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Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları

Yıl 2021, Cilt: 9 Sayı: 6 - ICAIAME 2021, 370 - 382, 31.12.2021
https://doi.org/10.29130/dubited.1015406

Öz

Hayvancılık, nesillerdir devam eden ve insanoğlunun temel gıda ihtiyacını karşılamasını sağlayan tarımın bir alt koludur. Ekonomik değer taşıyan hayvanların beslenmesi, bakımı ve üretimi yapılmaktadır. Büyükbaş, küçükbaş, tavukçuluk ve arıcılıkta hayvancılık kapsamında yer almaktadır. Temelinde hayvanların bakımı ve beslenmesi gibi gereksinimlerini karşılayarak insanların gıda ihtiyaçlarının sağlanması amaçlanmıştır. Bu ihtiyaçları karşılamak için hayvan çiftlikleri kurulmaktadır. Çiftliklerde hayvanların gereksinimlerinin sağlanması insan gücüne dayalı olarak sürdürülmektedir. Ancak günümüzde teknolojinin gelişmesiyle insan gücünün yerine makineler geçmektedir. Gömülü sistemler, robotik ve yapay zeka gibi konu alanlarının hayatımıza girmesiyle beraber karşılaşılan sorunlara daha kapsamlı çözümler bulunmaktadır. İnsan hatasından kaynaklanan ve kullanılan iş gücünü azaltarak en doğru bir şekilde mevcut teknolojiden faydalanılarak hayvancılık yapılması önerilmiştir. Çalışmamızda, literatürde bulunan hayvancılık kapsamında yapay zeka uygulamaları, görüntü işleme tabanlı sistemler, otonom çiftlik sistemleri incelenmiştir. İncelemelerden yola çıkarak insan hatasını minimize ederek yapay zeka tabanlı bir çiftliğin üretim kalitesi ve hızı yüksek oranda arttığı sonucuna varılmıştır. Mevcut çiftliklerde kendi kararını verebilen yapay zekaya sahip sistemlerin kullanılması üretim ve beslemenin yanı sıra hastalık tespiti de yapabilmektedir. Tamamen sayısal verilerden yola çıkarak maksimum verim elde etmek hedeflenmektedir.

Destekleyen Kurum

Kosgeb

Proje Numarası

4BHLI

Teşekkür

Bu çalışma, 4BHLI nolu KOSGEB Ar-Ge ve İnovasyon Destek Programı kapsamında desteklenmektedir.

Kaynakça

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Robotic Systems and Artificial Intelligence Applications in Livestock

Yıl 2021, Cilt: 9 Sayı: 6 - ICAIAME 2021, 370 - 382, 31.12.2021
https://doi.org/10.29130/dubited.1015406

Öz

Livestock farming is a sub-branch of agriculture that has been going on for generations and enables human beings to meet their basic food needs. Animals with economic value are fed, cared for and produced. Cattle, ovine, poultry and beekeeping are included in animal husbandry. It is aimed to meet the food needs of people by meeting the needs of animals such as care and feeding. Animal farms are established to meet these needs. Providing the needs of animals in farms is based on human power. However, today, with the development of technology, machines are replacing human power. With the introduction of subject areas such as embedded systems, robotics and artificial intelligence into our lives, there are more comprehensive solutions to the problems encountered. It has been suggested that animal husbandry should be done by making use of the existing technology in the most accurate way by reducing the labor force caused by human error. In this study, artificial intelligence applications, image processing based systems, autonomous farm systems were examined within the scope of animal husbandry in the literature. Based on the investigations, it was concluded that the production quality and speed of an artificial intelligence-based farm increased at a high rate by minimizing human error. The use of systems with artificial intelligence, which can make its own decision in existing farms, can detect diseases as well as production and feeding. It is aimed to achieve maximum efficiency based on purely numerical data.

Proje Numarası

4BHLI

Kaynakça

  • [1] M. Tuncay, Aristoteles: Politika. 1975.
  • [2] A. Hodges, Alan Turing: The Enigma. Princeton University Press, 2014.
  • [3] A. Süslü, “Doğa ve İnsan Bilimlerinde Yapay Zekâ Uygulamaları,” Akad. Doğa ve İnsan Bilim. Derg., vol. 5, no. 1, pp. 1–10, Dec. 2019.
  • [4] A. Kaplan and M. Haenlein, “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence,” Bus. Horiz., vol. 62, no. 1, pp. 15–25, Jan. 2019, doi: 10.1016/J.BUSHOR.2018.08.004.
  • [5] H. Pirim, “Yapay Zeka,” J. Yaşar Univ., vol. 1, no. 1, pp. 81–93, 2006.
  • [6] R. Hoehndorf and N. Queralt-Rosinach, “Data Science and symbolic AI: Synergies, challenges and opportunities,” Data Sci., vol. 1, no. 1–2, pp. 27–38, Jan. 2017, doi: 10.3233/DS-170004.
  • [7] Vijay Kotu and Bala Deshpande, “Data Science: Concepts and Practice ,” in 2. Edition. Elsevier, USA, 2019.
  • [8] M. A. Tabak et al., “Machine learning to classify animal species in camera trap images: Applications in ecology,” Methods Ecol. Evol., vol. 10, no. 4, pp. 585–590, Apr. 2019, doi: 10.1111/2041-210X.13120.
  • [9] P. Valdes-Donoso, K. VanderWaal, L. S. Jarvis, S. R. Wayne, and A. M. Perez, “Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US,” Front. Vet. Sci., vol. 0, no. JAN, p. 2, Jan. 2017, doi: 10.3389/FVETS.2017.00002.
  • [10] D. B. Jensen, H. Hogeveen, and A. De Vries, “Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis,” J. Dairy Sci., vol. 99, no. 9, pp. 7344–7361, Sep. 2016, doi: 10.3168/JDS.2015-10060.
  • [11] M. Ebrahimi, M. Mohammadi-Dehcheshmeh, E. Ebrahimie, and K. R. Petrovski, “Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models,” Comput. Biol. Med., vol. 114, p. 103456, Nov. 2019, doi: 10.1016/J.COMPBIOMED.2019.103456.
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  • [13] M. S. Shahriar et al., “Detecting heat events in dairy cows using accelerometers and unsupervised learning,” Comput. Electron. Agric., vol. 128, pp. 20–26, Oct. 2016, doi: 10.1016/J.COMPAG.2016.08.009.
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  • [15] E. Işık and T. Güler, “Farklı Vakum Değerlerinde İneklerde Sağım Sonrası Meme Başı Deformasyonun Görüntü İşleme Tekniğiyle Saptanması,” Uludağ Üniversitesi Ziraat Fakültesi Derg., vol. 23, no. 1, pp. 33–41, Apr. 2009.
  • [16] E. Dandıl, M. Turkan, M. Boğa, and K. K. Çevik, “Daha Hızlı Bölgesel-Evrişimsel Sinir Ağları ile Sığır Yüzlerinin Tanınması,” Bilecik Şeyh Edebali Üniversitesi Fen Bilim. Derg., vol. 6, pp. 177–189, Sep. 2019, doi: 10.35193/bseufbd.592099.
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  • [20] R. Raksha and P. Surekha, “A cohesive farm monitoring and wild animal warning prototype system using IoT and machine learning,” 2020 Int. Conf. Smart Technol. Comput. Electr. Electron., pp. 472–476, Oct. 2020, doi: 10.1109/ICSTCEE49637.2020.9277267.
  • [21] O. Debauche, M. Elmoulat, S. Mahmoudi, J. Bindelle, and F. Lebeau, “Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review,” Rev. d’Intelligence Artif., vol. 35, no. 3, pp. 243–253, Jun. 2021, doi: 10.18280/RIA.350308.
  • [22] D. Warner, E. Vasseur, D. M. Lefebvre, and R. Lacroix, “A machine learning based decision aid for lameness in dairy herds using farm-based records,” Comput. Electron. Agric., vol. 169, p. 105193, Feb. 2020, doi: 10.1016/J.COMPAG.2019.105193.
  • [23] G. Chen and T. T. Pham, Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. CRC Press, 2000.
  • [24] İsmail H. ALTAŞ, “Bulanık Mantık : Bulanıklılık Kavram,” Bilesim yayıncılık A.Ş, vol. 62, pp. 80–85, 1999.
  • [25] Akıllı Aslı, Atıl Hülya, and Harun Kesenkaş, “Çiğ Süt Kalite Değerlendirmesinde Bulanık Mantık Yaklaşımı,” Kafkas Üniversitesi Vet. Fakültesi Derg., vol. 20(2), pp. 223–229, 2014.
  • [26] K. M. Wade, R. Lacroix, and M. Strasser, “Fuzzy logic membership values as a ranking tool for breeding purposes in dairy cattle,” in Proceedings of the 6th World Congress on Genetics Applied to Livestock Production, 1998, vol. 27, pp. 433–436.
  • [27] M. Strasser, R. Lacroix, R. Kok, and K. M. Wade, “A second generation decision support system for the recommendation of dairy cattle culling decisions,” 1997.
  • [28] I. Morag, Y. Edan, and E. Maltz, “An individual feed allocation decision support system for the dairy farm,” J. Agric. Eng. Res., vol. 79, no. 2, pp. 167–176, 2001.
  • [29] M. M. Sangatash, M. Mohebbi, F. Shahidi, A. V. Kamyad, and M. Q. Rohani, “Application of fuzzy logic to classify raw milk based on qualitative properties,” Int. J. AgriScience, vol. 2(12), pp. 1168–1178, 2012.
  • [30] E. Kramer, D. Cavero, E. Stamer, and J. Krieter, “Mastitis and lameness detection in dairy cows by application of fuzzy logic,” Livest. Sci., vol. 125, no. 1, pp. 92–96, Oct. 2009, doi: 10.1016/J.LIVSCI.2009.02.020.
  • [31] N. Mikail and İ. Keskin, “İneklerde bulanık mantık modeli ile hareketlilik ölçüsünden yararlanılarak kızgınlığın tespiti,” Kafkas Üniversitesi Vet. Fakültesi Derg., vol. 17, no. 6, pp. 1003–1008, 2011.
  • [32] R. M. De Mol and W. E. Woldt, “Application of Fuzzy Logic in Automated Cow Status Monitoring,” J. Dairy Sci., vol. 84, no. 2, pp. 400–410, Feb. 2001, doi: 10.3168/JDS.S0022-0302(01)74490-6.
  • [33] D. Cavero, K. H. Tölle, C. Buxadé, and J. Krieter, “Mastitis detection in dairy cows by application of fuzzy logic,” Livest. Sci., vol. 105, no. 1–3, pp. 207–213, Dec. 2006, doi: 10.1016/J.LIVSCI.2006.06.006.
  • [34] H. A. Zarchi, R. I. Jónsson, and M. Blanke, “Improving Oestrus Detection in Dairy Cows by Combining Statistical Detection with Fuzzy Logic Classification,” in Proceedings of the 7th Workshop on Advanced Control and Diagnosis, 2009, p. 20.
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  • [36] M. Zaninelli, F. M. Tangorra, A. Costa, L. Rossi, V. Dell’Orto, and G. Savoini, “Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis,” Sensors 2016, Vol. 16, Page 1079, vol. 16, no. 7, p. 1079, Jul. 2016, doi: 10.3390/S16071079.
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Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ali Hakan Isık 0000-0003-3561-9375

Ferdi Alakus Bu kişi benim 0000-0002-6096-4659

Ömer Can Eskicioğlu 0000-0001-5644-2957

Proje Numarası 4BHLI
Yayımlanma Tarihi 31 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 9 Sayı: 6 - ICAIAME 2021

Kaynak Göster

APA Isık, A. H., Alakus, F., & Eskicioğlu, Ö. C. (2021). Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları. Duzce University Journal of Science and Technology, 9(6), 370-382. https://doi.org/10.29130/dubited.1015406
AMA Isık AH, Alakus F, Eskicioğlu ÖC. Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları. DÜBİTED. Aralık 2021;9(6):370-382. doi:10.29130/dubited.1015406
Chicago Isık, Ali Hakan, Ferdi Alakus, ve Ömer Can Eskicioğlu. “Hayvancılıkta Robotik Sistemler Ve Yapay Zekâ Uygulamaları”. Duzce University Journal of Science and Technology 9, sy. 6 (Aralık 2021): 370-82. https://doi.org/10.29130/dubited.1015406.
EndNote Isık AH, Alakus F, Eskicioğlu ÖC (01 Aralık 2021) Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları. Duzce University Journal of Science and Technology 9 6 370–382.
IEEE A. H. Isık, F. Alakus, ve Ö. C. Eskicioğlu, “Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları”, DÜBİTED, c. 9, sy. 6, ss. 370–382, 2021, doi: 10.29130/dubited.1015406.
ISNAD Isık, Ali Hakan vd. “Hayvancılıkta Robotik Sistemler Ve Yapay Zekâ Uygulamaları”. Duzce University Journal of Science and Technology 9/6 (Aralık 2021), 370-382. https://doi.org/10.29130/dubited.1015406.
JAMA Isık AH, Alakus F, Eskicioğlu ÖC. Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları. DÜBİTED. 2021;9:370–382.
MLA Isık, Ali Hakan vd. “Hayvancılıkta Robotik Sistemler Ve Yapay Zekâ Uygulamaları”. Duzce University Journal of Science and Technology, c. 9, sy. 6, 2021, ss. 370-82, doi:10.29130/dubited.1015406.
Vancouver Isık AH, Alakus F, Eskicioğlu ÖC. Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları. DÜBİTED. 2021;9(6):370-82.