Accuracy of body measurements using digital image analysis in female Holstein calves
Serkan OzkayaDepartment of Animal Science, Faculty of Agriculture, Suleyman Demirel University, Isparta, Turkey. Email: serkanozkaya@sdu.edu.tr
Animal Production Science 52(10) 917-920 https://doi.org/10.1071/AN12006
Submitted: 10 January 2012 Accepted: 2 April 2012 Published: 16 July 2012
Abstract
The objective of this study was to determine the accuracy of body measurements (BM) in Holstein female calves using digital image analysis. BM including body length, wither height, chest depth, hip height, and hip width of calves were recorded by stick and tape measurements at birth, weaning and 24 weeks of age. Then photos of calves were taken while calves were standing in a squeeze chute by a digital camera and were analysed by image analysis software to obtain BM of each calf from the image in centimetres. After comparing the actual and predicted BM, the accuracy was determined as 71, 97 and 99% for body length, 69, 87 and 99% for wither height, 43, 98 and 99% for chest depth, 74, 99 and 99% for hip height and 53, 99 and 98% for hip width at birth, weaning and 24 weeks of age, respectively. The difference between actual and predicted BM was significant at birth (P < 0.01). Although there were numerical differences between actual and predicted BM, the differences were not significant at weaning and 24 weeks of age (P > 0.01). According to these results, the BM estimation of Holstein female calves using digital image analysis produced high prediction accuracy at weaning and 24 weeks of age, but not at birth. The data presented in this study indicate that the digital image analysis provides very close agreement and reality for prediction of BM of Holstein female calves.
References
Aktan S (2004) Determining storage related egg quality changes via digital image analysis. South African Journal of Animal Science 34, 70–74.| Determining storage related egg quality changes via digital image analysis.Crossref | GoogleScholarGoogle Scholar |
Bewley JM, Peacock AM, Lewis O, Boyce RE, Roberts DJ, Coffey MP, Kenyon SJ, Schutz MM (2008) Potential for estimation of body condition score in dairy cattle from digital images. Journal of Dairy Science 91, 3439–3453.
| Potential for estimation of body condition score in dairy cattle from digital images.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtFamsrfK&md5=7c7d8847ad855c67801241917c4bd468CAS |
Bozkurt Y, Ozkaya S (2005) An assessment of the ARC metabolizable energy system to predict live weight gain of Brown Swiss cattle grown under feedlot conditions in Turkey. The Journal of Biological Sciences 5, 411–416.
| An assessment of the ARC metabolizable energy system to predict live weight gain of Brown Swiss cattle grown under feedlot conditions in Turkey.Crossref | GoogleScholarGoogle Scholar |
Bozkurt Y, Aktan S, Ozkaya S (2007) Body weight prediction using digital image analysis for slaughtering beef cattle. Journal of Applied Animal Research 32, 195–198.
| Body weight prediction using digital image analysis for slaughtering beef cattle.Crossref | GoogleScholarGoogle Scholar |
Cannell RC, Belk KE, Tatum JD, Wise JW, Chapman PL, Scanga JA, Smith GC (2002) Online evaluation of a commercial video image analysis system (Computer vision system) to predict beef carcass red meat yield and for augmenting the assignment of USDA yield grades. Journal of Animal Science 80, 1195–1201.
De Wet L, Vranken E, Chedad A, Aerts JM, Ceunen J, Berckmans D (2003) Computer-assisted image analysis to quantify daily growth rates of broiler chickens. British Poultry Science 44, 524–532.
| Computer-assisted image analysis to quantify daily growth rates of broiler chickens.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD3srgvVSqsg%3D%3D&md5=be051369643d8ab371af80686407aba6CAS |
Karnuah AB, Moriya K, Nakanishi N, Nade T, Mitsuhashi T, Sasaki Y (2001) Computer image analysis for prediction of carcass composition from carcass-section of Japanese Black steers. Journal of Animal Science 79, 2851–2856.
Kuchida K, Suzuki K, Yamaki K, Shirohara H, Yamagishi T (1991) Prediction for chemical component of pork meat by personal computer color image analysis. Animal Science and Technology 62, 477–479.
McDonald T, Chen YR (1990) Separating connected muscle tissues in image of beef carcass rib eyes. Transactions of the ASAE. American Society of Agricultural Engineers 33, 187–193.
Minitab (2001) ‘Institute Inc. Minitab user’s guide. Release 13 for windows.’ (Minitab Inc.: State Collage, PA)
Mollah BR, Hasan A, Salam A, Ali A (2010) Digital image analysis to estimate the live weight of broiler. Computers and Electronics in Agriculture 72, 48–52.
| Digital image analysis to estimate the live weight of broiler.Crossref | GoogleScholarGoogle Scholar |
Negretti P, Bianconi G (2007) Evaluation of horse live weight (Sella Italiano) by visual image analysis. Convegno Nuove acquisizioni in material di Ippologia 9, 151–157.
Negretti P, Bianconi G, Finzi A (2007) Visual image analysis to estimate morphological and weight measurements in rabbits. World Rabbit Science 15, 37–41.
Negretti P, Bianconi G, Bartocci S, Terramoccia S, Verna M (2008) Determination of live weight and body condition score in lactating Mediterranean buffalo by Visual Image Analysis. Livestock Science 113, 1–7.
| Determination of live weight and body condition score in lactating Mediterranean buffalo by Visual Image Analysis.Crossref | GoogleScholarGoogle Scholar |
Negretti P, Bianconi G, Bartocci S, Terramoccia S, Noe L (2011) New morphological and weight measurements by visual image analysis in sheep and goats. New trend for innovation in the Mediterranean animal production EAAP publication 129, 227–232.
| New morphological and weight measurements by visual image analysis in sheep and goats.Crossref | GoogleScholarGoogle Scholar |
Ozkaya S (2006) Prediction of live weight and carcass performance of beef cattle by using digital image analysis and comparing other prediction models. Masters Thesis, The Suleyman Demirel University of Institute of Science and Technology, Isparta, Turkey.
Ozkaya S, Bozkurt Y (2008) The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle. Archives of Animal Breeding. Dummerstorf 51, 120–128.
Rook AJ, Dhanoa MS, Gill M (1990) Prediction of voluntary intake of grass silages by beef cattle. 3. Precision of alternative prediction models. Animal Production 50, 455–466.
| Prediction of voluntary intake of grass silages by beef cattle. 3. Precision of alternative prediction models.Crossref | GoogleScholarGoogle Scholar |
Shackelford SD, Wheeler TL, Koohmaraie M (1998) Coupling if image analysis and tenderness classification to simultaneously evaluate carcass cutability, longissimus area, subprimal cut weights and tenderness of beef. Journal of Animal Science 76, 2631–2640.
Shiranita K, Miyajima T, Takiyama R (1998) Determination of meat quality by texture analysis. Pattern Recognition Letters 19, 1319–1324.
| Determination of meat quality by texture analysis.Crossref | GoogleScholarGoogle Scholar |
Stajnko D, Brus M, Hocevar M (2008) Estimation of bull live weight through thermographically measured body dimensions. Computers and Electronics in Agriculture 61, 233–240.
| Estimation of bull live weight through thermographically measured body dimensions.Crossref | GoogleScholarGoogle Scholar |
Tasdemir S, Urkmez A, Inal S (2011) Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Computers and Electronics in Agriculture 76, 189–197.
| Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis.Crossref | GoogleScholarGoogle Scholar |
Teira GA, Tinois E, Lotufo RA, Felicio PE (2003) Digital image analysis to predict weight and yields of boneless subprimal beef cuts. Science Agriculture 60, 403–408.
Wang Y, Yang W, Winter P, Walker L (2008) Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering 100, 117–125.
| Walk-through weighing of pigs using machine vision and an artificial neural network.Crossref | GoogleScholarGoogle Scholar |
