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Süt Sığırcılığında Yapay Zeka Teknolojisi: Bulanık Mantık ve Yapay Sinir Ağları

Year 2014, Volume: 55 Issue: 1, 39 - 45, 28.11.2014

Aslı Akıllı Hülya Atıl

https://doi.org/10.29185/hayuretim.363911
Cited By: 1

Abstract



Yapay zeka, bilimsel araştırmalarda karmaşık problemlerin
çözümlenmesi amacıyla oluşturulan modellerde yaygın olarak kullanılmaktadır.
Tarımsal alanda, özellikle hayvansal veriler üzerinde anlamlı ilişkilerin
ortaya çıkarılması ve etkili hesaplama yöntemleri sayesinde araştırıcılara
büyük faydalar sağlamaktadır. Bu çalışmada yetiştirici ve araştırmacılara,
karar verme ve değerlendirme süreçlerinde kullanılan karar destek sistemleri
sayesinde büyük kolaylıklar sağlayan “bulanık mantık” ile oldukça başarılı verim
tahminleri ve çeşitli sınıflandırmalar gerçekleştiren “yapay sinir ağları”
yöntemleri tanıtılacak ve süt sığırcılığı alanında gerçekleştirilen
uygulamalara yer verilecektir. 



Keywords

Bulanık mantık bulanık küme yapay sinir ağları süt sığırcılığı

References

  • Akkaptan, A. 2012. Hayvancılıkta bulanık mantık tabanlı karar destek sistemi. Yüksek Lisans Tezi, Ege Üniv. Fen Bil. Enst. İzmir.
  • Akıllı, A., Atıl, H., Kesenkaş, H. 2014. Çiğ süt kalite değerlendirmesinde bulanık mantık yaklaşımı. Kafkas Üniv. Vet. Fak. Derg. 20(2): 223-229.
  • Baykal, N., Beyan, T. 2004. Bulanık Mantık İlke ve Temelleri. Bıçaklar Kitabevi, Ankara.
  • Brown- Brandl,T. M., Jones, D. D., Woldt, W. E. 2005. Evaluating modelling techniques for cattle heat stress prediction. Biosystems Engineering 91(4): 513-524.
  • Cavero, D., Tölle, K. T., Buxade, C., Krieter, J. 2006. Mastitis detection in dairy cows by application of fuzzy logic. Livest. Prod. Sci. 105(1-3): 207-213.
  • Cha, M., Park, S. T., Kim, T., Jayarao, B.M. 2008. Evaluation of bulk tank milk quality based on fuzzy logic. Proceedings of the 2008 International Conference on Artificial Intelligence, 14-17 July 2008, pp.722-727, Las Vegas, Nevada, USA. http://nguyendangbinh.org/Proceedings/IPCV08/Papers/MLM3028.pdf (Erişim: 05.06.2011).
  • Chen, L. J., Cui, L. Y., Xing, L., Han, L. J. 2008. Prediction of the nutrient content in dairy manure using artificial neural network modeling. J. Dairy. Sci. 91: 4822-4829.
  • Craninx, M., Fievez, V., Vlaeminck, B., De Baets, B. 2008. Artificial neural network models of the rumen fermentation pattern in dairy cattle. Comput. Electron. Agric. 60: 226-238.
  • de Mol, R.M., Woldt, W.E. 2001. Application of fuzy logic in automated cow status monitoring. J. Dairy Sci. 84: 400-410.
  • Dong, R., Zhao, G. 2014. The use of artificial neural in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables. Livest. Prod. Sci. 162: 159-167.
  • Firk, R., Stamer, E., Junge, W., Krieter, J. 2003. Improving oestrus detection by combination of activity measurements with information about previous oestrus cases. Livest. Prod. Sci. 82(1): 97-103.
  • Grinspan, P., Edan, Y., Kahne, H., Maltz, E. 1994. A fuzzy logic expert system for dairy cow transfer between feding groups. Transactions of The ASAE 37(5): 1647–1654.
  • Grzesiak, W., Blaszczyk, P., Lacroix, R. 2006. Methods of predicting milk yield in dairy cows- Predictive capabilities of Wood’s lactation curve and artificial neural networks (ANNs). Comput. Electron. Agric. 54: 69-83.
  • Grzesiak, W., Lacroix, R., Wójcik, J., Blaszczyk, P. 2003. A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Can. J. Anim. Sci. 83: 307-310.
  • Harris, J. 1998. Raw milk grading using fuzzy logic. Int. J. Dairy. Technol. 51(2): 52-56. Hassan, K. J., Samarasinghe, S., Lopez- Benavidest, M. G. 2009. Use of neural networks to detect minor and major pathogens that cause bovine mastitis. J. Dairy. Sci. 92: 1493-1499.
  • Hosseinia, P., Edrisi, M., Edriss, M. A. Nilforooshan, M. A. 2007. Prediction of second parity milk yield and fat percentage of dairy cows based on first parity information using neural networks system. J. Appl. Sci. 7: 3274-3279.
  • Klir, J. G., Yuan, B. 1995. Fuzzy sets and fuzzy logic: Theory and application, Prentice Hall, New Jersey, 574p.
  • Kramer, E., Cavero, D., Stamer, E., Krieter, J. 2009. Mastitis and lameness detection in dairy cows by application of fuzzy logic. Livest. Prod. Sci. 125: 92-96.
  • Mehraban, S. M., Mohebbi, M., Shahidi, F., Vahidian, K. A., Qhods, R. M. 2012. Application of fuzzy logic to classify raw milk based on qualitative properties. Int. J. Agri. Sci. 2(12): 1168-1178.
  • Memmedova, N., Keskin İ. 2011. İneklerde bulanık mantık modeli ile hareketlilik ölçüsünden yararlanılarak kızgınlığın tespiti. Kafkas Üniv. Vet. Fak. Derg. 17(6): 1003-1008.
  • Morag, I., Edan, Y., Maltz, E. 2001. An individual feed allocation decision support system for the dairy farm. J. Agric. Eng. Res. 79(2): 167-176.
  • Negnevitsky, M. 2002. Artificial Intelligence, A Guide to Intelligent Systems. Pearson Education, Harlow, 415 p.
  • Öztemel, E. 2006. Yapay sinir ağları. Papatya Yayıncılık, İstanbul.
  • Salehi, F., Lacroix, R., Wade, K. M. 1998. Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks. Comput. Electron. Agric. 20: 199-213.
  • Salehi, F., Lacroix, R., Wade, K.M. 2000. Development of neuro-fuzzifiers for qualitative analyses of milk yield. Comput. Electron. Agric. 28: 171-186.
  • Sanzogni, L., Kerr, D. 2001. Milk production estimates using feed forwrd artificial neural networks. Comput. Electron. Agric. 32: 21-30.
  • Sharma, A. K., Sharma, R. K., Kasana, H. S. 2007. Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Appl. Soft Comput. 7: 1112-1120.
  • Shahinfar, S., Mehrabani-Yeganeh, H., Lucas, C., Kalhor, A., Kazemian, M., Weigel, K. A. 2012. Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Comput. Math. Methods Med. Article ID 127130, 9 pages.
  • Spangler, M. L., Sapp, R. L., Rekaya, R., Bertland, J. K. 2006. Success at first insemination in Australian angus cattle: Analysis of uncertain binary responses. J. Anim. Sci. 84: 20-24.
  • Strasser, M., Lacroix, R., Kok, R., Wade, K. M. 1997. A second generation decision support system for the recommendation of dairy cattle culling decisions, http://www.mcgill.ca/files/animal/97r04.pdf (Erişim: 08.12.2011).
  • Takma, Ç., Atıl, H., Aksakal, V. 2012. Çoklu doğrusal regresyon ve yapay sinir ağı modellerinin laktasyon süt verimlerine uyum yeteneklerinin karşılaştırılması. Kafkas Üniv. Vet. Fak. Derg. 18(6): 941-944.
  • Wade, K. M., Lacroix, R., Strasser, M. 1998. Fuzzy logic membership values as a ranking tool for breeding purposes in dairy cattle. Proceedings of the 6th World Congress on Genetics Applied to Livestock Production, 11-16 Jan 1998, 27: 433-436, Armidale, Australia.
  • Yang, X. Z., Lacroix, R., Wade, K. M. 2000. Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks. Can. J. Anim. Sci. 80: 415–426.
  • Zadeh, L. 1965. Fuzzy sets. Inform. Control. 8(3): 338-353.
  • Zarchi, H. A., Jonsson, R., Blanke M. 2009. Improving oestrus detection in dairy cows by combining statistical detection with fuzzy logic classification. http://orbit.dtu.dk/fedora/objects/orbit:56420/datastreams/file_4054429/content, (Erişim: 19.04.2013).

Year 2014, Volume: 55 Issue: 1, 39 - 45, 28.11.2014

Aslı Akıllı Hülya Atıl

https://doi.org/10.29185/hayuretim.363911
Cited By: 1

Abstract

References

  • Akkaptan, A. 2012. Hayvancılıkta bulanık mantık tabanlı karar destek sistemi. Yüksek Lisans Tezi, Ege Üniv. Fen Bil. Enst. İzmir.
  • Akıllı, A., Atıl, H., Kesenkaş, H. 2014. Çiğ süt kalite değerlendirmesinde bulanık mantık yaklaşımı. Kafkas Üniv. Vet. Fak. Derg. 20(2): 223-229.
  • Baykal, N., Beyan, T. 2004. Bulanık Mantık İlke ve Temelleri. Bıçaklar Kitabevi, Ankara.
  • Brown- Brandl,T. M., Jones, D. D., Woldt, W. E. 2005. Evaluating modelling techniques for cattle heat stress prediction. Biosystems Engineering 91(4): 513-524.
  • Cavero, D., Tölle, K. T., Buxade, C., Krieter, J. 2006. Mastitis detection in dairy cows by application of fuzzy logic. Livest. Prod. Sci. 105(1-3): 207-213.
  • Cha, M., Park, S. T., Kim, T., Jayarao, B.M. 2008. Evaluation of bulk tank milk quality based on fuzzy logic. Proceedings of the 2008 International Conference on Artificial Intelligence, 14-17 July 2008, pp.722-727, Las Vegas, Nevada, USA. http://nguyendangbinh.org/Proceedings/IPCV08/Papers/MLM3028.pdf (Erişim: 05.06.2011).
  • Chen, L. J., Cui, L. Y., Xing, L., Han, L. J. 2008. Prediction of the nutrient content in dairy manure using artificial neural network modeling. J. Dairy. Sci. 91: 4822-4829.
  • Craninx, M., Fievez, V., Vlaeminck, B., De Baets, B. 2008. Artificial neural network models of the rumen fermentation pattern in dairy cattle. Comput. Electron. Agric. 60: 226-238.
  • de Mol, R.M., Woldt, W.E. 2001. Application of fuzy logic in automated cow status monitoring. J. Dairy Sci. 84: 400-410.
  • Dong, R., Zhao, G. 2014. The use of artificial neural in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables. Livest. Prod. Sci. 162: 159-167.
  • Firk, R., Stamer, E., Junge, W., Krieter, J. 2003. Improving oestrus detection by combination of activity measurements with information about previous oestrus cases. Livest. Prod. Sci. 82(1): 97-103.
  • Grinspan, P., Edan, Y., Kahne, H., Maltz, E. 1994. A fuzzy logic expert system for dairy cow transfer between feding groups. Transactions of The ASAE 37(5): 1647–1654.
  • Grzesiak, W., Blaszczyk, P., Lacroix, R. 2006. Methods of predicting milk yield in dairy cows- Predictive capabilities of Wood’s lactation curve and artificial neural networks (ANNs). Comput. Electron. Agric. 54: 69-83.
  • Grzesiak, W., Lacroix, R., Wójcik, J., Blaszczyk, P. 2003. A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Can. J. Anim. Sci. 83: 307-310.
  • Harris, J. 1998. Raw milk grading using fuzzy logic. Int. J. Dairy. Technol. 51(2): 52-56. Hassan, K. J., Samarasinghe, S., Lopez- Benavidest, M. G. 2009. Use of neural networks to detect minor and major pathogens that cause bovine mastitis. J. Dairy. Sci. 92: 1493-1499.
  • Hosseinia, P., Edrisi, M., Edriss, M. A. Nilforooshan, M. A. 2007. Prediction of second parity milk yield and fat percentage of dairy cows based on first parity information using neural networks system. J. Appl. Sci. 7: 3274-3279.
  • Klir, J. G., Yuan, B. 1995. Fuzzy sets and fuzzy logic: Theory and application, Prentice Hall, New Jersey, 574p.
  • Kramer, E., Cavero, D., Stamer, E., Krieter, J. 2009. Mastitis and lameness detection in dairy cows by application of fuzzy logic. Livest. Prod. Sci. 125: 92-96.
  • Mehraban, S. M., Mohebbi, M., Shahidi, F., Vahidian, K. A., Qhods, R. M. 2012. Application of fuzzy logic to classify raw milk based on qualitative properties. Int. J. Agri. Sci. 2(12): 1168-1178.
  • Memmedova, N., Keskin İ. 2011. İneklerde bulanık mantık modeli ile hareketlilik ölçüsünden yararlanılarak kızgınlığın tespiti. Kafkas Üniv. Vet. Fak. Derg. 17(6): 1003-1008.
  • Morag, I., Edan, Y., Maltz, E. 2001. An individual feed allocation decision support system for the dairy farm. J. Agric. Eng. Res. 79(2): 167-176.
  • Negnevitsky, M. 2002. Artificial Intelligence, A Guide to Intelligent Systems. Pearson Education, Harlow, 415 p.
  • Öztemel, E. 2006. Yapay sinir ağları. Papatya Yayıncılık, İstanbul.
  • Salehi, F., Lacroix, R., Wade, K. M. 1998. Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks. Comput. Electron. Agric. 20: 199-213.
  • Salehi, F., Lacroix, R., Wade, K.M. 2000. Development of neuro-fuzzifiers for qualitative analyses of milk yield. Comput. Electron. Agric. 28: 171-186.
  • Sanzogni, L., Kerr, D. 2001. Milk production estimates using feed forwrd artificial neural networks. Comput. Electron. Agric. 32: 21-30.
  • Sharma, A. K., Sharma, R. K., Kasana, H. S. 2007. Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Appl. Soft Comput. 7: 1112-1120.
  • Shahinfar, S., Mehrabani-Yeganeh, H., Lucas, C., Kalhor, A., Kazemian, M., Weigel, K. A. 2012. Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Comput. Math. Methods Med. Article ID 127130, 9 pages.
  • Spangler, M. L., Sapp, R. L., Rekaya, R., Bertland, J. K. 2006. Success at first insemination in Australian angus cattle: Analysis of uncertain binary responses. J. Anim. Sci. 84: 20-24.
  • Strasser, M., Lacroix, R., Kok, R., Wade, K. M. 1997. A second generation decision support system for the recommendation of dairy cattle culling decisions, http://www.mcgill.ca/files/animal/97r04.pdf (Erişim: 08.12.2011).
  • Takma, Ç., Atıl, H., Aksakal, V. 2012. Çoklu doğrusal regresyon ve yapay sinir ağı modellerinin laktasyon süt verimlerine uyum yeteneklerinin karşılaştırılması. Kafkas Üniv. Vet. Fak. Derg. 18(6): 941-944.
  • Wade, K. M., Lacroix, R., Strasser, M. 1998. Fuzzy logic membership values as a ranking tool for breeding purposes in dairy cattle. Proceedings of the 6th World Congress on Genetics Applied to Livestock Production, 11-16 Jan 1998, 27: 433-436, Armidale, Australia.
  • Yang, X. Z., Lacroix, R., Wade, K. M. 2000. Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks. Can. J. Anim. Sci. 80: 415–426.
  • Zadeh, L. 1965. Fuzzy sets. Inform. Control. 8(3): 338-353.
  • Zarchi, H. A., Jonsson, R., Blanke M. 2009. Improving oestrus detection in dairy cows by combining statistical detection with fuzzy logic classification. http://orbit.dtu.dk/fedora/objects/orbit:56420/datastreams/file_4054429/content, (Erişim: 19.04.2013).
There are 35 citations in total.

Details

Journal Section Reviews
Authors

Aslı Akıllı This is me

Hülya Atıl

Publication Date November 28, 2014
Submission Date August 28, 2014
Published in Issue Year 2014 Volume: 55 Issue: 1

Cite

APA Akıllı, A., & Atıl, H. (2014). Süt Sığırcılığında Yapay Zeka Teknolojisi: Bulanık Mantık ve Yapay Sinir Ağları. Hayvansal Üretim, 55(1), 39-45. https://doi.org/10.29185/hayuretim.363911

Cited By

ANN approach for estimation of cow weight depending on photogrammetric body dimensions
International Journal of Engineering and Geosciences
https://doi.org/10.26833/ijeg.427531
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