Swine live weight estimation by adaptive neuro-fuzzy inference system
 

DOI: 10.18805/ijar.v0iOF.7250    | Article Id: B-633 | Page : 923-928
Citation :- Swine live weight estimation by adaptive neuro-fuzzy inference system .Indian Journal Of Animal Research.2018.(52):923-928

Cedric Okinda,  Longhen Liu,  Guangyue Zhang and Mingxia Shen*

cedsean@hotmail.com
Address :

College of Engineering, Nanjing Agricultural University, Jiangsu 210031, P.R. China.

Submitted Date : 1-10-2016
Accepted Date : 19-11-2016

Abstract

Swine live weight is an important aspect in the production of pork products and also to Stockmen, with reference to market costs, feed conversion, and animal health. The objective of this study was to develop a contactless, stress-free method of swine live weight estimation by machine vision technology. This novel approach was based on image processing for features extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) for modelling. Firstly, the model determines which input combination holds the highest predictive ability, secondly, used the feature combination with the best predictive power to correlate to live-weight. The test results showed that the average relative error of our proposed system was about 3% and a standard deviation of 0.7%. Thus, development of a practical imaging system for swine live weight estimation by the proposed method is feasible.

Keywords

Adaptive Neuro-Fuzzy Inference System Contactless Features Modelling Predictive Power.

References

  1. Agostini, P., Gasa, J., Manzanilla, E., Da Silva, C. and de Blas, C. (2013). Descriptive study of production factors affecting performance traits in growing-finishing pigs in Spain. Span. J. Agric. Res.11: 371-381.
  2. Alawneh, I. (2011). Monitoring liveweight to optimise health and productivity in pasture [-] fed dairy herds: a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Massey University, Massey University.
  3. Apichottanakul, A., Pathumnakul, S. and Piewthongngam, K. (2012). The role of pig size prediction in supply chain planning. Biosyst. Eng.113: 298-307.
  4. Cveticanin, D. (2003). New approach to the dynamic weighing of livestock. Biosyst. Eng. 86: 247-252.
  5. Cveticanin, D. and Wendl, G. (2004). Dynamic weighing of dairy cows: using a lumped-parameter model of cow walk. Comput. Electron. Agric. 44: 63-69.
  6. D’Souza, D., Pethick, D., Dunshea, F., Suster, D., Pluske, J. and Mullan, B. (2004). The pattern of fat and lean muscle tissue deposition differs in the different pork primal cuts of female pigs during the finisher growth phase. Livest. Prod. Sci. 91: 1-8.
  7. Agostini, P., Gasa, J., Manzanilla, E., Da Silva, C. and de Blas, C. (2013). Descriptive study of production factors affecting performance traits in growing-finishing pigs in Spain. Span. J. Agric. Res.11: 371-381.
  8. Alawneh, I. (2011). Monitoring liveweight to optimise health and productivity in pasture [-] fed dairy herds: a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Massey University, Massey University.
  9. Apichottanakul, A., Pathumnakul, S. and Piewthongngam, K. (2012). The role of pig size prediction in supply chain planning. Biosyst. Eng.113: 298-307.
  10. Cveticanin, D. (2003). New approach to the dynamic weighing of livestock. Biosyst. Eng. 86: 247-252.
  11. Cveticanin, D. and Wendl, G. (2004). Dynamic weighing of dairy cows: using a lumped-parameter model of cow walk. Comput. Electron. Agric. 44: 63-69.
  12. D’Souza, D., Pethick, D., Dunshea, F., Suster, D., Pluske, J. and Mullan, B. (2004). The pattern of fat and lean muscle tissue deposition differs in the different pork primal cuts of female pigs during the finisher growth phase. Livest. Prod. Sci. 91: 1-8.
  13. Kashiha, M., Bahr, C., Ott, S., Moons, C. P., Niewold, T. A., Ödberg, F. O. and Berckmans, D. (2014). Automatic weight estimation of individual pigs using image analysis. Comput. Electron. Agric. 107: 38-44.
  14. Khamjan, S., Piewthongngam, K. and Pathumnakul, S. (2013). Pig procurement plan considering pig growth and size distribution. Comput. Ind. Eng. 64: 886-894.
  15. Li, Z., Mao, T., Liu, T. and Teng, G. (2015). Comparison and optimization of pig mass estimation models based on machine vision. Trans. CSAE. 31: 155-161.
  16. Menesatti, P., Costa, C., Antonucci, F., Steri, R., Pallottino, F. and Catillo, G. (2014). A low-cost stereovision system to estimate size and weight of live sheep. Comput. Electron. Agric. 103: 33-38.
  17. Minagawa, H. and Murakami, T. (2001). A hands-off method to estimate pig weight by light projection and image analysis. Livestock Environment VI, Proceedings of the 6th International Symposium 2001, ASABE.
  18. Mollah, M. B. R., Hasan, M. A., Salam, M. A. and Ali, M. A. (2010). Digital image analysis to estimate the live weight of broiler. Comput. Electron. Agric.72: 48-52.
  19. Negretti, P., Bianconi, G. and Finzi, A. (2010). Visual image analysis to estimate morphological and weight measurements in rabbits. World. Rabbit. Sci.15: 37-41.
  20. Oliveira, J., Yus, E. and Guitián, F. (2009). Effects of management, environmental and temporal factors on mortality and feed consumption in integrated swine fattening farms. Livest. Sci.123: 221-229.
  21. Pastorelli, G., Musella, M., Zaninelli, M., Tangorra, F. and Corino, C. (2006). Static spatial requirements of growing-    finishing and heavy pigs. Livest. Sci. 105: 260-264.
  22. Pathumnakul, S., Piewthongngam, K. and Khamjan, S. (2009). Integrating a shrimp-growth function, farming skills information, and a supply allocation algorithm to manage the shrimp supply chain. Comput. Electron. Agric. 66: 93-105.
  23. Plà-Aragonés, L. M. and Rodríguez-Sánchez, S. V. (2015). Optimal Delivery of Pigs to the Abattoir. Handbook of Operations Research in Agriculture and the Agri-Food Industry, Springer: 381-395.
  24. Ramaekers, P., Huiskes, J., Verstegen, M., Den Hartog, L., Vesseur, P. and Swinkels, J. (1995). Estimating individual body weights of group-housed growing-finishing pigs using a forelegs weighing system. Comput. Electron. Agric.13: 1-12.
  25. Tasdemir, S., Urkmez, A. and Inal, S. (2011). Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Comput. Electron. Agric.76: 189-197.
  26. TASDEMIR, S., ÜRKMEZ, A. and INAL, S. (2011). A fuzzy rule-based system for predicting the live weight of Holstein cows whose body dimensions were determined by image analysis. Turk. J. Electr. Eng. Co.19: 689-703.
  27. Topai, M. and Macit, M. (2004). Prediction of body weight from body measurements in Morkaraman sheep. J. Appl. Anim. Res. 25: 97-100.
  28. Tscharke, M. and Banhazi, T. (2013). Review of methods to determine weight and size of livestock from images. Aust. J. Multidscip. Eng.10: 1-17.
  29. Wang, Y., Yang, W., Winter, P. and Walker, L. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosyst. Eng.100: 117-125.
  30. White, R., Schofield, C., Green, D., Parsons, D. and Whittemore, C. (2004). The effectiveness of a visual image analysis (VIA) system for monitoring the performance of growing/finishing pigs. Anim. Sci. -GLASGOW THEN PENICUIK-    78: 409-418.
  31. Williams, S., Moore, G. and Currie, E. (1996). Automatic weighing of pigs fed ad libitum. J. Agr. Eng. Res. 64: 1-10.
  32. Wongsriworaphon, A., Arnonkijpanich, B. and Pathumnakul, S. (2015). An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Comput. Electron. Agric.115: 26-33.
  33. Zaragoza, L. E. O. (2009). Evaluation of the accuracy of simple body measurements for live weight prediction in growing-    finishing pigs, University of Illinois at Urbana-Champaign.

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