Abstract
Artificial Intelligence (AI) has emerged as a transformative and enabling technology across a wide range of sectors, and its impact on animal husbandry is particularly significant within the evolving framework of precision agriculture. The integration of advanced AI methodologies—such as supervised and unsupervised machine learning algorithms, deep learning architectures, smart sensor networks, and real-time data analytics—has empowered livestock producers to make data-driven, accurate, timely, and economically efficient decisions. These intelligent systems not only reduce human error and lower labor costs but also substantially enhance animal health monitoring, overall welfare, and farm productivity by automating complex biological and environmental analyses.Within livestock management, AI offers a multifaceted approach to solving persistent challenges through innovative tools and intelligent automation. Key applications include the continuous monitoring of animal behavior using accelerometers and vision-based systems, early disease detection via biometric pattern recognition (e.g., respiration rate, temperature anomalies), estrus prediction based on movement, vocalization, and hormonal cues, as well as personalized feeding strategies optimized through predictive algorithms. Moreover, AI enables biometric identification of individual animals through facial and vocal recognition, eliminating the need for invasive tagging methods and enhancing traceability and welfare standards.This study provides a comprehensive analysis of the principal subfields of AI—including Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), Computer Vision (CV), Robotics, and Natural Language Processing (NLP)—and illustrates their real-world applications in livestock production through a synthesis of empirical research, case studies, and quantitative modeling. Special emphasis is placed on the use of convolutional neural networks for visual diagnostics, reinforcement learning in adaptive feeding systems, and sensor fusion strategies in behavior recognition platforms.In addition to theoretical exploration, the study introduces a practical simulation framework developed in Python, utilizing a Multilayer Perceptron (MLP) neural network to estimate daily milk yield in dairy cows. This simulation is based on synthetic biometric input data, including heart rate, respiratory rate, body temperature, and eye temperature—variables known to correlate with physiological stress and productivity. The model's performance is evaluated using standard
Keywords
References
- Andrew W, Gao J, Mullan S, Campbell N, Dowsey AW, Burghardt T, 2021. Visual identification of individual Holstein-Friesian cattle via deep metric learning. Comput Electron Agric, 185: 106133.
- Awad AI, Zawbaa HM, Mahmoud HA, Nabi EHH, Fayed RH, Hassanien AE, 2013. A robust cattle identification scheme using muzzle print images. Federated Conference on Computer Science and Information Systems, Krakow, Poland, ss. 529–534.
- Barbedo JGA, Koenigkan LV, 2018. Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook Agric, 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. Trans ASABE, 50(3): 1073–1080.
- Basheer IA, Hajmeer M, 2000. Artificial neural networks: Fundamentals, computing, design, and application. J Microbiol Methods, 43(1): 3–31.
- Benko A, Lanyi CS, 2009. History of artificial intelligence. In: Khosrow-Pour M (Ed.), Encyclopedia of Information Science and Technology (2nd ed., 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. J Dairy Sci, 100(7): 5664–5674.
- Chen LJ, Cui LY, Xing L, Han LJ, 2008. Prediction of the nutrient content in dairy manure using artificial neural network modeling. J Dairy Sci, 91(12): 4822–4829.
- Dandıl E, Turkan M, Boğa M, Çevik KK, 2019. Sığır yüzlerinin tanınması için daha hızlı bölgesel evrişimsel sinir ağları uygulaması. BŞEÜ Fen Bilim Derg, 6: 177–189.
- Dutta PA, 2021. Deep learning approach for animal breed classification – Sheep. Int J Res Appl Sci Eng Technol, 9(5): 73–76.
- 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.
- Fuentes S, Gonzalez Viejo C, Cullen B, Tongson E, Chauhan SS, Dunshea FR, 2020. Artificial intelligence 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 intelligence. Sensors, 21(20): 6844.
- 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. Int Joint Conf Neural Netw (IJCNN), ss. 1–8.
- Görgülü O, 2012. Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks. S Afr J Anim Sci, 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). Comput Electron Agric, 54(2): 69–83.
- Hamet P, Tremblay J, 2017. Artificial intelligence in medicine. Metabolism, 69(Suppl): S36–S40.
- He K, Gkioxari G, Dollár P, Girshick R, 2017. Mask R-CNN. Proc IEEE Int Conf Comput Vis, ss. 2961–2969.
- Hertz J, Krogh A, Palmer RG, 1991. Introduction to the Theory of Neural Computation. Westview Press.
- Hornik K, 1991. Approximation capabilities of multilayer feedforward networks. Neural Netw, 4(2): 251–257.
- Jung DH, Kim NY, Moon SH, Jhin C, Kim HJ, Yang JS, Park SH, 2021. Deep learning-based cattle vocal classification model and real-time livestock monitoring system with noise filtering. Animals, 11: 357.
- 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.
- McLennan K, Mahmoud M, 2019. Development of an automated pain facial expression detection system for sheep (Ovis aries). Animals, 9(4): 196.
- Memmedova N, 2012. Süt Sığırlarında Mastitisin Bazı Yapay Zekâ Yöntemleri Kullanılarak Erken Dönemde Tespiti. Doktora Tezi, Selçuk Üniversitesi, Konya, Türkiye.
- Nabiyev VV, 2012. Yapay Zekâ: İnsan–Bilgisayar Etkileşimi. Ankara: Seçkin Yayıncılık.
- Neethirajan S, 2021. Happy cow or thinking pig? WUR wolf–Facial coding platform for measuring emotions in farm animals. AI, 2(3): 342–354.
- Öztürk K, Şahin ME, 2018. Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. Takvim-i Vekayi, 6(2): 25–36.
- Xu B, Wang W, Falzon G, Kwan P, Guo L, Chen G, Schneider D, 2020. Automated cattle counting using Mask R-CNN in quadcopter vision system. Comput Electron Agric, 171: 105300.
- Zhang XD, 2020. Machine Learning. In: Zhang XD (Ed.), A Matrix Algebra Approach to Artificial Intelligence (pp. 223–440). Springer, Singapore.
- Zhang Z, 2018. Artificial Neural Network. In: Zhang Z (Ed.), Multivariate Time Series Analysis in Climate and Environmental Research (pp. 1–35). Springer.
Abstract
Yapay Zekâ (YZ), birçok sektörde olduğu gibi hayvancılık alanında da dönüştürücü ve destekleyici bir teknoloji olarak öne çıkmaktadır. Hassas tarımın evrilen çerçevesi içerisinde değerlendirildiğinde, gözetimli ve gözetimsiz öğrenme algoritmaları, derin öğrenme mimarileri, akıllı sensör ağları ve gerçek zamanlı veri analitiği gibi ileri düzey YZ yöntemlerinin entegrasyonu; hayvan yetiştiricilerine veriye dayalı, doğru, zamanında ve ekonomik olarak etkin kararlar alma imkânı sunmaktadır. Bu akıllı sistemler, insan hatasını azaltmakla kalmayıp iş gücü maliyetlerini düşürmekte; aynı zamanda biyolojik ve çevresel süreçlerin analizini otomatikleştirerek hayvan sağlığını, refahını ve çiftlik verimliliğini önemli ölçüde artırmaktadır.
Hayvancılıkta YZ uygulamaları, uzun süredir devam eden sorunlara yenilikçi çözümler getirerek çok yönlü bir yaklaşım sunmaktadır. Başlıca kullanım alanları arasında; ivmeölçerler ve görüntü tabanlı sistemlerle davranış takibi, solunum hızı ve sıcaklık gibi biyometrik kalıpların tanınması yoluyla erken hastalık tespiti, hareket ve ses sinyalleriyle kızgınlık (estrus) tahmini ve tahmine dayalı algoritmalarla kişiselleştirilmiş yemleme stratejileri yer almaktadır. Ayrıca, yüz ve ses tanıma gibi biyometrik kimliklendirme yöntemleri, invaziv işaretleme gerekliliğini ortadan kaldırmakta ve izlenebilirlik ile hayvan refahı standartlarını iyileştirmektedir.Bu çalışma, Yapay Öğrenme (ML), Derin Öğrenme (DL), Yapay Sinir Ağları (YSA), Bilgisayarla Görü (CV), Robotik ve Doğal Dil İşleme (NLP) gibi YZ’nin temel alt alanlarını kapsamlı bir biçimde analiz etmekte ve bunların hayvancılıktaki gerçek dünya uygulamalarını, ampirik araştırmalar, vaka analizleri ve nicel modelleme eşliğinde sunmaktadır. Özellikle; görüntü tanılamasında evrişimli sinir ağlarının (CNN) kullanımı, pekiştirmeli öğrenmeyle yem sistemlerinin adaptasyonu ve davranış tanımada sensör füzyonu stratejileri detaylı şekilde ele alınmıştır.Teorik incelemeye ek olarak, Python tabanlı pratik bir simülasyon çerçevesi de sunulmuştur. Bu çerçevede, çok katmanlı algılayıcı (MLP) yapay sinir ağı kullanılarak süt ineklerinde günlük süt verimi tahmin edilmiştir. Simülasyon, kalp atış hızı, solunum hızı, vücut sıcaklığı ve göz sıcaklığı gibi biyometrik girişlere dayalı olarak gerçekleştirilmiş; bu değişkenlerin fizyolojik stres ve verimlilikle olan korelasyonu doğrultusunda
Keywords
References
- Andrew W, Gao J, Mullan S, Campbell N, Dowsey AW, Burghardt T, 2021. Visual identification of individual Holstein-Friesian cattle via deep metric learning. Comput Electron Agric, 185: 106133.
- Awad AI, Zawbaa HM, Mahmoud HA, Nabi EHH, Fayed RH, Hassanien AE, 2013. A robust cattle identification scheme using muzzle print images. Federated Conference on Computer Science and Information Systems, Krakow, Poland, ss. 529–534.
- Barbedo JGA, Koenigkan LV, 2018. Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook Agric, 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. Trans ASABE, 50(3): 1073–1080.
- Basheer IA, Hajmeer M, 2000. Artificial neural networks: Fundamentals, computing, design, and application. J Microbiol Methods, 43(1): 3–31.
- Benko A, Lanyi CS, 2009. History of artificial intelligence. In: Khosrow-Pour M (Ed.), Encyclopedia of Information Science and Technology (2nd ed., 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. J Dairy Sci, 100(7): 5664–5674.
- Chen LJ, Cui LY, Xing L, Han LJ, 2008. Prediction of the nutrient content in dairy manure using artificial neural network modeling. J Dairy Sci, 91(12): 4822–4829.
- Dandıl E, Turkan M, Boğa M, Çevik KK, 2019. Sığır yüzlerinin tanınması için daha hızlı bölgesel evrişimsel sinir ağları uygulaması. BŞEÜ Fen Bilim Derg, 6: 177–189.
- Dutta PA, 2021. Deep learning approach for animal breed classification – Sheep. Int J Res Appl Sci Eng Technol, 9(5): 73–76.
- 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.
- Fuentes S, Gonzalez Viejo C, Cullen B, Tongson E, Chauhan SS, Dunshea FR, 2020. Artificial intelligence 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 intelligence. Sensors, 21(20): 6844.
- 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. Int Joint Conf Neural Netw (IJCNN), ss. 1–8.
- Görgülü O, 2012. Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks. S Afr J Anim Sci, 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). Comput Electron Agric, 54(2): 69–83.
- Hamet P, Tremblay J, 2017. Artificial intelligence in medicine. Metabolism, 69(Suppl): S36–S40.
- He K, Gkioxari G, Dollár P, Girshick R, 2017. Mask R-CNN. Proc IEEE Int Conf Comput Vis, ss. 2961–2969.
- Hertz J, Krogh A, Palmer RG, 1991. Introduction to the Theory of Neural Computation. Westview Press.
- Hornik K, 1991. Approximation capabilities of multilayer feedforward networks. Neural Netw, 4(2): 251–257.
- Jung DH, Kim NY, Moon SH, Jhin C, Kim HJ, Yang JS, Park SH, 2021. Deep learning-based cattle vocal classification model and real-time livestock monitoring system with noise filtering. Animals, 11: 357.
- 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.
- McLennan K, Mahmoud M, 2019. Development of an automated pain facial expression detection system for sheep (Ovis aries). Animals, 9(4): 196.
- Memmedova N, 2012. Süt Sığırlarında Mastitisin Bazı Yapay Zekâ Yöntemleri Kullanılarak Erken Dönemde Tespiti. Doktora Tezi, Selçuk Üniversitesi, Konya, Türkiye.
- Nabiyev VV, 2012. Yapay Zekâ: İnsan–Bilgisayar Etkileşimi. Ankara: Seçkin Yayıncılık.
- Neethirajan S, 2021. Happy cow or thinking pig? WUR wolf–Facial coding platform for measuring emotions in farm animals. AI, 2(3): 342–354.
- Öztürk K, Şahin ME, 2018. Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. Takvim-i Vekayi, 6(2): 25–36.
- Xu B, Wang W, Falzon G, Kwan P, Guo L, Chen G, Schneider D, 2020. Automated cattle counting using Mask R-CNN in quadcopter vision system. Comput Electron Agric, 171: 105300.
- Zhang XD, 2020. Machine Learning. In: Zhang XD (Ed.), A Matrix Algebra Approach to Artificial Intelligence (pp. 223–440). Springer, Singapore.
- Zhang Z, 2018. Artificial Neural Network. In: Zhang Z (Ed.), Multivariate Time Series Analysis in Climate and Environmental Research (pp. 1–35). Springer.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Zootechny (Other) |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | July 25, 2025 |
| Publication Date | July 24, 2025 |
| Submission Date | May 20, 2025 |
| Acceptance Date | June 12, 2025 |
| Published in Issue | Year 2025 Volume: 4 Issue: 2 |
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