Weight estimation system of individual broiler chickens using digital image processing and multi-regression analysis

Document Type : Research Paper

Abstract

The purpose of this study was to identify daily changes in body weight of broiler chickens using image processing and multi-regression analysis. Therefore, thirty 1-day-old broiler chickens were reared under standard rearing condition and after acquiring images they were weighted, individually. From 2490 digital images, six features (perimeter, area convex, major axis, minor axis, eccentricity) were extracted. Linear equations between body weight and these features indicated that R2 values for these features (except for eccentricity) for the individual birds were higher than 0.9. Furthermore, stepwise selection process was utilized to develop multi-regression model and to remove non-significant factors from the regression equation. According to the developed equation, area, perimeter, area convex, major axis, minor axis, interaction between area and major axis, and convex area and perimeter, major and minor axis were capable of predicting weight with R2= 0.945 in the confidence level of %5. This shows that the digital image processing and multi-regression analysis could predict weight of life chickens, promisingly.

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References

Abdanan Mehdizadeh, S., Neves, D. P., Tscharke, M., Nääs, I. A. & Banhazi, T. M. (2015).  Image analysis method to evaluate beak and head motion of broiler chickens during feeding. Computers and Electronics in Agriculture, 114, 85-95.
Abdolahzare, Z., & Abdanan Mehdizadeh, S. A. (2016). Nonlinear mathematical modeling of seed spacing uniformity of a pneumatic planter using genetic programming and image processing. Neural Computing and Applications, 1-13.
Ali, N.M. (1993). Variance in pigs dimensions as measured by image analysis. Livestock Environment IV, Fourth International Symposium, University of Warwick, Coventry, UK, American Society of Agricultural Engineers, MI, pp. 151-158.
Alonso, J., Castañón, Á. R., & Bahamonde, A. (2013). Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter. Computers and Electronics in Agriculture, 91, 116-120.
Anonymous. (1982). Live bird weighing simplified. Poultry International, 6: 44-50.
Botreau, R., Veissier, I., Butterworth, A., Bracke, M.B.M., & Keeling, L.J. (2007). Definition of criteria for overall assessment of animal welfare. Journal of Animal Welfare, 16 (2), 225–228.
Brandl, N., & Jørgensen, E. (1996). Determination of live weight of pigs from dimensions measured using image analysis. Computers and electronics in agriculture, 15(1), 57-72.
Chedad, A., Aerts, J.M., Vranken, E., Lippens, M., Zoons, J., & Berckmans, D. (2003). Do heavy broiler chickens visit automatic weighing systems less than lighter birds? British Poultry Science, 44, 663–668.
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models-their training and application. Computer vision and image understanding, 61(1), 38-59.
Deshazer, J.A., Moran, P., Onyango, C.M., Randall, J.M., & Schofield, C.P. (1988). Imaging Systems to Improve Stockmanship in Pig Production. AFRC Institute of Engineering Research, pp. 24.
Doyle, I. &Leeson, S. (1989). Automatic weighing of poultry reared on a litter floor. Canadian Journal of Animal Science, 69, 1075-1081.
Feighner, S.D., Godowsky, E.F., & Miller, B.M. (1986). Portable microcomputer-based weighing systems: applications in poultry science. Poultry Science, 65, 868-873.
Flood, C.A., Koon, J.L., Trumbull, R.D., & Brewer, R.N. (1992). Broiler growth data-1986-1991. Transactions of the ASAE, 35(2), 703-709.
Harris, P.J., Schaare, P.N., Cook, C.J., & Henderson, J.D. (2001). An ambulatory physiological monitor for animal welfare studies. Computers and Electronics in Agriculture, 32 (3), 181–194.
Kashiha, M., Bahr, C., Ott, S., Moons, C. P., Niewold, T. A., Ödberg, F. O., &Berckmans, D. (2014). Automatic weight estimation of individual pigs using image analysis. Computers and Electronics in Agriculture, 107, 38-44.‏
Khaledi, M., Shokatfadaie, M., &Nekofar, F. (2011) Investigation of Iran marketing chicken performance. Journal of Agricultural Economics and Development (Agricultural Science and Technology), 24(4), 448-445.
Kuzuhara, Y., Kawamura, K., Yoshitoshi, R., Tamaki, T., Sugai, S., Ikegami, M., & Yasuda, T. (2015). A preliminarily study for predicting body weight and milk properties in lactating Holstein cows using a three-dimensional camera system. Computers and Electronics in Agriculture, 111, 186-193
Lott, B.D., Reece, F. N. & Mcnaughton, J.L. (1982). Anautomated weighing system for use in poultry research. Poultry Science, 61-236-238.
Marchant, J.A. & Schofield, C.P. (1993). Extending the image process algorithm for outlining pigs in scenes. Computers and Electronics in Agriculture, 8, 261-275.
Marchant, J.A., Schofield, C.P., & White, R.P. (1999). Pig growth and conformation monitoring using image analysis. Jornal of Animal Science, 68, 141–150.
Mollah, M. B. R., Hasan, M. A., Salam, M. A., & Ali, M. A. (2010). Digital image analysis to estimate the live weight of broiler. Computers and electronics in agriculture, 72(1), 48-52.
Morris, J.E., Cronin, G.M., & Bush, R.D. (2012). Improving sheep production and welfare in extensive systems through precision sheep management. Animal Production Science, 52 (7), 665–670.
Newberry, R.C., Hunt, J.R. &Garriner, E.E. (1985). Behaviour of roaster chickens towards an automatic weighing perch. British Poultry Science, 26, 229-237.
NRC. (1994). Nutrient Requirements of Poultry. 9th edition. National. Academy. Press, Washington, DC.
Prakash, A., & Stigler, M. (2012). Fao Statistical Yearbook. Food and Agriculture Organization of the United Nations. http://faostat.fao.org/
Schofield, C.P., (1990). Evaluation of image analysis as a means of estimating the weight of pigs. Journal of Agricultural Engineering Research, 47, 287–296.
Schofield, C.P., Marchant, J.A., White, R.P., Brandl, N., & Wilson, M. (1999). Monitoring pig growth using a prototype imaging system. Journal of Agricultural Engineering Research, 72, 205–210.
Shen, H., Li, S., Gu, D., & Chang, H. (2012). Bearing defect inspection based on machine vision. Measurement, 45(4), 719-733.
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(2), 189-197.
Tillet, R.D., Onyango, C.M. &Marchant, J.A. (1997) Using model-based-image processing to track animal movements. Computers and Electronics in Agriculture, 17, 249-261
Turner, M.J.B., Gurney, P. &Belyavin, C.G. (1983). Automatic weighing of layer replacement pullets housed on litter or in cages. British Poultry Science, 24, 33-45.
Viazzi, S., Van Hoestenberghe, S., Goddeeris, B. M., &Berckmans, D. (2015). Automatic mass estimation of Jade perch Scortumbarcoo by computer vision. Aquacultural Engineering, 64, 42-48.
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(1), 117-125.
White, R.P., Schofield, C.P., Green, D.M., Parsons, D.J., &Whittemore, C.T. (2004). The effectiveness of a visual image analysis (VIA) system for monitoring the performance of growing/finishing pigs. Animal Science, 78, 409–418.
Iranian Journal of Biosystems Engineering
Volume 47, Issue 4 - Serial Number 4
January 2017
Pages 623-615

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History

  • Receive Date: 06 February 2016
  • Revise Date: 04 August 2016
  • Accept Date: 25 September 2016

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