Estimating pig weights from images without constraint on posture and illumination

This paper proposes an image based pig weight estimation method different from the previous works in three ways. The first difference is no constraint on pig posture and image capture environment, reducing the stress of the pigs. The second one is that the features obtained from 2D images are used without depending on 3D depth information. And the third is that our estimation model is constructed by exploiting the recent advances in machine learning. Besides the pig area size which had been a major feature parameter for the estimation, two new features, curvature and deviation, are introduced because those are related with the postures, thus being able to quantify the weight adjustment. A set of experiments are conducted to investigate how the performance is affected by the combination of the features and the neural network configurations. By using 477 training and 103 test images, the average estimation error of 3.15 kg was achieved, and the coefficient of determination of the model was R2=0.79.