Evaluate of The Reproductive Efficiency of Cows With Fuzzy Logic

Year 2021, Volume: 9 Issue: 2, 19 - 25, 30.06.2021

Betül Ağaoğlu , Bahat Comba , Hasan Koyun

https://doi.org/10.18100/ijamec.801610

Cited By: 1

Abstract

Fuzzy Logic (Fuzzy Logic) is a branch of science based on thinking like human beings and solving them with mathematical functions. Fuzzy logic theory is a mathematical theory. Based on fuzzy set theory, it also uses intermediate values. The fuzzy logic that emerged in 1965 is used in many fields. In the production of pacemakers, in the production of artificial organs, in many electronic devices, company efficiency estimation, etc. situations are used. Fuzzy logic, which is frequently used in the solution of problems that occur in uncertain situations such as quality assessment in recent years, is one of the artificial intelligence methods. With the help of machines, people-specific data and experiences are studied using the fuzzy logic approach. In this study, by using Matlab Fuzzy Toolbox, it was aimed to design a system that gives information about the breeding performances of cows. The expert system was designed based on the optimal values under the ideal conditions specified in the literature. The architecture of the system presented in this paper is designed as three input parameters and one output. The designed system was tested with 100 sample values. Afterwards, expert results were evaluated and system decisions were compared. The success of the decision support system was 94%. As a result, the reproductive efficiency of cows can be determined with this designed system. With this determination, the handling or disposal of cows can be determined.

Keywords

Cow, Expert system, Farm yield, Fuzzy logic, Reproductive efficiency

References

• L. Wang, A course in fuzzy systems and control prentice hall, Facsimile edition, 1997.
• A. Akilli, H. Atil, and H. Kesenkaş, “Çiğ süt kalite değerlendirmesinde bulanık mantık yaklaşımı”, Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 20(2): p. 223-229.2014.
• İ.H. Altaş, Bulanık Mantık: Bulanıklılık Kavramı, Enerji, Elektrik, Elektromekanik-3e, 62: p. 80-85, 1999.
• H.J. Zimmermann, Fuzzy set theory and its applications, Springer Science & Business Media, 2011.
• İ. Ertuğrul, “Akademik performans değerlendirmede bulanik mantik yaklaşimi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(1): p. 155-176, 2006.
• I. Morag, Y. Edan, and E. Maltz, “IT—Information technology: an individual feed allocation decision support system for the dairy farm”, Journal of Agricultural Engineering Research, 79(2): p. 167-176, 2001.
• M. Sangatash, “Application of fuzzy logic to classify raw milk based on qualitative properties”, International Journal of AgriScience, 2(12): p. 1168-1178, 2012.
• Ç. Takma, H. Atıl, and V. Aksakal, “Çoklu doğrusal regresyon ve yapay sinir ağı modellerinin laktasyon süt verimlerine uyum yeteneklerinin karşılaştırılması”, Veterinerlik Fakültesi Dergisi, Kafkas Üniversitesi, 18(6): p. 941-944, 2012.
• P. Grinspan, “A fuzzy logic expert system for dairy cow transfer between feeding groups”, Transactions of the ASAE, 37(5): p. 1647-1654, 1994.
• H. Atil, and A. Akilli, “Investigation of dairy cattle traits by using artificial neural networks and cluster analysis”, HAICTA, 2015.
• R. De Mol, and W. Woldt, “Application of fuzzy logic in automated cow status monitoring” Journal of Dairy Science, 84(2): p. 400-410, 2001.
• L. Sanzogni, and D. Kerr, “Milk production estimates using feed forward artificial neural networks”, Computers and Electronics in Agriculture, 32(1): p. 21-30,2001.
• K. Hassan, S. Samarasinghe, and M. Lopez-Benavides, “Use of neural networks to detect minor and major pathogens that cause bovine mastitis” Journal of Dairy Science, 92(4): p. 1493-1499, 2009.
• X. Yang, R. Lacroix, and K. Wade, “Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks”, Canadian Journal of Animal Science, 80(3): p. 415-426, 2000.
• S. Shahinfar, “Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems”, Computational and Mathematical Methods in Medicine, 2012.
• A.M. Uygur, “Süt sığırcılığı sürü yönetiminde döl verimi”, Ege Tarımsal Araştırma Enstitüsü- Hayvansal Üretim, 45(2): p. 23-27, 2004.
• Ç. Elmas, Bulanık Mantık Denetleyiciler:(Kuram, Uygulama. Sinirsel Bulanık Mantık), Seçkin Yayıncılık, 2003.
• A. Akkaptan, “Hayvancılıkta bulanık mantık tabanlı karar destek sistemi” Yüksek Lisans Tezi, 2012.
• T.J. Ross, Fuzzy logic with engineering applications. John Wiley & Sons., 2005.
• N. Baykal, and T. Beyan, Bulanık mantık ilke ve temelleri, Bıçaklar Kitabevi, 2004.
• A. Önenç, Süt sığırcılığında sürü izlence tablolarından yararlanma olanakları, US Feed Grains Council, 99, 1996.
• F. Salehi, R. Lacroix, and K. Wade, “Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks”, Computers and Electronics in Agriculture, 20(3): p. 199-213, 1998.
• N. Mikail, and İ. Keskin, “İneklerde bulanık mantık modeli ile hareketlilik ölçüsünden yararlanılarak kızgınlığın tespiti”, Kafkas Universitesi Vet. Fak. Dergisi, 17 (6): 1003-1008, 2011.
• O. Gorgulu, and A. Akilli, “Estimation of 305-days milk yield using fuzzy linear regression in jersey dairy cattle”, Journal of Animal and Plant Sciences, 28(4): p. 1174-1181, 2018.
• A. Akıllı, “Fuzzy logic-based decision support system for dairy cattle”, Kafkas Universitesi Veteriner Fakültesi Dergisi, 22(1): p. 13-19, 2016.
• R. Firk, “Improving oestrus detection by combination of activity measurements with information about previous oestrus cases” Livestock Production Science, 82(1): p. 97-103, 2003.
• H.A. Zarchi, R.I. Jónsson, and M. Blanke. “Improving oestrus detection in dairy cows by combining statistical detection with fuzzy logic classification”, Advanced Control and Diagnosis, 2009.
• D. Cavero, “Mastitis detection in dairy cows by application of fuzzy logic”, Livestock Science, 105(1-3): p. 207-213,2006.
• E. Kramer, “Mastitis and lameness detection in dairy cows by application of fuzzy logic”, Livestock Science, 125(1): p. 92-96, 2009.