Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T17:58:33.717Z Has data issue: false hasContentIssue false
  • English
  • Français

Assessing the potential of photogrammetry to monitor feed intake of dairy cows

Published online by Cambridge University Press:  18 February 2019

Victor Bloch ,
Harel Levit  and
Ilan Halachmi
Victor Bloch
Affiliation:
Precision Livestock Farming Lab, Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel
Harel Levit
Affiliation:
Precision Livestock Farming Lab, Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel
Ilan Halachmi*
Affiliation:
Precision Livestock Farming Lab, Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel
*
Authors for correspondence: Ilan Halachmi, Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We address the hypothesis that individual cow feed intake can be measured in commercial farms through the use of a photogrammetry method. Feed intake and feed efficiency have a significant economic value for the farmer. A common method for measuring feed mass in research is a feed mass weighing system, which is excessively expensive for commercial farms. However, feed mass can be estimated by its volume, which can be measured by photogrammetry. Photogrammetry applies cameras along the feed-lane, photographing the feed before and after the cow visits the feed-lane, and calculating the feed volume. In this study, the precision of estimating feed mass by its volume was tested by comparing measured mass and calculated volume of feed heaps. The following principal factors had an impact on the precision of this method: camera quality, lighting conditions, image resolution, number of images, and feed density. Under laboratory conditions, the feed mass estimation error was 0·483 kg for heaps up to 7 kg, while in the cowshed the estimation error was 1·32 kg for up to 40 kg. A complementary experiment showed that the natural feed compressibility causes about 85% of uncertainty in the mass estimation error.

Keywords

Dairy cowindividual cow feed intakecow feed mass estimationphotogrammetryfeed compressibilityprecision livestock farming
Type
Research Article
Information
Journal of Dairy Research , Volume 86 , Issue 1 , February 2019 , pp. 34 - 39
Copyright
Copyright © Hannah Dairy Research Foundation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bach, A, Iglesias, C & Busto, I (2004) Technical note: A computerized system for monitoring feeding behavior and individual feed intake of dairy cattle. Journal of Dairy Science 87 4207–4209.Google Scholar
Borchersen, S, Hansen, NW & Borggaard, C (2018) System for determining feed consumption of at least one animal. Google Patents.Google Scholar
Buza, MH, Holden, LA, White, RA & Ishler, VA (2014) Evaluating the effect of ration composition on income over feed cost and milk yield. Journal of Dairy Science 97 3073–3080.Google Scholar
Chapinal, N, Veira, DM, Weary, DM & von Keyserlingk, MA (2007) Technical note: validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. Journal of Dairy Science 90 5732–5736.Google Scholar
DeVries, TJ, von Keyserlingk, MA, Weary, DM & Beauchemin, KA (2003) Technical note: validation of a system for monitoring feeding behavior of dairy cows. Journal of Dairy Science 86 3571–3574.Google Scholar
Ferris, CP, Keady, TWJ, Gordon, FJ & Kilpatrick, DJ (2006) Comparison of a Calan gate and a conventional feed barrier system for dairy cows: feed intake and cow behaviour. Irish Journal of Agricultural and Food Research 45 149–156.Google Scholar
Halachmi, I, Edan, Y, Maltz, E, Peiper, UM, Moallem, U & Brukental, I (1998) A real-time control system for individual dairy cow food intake. Computers and Electronics in Agriculture 20 131–144.Google Scholar
Halachmi, I, Meir, YB, Miron, J & Maltz, E (2016) Feeding behavior improves prediction of dairy cow voluntary feed intake but cannot serve as the sole indicator. Journal of Animal Science 10 1501–1506.Google Scholar
Herd, RM, Archer, JA & Arthur, PF (2003) Reducing the cost of beef production through genetic improvement in residual feed intake: opportunity and challenges to application. Journal of Animal Science 81 9–17.Google Scholar
Gonzalez, LA, Tolkamp, BJ, Coffey, MP, Ferret, A & Kyriazakis, I (2008) Changes in feeding behavior as possible indicators for the automatic monitoring of health disorders in dairy cows. Journal of Dairy Science 91 1017–1028.Google Scholar
Krawczel, PD, Klaiber, LM, Thibeau, SS & Dann, HM (2012) Technical note: data loggers are a valid method for assessing the feeding behavior of dairy cows using the Calan Broadbent Feeding System. Journal of Dairy Science 95 4452–4456.Google Scholar
Lassen, J, Thomasen, JR, Hansen, RH, Nielsen, GGB, Olsen, E, Stentebjerg, PRB, Hansen, NW & Søren, B (2018) Individual measure of feed intake on in-house commercial dairy cattle using 3D camera system. In The World Congress on Genetics Applied to Livestock Production vol. Technologies - Novel Phenotypes, p. 635, (Ed. Blair, H). Auckland, New Zealand.Google Scholar
Mendes, EDM, Carstens, GE, Tedeschi, LO, Pinchak, WE & Friend, TH (2011) Validation of a system for monitoring feeding behavior in beef cattle. Journal of Animal Science 89 2904–2910.Google Scholar
Mikhail, EM, Bethel, JS & McGlone, JC (2001) Introduction to Modern Photogrammetry. John Wiley & Sons, New York.Google Scholar
National Research Council (2001) Nutrient Requirements of Dairy Cattle: Seventh Revised Edition. Washington, DC: The National Academies Press.Google Scholar
National Research Council (2007) Nutrient Requirements of Small Ruminants: Sheep, Goats, Cervids, and New World Camelids. Washington, DC: The National Academies Press.Google Scholar
Shelley, AN (2013) Monitoring Dairy Cow Feed Intake Using Machine Vision. Theses and Dissertations – Electrical and Computer Engineering, University of Kentucky, University of Kentucky, Lexington, Kentucky, USA. Paper 24.Google Scholar
Shelley, AN, Lau, DL, Stone, AE & Bewley, JM (2016) Short communication: measuring feed volume and weight by machine vision. Journal of Dairy Science 99 386–391.Google Scholar
Stajnko, D, Vindiš, P, Marjan, J & Maksimiljan, B (2015) Non Invasive Estimating of Cattle Live Weight Using Thermal Imaging. New Trends in Technologies: Control, Management, Computational Intelligence and Network Systems. Chapter 13.Google Scholar
Vandehaar, MJ (1998) Efficiency of nutrient use and relationship to profitability on dairy farms. Journal of Animal Science 81 272–282.Google Scholar
Volden, H (ed.) (2011) NorFor-The Nordic Feed Evaluation System. The Netherlands: Wageningen Academic Publishers.Google Scholar
Wang, Z, Nkrumah, JD, Li, C, Basarab, JA, Goonewardene, LA, Okine, EK, Crews, DH & Moore, SS (2006) Test duration for growth, feed intake, and feed efficiency in beef cattle using the GrowSafe System. Journal of Animal Science 84 2289–2298.Google Scholar
Supplementary material: PDF

Bloch et al. supplementary material

Tables S1-S4 and Figure S1

Download Bloch et al. supplementary material(PDF)
PDF 291.7 KB