected using automatic milk feeding machines and ultrawideband location sensors. Health assessments were carried out twice weekly using a modified Wisconsin scoring system and calves were classified as sick if they had a Wisconsin score of five or above and/or a rectal temperature of 39.5 °C or higher. A gradient boosting machine classification algorithm produced moderate to high performance: accuracy (0.773), precision (0.776), sensitivity (0.625), specificity (0.872), and F1-score (0.689). The most important 30 features were 40% feeding, 50% movement, and 10% social behavioural features. Movement behaviours, specifically the distance walked per day, were most important for model prediction, whereas feeding and social features aided in the model's prediction minimally. These results highlighting the predictive potential in this area but the need for further improvement before behavioural changes can be used to reliably predict the onset of BRD in pre-weaned calves.
(© 2024. The Author(s).)

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BB/W020459/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council; BB/W020459/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council; BB/W020459/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council; BB/T0083690/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council; BB/W020459/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council

Keywords: Animal health; Calf; Machine learning; Precision livestock farming; Respiratory disease

Date Created: 20240428 Date Completed: 20240428 Latest Revision: 20240501

20240501

PMC11056383

10.1038/s41598-024-58206-4

38679647