Pig growth and conformation monitoring using image analysis

Machine vision can be used to collect images of pigs and analyse them to identify and measure specific areas and dimensions related to their growth, shape and hence conformation. This information could improve the stockman's ability to maximize production efficiency and also to monitor health by defecting abnormalities in growth rates. This work introduces fully automated algorithms which find the plan view outline of animals in a normal housing situation, divide the outline into major body components and measure specified dimensions and areas. Special attention is paid to determining whether the results are sufficiently repeatable to be useful in estimating these parameters. Problems in compensating for changes in the optical geometry are outlined and methods proposed to deal with them. The repeatability of the image analysis process coupled with the subsequent signal processing for outlier rejection gives s.e. values on areas of < 0.005 and on linear dimensions of < 0.0025. For example the plan view area less head and neck (A4) can be used to predict the weight of the group of pigs at 34 kg, 66 kg and 98 kg with standard errors of 0.25 kg, 0.17 kg and 0.39 kg respectively when using manual weighing results to calibrate the system. If an individual pig is weighed once at 75 days (e.g. 34 kg) to calibrate the A4-to-weight relationship, subsequent A4 measurements can be used to predict its weight when 125 days old (approx. 80 kg) to within l kg. This matches the accuracy of the manual weighing system used in the trials. The effect of pig gender on the area to weight relationships is not significant (P = 0.074), but there is a small yet significant gender effect with the linear dimensions.