Cows on concrete slats of the waiting area in a dairy barn estimated by use of image analysis

Slatted concrete floors are commonly used in dairy barns for aisles, feeding and waiting areas. Maximum slot opening in Sweden is 35 mm with a maximum of 28% opening area for adult cattle in order to provide the adequate claw support. The construction of the slats has to consider this together with the length of the slats and the load from the weight of the animals on the slats. Presently, the calculation of strength of slats assumes construction of multiple slat units instead of single beams. There is presently no use of empirical data on the distribution of animals’ claws on the surface to estimate the load of the animals on the slat beams. The purpose of this study was to investigate possibilities of using machine learning algorithms and image analysis for assessing actual distribution of animals in the areas of interest and maximal weight load per slat element per unit of time. Images for the study were acquired from three surveillance cameras placed in the ceiling above the common waiting area with entrances to four automatic milking systems (AMS). Then images were used to train a convolutional neural net classifier to detect and locate the cows in the images. Then, a probability distribution of where the hooves might be located was constructed. By using this distribution in a Monte Carlo simulation, a probability distribution of the number of hooves on each slat was estimated, and from that, a worst-case estimate of the actual weight load was constructed.