Abstract
Automatic body condition scoring system for dairy cows based on depth-image analysis
Highlights
The metabolizable energy stored in fat and muscle is vital to maintaining dairy cows
The Body condition score (BCS) regression model was fitted to a training dataset (DT), and the result was as follows: BCS=3.94–0.35×visibility of the thurl (VTH)–0.71×VSL–0.67×CHB–0.72×CPB (5) The parameters in the model showed that the highest theoretical score in the herd was 3.94 and that a sharp thurl, sacral ligament, hook bone, and pin bone will reduce the BCS by 0.35, 0.71, 0.67, and 0.72, respectively
The BP network achieved higher accuracy than linear regression regarding all the performance indicators, especially the proportion of results within 0.25 BCS points of manual scores; in this respect, the BP network was over 5% more accurate than linear regression
Summary
The metabolizable energy stored in fat and muscle is vital to maintaining dairy cows. BCS evaluations can be used to determine whether a cow is in the proper condition for each stage of the lactation cycle. At dry-off, parturition, and throughout the lactation cycle, BCS evaluations can be used to identify cows that are at risk of milk fever, mastitis, lameness, and infertility[9]; BCS is an important management tool for maximizing milk production and reproductive efficiency[9,10] and even preventing potential disease and lameness[6,11]. The first 1200 camera depth frames without cows were used to build the background image. The depth value of each pixel on the cow body was converted into the distance to the floor by adding the height of the camera, which was 3050 mm. The symmetry of the rotated cow was tested by calculating the overall difference between the left and right of the cow as defined by the line of symmetry[24]
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