Body weight prediction in crossbred pigs from digital images using computer vision

Abstract

The development of technologies made it possible to capture digital images by sensors in order to obtain morphometric measurements that can be used in the development of mathematical models for the prediction of body weight (BW) in animals. The objective was to develop mathematical models to predict body weight in crossbred pigs from morphometric measurements obtained with two-dimensional digital images. Data collection was carried out in the swine sector of IF Goiano, Urutai campus, in 52 crossbred pigs housed in the finishing phase. The animals were randomly weighed and filmed by the Microsoft Kinect® v1 sensor camera in the dorsal view. Subsequently, they were conducted for the measurement of manual morphometric measurements, without restraint, being dorsal length, chest width and flank width. The morphometric measurements predicted from the dorsal images were dorsal length, chest width, flank width, dorsal area and perimeter. Through Mask R-CNN algorithm trains the network by input images, was compute the area of the evaluated object in pixels, and thus, can correlate with measures of interest. The Mask R-CNN algorithm were performed with the Collaboratory Google using Phyton 3.0. Mathematical models were developed using multiple linear regression (MLR) and support vector regression (SVR) methodologies for body weight prediction. All Pearson's correlations between real body weight and real and predicted morphometric measurements by digital images were moderate to high magnitude positive and statistically significant (P<0.05). The difference between the real and predicted BW by the SVR was -1.74 kg (R² = 91%) and -2.39 kg (R² = 88%), respectively. The MLR model with real morphometrics measurements explained 60% of BW variance and estimated a BW 18.76 kg below from the real BW mean. The MLR model with predicted measurements explained 53% of BW variance and estimated a BW 22.51 kg below from the real BW mean. The mathematical model developed from SVR has the potential to estimate the body weight of crossbred pigs using morphometric measurements predicted by digital images such as dorsal length, width, dorsal area and perimeter.