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Journal of Dairy Science
Bercovich, A., Department of Industrial Engineering and Management, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
Edan, Y., Department of Industrial Engineering and Management, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
Alchanatis, V., Institute of Agricultural Engineering, Bet Dagan 50250, Israel
Moallem, U., Institute of Animal Science, ARO, The Volcani Center, PO Box 6, Bet Dagan 50250, Israel
Parmet, Y., Department of Industrial Engineering and Management, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
Honig, H., Institute of Animal Science, ARO, The Volcani Center, PO Box 6, Bet Dagan 50250, Israel
Maltz, E., Institute of Agricultural Engineering, Bet Dagan 50250, Israel
Antler, A., Institute of Agricultural Engineering, Bet Dagan 50250, Israel
Halachmi, I., Institute of Agricultural Engineering, Bet Dagan 50250, Israel
Body condition evaluation is a common tool to assess energy reserves of dairy cows and to estimate their fatness or thinness. This study presents a computer-vision tool that automatically estimates cow's body condition score. Top-view images of 151 cows were collected on an Israeli research dairy farm using a digital still camera located at the entrance to the milking parlor. The cow's tailhead area and its contour were segmented and extracted automatically. Two types of features of the tailhead contour were extracted: (1) the angles and distances between 5 anatomical points; and (2) the cow signature, which is a 1-dimensional vector of the Euclidean distances from each point in the normalized tailhead contour to the shape center. Two methods were applied to describe the cow's signature and to reduce its dimension: (1) partial least squares regression, and (2) Fourier descriptors of the cow signature. Three prediction models were compared with manual scores of an expert. Results indicate that (1) it is possible to automatically extract and predict body condition from color images without any manual interference; and (2) Fourier descriptors of the cow's signature result in improved performance (R2=0.77). © 2013 American Dairy Science Association.
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Development of an automatic cow body condition scoring using body shape signature and Fourier descriptors
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Bercovich, A., Department of Industrial Engineering and Management, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
Edan, Y., Department of Industrial Engineering and Management, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
Alchanatis, V., Institute of Agricultural Engineering, Bet Dagan 50250, Israel
Moallem, U., Institute of Animal Science, ARO, The Volcani Center, PO Box 6, Bet Dagan 50250, Israel
Parmet, Y., Department of Industrial Engineering and Management, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
Honig, H., Institute of Animal Science, ARO, The Volcani Center, PO Box 6, Bet Dagan 50250, Israel
Maltz, E., Institute of Agricultural Engineering, Bet Dagan 50250, Israel
Antler, A., Institute of Agricultural Engineering, Bet Dagan 50250, Israel
Halachmi, I., Institute of Agricultural Engineering, Bet Dagan 50250, Israel
Development of an automatic cow body condition scoring using body shape signature and Fourier descriptors
Body condition evaluation is a common tool to assess energy reserves of dairy cows and to estimate their fatness or thinness. This study presents a computer-vision tool that automatically estimates cow's body condition score. Top-view images of 151 cows were collected on an Israeli research dairy farm using a digital still camera located at the entrance to the milking parlor. The cow's tailhead area and its contour were segmented and extracted automatically. Two types of features of the tailhead contour were extracted: (1) the angles and distances between 5 anatomical points; and (2) the cow signature, which is a 1-dimensional vector of the Euclidean distances from each point in the normalized tailhead contour to the shape center. Two methods were applied to describe the cow's signature and to reduce its dimension: (1) partial least squares regression, and (2) Fourier descriptors of the cow signature. Three prediction models were compared with manual scores of an expert. Results indicate that (1) it is possible to automatically extract and predict body condition from color images without any manual interference; and (2) Fourier descriptors of the cow's signature result in improved performance (R2=0.77). © 2013 American Dairy Science Association.
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