An approach based on digital image analysis to estimate the live weights of pigs in farm environments

• We develop a machine vision-based method to estimate the live weight of pigs.
• The proposed approach provides circumstance-free image processing.
• It is practical to apply on farms without interrupting the routine life of pigs.

In this study, an estimation system for the live weights of pigs is proposed that could be practically employed in a real farm environment without disturbing the animals. This approach is based on computer-assisted visual image capture and a supervised learning algorithm known as vector-quantized temporal associative memory (VQTAM). The method is composed of three parts, which are boundary detection, feature extraction, and pattern recognition. To identify an image’s edge, a method that is based on user interaction via mouse-clicking on the pig image is employed to avoid edge detection errors if the pig’s image and its background are not in contrast. Two image features, (1) the average distance from the pig’s centroid to the boundary points and (2) the pig’s perimeter length, are extracted and used as the inputs of VQTAM. Next, the solutions from VQTAM are improved by an autoregressive model (AR) and locally linear embedding (LLE). This approach has been examined using a specific farm for a case study. The results indicate that the method based on VQTAM and improved by LLE provides the most accurate prediction with an error rate of less than 3% on average.