Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları

Year 2024, Volume: 3 Issue: 2, 63 - 72, 01.07.2024

Hatice Dilaver Kamil Fatih Dilaver

https://doi.org/10.5281/zenodo.12518170

Abstract

Hayvancılık, tarımın bir parçası olarak insanların temel gıda ihtiyaçlarını karşılamak amacıyla yüzyıllardır var olan bir faaliyettir. Bu sektörde büyükbaş, küçükbaş, tavukçuluk ve arıcılık gibi farklı alt dallar bulunmaktadır. Geleneksel olarak insan gücüyle yapılan hayvan bakımı ve üretimi, teknolojinin ilerlemesiyle makineler ve yapay zekâ gibi teknolojilerle desteklenmeye başlamıştır. Yapay zekâ uygulamaları, görüntü işleme sistemleri ve otonom çiftlik sistemleri gibi yenilikler, insan hatalarını azaltarak üretimde kalite ve hız artışı sağlamıştır. Özellikle sığır yetiştiriciliği alanında robotik sistemler ve yapay zekâ uygulamaları, işgücü maliyetlerini düşürmekte, verimliliği artırmakta ve çevresel etkileri minimize etmektedir. Gelecekte daha da gelişmiş robotik sistemler ve yapay zekâ algoritmalarıyla hayvancılık endüstrisi daha sürdürülebilir bir hale gelecektir Bu teknolojiler, hastalık tespiti gibi alanlarda da etkili olmuştur. Sığır yetiştiriciliği özelinde, robotik sistemlerin ve yapay zekâ uygulamalarının işgücü maliyetlerini azalttığı, verimliliği artırdığı ve çevresel etkileri minimize ettiği vurgulanmıştır. Gelecekte, daha da gelişmiş robotik sistemler ve yapay zekâ algoritmalarının endüstriyi daha sürdürülebilir hale getireceği tahmin edilmektedir. Robotik sistemlerin ve yapay zekâ uygulamalarının sığır yetiştiriciliği endüstrisine bir dizi faydası vardır. Bu teknolojiler, işgücü maliyetlerini azaltır, verimliliği artırır, hayvan refahını iyileştirir ve çevresel etkileri minimize eder. Ayrıca, daha sağlıklı hayvanlar ve daha yüksek kaliteli ürünler elde edilmesine olanak tanır. Sığır yetiştiriciliği endüstrisi, robotik sistemlerin ve yapay zekâ uygulamalarının daha da yaygınlaşmasıyla önemli değişikliklere tanık olmaya devam edecektir. Gelecekte, daha gelişmiş robotik sistemler ve yapay zekâ algoritmaları, sığır yetiştiriciliği süreçlerini daha da optimize edecek ve endüstriyi daha sürdürülebilir hale getirecektir. Robotik sistemler ve yapay zekâ uygulamaları, sığır yetiştiriciliği endüstrisinde önemli bir dönüşüm sağlamaktadır.

Keywords

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

Robotics Systems and Artificial Intelligence Applications in Livestock Farming

Abstract

Cattle farming is a significant activity in the food industry, widely practiced worldwide. However, traditional methods of cattle farming face several challenges such as labor intensity, pressures on resources, and environmental impacts. To overcome these challenges, in recent years, robotic systems and artificial intelligence (AI) applications have brought about revolutionary changes in the cattle farming industry.Robotic systems play a crucial role in enhancing efficiency and reducing human intervention in cattle farming processes. For instance, automatic milking machines ensure regular milking of cattle, reducing labor costs and increasing milk productivity. Additionally, robotic feed distribution systems optimize feeding processes by automatically providing feed to animals.Artificial intelligence has many significant applications in cattle farming. For example, image recognition systems can be used to monitor the health status of animals and detect signs of illness. Furthermore, big data analytics and machine learning algorithms can provide valuable insights from cattle farming data, optimizing farm management. Robotic systems and artificial intelligence applications offer a range of benefits to the cattle farming industry. These technologies reduce labor costs, increase efficiency, improve animal welfare, and minimize environmental impacts. Additionally, they enable the production of healthier animals and higher-quality products.The cattle farming industry will continue to witness significant changes with the further proliferation of robotic systems and artificial intelligence applications. In the future, more advanced robotic systems and AI algorithms will further optimize cattle farming processes, making the industry more sustainable.Robotic systems and artificial intelligence applications are driving a significant transformation in the cattle farming industry

Keywords

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

References

• Akıllı A, Atıl H, & Kesenkaş H, (2014) Çiğ süt kalite değerlendirmesinde bulanık mantık yaklaşımı, Kafkas Üniversitesi Veteriner Fakültesi Dergisi, c. 20, s. 2, ss. 223–229.
• Altaş, İ. H., (1999). Bulanık Mantık: Bulanıklılık Kavram, Enerji, Elektrik, Elektromekanik-3e, c. 62, ss. 80–85.
• Borshch, O. O., Gutyj, B. V., Sobolev, O. I., Borshch, O. V., Ruban, S. Y., Bilkevich, V. V., & Nahirniak, T. (2020). Adaptation strategy of different cow genotypes to the voluntary milking system, Ukrainian Journal of Ecology, vol. 10, no. 1, pp. 145–150.
• Butler D., Holloway L., & Bear C., (2012). The impact of technological change in dairy farming: Robotic milking systems and the changing role of the stockperson, Journal of the Royal Agricultural Society of England, vol. 173, pp. 1-6.
• Cavero D, Tölle K. H, Buxadé C., & Krieter J., (2006). Mastitis detection in dairy cows by application of fuzzy logic, Livestock Science, vol. 105, no. 1–3, pp. 207–213.
• Chen G. & Pham T. T, (2000). Introduction To Fuzzy Sets, Fuzzy Logic, And Fuzzy Control Systems, CRC Press, Florida, ABD,
• Cihan P., Gökçe E., & Kalipsiz, O., (2017), Veteriner hekimlik alanında makine öğrenmesi uygulamaları üzerine bir derleme,, Kafkas Üniversitesi Veteriner Fakültesi Dergisi, c. 23, s. 4, ss. 673–680.
• Dandıl, E., Turkan, M. Boğa M, & Çevik K. K., (2019). Daha hızlı bölgesel-evrişimsel sinir ağları ile sığır yüzlerinin tanınması’’, Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, c. 6, ss. 177–189.
• De Mol R. M & W. E. Woldt, (2001). Application of fuzzy logic in automated cow status monitoring, Journal of Dairy Science, vol. 84, no. 2, pp. 400–410.
• Debauche O., Elmoulat M., Mahmoudi S, Bindelle J., & Lebeau F., (2021). Farm animals’ behaviors and welfare analysis with AI algorithms: A Review, Revue d'Intelligence Artificielle, vol. 35, no. 3, pp. 243–253.
• Ebrahimi M., Mohammadi M., Dehcheshmeh, E. Ebrahimie, & Petrovski K. R., (2019). Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models, Computers in Biology and Medicine, vol. 114.
• Hamrita T. K & Tollner, E.W., (2000), Toward fulfilling the robotic farming vision: Advances in sensors and controllers for agricultural applications, IEEE Transactions on Industry Applications, vol. 36, no. 4, pp. 1026–1032.
• Harsani P., Mulyana I., & Zakaria D, (2018). Fuzzy logic and A* algorithm implementation on goat foraging games, in IOP Conference Series: Materials Science and Engineering, Tangerang Selatan, Indonesia, vol. 332, no. 1, p. 012054.
• Hodges. A., (2014). Alan Turing: The Enigma Updated Edition, Princeton New Jersey, USA: Princeton University Press.
• Hoehndorf R. & Queralt-Rosinach N. (2017). Data Science and symbolic AI: Synergies, challenges and opportunities, Data Science, vol. 1, no. 1–2, pp. 27–38.
• Hyde J. & Engel P., (2002). Investing in a robotic milking system: A Monte Carlo simulation analysis, Journal of Dairy Science., vol. 85, no. 9, pp. 2207–2214.
• Işık, E., & Güler. T., (2009). Farklı vakum değerlerinde ineklerde sağım sonrası meme başı deformasyonun görüntü işleme tekniğiyle saptanması, Uludağ Üniversitesi Ziraat Fakültesi Dergisi, c. 23, s. 1, ss. 33–41.
• Jensen D. B., Hogeveen H., & De Vries, A., (2016). Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis, Journal of Dairy Science, vol. 99, no. 9, pp. 7344–7361.
• Kaplan A. & Haenlein M., (2019). Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence, Business Horizons, vol. 62, no. 1, pp. 15–25.
• Kotu V. & Deshpande B., (2019). “Data Science: Concepts and practice, in 2. Edition. Elsevier, USA.
• Kounalakis T., Triantafyllidis G. A., & Nalpantidis L, (2019). Deep learning-based visual recognition of rumex for robotic precision farming, Computers and Electronics in Agriculture, vol. 165, p. 104973.
• Kramer E, Cavero D., Stamer E., & Krieter J., (2009). Mastitis and lameness detection in dairy cows by application of fuzzy logic, Livestock Science, vol. 125, no. 1, pp. 92–96.
• Brunassi, L. D. A., Moura, D. J. D., Nääs, I. D. A., Vale, M. M. D., Souza, S. R. L. D., Lima, K. A. O. D., & Bueno, L. G. D. F. (2010). Improving detection of dairy cow estrus using fuzzy logic, Scientia Agricola, vol. 67, no. 5, pp. 503–509.
• Lauguico S. C, Concepcion R. S., MacAsaet D. D., Alejandrino J. D,. Bandala A. A, & Dadios E. P, (2019). Implementation of ınverse kinematics for crop-harvesting robotic arm in vertical farming, in 9th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) Robotics, Automation and Mechatronics (RAM), Bangkok, Thailand, pp. 298–303.
• Tabak, M. A., Norouzzadeh, M. S., Wolfson, D. W., Sweeney, S. J., VerCauteren, K. C., Snow, N. P., ... & Miller, R. S. (2019). Machine learning to classify animal species in camera trap images: Applications in ecology. Methods in Ecology and Evolution, 10(4), 585-590.
• Mikail N. & Keskin İ., (2011). İneklerde bulanık mantık modeli ile hareketlilik ölçüsünden yararlanılarak kızgınlığın tespiti,, Kafkas Üniversitesi Veteriner Fakültesi Dergisi, c. 17, s. 6, ss. 1003–1008.
• Morag-I, Edan Y., & E. Maltz, (2001). “An individual feed allocation decision support system for the dairy farm,” Journal of Agricultural Engineering Research, vol. 79, no. 2, pp. 167–176.
• Mundan D. Selçuk H, Orçin K., Karakafa E, & Akdağ F, (2014). Modern süt sığırı işletmelerinde robotlu sağım sistemlerinin ekonomik açıdan değerlendirilmesi Harran Üniversitesi Veteriner Fakültesi Dergisi, c. 3, s. 1, ss. 42–48.
• Nguyen V., Q. Vu, O. Solenaya, & Ronzhin A., (2017), Analysis of main tasks of precision farming solved with the use of robotic means, in MATEC Web of Conferences, vol. 113, p. 02009.
• Orsini, R., Basili, D., Belletti, M., Bentivoglio, D., Bozzi, C. A., Chiappini, S., & Zingaretti, P. (2019, May). Setting of a precision farming robotic laboratory for cropping system sustainability and food safety and security: Preliminary results. In IOP Conference Series: Earth and Environmental Science (Vol. 275, No. 1, p. 012021). IOP Publishing.
• Pirim.H. (2006). Yapay Zeka, Journal of Yaşar University, c. 1, s. 1, ss. 81–93.
• Raksha, R., & Surekha, P., (2020). A cohesive farm monitoring and wild animal warning prototype system using IoT and machine learning, in 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics, Bengaluru, India, pp. 472–476.
• Rao Y., Jiang M., Wang W., Zhang W, & Wang R., (2020). On-farm welfare monitoring system for goats based on Internet of Things and machine learning, International Journal of Distributed Sensor Networks, vol. 16, no. 7.
• Rossing W., Hogewerf P. H, Ipema A. H, Lauwere K.-D, & De Koning C. J. A. M, (1997). Robotic milking in dairy farming. Netherlands Journal of Agricultural Science., vol. 45, no. 1, pp. 15–31.
• Sangatash M. M, Mohebbi M., Shahidi F., Kamyad A. V, & Rohani M. Q., (2012). “Application of fuzzy logic to classify raw milk based on qualitative properties,” International Journal of AgriScience, vol. 2, no. 12, pp. 1168–1178.
• Santos, S. A., de Lima, H. P., Massruhá, S. M., de Abreu, U. G., Tomás, W. M., Salis, S. M., ... & Pellegrin, L. A. (2017). A fuzzy logic-based tool to assess beef cattle ranching sustainability in complex environmental systems. Journal of Environmental Management, 198, 95-106.
• Shahriar, M. S., Smith, D., Rahman, A., Freeman, M., Hills, J., Rawnsley, R., ... & Bishop-Hurley, G. (2016). Detecting heat events in dairy cows using accelerometers and unsupervised learning. Computers and electronics in agriculture, 128, 20-26.
• Strasse M. r, R. Lacroix, R. Kok, & Wade. K.M., (1997). A second-generation decision support system for the recommendation of dairy cattle culling decisions [Online]. Available: https://www.mcgill.ca/animal/files/animal/97r04.pdf
• Süslü A., (2019). Doğa ve insan bilimlerinde yapay zekâ uygulamaları, Akademia Doğa ve İnsan Bilimleri Dergisi, c. 5, s. 1, ss. 1–10.
• Tuncay, M., (2019), Aristoteles: Politika, 21. baskı, İstanbul, Türkiye: Remzi Yayınevi.
• Valdes-Donoso P., VanderWaal K, Jarvis L. S., Wayne S. R., & Perez A. M., (2017). Using machine learning to predict swine movements within a regional program to improve control of infectious diseases in the US, Frontiers in Veterinary Science, vol. 4, no. 2, pp. 1-13.
• Volkmann N., Kulig B., Hoppe S., Stracke J., Hensel O., & Kemper N., (2021), “On-farm detection of claw lesions in dairy cows based on acoustic analyses and machine learning,” Journal of Dairy Science, vol. 104, no. 5, pp. 5921–5931.
• Wade K. M., Lacroix R., & Strasser M., (1998). 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, Armidale, Australia, vol. 27, pp. 433–436.
• Wagner-Storch A. M. & Palmer R. W., (2003). Feeding behavior, milking behavior, and milk yields of cows milked in a parlor versus an automatic milking system, Journal of Dairy Science., vol. 86, no. 4, pp. 1494–1502.
• Warner D., Vasseur E, Lefebvre D. M, & Lacroix R., (2020), A machine learning based decision aid for lameness in dairy herds using farm-based records, Computers and Electronics in Agriculture, vol. 169, pp. 1-7.
• Yildiz K., (2016). Büyükbaş hayvanlarda kızgınlığın (östrus) hareketlilik ve çevre verilerinden yararlanarak yapay sinir ağları ile belirlenmesi, Doktora tezi, Biyosistem Mühendisliği Bölümü, Gaziosmanpaşa Üniversitesi, Tokat, Türkiye.
• Zaninelli M, Rossi L., Tangorra F. M, Costa A., Agazzi A., & Savoini G., (2016), On-Line monitoring of milk electrical conductivity by fuzzy logic technology to characterise health status in dairy goats, Italian Journal of Animal Science, vol. 13, no. 2, pp. 340–347.
• Zaninelli M, Tangorra F. M, Costa A, Rossi L., Dell’Orto, V. & Savoini, (2016), Improved fuzzy logic system to evaluate milk electrical conductivity signals from on-line sensors to monitor dairy goat mastitis, Sensors 2016, vol. 16, no. 7, pp. 1079.
• Zaninelli M., Rossi L., Costa A., Tangorra F. M, . Agazzi A, & Savoini, G., (2015). Use of electrical coductivity sensors to monitor health status and quality of milk in dairy goats, International Journal of Health, Animal Science and Food Safety, vol. 2, no. 2s, pp. 8-9.
• Zarchi H., Jónsson I., & Blanke M., (2009). 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, Zielona, Poland, pp. 20.