AUTOMATED WEIGHING OF GROUP-HOUSED, GROWING-FINISHING SWINE

A system for automated acquisition of the mean pig weight from a group of growing-finishing animals was developed and tested. Each time an a nimal entered a crate to obtain a drink of water, a time series of wei ghts was recorded. A stare machine representation was implemented to c apture weight events and to put time series weight recordings into a q ueue. Three algorithms (mean, histogram, and median) were devised and evaluated to extract animal weight from the time series recording. Fou r tests were performed-short-term tests included test 1 with 12 animal s of 64.5 to 96.4 kg initial weight; test 2 with 8 animals of 70.9 to 75 kg; test 3 with 3 individual animals of 45, 68, and 93 kg; and test 4 was for a full growth period with 10 animals. The first three exper iments were used to develop and refine the system, and test 4 was used to assess the long-term performance and feasibility. Comparisons betw een estimated weight and ''spot checks'' with a mechanical scale were typically within about +/-1% over test 4. The histogram algorithm was found to be the best for estimating average weight. Accuracy was not a ffected by variation in animal weight (tests 1 and 2); however, differ ent length data windows affected accuracy and a 4-h window was best. A time series queue of at most 150 s of data was found to be adequate a nd the histogram algorithm was successful with as few as 10 data point s. Locating the sole water supply within the weigh crate did not affec t growth rate compared to a control group. A change rate (CR) index wa s devised to compare daily activity and to flag pen health problems.