Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns

Abstract

As herd sizes have increased in the last decades, computerized monitoring solutions, which provide fast, objective and accurate evaluations of the herd status, gain more and more importance. This study analyzes the feasibility of a Time-of-Flight-camera-based system for gathering body traits in dairy cows for use under cow barn conditions. Recording, determination of body condition score on a 5 point scale by visual and manual inspection, and measuring the backfat thickness with ultrasound took place from July 2011 to May 2012 at the dairy research farm Karkendamm of the Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel (Germany) and between August 2010 and July 2012 at the Institute for Agricultural Engineering and Animal Husbandry of Bavarian State Research Center for Agriculture in Grub (Germany). The two breeds Holstein Friesian cows (Karkendamm) and Fleckvieh (Grub) were considered in this study. Software for recording, image sorting and evaluation, determining the body parts needed, and extracting traits from the images was written and assembled to an automated system. Sorting the images and finding ischeal tuberosities, base of the tail, and dishes of the rump, backbone, and hips had error rates of 0.2%, 1.5%, 0.1%, and 2.6%, respectively. 13 traits were extracted and compared to backfat thickness and body condition score as well as between breeds. All traits depend significantly on the animal and showed very large effect sizes. Coefficients of determination restricted to individual animals were reaching up to 0.89. The precision in measuring the traits and gathering backfat thickness was comparable. Results indicated that the application of Time-Of-Flight in determination of body traits is feasible.

Development of automatic body condition scoring using a low-cost 3-dimensional Kinect camera

“…Several attempts have been made to automate the BCS process, mainly by including an analysis of the cow's contour and shape (Sharony, 2003;Peacock et al, 2006;Halachmi et al, 2008;Bercovich et al, 2013;Salau et al, 2014;Tedín et al, 2014;Weber et al, 2014;Caccamo et al, 2015). However, these systems still require some level of human interference.…”

Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review

“…In the medium level are, 11,13,20 where the input images used are manually selected, but the rest of the process is automatic. Finally, in the highest level are, 14,[16][17][18][19] where the process is completely automated. Among the latter studies, only 17,18 carry out real time estimations (i.e.…”

“…In the Table 1 ii. Mostly, there are two types of models used: regression analysis models (as in) [10][11][12][13][14][15][16] and algorithms that measure cow's body angularity (as in) [17][18][19] according to the hypothesis that the body shape of a fatter cow is rounder than that of a thin cow. Moreover, three automation levels are described.…”

Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques

“…Thus, in this work models composed by one (Depth) and two (Depth and Edge) input channels were also analyzed, using the same architecture model defined in [15] (Figure 3). Then, new models were identified as follow: Other combinations of one and two input channels were not analyzed because, firstly it is important to preserve a part or a partial variation of the original data (depth data), and secondly because data corresponding to the contour of the cows (edge data) have also proven to be a determinant feature (in machine learning terms) to guide the estimation of BCS from images, as evidenced in previous works [4,[6][7][8][9]24,25].…”