No abstract

Stockman or farmers always have difficulty recognition of pig mass in their farms. The typical approach is to approximate from age of pigs, daily-given feed, or from experience of human vision. Another practical approach to instantly measure mass of pigs is to use machine vision. The objective of this paper is to use a developed machine vision to analyze pig mass for detection of size and weight of pigs in farm. The pig mass is processed from physical features captured from digital image and their liveweights are approximated from artificial neural network. This neural network model is based on vector-quantized temporal associative memory (VQTAM) and locally linear embedding (LLE). The elementary results showed that the mass approximation of pig weight had acceptable accuracy and it was practical in pig farms.

In this study, artificial neural network with a supervised learning algorithm called vector-quantized temporal associative memory (VQTAM) is proposed to estimate chilled weight loss during chilling process of pig slaughtering plant. Four models based on carcass weights are developed. The results show that the proposed algorithms can accurately predict chilled weight loss with an error rate of less than 5% on average. The models are also employed to determine the suitable chilling times for each weight class.