Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı

Niyazi Hayrullah Tuvay; Orhan Ermetin

doi:10.29185/hayuretim.1034328

TR EN

Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı

Öz

Yapay zekâ teknolojisi sayesinde üretilen yazılımlar, çeşitli sensörler ve akıllı makineler birçok sektörde başarılı bir şekilde kullanılmaktadır. Yapay zekâ uygulamaları ile hayvancılık alanında sağlıklı kararlar verebilmek, doğru yorumlar yapabilmek ve çok daha fazla sayıda değişkeni daha kısa zamanda inceleyip sonuca varmak mümkün olabilmektedir. Bu teknolojiler, insan işgücünü ve insan kaynaklı hataları büyük ölçüde azaltarak verimlilik ve ürün kalitesinin iyileştirilmesine de yardımcı olmaktadır. Yapay zekâ teknolojileri, sağladığı avantaj ve kolaylıklarla hayvancılık alanında giderek yaygın bir şekilde kullanılmaya başlanmıştır. Hayvan yetiştiriciliğinde uygulamaları gittikçe artan yapay zekâ programları ile hayvanların duygusal durumları, beslenme alışkanlıkları, süt verimlerinin kontrolü ve sürü yönetimi gibi pek çok alanda insan müdahale ve hatası ortadan kaldırılmaktadır. Hayvanları tanımlamak için uygulanan küpe, işaret, etiket ve benzeri dış etmenleri de ortadan kaldırarak, hem iş yükünü ve maliyeti azaltmakta hem de hayvan refahına katkı sunmaktadır. Ayrıca biyogüvenlik, hastalık takibi ve kontrolü, hayvanların izlenmesi, çiftlik yönetimi, çiftlik hayvanlarında büyümenin kontrolü ve benzeri konularda kullanılmaktadır. Bu çalışmada hayvan yetiştiriciliğinde yapay zekâ uygulamaları hakkında bilgilere ve örneklere yer verilmiştir.

Anahtar Kelimeler

Yapay zeka, hayvancılık, yapay sinir ağları, derin öğrenme

Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı

Öz

Software, various sensors, and smart machines produced by artificial intelligence technology are used successfully in many sectors. With artificial intelligence applications, it is possible to make healthy decisions in the field of animal husbandry, to make correct interpretations, and to analyze many more variables in a shorter time and reach a conclusion. These technologies also help improve productivity and product quality by greatly reducing human labor and human error. Artificial intelligence technologies have been increasingly used in the field of animal husbandry with the advantages and conveniences it provides. With artificial intelligence programs, which are increasingly used in animal husbandry, human intervention and error are eliminated in many areas such as the emotional state of animals, nutritional habits, control of milk yields, and herd management. By eliminating external factors such as earrings, signs, tags, etc. which are applied to identify animals, these technologies not only reduce the workload and cost, but also contribute to animal welfare. They are also used in biosecurity, disease monitoring and control, animal monitoring, farm management, control of growth in farm animals, and similar issues. In this study, information and examples about artificial intelligence applications in animal husbandry are presented.

Anahtar Kelimeler

Artificial intelligence, animal husbandry, artificial neural network, deep learning

Kaynakça

Ağyar Z. 2015. Yapay sinir ağlarının kullanım alanları ve bir uygulama. Mühendis ve Makine, 56(662): 22-23.
Andrew NG, Ngiam J, Foo CY, Mai Y, Suen C, Coates A, Tandon S. 2013. Unsupervised feature learning and deep learning. http://deeplearning.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork. (25.08.2021).
Andrew W, Gao J, Mullan S, Campbell N, Dowsey AW, Burghardt T. 2021. Visual identification of individual Holstein-Friesian cattle via deep metric learning. Computers and Electronics in Agriculture, 185, 106133.
Awad AI, Zawbaa HM, Mahmoud HA, Nabi EHHA, Fayed RH, Hassanien AE. 2013. A robust cattle identification scheme using muzzle print images. 2013 Federated Conference on Computer Science and Information Systems, Krakow, Poland. pp. 529-534.
Barbedo JGA, Koenigkan LV. 2018. Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook on Agriculture, 47(3), 214-222.
Barry B, Gonzales-Barron UA, McDonnell K, Butler F, Ward S. 2007. Using muzzle pattern recognition as a biometric approach for cattle identification. Transactions of the ASABE, 50(3), 1073-1080.
Basheer IA, Hajmeer M. 2000. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31.
Benko A, Lanyi CS. 2009. History of artificial intelligence. In Encyclopedia of Information Science and Technology, Second Edition, pp. 1759-1762. IGI Global.
Borchers MR, Chang YM, Proudfoot KL, Wadsworth BA, Stone AE, Bewley JM. 2017. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. Journal of Dairy Science 100(7):5664–5674.
Bretschneider G, Cuatrin A, Arias D, Vottero D. 2014. Estimation of body weight by an indirect measurement method in developing replacement Holstein heifers raised on pasture. Archivos de Medicina Veterinaria, 46(3), 439-443.

Brunassi LDA, Moura DJD, Naas IDA, Vale MMD, Souza SRLD, Lima KAOD, Carvalho TMRD, Bueno LGDF. 2010. Improving detection of dairy cow estrus using fuzzy logic. Scientia Agicola, 67(5), 503-509.
Cavero D, Tölle KH, Henze C, Buxadé C, Krieter J. 2008. Mastitis detection in dairy cows by application of neural networks. Livestock Science, 114(2-3), 280-286.
Chen LJ, Cui LY, Xing L, Han LJ. 2008. Prediction of the nutrient content in dairy manure using artificial neural network modeling. Journal of Dairy Science, 91(12), 4822-4829.
Chowdhary, KR., 2012. Sub-fields and commercial applications of AI. http://www.krchowdhary.com/ai/ai14/2-ai-applic.pdf (20.07.2021).
Çetin FA, Mikail N. 2016. Hayvancılıkta veri madenciliği uygulamaları. Türkiye Tarımsal Araştırmalar Dergisi, 3(1): 79-88.
Dandıl E, Turkan M, Boğa M, Çevik KK. 2019. Daha hızlı bölgesel-evrişimsel sinir ağları ile sığır yüzlerinin tanınması. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 6, 177-189.
Dutta PA. 2021. Deep learning approach for animal breed classification - sheep. International Journal for Research in Applied Science and Engineering Technology, 9(5), 73-76.
Ermetin O, Mülayim M. 2021. Küçükbaş Hayvan Yetiştiriciliğinde Çoban, Hayvan Davranışları, Sürü Yönetimi ve Teknik Mera Kullanımı. Nobel Yayınları. Yayın No.: 3678, Gıda, Tarım ve Hayvancılık No.: 040. ISBN: 978-625-439-943-5, E-ISBN: 978-625-439-944-2. Ankara.
El Naqa I, Murphy MJ. 2015. What is machine learning? In: El Naqa I, Li R, Murphy MJ (eds), Machine Learning in Radiation Oncology. pp. 3-11. Springer, Cham.
Franco MDO, Marcondes MI, Campos JMDS, Freitas DRD, Detmann E, Filho SDCV. 2017. Evaluation of body weight prediction Equations in growing heifers. Acta Scientiarum. Animal Sciences, 39(2), 201-206.
Fuentes S, Gonzalez Viejo C, Cullen B, Tongson E, Chauhan SS, Dunshea FR. 2020. Artificial ıntelligence applied to a robotic dairy farm to model milk productivity and quality based on cow data and daily environmental parameters. Sensors, 20(10), 2975.
Fuentes S, Gonzalez Viejo C, Tongson E, Lipovetzky N, Dunshea, FR. 2021. Biometric physiological responses from dairy cows measured by visible remote sensing are good predictors of milk productivity and quality through artificial ıntelligence. Sensors, 21(20), 6844.
Gardner JW, Barlett PN. 1999. Electronic Noses: Principals and Applications. Oxford University Press, Oxford, UK.
Giger ML. 2020. Deep learning. High-dimensional fuzzy clustering. Chicago International Breast Course The Westin Chicago River North.
Gjergji M, de Moraes Weber V, Silva, LOC, da Costa Gomes R, de Araújo TLAC, Pistori H, Alvarez M. 2020. Deep learning techniques for beef cattle body weight prediction. In 2020 International Joint Conference on Neural Networks (IJCNN), 19-24 July 2020, 1-8.
Görgülü O. 2012. Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks. South African Journal of Animal Science, 42(3): 280-287.
Grzesiak W, Błaszczyk P, Lacroix R. 2006. Methods of predicting milk yield in dairy cows—Predictive capabilities of Wood's lactation curve and artificial neural networks (ANNs). Computers and Electronics in Agriculture, 54(2), 69-83.
Hamet P, Tremblay J. 2017. Artificial intelligence in medicine. Metabolism, 69, S36-S40.
He K, Gkioxari G, Dollár P, Girshick R. 2017. Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision. 2961-2969.
Hertz J, Krogh A, Palmer RG. 1991. Introduction to the Theory of Neural Computation (Westview Press).
Hewitt C, Mahmoud M. 2019. Pose-informed face alignment for extreme head pose variations in animals. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 1-6.
Hornik K. 1991. Approximation capabilities of multilayer feedforward networks. Neural Networks. 4(2), 251–257.
Ikeda Y, Ishii Y. 2008. Recognition of two psychological conditions of a single cow by her voice. Computers and Electronics in Agriculture, 62(1), 67–72.
İnik Ö, Ülker E. 2017. Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.
Jones LD, Golan D, Hanna SA, Ramachandran M. 2018. Artificial intelligence, machine learning and the evolution of healthcare: a bright future or cause for concern? Bone & Joint Research, 7(3), 223-225.
Jung DH, Kim NY, Moon SH, Jhin C, Kim HJ, Yang JS, Kim HS, Lee TS, Lee JY, Park SH. 2021. Deep learning-based cattle vocal classification model and real-time livestock monitoring system with noise filtering. Animals, 11, 357.
Kaler J, Daniels SLS, Wright JL, Green LE. 2010. Randomised clinical trial of long-acting oxytetracycline, foot trimming, and flunixine meglumine on time to recovery in sheep with footrot. Journal of Veterinary Internal Medicine, 24(2), 420-425.
Kumar S, Singh SK. 2017. Visual animal biometrics: survey. IET Biometrics, 6(3), 139-156.
Kumar S, Singh SK, Dutta T, Gupta, HP. 2016. A fast cattle recognition system using smart devices. Proceedings of the 24th ACM International Conference on Multimedia. 742-743.
Kumar S, Pandey A, Satwik KSR, Kumar S, Singh SK, Singh AK, Mohan A. 2018. Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement, 116: 1-17.
Langford DJ, Bailey AL, Chanda ML, Clarke SE, Drummond TE, Echols S, Glick S, Ingrao J, Klassen-Ross T, Lacroix-Fralish ML, Matsumiya L, Sorge RE, Sotocinal SG, Tabaka JM, Wrong D, van den Maagdenberg AMJM, Ferrari MD, Craig KD, Mogil JS. 2010. Coding of facial expressions of pain in the laboratory mouse. Nature Methods, 7(6), 447-449.
Liddy ED. 2001. Natural Language Processing. In Encyclopedia of Library and Information Science, 2nd Ed. NY. Marcel Decker, Inc.
Ma C, Sun X, Yao C, Tian M, Li L. 2020. Research on sheep recognition algorithm based on deep learning in animal husbandry. Journal of Physics: Conference Series Vol. 1651, 012129. IOP Publishing.
Madan P, Madhavan S. 2020. An introduction to deep learning. https://developer.ibm.com/articles/an-introduction-to-deep-learning/ (25.08.2021).
Mahmoud M, Lu Y, Hou X, McLennan K, Robinson P. 2018. Estimation of Pain in Sheep Using Computer Vision. In: Moore RJ (ed.). Handbook of Pain and Palliative Care: Biopsychosocial and Environmental Approaches for the Life Course. pp. 145-157. Springer, Cham.
Mataric MJ. 2007. The Robotics Primer. The MIT Press. 328 pp.
McLennan K, Mahmoud M. 2019. Development of an automated pain facial expression detection system for sheep (Ovis Aries). Animals, 9(4), 196.
Memmedova N, Keskin İ. 2011. İneklerde bulanık mantık modeli ile hareketlilik ölçüsünden yararlanılarak kızgınlık tespiti. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 17(6), 1003-1008.
Memmedova N. 2012. Süt Sığırlarında Mastitisin Bazı Yapay Zekâ Yöntemleri Kullanılarak Erken Dönemde Tespiti. Doktora Tezi, Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Zootekni Anabilim Dalı. Konya.
Nabiyev VV. 2012. Yapay Zekâ: İnsan-Bilgisayar Etkileşimi. Seçkin Yayıncılık. ISBN: 9789750220340.
Neethirajan S. 2021. Happy cow or thinking pig? WUR wolf–Facial coding platform for measuring emotions in farm animals. AI, 2(3), 342–354.
Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Palmer MS, Packer C, Clune J. 2018. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences, 115(25), E5716-E5725.
O’Mahony N, Campbell S, Carvalho A, Harapanahalli S, Hernandez GV, Krpalkova L, Walsh J. 2019. Deep learning vs. traditional computer vision. In Science and Information Conference, CVC 2019, pp. 128-144. Springer, Cham.
Öztürk K, Şahin ME. 2018. Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. Takvim-i Vekayi, 6(2), 25-36.
Pan L, Yang SX. 2007. A new intelligent electronic nose system for measuring and analysing livestock and poultry farm odours. Environmental Monitoring and Assessment, 135: 399–408.
Poole GD, Craig KD. 1992. Judgments of genuine, suppressed, and faked facial expressions of pain. Journal of Personality and Social Psychology, 63(5), 797-805.
Porter S, ten Brinke L, Wallace B. 2012. Secrets and lies: Involuntary leakage in deceptive facial expressions as a function of emotional intensity. Journal of Nonverbal Behavior, 36(1), 23-37.
Powers WJ, Bastyr S. 2004. Downwind air quality measurements from poultry and livestock facilities. Animal Industry Report: ASL-R1927. Ames, IA: Iowa State University.
Preece J, Rombach HD. 1994. A taxonomy for combining software engineering and human-computer interaction measurement approaches: towards a common framework. International Journal of Human-Computer Studies, 41(4), 553-583.
Qu G, Feddes JJR, Armstrong WW, Coleman RN, Leonard JJ. 2001. Measuring odour concentration with an electronic nose. Transactions of the ASAE, 44(6), 1807–1812.
Reis GL, Albuquerque FHMAR, Valente BD, Martins GA, Teodoro RL, Ferreira MBD, Monteiro JBN, e Silva MDA, Madalena FE. 2008. Predição do peso vivo a partir de medidas corporais em animais mestiços Holandês/Gir. Ciência Rural, 38(3), 778-783.
Ren S, He K, Girshick R, Sun J. 2017. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149.
Roberts HC, Elbers ARW, Conraths FJ, Holsteg M, Hoereth-Boentgen D, Gethmann J, van Schaik G. 2014. Response to an emerging vector-borne disease: Surveillance and preparedness for Schmallenberg virus. Preventive Veterinary Medicine, 116(4), 341-349.
Takma Ç, Atıl H, Aksakal V. 2012. Çoklu doğrusal regresyon ve yapay sinir ağı modellerinin laktasyon süt verimine uyum yeteneklerinin karşılaştırılması. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 18(6): 941-944.
Terzi İ, Özgüven MM, Altaş Z, Uygun T. 2019. Tarımda yapay zekâ kullanımı. International Erciyes Agriculture, Animal & Food Sciences Conference 24-27 April 2019 - Erciyes University – Kayseri, Turkey. 245-255.
van Gerven M, Bohte S. 2017. Editorial: Artificial neural networks as models of neural information processing. Frontiers in Computational Neuroscience, 11: 114.
Xu B, Wang W, Falzon G, Kwan P, Guo L, Chen G, Tait A, Schneider D. 2020. Automated cattle counting using Mask R-CNN in quadcopter vision system. Computers and Electronics in Agriculture, 171, 105300.
Yıldız AK. 2016. Büyükbaş Hayvanlarda Kızgınlığın (Östrus) Hareketlilik ve Çevre Verilerinden Yararlanarak Yapay Sinir Ağları İle Belirlenmesi. Doktora Tezi. Gaziosmanpaşa Üniversitesi Fen Bilimleri Enstitüsü, Biyosistem Mühendisliği Ana Bilim Dalı. Tokat.
Zhang Z. 2018. Artificial Neural Network. In: Multivariate Time Series Analysis in Climate and Environmental Research. Springer, Cham. pp. 1-35.
Zhang XD. 2020. Machine Learning. In A Matrix Algebra Approach to Artificial Intelligence. Springer, Singapore. pp. 223-440.

Ayrıntılar

Birincil Dil

Türkçe

Konular

Hayvansal Üretim (Diğer)

Bölüm

Derleme

Yazarlar

Niyazi Hayrullah Tuvay Bu kişi benim
0000-0002-7603-8721
Türkiye

Orhan Ermetin ^*
0000-0002-3404-0452
Türkiye

Erken Görünüm Tarihi

24 Ekim 2023

Yayımlanma Tarihi

30 Ekim 2023

Gönderilme Tarihi

8 Aralık 2021

Kabul Tarihi

7 Nisan 2022

Yayımlandığı Sayı

Yıl 2023 Cilt: 64 Sayı: 1

DOI

https://doi.org/10.29185/hayuretim.1034328

IZ

https://izlik.org/JA88TZ26MU

APA

Tuvay, N. H., & Ermetin, O. (2023). Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı. Journal of Animal Production, 64(1), 48-58. https://doi.org/10.29185/hayuretim.1034328

AMA

1.Tuvay NH, Ermetin O. Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı. Journal of Animal Production. 2023;64(1):48-58. doi:10.29185/hayuretim.1034328

Chicago

Tuvay, Niyazi Hayrullah, ve Orhan Ermetin. 2023. “Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı”. Journal of Animal Production 64 (1): 48-58. https://doi.org/10.29185/hayuretim.1034328.

EndNote

Tuvay NH, Ermetin O (01 Ekim 2023) Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı. Journal of Animal Production 64 1 48–58.

IEEE

[1]N. H. Tuvay ve O. Ermetin, “Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı”, Journal of Animal Production, c. 64, sy 1, ss. 48–58, Eki. 2023, doi: 10.29185/hayuretim.1034328.

ISNAD

Tuvay, Niyazi Hayrullah - Ermetin, Orhan. “Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı”. Journal of Animal Production 64/1 (01 Ekim 2023): 48-58. https://doi.org/10.29185/hayuretim.1034328.

JAMA

1.Tuvay NH, Ermetin O. Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı. Journal of Animal Production. 2023;64:48–58.

MLA

Tuvay, Niyazi Hayrullah, ve Orhan Ermetin. “Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı”. Journal of Animal Production, c. 64, sy 1, Ekim 2023, ss. 48-58, doi:10.29185/hayuretim.1034328.

Vancouver

1.Niyazi Hayrullah Tuvay, Orhan Ermetin. Yapay Zeka Teknolojilerinin Hayvancılıkta Kullanımı. Journal of Animal Production. 01 Ekim 2023;64(1):48-5. doi:10.29185/hayuretim.1034328

KAMU YÖNETİMİNDE YAPAY ZEKÂ UYGULAMALARININ TÜRKİYE ÖRNEĞİNDE ANALİZİ: DİJİTAL DÖNÜŞÜM, STRATEJİK UYGULAMALAR VE ÇOK BOYUTLU ETKİLER

Izmir Democracy University Social Sciences Journal

https://doi.org/10.61127/idusos.1754003

Dijital (Hassas) Teknolojiler ve Süt Sığırcılığında Kullanımı

Erciyes Üniversitesi Veteriner Fakültesi Dergisi

https://doi.org/10.32707/ercivet.1809972

26405

Creative Commons License Journal of Animal Production is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

26407 26406 26408 26409 26410 26399 26411 26412 26413 26414 26415