Testing the potential of the Sow Stance Information System (SowSIS) based on a force plate system built into an electronic sow feeder for on-farm automatic lameness detection in breeding sows
| Autor: | Annelies Van Nuffel, Stephanie Van Weyenberg, Jürgen Vangeyte, Frank Tuyttens, Bart Ampe, Sam Millet, Jarissa Maselyne, Pieterjan De Geest, Olga Szczodry, Petra Briene |
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| Rok vydání: | 2021 |
| Předmět: |
010401 analytical chemistry
Soil Science 04 agricultural and veterinary sciences 01 natural sciences Predictive value 0104 chemical sciences Gait (human) Control and Systems Engineering Lameness Statistics 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Agronomy and Crop Science Food Science Mathematics |
| Zdroj: | Biosystems Engineering. 204:270-282 |
| ISSN: | 1537-5110 |
| DOI: | 10.1016/j.biosystemseng.2021.01.024 |
| Popis: | Lameness is a common problem in breeding sows, which often goes undetected for long periods with severe consequences for animal welfare and farm productivity. Automatic lameness detection could help pig farmers to recognise and treat lameness sooner. The SowSIS (Sow Stance Information System) is a device consisting of four force plates and providing non-invasive force measurements per leg of the sow. In this study, the SowSIS was built into electronic sow feeders and validated for lameness detection in group-housed gestating sows. Data was automatically collected for 71 sows. Visual gait scoring was performed twice a week using a 150-mm tagged visual analogue scale to determine the sows' lameness status. Only data from 32 gait scoring days were included, adding up to 674 sow days. A sow was classified as lame using >60 mm as the cut-off value for the visual gait scores. Stance variables were calculated from the SowSIS data per sow per day. First, a multivariable linear mixed model was used to detect lameness, using stance variables with significant influence on the gait score. The model's performance was 78.5% sensitivity, 81.4% specificity, 80.7% accuracy, 57.4% lame predictive value and 92.2% non-lame predictive value. Second, five types of classification models were tested to determine the lame leg on a sub-dataset. The random forest model could predict the lame leg correctly 90% of the time. The SowSIS shows great promise as an on-farm lameness detection system, as it allows continuous non-invasive data collection in a practical setting. |
| Databáze: | OpenAIRE |
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