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Report, 2016

Cow claws on concrete slats in the waiting area in a dairy barn estimated by use of Image analysis

Ardö, Håkan; Guzhva, Oleksiy; Nilsson, Mikael; Herlin, Anders Henrik

Abstract

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 dimension of the load strength of slats is based on assumptions and experience. An alternative approach is to estimate the true load of the animals on the slats by observation of animal distribution on slatted floors. 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 (size 6x18 m) 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 claws might be located was constructed. By using this distribution in a Monte Carlo simulation, a probability distribution of the number of claws on each slat could be estimated, and from that, a worst-case estimate of the actual weight load was constructed. Results indicate that the 95% percentile number of claws on 160 mm wide slat area (slat width including the opening) was estimated to 3.03 and on a 560 mm slat area width was 5.63. Cows mounting was found in 7 of 9215 (0.2 %) examined pictures. The method proposed in this report was promising and for this purpose and could be used for practical assessment of animal distribution and loading and thus be a part of the dimensioning of construction.

Keywords

dairy barn flooring; deep learning; weight distribution; standards for concrete; precision livestock farming

Published in


Publisher: Department of Biosystems and Technology, Swedish University of Agricultural Sciences

Authors' information

Ardö, Håkan
Lund University
Swedish University of Agricultural Sciences, Department of Biosystems and Technology
Nilsson, Mikael
Lund University
Swedish University of Agricultural Sciences, Department of Biosystems and Technology

UKÄ Subject classification

Other Materials Engineering
Animal and Dairy Science

URI (permanent link to this page)

https://res.slu.se/id/publ/80706