Comparison of artificial neural network accuracy and linear regression model in estimating the weight of dromedary camel using body measurements

Document Type : Research Paper

Authors

1 member of scientific board of Agriculture and Natural Resources Research and Education Center in Qom

2 scientific board member of agricultural research center of Qom, Qom, Iran

3 Animal Science Department, Agricultural Research and Education Center of Qom, AREEO, Qom, Iran.

4 Department of Rearing Management, Animal Science Research Institute of Karaj. Agricultural Research, Education and Extension Organization (AREEO) of Jihad-e-Keshavarzi Ministry

5 Executive member of Jihad-e-Keshavarzi Organizatuin of Qom province, Qom, Iran.

Abstract

In the management of camel breeding, weightlifting plays a decisive role in grouping livestock, regulating nutritional needs and also annual evaluation of animals. Due to the many difficulties and risks, camel owners usually prefer alternative methods such as using apparent estimates or weight -meter to estimate the weight of camels. Since the accuracy of mathematical models in estimating the weight of camels is not equal, so the present research was conducted to compare the accuracy of estimating artificial neural network and multiple linear regression model in estimating the weight of dromedary camels from their body dimensions. For this purpose, 26 camels with 203 records were used from a private farm for one year. Weighing and determining the body dimensions of camels (body length, shoulder height, back height, hump height to the ground, chest and abdomen girth) were measured monthly. To estimate the weight of camels from their body dimensions, the data were analyzed using multiple linear regression model and artificial neural network. The weight of camels on their body dimensions was estimated with accuracy of 0.94 and 0.99, respectively, using multivariate linear regression model and artificial neural network model. The weight of camels on their body dimensions was estimated with accuracy of 0.94 and 0.99, respectively, using multivariate linear regression model and artificial neural network model. The results of this research showed that the artificial neural network has the proper ability to estimate the weight of camels based on their body dimensions and can replace conventional regression methods.

Keywords

References

  1. Abegaz, S. and Awgichew, K. (2009). Technical bulletin no. 23. Estimation of weight and age of sheep and goat. Ethiopia sheep and goat productivity improvement program. ESGPIP, Ethiopia.
  2. Afolayan, R.A., Adeyinka, I. A. and Lakpini, C. A. M. (2006). The estimation of live weight from body measurements in Yankasa sheep. Czech J.Anim.Scie.51 (8):343-348.
  3. Atta, M. and el-Khidir, O. A. (2004). Use of heart girth wither height and scapuloischial length for prediction of love weight of Nilotic sheep. Small ruminant research, 55(1):233-237.
  4. Bahashwan, S., Alrawas, A. S., Alfadli, S. and Johnson, E. S. (2016). Dofari cattle growth curve prediction by different non linear model functions. Livestock Research.27 (12)
  5. Borah, S., Hines, E. L. and Bhuyan, M. (2007). Wavelet transform based image texture analysis for size estimation applied to the sorting of tea granules. Journal of Food Engineering, 79: 629–639.
  6. Cannas, A. and Boe, F. (2003). Prediction of the relationship between body weight and body condition score in sheep. Italian Journal of Animal Sciences, 2:527-529.
  7. De Villers, J. F., Gcumisa, S. T. and Gumede, S. A. (2010). Weight band to estimate the live weight of meat goats. Agri update: information from the KZN department of Agriculture environmental affairs and rural development, South Africa.
  8. Franicis, J., Sibanda, S., Hermansen, J. T, and Kristrerrsen, E. (2002). Estimating body weight of cattle using linear body measurements. Zimbabwe Veterinary Journal.33:15-21.
  9. Grzesiak, W., Lacroix, R., Wójcik, J. and Blaszczyk, P. (2003). A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Canadian Journal of Animal Science, 2003, 83(2): 307-310.
  10. Kadim, I. T, Mahgoub, O. and Purchas, R. W. (2008). A review of the growth and the carcass and meat quality characteristics of the one humped camel. Meat. Science 80(3):555-69.
  11. Kohler-Rollefson, I., Mundy, P. and Mathias, E. (2001). A Field Manual of Camel Diseases. London, U.K.; Intermediate Technology Group Publishing. pp. 2 54.
  12. Iqbal, Z. M., Javed, K., Abdollah, M., Ahmad, N., Ali, A., Khalique, A., Aslamand, N. and Younas, U. (2014). Estimation of body weight from different morphometric measurement in Kali lambs. Journal of animal and plant Science.24 (3):700-703.
  13. Mahmoud, M. A., Shaba, P. and Zubairu, U.V. (2014). Live body weight estimation in small ruminant: a review. Global journal of animal scientific research. 2(2).
  14. Mehta, S. C., Bapna, D. L. and Bhure, S. K. (2010). Mathematical function of growth in India dromedary genotypes. Journal of Biopesticides.80 (2)148-151.
  15. Norouzian, M. A.and Vakili, M. (2016). Comparison of artificial neural network and multiple regression to estimate fat tail weight of sheep. Iranian Journal of applied animal science.4 (6):895-900.
  16. Otoikhian, C. S. O., Otoikhian, A. M., Akporhuarho, O. P. and Isidahoman, C. (2008). Correlation of body weight and some body measurement parameters in Quda sheep under extensive management system. African journal .Gen.Agr4 (3):129-133.
  17. Salau, J., Haas, J., Junge, W., Bauer, U., Harms, J. and Bieletzki, S. (2014). Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns. Springer Plus, 3, 225.
  18. Slosarz, P., Stanisz, M., Boniecki, P., Prezybylak, A., Lisiak, D., Ludwiczak, A. (2011). Artificial neural network analysis of ultrasound image for the estimation of intramuscular fat content in lamb muscle. African Journal of Biotechnology.10 (55):11792-11796.
  19. Snedecor, S.W. and Cochran, W.G. (1989). Statistical method s. 8th ed. Iowa state University Press .USA.
  20. Tsegaye, D., Belay, B., and Haile, A. (2013). Linear body measurement as predictor of body weight in Haraghe Highland goat under farmer's environment Ethiopia. Glob. Veterinaria.11 (5):649-656.
  21. Wangchuk. K., Wangdi, J. and Mindu, M. (2017).comparison and reliability of techniques to estimate live cattle body weight. Journal of Applied Animal Research.
Animal Sciences Journal
Volume 35, Issue 136
December 2022
Pages 3-16

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