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Current IssueEditorial BoardOnline First ArticlesArchive

volume 49 issue 5 (october 2015) : 671-679,   Doi: 10.18805/ijar.5581

Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle

N
Nazira M. Mammadova
I
Ismail Keskin
1Department of Animal Science, Faculty of Agriculture, Selcuk University, 42075, Konya, Turkey.
Cite article:- Mammadova M. Nazira, Keskin Ismail (2025). Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle. Indian Journal of Animal Research. 49(5): 671-679. doi: 10.18805/ijar.5581.

ABSTRACT

Mastitis is an important problem, while I guess AI is a possible solution to detect subclinical mastitis in Holstein cows milked with automatic milking systems. Mastitis alerts were generated via ANN and ANFIS model with the input data of lactation rank (current lactation number), milk yield, electrical conductivity, average milking duration and season. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the sampling period. Cattle were judged healthy or infected based on somatic cell counts. This study undertook a detailed scrutiny of ANN, and ANFIS AI methodology; constructed and examined models for each; and chose optimal methods based on that examination. The two mastitis detection models were evaluated as to sensitivity, specificity and error rate. The ANN model yielded 80% sensitivity, 91% specificity, and 64% error and the ANFIS, 55%, 91% and 35%. These results suggest the ANN model is better predictor of subclinical mastitis than ANN based on Z-test (the hypothesis control for the difference between rates). AI models such as these are useful tools in the development of mastitis detection models. Prediction error rates can be decreased through the use of more informative parameters.

REFERENCES

  1. Allahverdi, N. (2002). Expert systems, artificial intelligence application. Atlas Pub. Co., Istanbul.
  2. Atasever, S. and Erdem, H. (2008). Relationships between mastitis and electrical conductivity of raw milk in dairy cows. J. Fac. Agric., OMU, 23 (2): 131-136.
  3. Bademkiran, S., S. Yesilmen and Gürbulak, K. (2005). The effect of daily milking frequency on clinical mastitis and milk yield of dairy cows, Y.Y. Üniv., J. the Fac. Vet. Med., 16(2): 17-21.
  4. Batra, T.R. (1986). Relationship of somatic cell concentration with milk yield in dairy cows. Canadian Journal Animal Science, 66: 607-614.
  5. Bruckmaier, R.M., C.E. Ontsouka and Blum, J.W. (2004). Fractionized milk composition in dairy cows with subclinical mastitis. Vet. Med. - Czech., 49(8): 283-290.
  6. Cavero, D., K.H. Tölle, C. Buxade and Krieter, J. (2006). Mastitis detection in dairy cows by application of fuzzy logic. Livestock Science, 105: 207 – 213.
  7. Cedden, F., A. Kor and Keskin, S. (2002). Somatic cell counts in goat milk during late lactation period and its relationship with milk yield, age and some udder measurements, J. Agric. Sci., 12(2): 63-67.
  8. Eyduran, E., T. Özdemir, K. Yazgan and Keskin, S. (2005). The effects of lactation rank and period on somatic cell count (SCC) in milks of Holstein cows. Y.Y. Üniv., J. the Fac. Vet. Med., 16(1): 61-65.
  9. Göncü, S. and Özkütük, K. (2002). Factors effective at somatic cell count (SCC) in the milk of black and white cows kept in intensive dairy farms at Adana province and their relationships with mastitis. Hayvansal Üretim (Anim. Prod.), 43(2): 44-53.
  10. Ilie, L.I., L. Tudor and Galis, A.M. (2010). The electrical conductivity of cattle milk and the possibility of mastitis diagnosis in Romania. Vet. Med. Sci., Works; 43: 220-227.
  11. Jacceh, M. (2003). Neuro-Fuzzy System with learning tolerant to imprecision. Fuzzy Sets and Systems, 138: 427–439.
  12. Janzekovic, M., M. Brus, B. Mursec, P. Vinis, D. Stajnko and Cus, F. (2009). Mastitis detection based on electric conductivity of milk. JAMME; 34: 39-46.
  13. Jaos, V. (1998). Dynamic System Control and Identification for Fuzzy Inference Systems. IEEE.
  14. Jones, G.M., R.E. Pearson, G.A. Clabaugh and Heald, C.M. (1984). Relationship between somatic cell counts and milk production. Journal Dairy Science, 67: 1823-1831.
  15. Kesici, T. and Kocabas, Z. (2007). Biostatistics. Ankara Univ., Faculty of Pharmacy, Biostatistics Pub. No: 94, Ankara.
  16. Krieter, J., D. Cavero and Henze, C. (2007). Mastitis detection in dairy cows using neural networks. GIL Jahrestagung, 101: 123-126.
  17. Kul, E., H. Erdem and Atasever, S. (2006). Effect of different udder traits on mastitis and somatic cell count in dairy cows. J. Fac. Agric., OMU, 21(3): 350-356.
  18. Nielen, M., H. Deluyker, Y.H. Schukken and Brand, A. (1992). Electrical conductivity of milk: measurement, modifiers, and meta analysis of mastitis detection performance. Journal Dairy Science, 75: 606-614.
  19. Norberg, E., H. Hogeveen, I.R. Korsgaard, N.C. Friggens, K.H.M.N. Sloth and Lovendahl, P. (2004). Electrical conductivity of milk: ability to predict mastitis status. Journal Dairy Science, 87: 1099-1107.
  20. Osteras, O.S., V.L. Edge and Martin, S.W. (1999). Determinants success or failure in the elimination of major mastitis pathogens in selective dry cow therapy. Journal Dairy Science, vol. 82, no. 6, pp. 1221–1231.
  21. Porcionato, M.A.D., W.V.B. Soares, C.B.M. dos Reis, C.S. Cortinhas, L. Mestieri and dos Santos, M.V. (2010). Milk flow, teat morphology and subclinical mastitis prevalence in Gyr cows. Pesquisa Agropec. Bras. 45: 1507–1512.
  22. Raubertas, R.F. and Shook, G.E. (1982). Relationship between lactation measures of somatic cell concentration and milk yield. Journal of Dairy Science, 65: 419-425.
  23. Risvanli, A. and Kalkan, C. (2002). The effect of age and breed on somatic cell count and microbiological isolation rates in milk of dairy cows with subclinical mastitis, Y.Y. Univ., J. the Fac. Vet. Med., 13: 84-87.
  24. Sargeant, J.M., H.M. Scott, K.E. Leslie, M.J. Ireland and Bashiri, A. (1998). Clinical mastitis in dairy cattle in Ontario: Frequency of occurrence and bacteriological isolates. Canadian Vet. Journal, 39: 33-38.
  25. Schroeder, J.W. (2012). Mastitis control programs: Bovine mastitis and milking management. North Dakota State University, Fargo, North Dakota 1.5M-4-97, 200-7-12.
  26. Seker, I., A. Risvanli, S. Kul, M. Bayraktar and Kaygusuzoðlu, E. (2000). Relationships between CMT scores and udder traits and milk yield in Brown-Swiss cows. Lalahan Hay. Araº. Ens. Derg., 40: 29–38.
  27. Tekeli, T. (2005). Mastitis. Quality milk production and somatic cell count in the process of the European Union. Güzelis Pub. Co., Konya.
  28. Torlak, E. (2005). Quality milk production and somatic cell count in the process of the European Union. Güzelis Pub. Co., Konya. 2005.
  29. Uzmay, C., I. Kaya, Y. Akbas and Kaya, A. (2003). Effects of udder and teat morphology, parity and lactation stage on subclinical mastitis in Holstein cows. Tr. J. Vet. and Anim. Sci., 27: 695–701.
  30. Yang, X.Z., R. Lacroix and Wade, K.M. (2000). Investigation into the production and conformation traits associated with clinical mastitis using artificial neural Networks. Canadian Journal Animal Science, 80: 415–426.
  31. Wilson, D.J., R.N. González, J. Hertl, H.F. Schulte, G.J. Bennett, Y.H. Schukken and Gröhn, Y.T. (2004). Effect of clinical mastitis on the lactation curve: a mixed model estimation using daily milk weights. J. Dairy Sci, 87: 2073-2084.
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Indian Journal of Animal Research
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    volume 49 issue 5 (october 2015) : 671-679,   Doi: 10.18805/ijar.5581

    Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle

    N
    Nazira M. Mammadova
    I
    Ismail Keskin
    1Department of Animal Science, Faculty of Agriculture, Selcuk University, 42075, Konya, Turkey.
    Cite article:- Mammadova M. Nazira, Keskin Ismail (2025). Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle. Indian Journal of Animal Research. 49(5): 671-679. doi: 10.18805/ijar.5581.

    ABSTRACT

    Mastitis is an important problem, while I guess AI is a possible solution to detect subclinical mastitis in Holstein cows milked with automatic milking systems. Mastitis alerts were generated via ANN and ANFIS model with the input data of lactation rank (current lactation number), milk yield, electrical conductivity, average milking duration and season. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the sampling period. Cattle were judged healthy or infected based on somatic cell counts. This study undertook a detailed scrutiny of ANN, and ANFIS AI methodology; constructed and examined models for each; and chose optimal methods based on that examination. The two mastitis detection models were evaluated as to sensitivity, specificity and error rate. The ANN model yielded 80% sensitivity, 91% specificity, and 64% error and the ANFIS, 55%, 91% and 35%. These results suggest the ANN model is better predictor of subclinical mastitis than ANN based on Z-test (the hypothesis control for the difference between rates). AI models such as these are useful tools in the development of mastitis detection models. Prediction error rates can be decreased through the use of more informative parameters.

    REFERENCES

    1. Allahverdi, N. (2002). Expert systems, artificial intelligence application. Atlas Pub. Co., Istanbul.
    2. Atasever, S. and Erdem, H. (2008). Relationships between mastitis and electrical conductivity of raw milk in dairy cows. J. Fac. Agric., OMU, 23 (2): 131-136.
    3. Bademkiran, S., S. Yesilmen and Gürbulak, K. (2005). The effect of daily milking frequency on clinical mastitis and milk yield of dairy cows, Y.Y. Üniv., J. the Fac. Vet. Med., 16(2): 17-21.
    4. Batra, T.R. (1986). Relationship of somatic cell concentration with milk yield in dairy cows. Canadian Journal Animal Science, 66: 607-614.
    5. Bruckmaier, R.M., C.E. Ontsouka and Blum, J.W. (2004). Fractionized milk composition in dairy cows with subclinical mastitis. Vet. Med. - Czech., 49(8): 283-290.
    6. Cavero, D., K.H. Tölle, C. Buxade and Krieter, J. (2006). Mastitis detection in dairy cows by application of fuzzy logic. Livestock Science, 105: 207 – 213.
    7. Cedden, F., A. Kor and Keskin, S. (2002). Somatic cell counts in goat milk during late lactation period and its relationship with milk yield, age and some udder measurements, J. Agric. Sci., 12(2): 63-67.
    8. Eyduran, E., T. Özdemir, K. Yazgan and Keskin, S. (2005). The effects of lactation rank and period on somatic cell count (SCC) in milks of Holstein cows. Y.Y. Üniv., J. the Fac. Vet. Med., 16(1): 61-65.
    9. Göncü, S. and Özkütük, K. (2002). Factors effective at somatic cell count (SCC) in the milk of black and white cows kept in intensive dairy farms at Adana province and their relationships with mastitis. Hayvansal Üretim (Anim. Prod.), 43(2): 44-53.
    10. Ilie, L.I., L. Tudor and Galis, A.M. (2010). The electrical conductivity of cattle milk and the possibility of mastitis diagnosis in Romania. Vet. Med. Sci., Works; 43: 220-227.
    11. Jacceh, M. (2003). Neuro-Fuzzy System with learning tolerant to imprecision. Fuzzy Sets and Systems, 138: 427–439.
    12. Janzekovic, M., M. Brus, B. Mursec, P. Vinis, D. Stajnko and Cus, F. (2009). Mastitis detection based on electric conductivity of milk. JAMME; 34: 39-46.
    13. Jaos, V. (1998). Dynamic System Control and Identification for Fuzzy Inference Systems. IEEE.
    14. Jones, G.M., R.E. Pearson, G.A. Clabaugh and Heald, C.M. (1984). Relationship between somatic cell counts and milk production. Journal Dairy Science, 67: 1823-1831.
    15. Kesici, T. and Kocabas, Z. (2007). Biostatistics. Ankara Univ., Faculty of Pharmacy, Biostatistics Pub. No: 94, Ankara.
    16. Krieter, J., D. Cavero and Henze, C. (2007). Mastitis detection in dairy cows using neural networks. GIL Jahrestagung, 101: 123-126.
    17. Kul, E., H. Erdem and Atasever, S. (2006). Effect of different udder traits on mastitis and somatic cell count in dairy cows. J. Fac. Agric., OMU, 21(3): 350-356.
    18. Nielen, M., H. Deluyker, Y.H. Schukken and Brand, A. (1992). Electrical conductivity of milk: measurement, modifiers, and meta analysis of mastitis detection performance. Journal Dairy Science, 75: 606-614.
    19. Norberg, E., H. Hogeveen, I.R. Korsgaard, N.C. Friggens, K.H.M.N. Sloth and Lovendahl, P. (2004). Electrical conductivity of milk: ability to predict mastitis status. Journal Dairy Science, 87: 1099-1107.
    20. Osteras, O.S., V.L. Edge and Martin, S.W. (1999). Determinants success or failure in the elimination of major mastitis pathogens in selective dry cow therapy. Journal Dairy Science, vol. 82, no. 6, pp. 1221–1231.
    21. Porcionato, M.A.D., W.V.B. Soares, C.B.M. dos Reis, C.S. Cortinhas, L. Mestieri and dos Santos, M.V. (2010). Milk flow, teat morphology and subclinical mastitis prevalence in Gyr cows. Pesquisa Agropec. Bras. 45: 1507–1512.
    22. Raubertas, R.F. and Shook, G.E. (1982). Relationship between lactation measures of somatic cell concentration and milk yield. Journal of Dairy Science, 65: 419-425.
    23. Risvanli, A. and Kalkan, C. (2002). The effect of age and breed on somatic cell count and microbiological isolation rates in milk of dairy cows with subclinical mastitis, Y.Y. Univ., J. the Fac. Vet. Med., 13: 84-87.
    24. Sargeant, J.M., H.M. Scott, K.E. Leslie, M.J. Ireland and Bashiri, A. (1998). Clinical mastitis in dairy cattle in Ontario: Frequency of occurrence and bacteriological isolates. Canadian Vet. Journal, 39: 33-38.
    25. Schroeder, J.W. (2012). Mastitis control programs: Bovine mastitis and milking management. North Dakota State University, Fargo, North Dakota 1.5M-4-97, 200-7-12.
    26. Seker, I., A. Risvanli, S. Kul, M. Bayraktar and Kaygusuzoðlu, E. (2000). Relationships between CMT scores and udder traits and milk yield in Brown-Swiss cows. Lalahan Hay. Araº. Ens. Derg., 40: 29–38.
    27. Tekeli, T. (2005). Mastitis. Quality milk production and somatic cell count in the process of the European Union. Güzelis Pub. Co., Konya.
    28. Torlak, E. (2005). Quality milk production and somatic cell count in the process of the European Union. Güzelis Pub. Co., Konya. 2005.
    29. Uzmay, C., I. Kaya, Y. Akbas and Kaya, A. (2003). Effects of udder and teat morphology, parity and lactation stage on subclinical mastitis in Holstein cows. Tr. J. Vet. and Anim. Sci., 27: 695–701.
    30. Yang, X.Z., R. Lacroix and Wade, K.M. (2000). Investigation into the production and conformation traits associated with clinical mastitis using artificial neural Networks. Canadian Journal Animal Science, 80: 415–426.
    31. Wilson, D.J., R.N. González, J. Hertl, H.F. Schulte, G.J. Bennett, Y.H. Schukken and Gröhn, Y.T. (2004). Effect of clinical mastitis on the lactation curve: a mixed model estimation using daily milk weights. J. Dairy Sci, 87: 2073-2084.
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