Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları

Year 2021, Volume: 9 Issue: 6 - ICAIAME 2021, 370 - 382, 31.12.2021

Ali Hakan Isık Ferdi Alakus Ömer Can Eskicioğlu

https://doi.org/10.29130/dubited.1015406

Abstract

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.

Keywords

Hayvancılık, Çiftlik Sistemleri, Yapay Zeka Uygulamaları, Otonom Teknolojiler, Görüntü İşleme, Robotik Sistemler

Supporting Institution

Kosgeb

Project Number

4BHLI

Thanks

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

Robotic Systems and Artificial Intelligence Applications in Livestock

Abstract

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.

Keywords

Livestock, Farm Systems, Artificial Intelligence Applications, Autonomous Technologies, Image Processing, Robotic Systems

Project Number

4BHLI

References

• [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.
• [12] A. Koray Yildiz, “Determination of Estrus in Cattle With Neural Networks Using Mobility and Environmental Data,” 2016.
• [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.
• [14] L. dos A. Brunassi et al., “Improving detection of dairy cow estrus using fuzzy logic,” Sci. Agric., vol. 67, no. 5, pp. 503–509, 2010, doi: 10.1590/S0103-90162010000500002.
• [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.
• [17] P. Cihan, E. Gökçe, and O. Kalipsiz, “Veteriner Hekimlik Alanında Makine Öğrenmesi Uygulamaları Üzerine Bir Derleme,” Kafkas Univ. Vet. Fak. Derg., vol. 23, no. 4, pp. 673–680, 2017, doi: 10.9775/KVFD.2016.17281.
• [18] Y. Rao, M. Jiang, W. Wang, W. Zhang, and R. Wang, “On-farm welfare monitoring system for goats based on Internet of Things and machine learning,” Int. J. Distrib. Sens. Networks, vol. 16, no. 7, Jul. 2020, doi: 10.1177/1550147720944030. [19] N. Volkmann, B. Kulig, S. Hoppe, J. Stracke, O. Hensel, and N. Kemper, “On-farm detection of claw lesions in dairy cows based on acoustic analyses and machine learning,” J. Dairy Sci., vol. 104, no. 5, pp. 5921–5931, May 2021, doi: 10.3168/JDS.2020-19206.
• [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.
• [35] S. A. Santos et al., “A fuzzy logic-based tool to assess beef cattle ranching sustainability in complex environmental systems,” J. Environ. Manage., vol. 198, pp. 95–106, Aug. 2017, doi: 10.1016/J.JENVMAN.2017.04.076.
• [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.
• [37] M. Zaninelli, L. Rossi, F. M. Tangorra, A. Costa, A. Agazzi, and G. Savoini, “On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats,” Ital. J. Anim. Sci., vol. 13, no. 2, pp. 340–347, Mar. 2016, doi: 10.4081/IJAS.2014.3170.
• [38] P. Harsani, I. Mulyana, and D. Zakaria, “Fuzzy logic and A* algorithm implementation on goat foraging games,” IOP Conf. Ser. Mater. Sci. Eng., vol. 332, no. 1, p. 012054, Mar. 2018, doi: 10.1088/1757-899X/332/1/012054.
• [39] M. Zaninelli, L. Rossi, A. Costa, F. M. Tangorra, A. Agazzi, and G. Savoini, “Use of Electrical Coductivity Sensors to monitor Health Status and Quality of Milk in Dairy Goats,” Int. J. Heal. Anim. Sci. Food Saf., vol. 2, no. 2s, Nov. 2015, doi: 10.13130/2283-3927/6468.
• [40] D. Butler, L. Holloway, and C. Bear, “The impact of technological change in dairy farming: Robotic milking systems and the changing role of the stockperson,” Artic. J. R. Agric. Soc. Engl., 2012.
• [41] T. Kounalakis, G. A. Triantafyllidis, and L. Nalpantidis, “Deep learning-based visual recognition of rumex for robotic precision farming,” Comput. Electron. Agric., vol. 165, p. 104973, Oct. 2019, doi: 10.1016/J.COMPAG.2019.104973.
• [42] W. Rossing, P. H. Hogewerf, A. H. Ipema, C. C. K.-D. Lauwere, and C. J. A. M. De Koning, “Robotic milking in dairy farming,” Netherlands J. Agric. Sci., vol. 45, no. 1, pp. 15–31, Jul. 1997, doi: 10.18174/NJAS.V45I1.523.
• [43] T. K. Hamrita and E. W. Tollner, “Toward fulfilling the robotic farming vision: Advances in sensors and controllers for agricultural applications,” IEEE Trans. Ind. Appl., vol. 36, no. 4, pp. 1026–1032, Jul. 2000, doi: 10.1109/28.855956.
• [44] R. Orsini et al., “Setting of a precision farming robotic laboratory for cropping system sustainability and food safety and security: preliminary results,” IOP Conf. Ser. Earth Environ. Sci., vol. 275, no. 1, p. 012021, May 2019, doi: 10.1088/1755-1315/275/1/012021.
• [45] S. C. Lauguico, R. S. Concepcion, D. D. MacAsaet, J. D. Alejandrino, A. A. Bandala, and E. P. Dadios, “Implementation of Inverse Kinematics for Crop-Harvesting Robotic Arm in Vertical Farming,” Proc. IEEE 2019 9th Int. Conf. Cybern. Intell. Syst. Robot. Autom. Mechatronics, CIS RAM 2019, pp. 298–303, Nov. 2019, doi: 10.1109/CIS-RAM47153.2019.9095774.
• [46] V. Nguyen, Q. Vu, O. Solenaya, and A. Ronzhin, “Analysis of main tasks of precision farming solved with the use of robotic means,” MATEC Web Conf., vol. 113, p. 02009, Jun. 2017, doi: 10.1051/MATECCONF/201711302009.
• [47] D. Mundan, H. Selçuk, K. Orçin, E. Karakafa, and F. Akdağ, “Modern Süt Sığırı İşletmelerinde Robotlu Sağım Sistemlerinin Ekonomik Açıdan Değerlendirilmesi,” Harran Üniversitesi Vet. Fakültesi Derg., vol. 3, no. 1, pp. 42–48, Jan. 2014.
• [48] J. Hyde and P. Engel, “Investing in a Robotic Milking System: A Monte Carlo Simulation Analysis,” J. Dairy Sci., vol. 85, no. 9, pp. 2207–2214, Sep. 2002, doi: 10.3168/JDS.S0022-0302(02)74300-2.
• [49] A. M. Wagner-Storch and R. W. Palmer, “Feeding Behavior, Milking Behavior, and Milk Yields of Cows Milked in a Parlor Versus an Automatic Milking System,” J. Dairy Sci., vol. 86, no. 4, pp. 1494–1502, Apr. 2003, doi: 10.3168/JDS.S0022-0302(03)73735-7.
• [50] O. O. Borshch et al., “Adaptation strategy of different cow genotypes to the voluntary milking system,” Ukr. J. Ecol., vol. 10, no. 1, pp. 145–150, Feb. 2020, doi: 10.15421/2020_23.