Transforming Animal Husbandry: Leveraging Herd Management, Automation and Artificial Intelligence for Enhanced Productivity and Sustainability

Yıl 2024, Cilt: 5 Sayı: 1, 23 - 30, 28.06.2024

Kübra Benan Yılmaz

https://doi.org/10.58833/bozokvetsci.1396800

Öz

Herd management in livestock enterprises is a complex business endeavor that demands technical expertise, vigilant attention to animal health and welfare, quality assurance, and the monitoring of worker productivity and well-being. It necessitates the evaluation of diverse data through a well-defined logic and demands a professional approach for precise decision-making. As a result, herd management systems, automation, and artificial intelligence applications have progressively become indispensable tools on livestock farms. These applications play a pivotal role in ensuring the sustainability and profitability of production in both the short and long term, given the perpetual nature of this cycle. This article explores the evolution and benefits of herd management systems, automation, and artificial intelligence applications as advanced technologies in animal husbandry enterprises from the past to the present.

Anahtar Kelimeler

Artificial Intelligence, Herd Management, Husbandry

Hayvancılığı Dönüştürmek: Verimlilik ve Sürdürülebilirliği Geliştirmek için Sürü Yönetimi, Otomasyon ve Yapay Zekadan Yararlanmak

Öz

Hayvancılık işletmelerinde sürü yönetimi, teknik uzmanlık, hayvan sağlığı ve refahına odaklanmayı, kalite güvencesini sağlamayı ve çalışanların verimliliği ile refahını izlemeyi gerektiren karmaşık bir çabadır. Bu süreç, çeşitli verilerin düzenli bir mantıkla değerlendirilmesini ve kesin kararlar almak için profesyonel bir yaklaşımı içermektedir. Sonuç olarak, sürü yönetim sistemleri, otomasyon ve yapay zeka uygulamaları, hayvancılık çiftliklerinde giderek vazgeçilmez araçlar haline gelmiştir. Bu uygulamalar, üretimin hem kısa hem de uzun vadede sürdürülebilirliğini ve karlılığını sağlamada önemli bir rol oynamaktadır.Bu çalışma, hayvancılık işletmelerinde sürü yönetim sistemleri, otomasyon ve yapay zekâ uygulamalarının geçmişten günümüze olan gelişimini ve sağladığı faydaları detaylı bir şekilde incelemektedir.

Anahtar Kelimeler

Yapay Zeka, Sürü Yönetimi, Hayvancılık

Kaynakça

• 1. Alonso ME, González-Montaña JR, Lomillos JM. Consumers' Concerns and Perceptions of Farm Animal Welfare. Animals (Basel). 2020;10(3).
• 2. Wang K, Lu X, Lu Y, Wang J, Lu Q, Cao X, et al. Nanomaterials in Animal Husbandry: Research and Prospects. Front Genet. 2022;13:915911.
• 3. Hajnal É, Kovács L, Vakulya G. Dairy Cattle Rumen Bolus Developments with Special Regard to the Applicable Artificial Intelligence (AI) Methods. Sensors (Basel). 2022;22(18).
• 4. Doanh PN, Nawa Y. Clonorchis sinensis and Opisthorchis spp. in Vietnam: current status and prospects. Trans R Soc Trop Med Hyg. 2016;110(1):13-20.
• 5. Dahl GE, Tao S, Laporta J. Heat Stress Impacts Immune Status in Cows Across the Life Cycle. Front Vet Sci. 2020;7:116.
• 6. Munksgaard L, Weisbjerg MR, Henriksen JCS, Løvendahl P. Changes to steps, lying, and eating behavior during lactation in Jersey and Holstein cows and the relationship to feed intake, yield, and weight. J Dairy Sci. 2020;103(5):4643-53.
• 7. Denißen J, Beintmann S, Hoppe S, Pries M, Hummel J, Südekum KH. Influence of the addition of water to total mixed rations on the feeding behaviour, feed intake and milk performance of high-yielding dairy cows. Livestock Science. 2021;254:104743.
• 8. Chiu M-C, Yan W-M, Bhat SA, Huang N-F. Development of smart aquaculture farm management system using IoT and AI-based surrogate models. Journal of Agriculture and Food Research. 2022;9:100357.
• 9. McDougall S, Heuer C, Morton J, Brownlie T. Use of herd management programmes to improve the reproductive performance of dairy cattle. Animal. 2014;8 Suppl 1:199-210.
• 10. Crittenden VL, Crittenden WF. Building a capable organization: The eight levers of strategy implementation. Business Horizons. 2008;51(4):301-9.
• 11. Teece DJ. Business Models, Business Strategy and Innovation. Long Range Planning. 2010;43(2):172-94.
• 12. Maltz E. Individual dairy cow management: achievements, obstacles and prospects. Journal of Dairy Research. 2020;87(2):145-57.
• 13. Malenje EM, Missohou A, Tebug SF, König EZ, Jung’a JO, Bett RC, et al. Economic analysis of smallholder dairy cattle enterprises in Senegal. Tropical Animal Health and Production. 2022;54(4):221.
• 14. Kaya A, Gunes E, Memili E. Application of reproductive biotechnologies for sustainableproduction of livestock in Turkey. Turkish Journal of Veterinary & Animal Sciences. 2018;42(3):143-51.
• 15. Kgari RD, Muller C, Dzama K, Makgahlela M. Evaluation of female fertility in dairy cattle enterprises–A review. South African Journal of Animal Science. 2020;50(6).
• 16. Halachmi I, Guarino M, Bewley J, Pastell M. Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production. Annual Review of Animal Biosciences. 2019;7(1):403-25.
• 17. Conradt L. Models in animal collective decision-making: information uncertainty and conflicting preferences. Interface Focus. 2012;2(2):226-40.
• 18. Neethirajan S. Transforming the Adaptation Physiology of Farm Animals through Sensors. Animals (Basel). 2020;10(9).
• 19. Neethirajan S. The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research. 2020;29:100367.
• 20. Voulodimos AS, Patrikakis CZ, Sideridis AB, Ntafis VA, Xylouri EM. A complete farm management system based on animal identification using RFID technology. Computers and Electronics in Agriculture. 2010;70(2):380-8.
• 21. Neves RC, LeBlanc SJ. Reproductive management practices and performance of Canadian dairy herds using automated activity-monitoring systems. Journal of Dairy Science. 2015;98(4):2801-11.
• 22. Akhigbe BI, Munir K, Akinade O, Akanbi L, Oyedele LO. IoT Technologies for Livestock Management: A Review of Present Status, Opportunities, and Future Trends. Big Data and Cognitive Computing [Internet]. 2021; 5(1).
• 23. García R, Aguilar J, Toro M, Pérez N, Pinto A, Rodríguez P. Autonomic computing in a beef-production process for Precision Livestock Farming. Journal of Industrial Information Integration. 2023;31:100425.
• 24. McDougall S, Heuer C, Morton J, Brownlie T. Use of herd management programmes to improve the reproductive performance of dairy cattle. animal. 2014;8(s1):199-210.
• 25. Plà LM. Review of mathematical models for sow herd management. Livestock Science. 2007;106(2):107-19.
• 26. Veissier I, Butterworth A, Bock B, Roe E. European approaches to ensure good animal welfare. Applied Animal Behaviour Science. 2008;113(4):279-97.
• 27. Gómez Y, Stygar AH, Boumans I, Bokkers EAM, Pedersen LJ, Niemi JK, et al. A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare. Front Vet Sci. 2021;8:660565.
• 28. Awad AI. From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture. 2016;123:423-35.
• 29. Yongqiang C, Shaofang L, Hongmei L, Pin T, Yilin C, editors. Application of Intelligent Technology in Animal Husbandry and Aquaculture Industry. 2019 14th International Conference on Computer Science & Education (ICCSE); 2019 19-21 Aug. 2019.
• 30. Giersberg MF, Meijboom FLB. Smart Technologies Lead to Smart Answers? On the Claim of Smart Sensing Technologies to Tackle Animal Related Societal Concerns in Europe Over Current Pig Husbandry Systems. Front Vet Sci. 2020;7:588214.
• 31. Goncu S, Gungor C. The innovative techniques in animal husbandry. Anim Husb Nut. 2018;1.
• 32. Cornou C. Automation Systems for Farm Animals: Potential Impacts on the Human—Animal Relationship and on Animal Welfare. Anthrozoös. 2009;22(3):213-20.
• 33. Wellmann R. Optimum contribution selection for animal breeding and conservation: the R package optiSel. BMC Bioinformatics. 2019;20(1):25.
• 34. Wardal WJ, Mazur KE, Roman K, Roman M, Majchrzak M. Assessment of Cumulative Energy Needs for Chosen Technologies of Cattle Feeding in Barns with Conventional (CFS) and Automated Feeding Systems (AFS). Energies [Internet]. 2021; 14(24).
• 35. Schulze C, Spilke J, Lehner W. Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks. Computers and Electronics in Agriculture. 2007;59:39-55.
• 36. Rutten CJ, Velthuis AGJ, Steeneveld W, Hogeveen H. Invited review: sensors to support health management on dairy farms. J Dairy Sci. 2013;96(4):1928-52.
• 37. Bao J, Xie Q. Artificial intelligence in animal farming: A systematic literature review. Journal of Cleaner Production. 2022;331:129956.
• 38. Ayoub Shaikh T, Rasool T, Rasheed Lone F. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture. 2022;198:107119.
• 39. Becker CA, Aghalari A, Marufuzzaman M, Stone AE. Predicting dairy cattle heat stress using machine learning techniques. Journal of Dairy Science. 2021;104(1):501-24.
• 40. Fuentes S, Gonzalez Viejo C, Cullen B, Tongson E, Chauhan SS, Dunshea FR. Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters. Sensors [Internet]. 2020; 20(10).
• 41. Neethirajan S, Kemp B. Digital Livestock Farming. Sensing and Bio-Sensing Research. 2021;32:100408.
• 42. Bello R-W, Mohamed ASA, Talib A. Contour Extraction of Individual Cattle From an Image Using Enhanced Mask R-CNN Instance Segmentation Method. IEEE Access. 2021;9:56984-7000.
• 43. Kumar S, Singh S, Singh R, Singh A. Recognition of Cattle Using Face Images. 2017. p. 79-110.
• 44. Qiao Y, Su D, Kong H, Sukkarieh S, Lomax S, Clark C. Individual Cattle Identification Using a Deep Learning Based Framework. IFAC-PapersOnLine. 2019;52:318-23.
• 45. Shen W, Hu H, Dai B, Wei X, Sun J, Jiang L, et al. Individual identification of dairy cows based on convolutional neural networks. Multimedia Tools and Applications. 2020;79.
• 46. Xiao J, Liu G, Wang K, Si Y. Cow identification in free-stall barns based on an improved Mask R-CNN and an SVM. Computers and Electronics in Agriculture. 2022;194:106738.