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Research Article

volume 54 issue 9 (september 2020) : 1165-1170,   Doi: 10.18805/ijar.B-1199

Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models

J
Jayanta Kumar Basak
E
Elanchezhian Arulmozhi
F
Fawad Khan
F
Frank Gyan Okyere
J
Jihoon Park
D
Deog Hyun Lee
H
Hyeon Tae Kim
1Department of Bio-systems Engineering, Gyeongsang National University (Institute of Agriculture and Life Science), Jinju 52828, Korea.
Cite article:- Basak Kumar Jayanta, Arulmozhi Elanchezhian, Khan Fawad, Okyere Gyan Frank, Park Jihoon, Lee Hyun Deog, Kim Tae Hyeon (2020). Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models. Indian Journal of Animal Research. 54(9): 1165-1170. doi: 10.18805/ijar.B-1199.

ABSTRACT

An experiment was conducted to find out the most influential factors affecting pig’s body temperature (PBT). For this purpose, eight environmental parameters and three growth related factors were considered as variables. Among these factors, seven environmental parameters, including temperature, CO2, temperature-humidity index inside and outside the pig’s barn and relative humidity inside the barn were taken as input variables for artificial neural networks (ANN) and multiple linear regression (MLR) models due to their good correlation (r ³ 0.5) with PBT. The results showed that ANN and MLR models had the lowest R2 values (0.81 and 0.69, respectively) and the highest RMSE (1.17 and 1.48, respectively) when they were run without temperature-humidity index; however, the maximum R2 (0.90 and 0.75, respectively) and minimum RMSE (0.92 and 1.40, respectively) were found without relative humidity. Based on the results, the temperature-humidity index could represent an important indicator in registering early warning signs of PBT status alternations.   

REFERENCES

  1. Abdipour, M., Hmazekhanlu, M.Y., Ramazani, S.H.R., Omidi, A.H. (2019). Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamustinctorius L.). Industrial Crops and Products. 127: 185-194. DOI: 10.1016/j.indcrop.2018.10.050.
  2. Amezcua, M.D.R., Walsh, S., Luimes, P.H., Friendship, R.M. (2014). Infrared thermography to evaluate lameness in pregnant sows. The Canadian Veterinary Journal. 55(3): 268-272.
  3. Asamoah, M.K. (2014). Re-examination of the limitations associated with correlational research. Journal of Educational Research and Reviews. 2(4): 45-52. 
  4. Basak, J.K., Qasim, W., Okyere, F.G., Khan, F.G., Lee, Y.J., Park, J., KIM, H.T. (2019). Regression analysis to estimate morphology parameters of pepper plant in a controlled greenhouse system. Journal of Biosystems Engineering. 44: 57-68. DOI: 10.1007/s42853-019-00014-0.
  5. Bishop, C.M. (1996). Neural Networks for Pattern Recognition. Oxford University Press, USA, ISBN: 978-0198538646.
  6. Bertoldo, M.J., Holyoake, P.K., Evans, G., Grupen, C.G. (2012). Seasonal variation in the ovarian function of sows. Reproduction, Fertility and Development. 24: 822–834. DOI: 10.1071/RD11249. 
  7. Chakraborty, A., Baruah, A., Sarmah, B.C., Goswami, J., Bora, A., Dutta, D.J., Biswas, R.K., et al. (2018). Physiological responses in pigs on antioxidant supplementation during summer and winter. Indian Journal of Animal Research. 52(11): 1557-1559. DOI: 10.18805/ijar.B-3401. 
  8. Darlington, R.B. and Hayes, A.F. (2016). Regression Analysis and Linear Models: Concepts, Applications and Implementation. Guilford Publications.
  9. Gourdine, J.L., Bidanel, J.P., Noblet, J., Renaudeau, D. (2006). Effects of breed and season on performance of lactating sows in a tropical humid climate. Journal of Animal Science. 84: 360–369. DOI: 10.2527/2006.842360x.
  10. Hydrology, ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering. 5(2): 115-123. DOI: 10.1061/(ASCE)1084-0699(2000)5:2(115).
  11. Huynh, T.T., Aarnink, A.J., Verstegen, M.W., Gerrits, W.J., Heetkamp, M.J., Kemp, B., Canh, T.T., (2005). Effects of increasing temperatures on physiological changes in pigs at different relative humidities. Journal of Animal Science. 83: 1385–    1396. DOI: 10.2527/2005.8361385x.
  12. Jang, J.C., Lee, M.H., Lee, J.Y., Choi, H.C., Choi, D.Y., Kim, H.J., Kim, H.T. (2015). Monitoring pig body temperature using infrared sensors. Journal of Biosystems Engineering. 40(4): 368-372. DOI: 10.5307/JBE.2015.40.4.368.
  13. Kessel, L., Johnson, L., Arvidsson, H., Larsen, M. (2010). The relationship between body and ambient temperature and corneal temperature. Investigative Ophthalmology & Visual Science. 51(12): 6593-6597. DOI: 10.1167/iovs.10-    5659.
  14. Le, P.D., Aarnink, A.J.A., Jongbloed, A.W. (2009). Odour and ammonia emission from pig manure as affected by dietary crude protein level. Livestock Science. 121: 267-274. DOI: 10.1016/j.livsci.2008.06.021.
  15. Mader, T.L., Davis, M.S., Brown-Brandl, T. (2006). Environmental factors influencing heat stress in feedlot cattle. Journal of Animal Science. 84: 712–719. DOI: 10.2527/2006. 843712x.
  16. McClure, M.L., Burdett, C.L., Farnsworth, M.L., Lutman, M.W., Theobald, D.M., Riggs, P.D., Grear, D.A., Miller, R.S. (2015). Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States. PLoS ONE. 10(8): 1-17. DOI: 10.1371/journal.pone.0133771.
  17. Moon, B.E., Kim, H.T., Kim, J.G., Ryou, Y.S., Kim, H.T. (2016). A fundamental study for development of unglazed transpired collector control system in windowless pig house. Journal of Agriculture & Life Science. 50 (2): 175-185. DOI: 10.14397/jals.2016.50.2.175.
  18. Renaudeau, D., Gourdine, J.L., St-Pierre, N.R. (2011). A meta-    analysis of the effect of high ambient temperature on growing–finishing pigs. Journal of Animal Science. 89: 2220-2230. DOI: 10.2527/jas.2010-3329.
  19. Ramesh, V., Saseendran, P.C. (2002). Effect of enrichment of environment for better economic reproductive performance of sows. Indian Journal of Animal Research. 36(2): 98-101. 
  20. Renaudeau, D., Anais, C., Tel, L., Gourdine, J.L. (2010). Effect of temperature on thermal acclimation in growing pigs estimated using a nonlinear function. Journal of Animal Science. 88: 3715–3724. DOI: 10.2527/jas.2009-2169. 
  21. Ross, J.W., Hale, B.J., Gabler, N.K., Rhoads, R.P., Keating, A.F., Baumgard, L.H. (2015). Physiological consequences of heat stress in pigs. Animal Production Science. 55: 1381-    1390. DOI: 10.1071/AN15267.
  22. Soerensen, D.D. and Pedersen, L.J. (2015). Infrared skin temperature measurements for monitoring health in pigs: a review. Acta Veterinaria Scandinavica. 57(5): 1-11. DOI: 10.1186/    s13028-015-0094-2
  23. White, H.M., Richert, B.T., Schinckel, A.P., Burgess, J.R., Donkin, S.S., Latour, M.A. (2008). Effects of temperature stress on growth performance and bacon quality in grow-finish pigs housed at two densities. Journal of Animal Science. 86: 1789-1798. DOI: 10.2527/jas.2007-0801.
  24. Yazgan, K. (2017). Determining heat stress effect in Holstein dairy cattle using daily milk yield and meteorological data obtained from public weather station in Sanliurfa province of Turkey. Indian Journal of Animal Research. 51(6): 1002-1011. DOI: 10.18805/ijar.v0i0f.38.
  25. Zhang, Z., Zhang, H., Liu, T. (2019). Study on body temperature detection of pig based on infrared technology: A review. Artificial Intelligence in Agriculture. 1: 14-26. DOI: DOI: 10.1016/j.aiia.2019.02.002.
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Indian Journal of Animal Research
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    Research Article

    volume 54 issue 9 (september 2020) : 1165-1170,   Doi: 10.18805/ijar.B-1199

    Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models

    J
    Jayanta Kumar Basak
    E
    Elanchezhian Arulmozhi
    F
    Fawad Khan
    F
    Frank Gyan Okyere
    J
    Jihoon Park
    D
    Deog Hyun Lee
    H
    Hyeon Tae Kim
    1Department of Bio-systems Engineering, Gyeongsang National University (Institute of Agriculture and Life Science), Jinju 52828, Korea.
    Cite article:- Basak Kumar Jayanta, Arulmozhi Elanchezhian, Khan Fawad, Okyere Gyan Frank, Park Jihoon, Lee Hyun Deog, Kim Tae Hyeon (2020). Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models. Indian Journal of Animal Research. 54(9): 1165-1170. doi: 10.18805/ijar.B-1199.

    ABSTRACT

    An experiment was conducted to find out the most influential factors affecting pig’s body temperature (PBT). For this purpose, eight environmental parameters and three growth related factors were considered as variables. Among these factors, seven environmental parameters, including temperature, CO2, temperature-humidity index inside and outside the pig’s barn and relative humidity inside the barn were taken as input variables for artificial neural networks (ANN) and multiple linear regression (MLR) models due to their good correlation (r ³ 0.5) with PBT. The results showed that ANN and MLR models had the lowest R2 values (0.81 and 0.69, respectively) and the highest RMSE (1.17 and 1.48, respectively) when they were run without temperature-humidity index; however, the maximum R2 (0.90 and 0.75, respectively) and minimum RMSE (0.92 and 1.40, respectively) were found without relative humidity. Based on the results, the temperature-humidity index could represent an important indicator in registering early warning signs of PBT status alternations.   

    REFERENCES

    1. Abdipour, M., Hmazekhanlu, M.Y., Ramazani, S.H.R., Omidi, A.H. (2019). Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamustinctorius L.). Industrial Crops and Products. 127: 185-194. DOI: 10.1016/j.indcrop.2018.10.050.
    2. Amezcua, M.D.R., Walsh, S., Luimes, P.H., Friendship, R.M. (2014). Infrared thermography to evaluate lameness in pregnant sows. The Canadian Veterinary Journal. 55(3): 268-272.
    3. Asamoah, M.K. (2014). Re-examination of the limitations associated with correlational research. Journal of Educational Research and Reviews. 2(4): 45-52. 
    4. Basak, J.K., Qasim, W., Okyere, F.G., Khan, F.G., Lee, Y.J., Park, J., KIM, H.T. (2019). Regression analysis to estimate morphology parameters of pepper plant in a controlled greenhouse system. Journal of Biosystems Engineering. 44: 57-68. DOI: 10.1007/s42853-019-00014-0.
    5. Bishop, C.M. (1996). Neural Networks for Pattern Recognition. Oxford University Press, USA, ISBN: 978-0198538646.
    6. Bertoldo, M.J., Holyoake, P.K., Evans, G., Grupen, C.G. (2012). Seasonal variation in the ovarian function of sows. Reproduction, Fertility and Development. 24: 822–834. DOI: 10.1071/RD11249. 
    7. Chakraborty, A., Baruah, A., Sarmah, B.C., Goswami, J., Bora, A., Dutta, D.J., Biswas, R.K., et al. (2018). Physiological responses in pigs on antioxidant supplementation during summer and winter. Indian Journal of Animal Research. 52(11): 1557-1559. DOI: 10.18805/ijar.B-3401. 
    8. Darlington, R.B. and Hayes, A.F. (2016). Regression Analysis and Linear Models: Concepts, Applications and Implementation. Guilford Publications.
    9. Gourdine, J.L., Bidanel, J.P., Noblet, J., Renaudeau, D. (2006). Effects of breed and season on performance of lactating sows in a tropical humid climate. Journal of Animal Science. 84: 360–369. DOI: 10.2527/2006.842360x.
    10. Hydrology, ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering. 5(2): 115-123. DOI: 10.1061/(ASCE)1084-0699(2000)5:2(115).
    11. Huynh, T.T., Aarnink, A.J., Verstegen, M.W., Gerrits, W.J., Heetkamp, M.J., Kemp, B., Canh, T.T., (2005). Effects of increasing temperatures on physiological changes in pigs at different relative humidities. Journal of Animal Science. 83: 1385–    1396. DOI: 10.2527/2005.8361385x.
    12. Jang, J.C., Lee, M.H., Lee, J.Y., Choi, H.C., Choi, D.Y., Kim, H.J., Kim, H.T. (2015). Monitoring pig body temperature using infrared sensors. Journal of Biosystems Engineering. 40(4): 368-372. DOI: 10.5307/JBE.2015.40.4.368.
    13. Kessel, L., Johnson, L., Arvidsson, H., Larsen, M. (2010). The relationship between body and ambient temperature and corneal temperature. Investigative Ophthalmology & Visual Science. 51(12): 6593-6597. DOI: 10.1167/iovs.10-    5659.
    14. Le, P.D., Aarnink, A.J.A., Jongbloed, A.W. (2009). Odour and ammonia emission from pig manure as affected by dietary crude protein level. Livestock Science. 121: 267-274. DOI: 10.1016/j.livsci.2008.06.021.
    15. Mader, T.L., Davis, M.S., Brown-Brandl, T. (2006). Environmental factors influencing heat stress in feedlot cattle. Journal of Animal Science. 84: 712–719. DOI: 10.2527/2006. 843712x.
    16. McClure, M.L., Burdett, C.L., Farnsworth, M.L., Lutman, M.W., Theobald, D.M., Riggs, P.D., Grear, D.A., Miller, R.S. (2015). Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States. PLoS ONE. 10(8): 1-17. DOI: 10.1371/journal.pone.0133771.
    17. Moon, B.E., Kim, H.T., Kim, J.G., Ryou, Y.S., Kim, H.T. (2016). A fundamental study for development of unglazed transpired collector control system in windowless pig house. Journal of Agriculture & Life Science. 50 (2): 175-185. DOI: 10.14397/jals.2016.50.2.175.
    18. Renaudeau, D., Gourdine, J.L., St-Pierre, N.R. (2011). A meta-    analysis of the effect of high ambient temperature on growing–finishing pigs. Journal of Animal Science. 89: 2220-2230. DOI: 10.2527/jas.2010-3329.
    19. Ramesh, V., Saseendran, P.C. (2002). Effect of enrichment of environment for better economic reproductive performance of sows. Indian Journal of Animal Research. 36(2): 98-101. 
    20. Renaudeau, D., Anais, C., Tel, L., Gourdine, J.L. (2010). Effect of temperature on thermal acclimation in growing pigs estimated using a nonlinear function. Journal of Animal Science. 88: 3715–3724. DOI: 10.2527/jas.2009-2169. 
    21. Ross, J.W., Hale, B.J., Gabler, N.K., Rhoads, R.P., Keating, A.F., Baumgard, L.H. (2015). Physiological consequences of heat stress in pigs. Animal Production Science. 55: 1381-    1390. DOI: 10.1071/AN15267.
    22. Soerensen, D.D. and Pedersen, L.J. (2015). Infrared skin temperature measurements for monitoring health in pigs: a review. Acta Veterinaria Scandinavica. 57(5): 1-11. DOI: 10.1186/    s13028-015-0094-2
    23. White, H.M., Richert, B.T., Schinckel, A.P., Burgess, J.R., Donkin, S.S., Latour, M.A. (2008). Effects of temperature stress on growth performance and bacon quality in grow-finish pigs housed at two densities. Journal of Animal Science. 86: 1789-1798. DOI: 10.2527/jas.2007-0801.
    24. Yazgan, K. (2017). Determining heat stress effect in Holstein dairy cattle using daily milk yield and meteorological data obtained from public weather station in Sanliurfa province of Turkey. Indian Journal of Animal Research. 51(6): 1002-1011. DOI: 10.18805/ijar.v0i0f.38.
    25. Zhang, Z., Zhang, H., Liu, T. (2019). Study on body temperature detection of pig based on infrared technology: A review. Artificial Intelligence in Agriculture. 1: 14-26. DOI: DOI: 10.1016/j.aiia.2019.02.002.
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