Vision analysis and prediction for estimation of pig weight in slaughter pens.

The potential of using computer vision to estimate the weight of pigs has been demonstrated in several studies. In this paper, we propose a Vision Analysis and Prediction (VAP) system, which consisted of two main parts: (1) computer vision – a vision algorithm was developed to segment the pigs and identify which pig area could be used to estimate the corresponding weight; (2) statistical analysis – a statistical method for predicting pig weight using the area was presented, including BLUP model and linear regression as well as a prediction function. The results showed that our approach has successfully estimated the weight of growing pigs (approximate ranges from 20 to 105 kg) with an accuracy of weight estimation of 97.76% on average for the predicted mean weight at pen level. The proposed system does not require modifications to the building and shows a strong potential to be utilized in pig weight estimation in slaughter pens. • A VAP system has great potential for pig weight estimation in slaughter pens. • The VAP includes two parts: a computer vision algorithm and a predicting model. • The VAP can accurately predict pig weights from 20 to 105 kg in a slaughter pen. • The VAP can help for optimal decision making to improve animal productivity. [ABSTRACT FROM AUTHOR]