The use of an embedded microcomputer-based force plate system for accurate sow lameness identification

摘要

The objectives of this thesis were: i) to determine the minimum time required to record data from each individual load cell in the force plate system in order to obtain accurate sow weight distributions on each leg to objectively detect lameness, and ii) to develop a lameness detection decision tree from the force plate output collected in a commercial setting. In the first study, lameness was induced in 12 multiparous sows using a chemical synovitis model. Weight applied to each foot was recorded twice per second for 15 min on days -1, +1, +6, and +10 relative to lameness induction. Results suggest that there could be potential data collection problems after 12 min; therefore, 10 min was considered the maximum time required for weight recordings. Utilizing a 30 sec burn-in period to allow sows to become acquainted with the force plate, 30 to 210 sec was the time period that had the best combination of different readings and speed of collection compared to 30 to 630 sec. In the second study, one force plate was installed under an electronic sow feeder (ESF) in a dynamic group sow housing system with 120 multiparous sows for 21 days. Force applied by each foot was recorded once per second after the sow stood squarely on the plate and applied pressure to all quadrants during her first daily visit to the ESF. Sows were visually lameness scored using a four-point scale on a weekly basis. A decision tree was created using the variables that were deemed as more important for accurate lameness detection. The classification tree was 96% similar to weekly visual lameness identification. When comparing the output from the daily classification tree to a weekly visual lameness assessment, the force plate was able to identify lameness almost 5 days before it was visually assessed. Results from this thesis can be used to improve the embedded microcomputer-based force plate use efficiency when evaluating sow lameness and could help to identify lameness before clinical signs become evident.