The effectiveness of a visual image analysis (VIA) system for monitoring the performance of growing/finishing pigs

Abstract

AbstractA visual image analysis (VIA) system provided continuous, automatic collection of size and shape data for a total of 116 pigs slaughtered serially from 25 to 115 kg live weight. Males and females of three types of pigs (‘Meishan’ type, ‘Pietrain’ type, and ‘Landrace’ type) were selected to provide variation in both composition and conformation (the three types being, respectively, ‘fat’, ‘blocky’, and ‘lean’). Results below are presented in this order. Regression analysis was used to relate VIA size to platform weigher (FIRE) measurements of live weight. Residual maximum likelihood (REML) analysis showed that at the observed growth rate, a change in pig state could be detected by VIA after 8, 9, and 10 days respectively for the three types, and by the platform weigher system after 12, 4, and 13 days (in both cases with a confidence of 95%). Artificial neural network and canonical variates analysis were used to test the ability of VIA to distinguish between pig types and sexes. With cross validation, the canonical variates analysis correctly classified the three types in 72, 83, and 64% of observations, and the neural network in 81, 81, and 64% of observations. The VIA system is considered to be a valuable monitoring system which may play a rôle in the construction of integrated management systems (IMS).