Prediction possibility of milk yield from udder measurements using digital image analysis on holstein cows

DOI: 10.5958/0976-0555.2015.00050.3    | Article Id: B-247 | Page : 388-391
Citation :- Prediction possibility of milk yield from udder measurements using digital image analysis on holstein cows.Indian Journal Of Animal Research.2015.(49):388-391
Address : Suleyman Demirel University, Agriculture Faculty, Department of Animal Science, 32260 Isparta, Turkey.


A photographic survey of 43 multiparous lactating cows was carried out by recording the images between morning and evening milking time (around noon time) using digital camera from posterior angle. Images were then processed using Image Tools V.3.00 software to determine Udder measurements (UMs) of cows from the images in centimetres. The linear parameters recorded were rear udder depth (UD), rear udder width (UW) and rear udder area (UA). Regression analysis of milk yield on each of the independent variables was performed. Regression coefficients, for all UMs, taken together were 65.5%. The single regression coefficients were23.0, 0.2 and 61.0for UD, UW and UA, respectively. The correlation coefficients between milk yield and UD, UW and UA were 0.48 (P >0.05), 0.04 (P >0.05) and 0.78 (P


Digital image analysis Holstein cows Milk yield Udder measurements.


  1. Berry D P, Buckley F, Dillon P, Evans R D and Veerkamp R F. (2004). Genetic relationships among linear type traits, milk yield, body weight, fertility and somatic cell count in primiparous dairy cows. Irish Journal of Agricultural and Food Research 43: 161-176.
  2. Brent Dove S. (2002). UTHSCSA Image Tool. Version 3.0. Dental Diagnostic Science. itdesc.html (Access: 07.01.2015).
  3. Cruickshank J, Weigel KA,Dentine MR and Kirkpatrick BW. (2002). Indirect prediction of herd life in Guernsey dairy cattle. Journal of Dairy Science85: 1307-1313.
  4. DeWet L, Vranken E,Chedad A,Aerts JM,Ceunen J andBerckmans D. (2003). Computer-assisted image analysis to quantify daily growth rates of broiler chickens. British Poultry Science44: 524-532.
  5. Minitab. (2001). Minitab User’s Guide. Release 13 for Windows.State Collage, PA, USA: Minitab Inc.
  6. Mollah BR, Hasan A,Salam A andAli A. (2010). Digital image analysis to estimate the live weight for broiler. Computers andElectronics in Agriculture72: 48-52.
  7. Negretti P, Bianconi G andFinzi A. (2007). Visual image analysis to estimate morphological and weight measurements in rabbits. World Rabbit Science15: 37-41.
  8. Negretti P, Bianconi G,Bartocci S,Terramoccia S andNoe L. (2011). New morphological and weight measurements visual image analysis in sheep and goats. New trend for innovation in the Mediterranean Animal Production EAAP Publication, 129: 227-232.
  9. Ozkaya S andBozkurtY. (2008). The relationship of parameters of body measure and body weight by using digital image analysis in pre slaughter cattle. ArchivTierzucht/Archives Animal Breeding51(2): 120-128.
  10. Ozkaya S. (2012). Accuracy of body measurements using digital image analysis in female Holstein calves. AnimalProduction Science52: 917-920.
  11. Ozkaya S. (2013). The prediction of live weight from body measurements on female Holstein calves by digital image analysis. The Journal ofAgricultural Science151: 570-576.
  12. Royal MD,Pryce JE,Woolliams JA andFlint APF. (2002). The genetic relationship between commencement of luteal activity and calving interval, body condition score, production and linear type traits in Holstein-Friesian dairy cattle. Journal of Dairy Science85: 3071-3080.
  13. Tasdemir S, Urkmez A andInal 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 inAgriculture76: 189-197.
  14. Teira GA, Tinois E,Lotufo RA andFelicio PE. (2003). Digital image analysis to predict weight and yields of boneless subprimal beef cuts. Scientia Agricola60(2): 403-408.
  15. Tilki M, Inal S,Colak M andGaripS. (2005). Relationships between milk yield and udder measurements in Brown Swiss cows. Turkish Journal of Veterinary and Animal Science29: 75-81.
  16. Wang Y, YangW,WinterP andWalkerL. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering100: 117-125.

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