Digital (Precision) Technologies and Their Usage in Dairy Farming

Abstract

This study aims to evaluate the usage of digital (precision) technologies in the livestock sector, particularly in dairy farming, and to highlight the advantages they offer. Starting with the individual identification of animals, the following control process has now reached the levels of electronic ear tags, virtual fences, computer vision, sensors, and artificial intelligence. Consequently, these technologies have the potential to make significant contributions to the economic sustainability of livestock production by improving animal health, welfare, productivity, and quality of production.

 

Keywords

• Biosensors
• early diagnosis
• precision dairy farming
• sensor technology

Dijital (Hassas) Teknolojiler ve Süt Sığırcılığında Kullanımı

Abstract

Bu çalışmada dijital (hassas) teknolojilerin hayvancılık sektöründe özellikle süt sığırcılığında kullanım durumunun değerlendirilmesi ve sağladığı avantajların ortaya konulması amaçlanmıştır. Hayvanların bireysel olarak kimliklendirilmesiyle başlayan takip ve kontrol süreci, günümüzde elektronik kulak küpeleri, sanal çitler, bilgisayarla görme, sensörler ve yapay zekâ seviyelerine kadar ulaşmıştır. Sonuçta, bahsedilen teknolojiler hayvan sağlığına, refahına, verimliliğe ve kaliteli üretime imkân vererek hayvansal üretimin ekonomik şartlarda sürdürülebilirliğine önemli katkılar sağlama potansiyeline sahiptir.

 

Keywords

• Biyosensörler
• erken teşhis
• hassas süt hayvancılığı
• sensör teknolojisi

Supporting Institution

Bu çalışma, herhangi bir finansman ya da destek almamıştır.

Ethical Statement

Etik Kurul İzni gerekmemektedir.

References

1. Adin G, Solomon R, Nikbachat M, Zenou A, Yosef E, Brosh A, Shabtay A, Mabjeesh SJ, Halachmi I, Miron J. Effect of feeding cows in early lactation with diets differing in roughage-neutral detergent fiber content on intake behavior, rumination, and milk production. J Dairy Sci 2009; 92(7): 3364-73.
2. Andriamandroso A, Bindelle J, Mercatoris B, Lebeau F. A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnol Agron Soc Environ 2016; 20(S1): 273- 82.
3. Anonim. Elektronik Künye - Hayvan Takip Sistemi. http://teta.com.tr/elektronik-kunye-hayvantakip-sistemi-1; Erişim Tarihi: 02.12.2022.
4. Antanaitis R, Juozaitiene V, Malašauskiene D, Televičius M, Urbutis M, Rutkaukas A, Šertvytyte G, Baumgartner W. Identification of changes in rumination behavior registered with an online sensor system in cows with subclinical mastitis. Vet Sci 2022; 9(9): 454.
5. Arel I, Rose DC, Karnowski TP. Deep machine learning - a new frontier in artificial intelligence research. IEEE Comput Intell Mag 2010; 5(4): 13-8.
6. Atkins G, Shannon J. Minimizing lameness through intermediate values for length of the diagonal. Adv Dairy Technol 2002; 14: 93-109.
7. Aydın A, Demir C. Hayvanların sağlığını, refahını ve üretimini değerlendirmek i̇çin temassız ve tahribatsız bir araç olarak kızılötesi termografi. Tarım Mak Bil Derg 2018; 14.2: 89-98.
8. Bewley JM. Precision dairy farming: advanced analysis solutions for future profitability. First North American Conf on Precision Dairy Management; 2010; USA.

9. Bewley JM. Precision technology dairy opportunities. GPS Dairy Forum; 2013; Kentucky, USA.
10. Billah M, Wang X, Yu J, Jiang Y. Real-time goat face recognition using convolutional neural network. Comput Electron Agric 2022; 194:106730.
11. Campbell DLM, Lea JM, Keshavarzi H, Lee C. Virtual fencing is comparable to electric tape fencing for cattle behavior and welfare. Front Vet Sci 2019; 6:445.
12. Cheng M, Yuan H, Wang Q, Cai Z, Liu Y, Zhang Y. Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect. Comput Electron Agric 2022; 198:107010.
13. Congdon JV, Hosseini M, Gading EF, Masousi M, Franke M, Macdonald SE. The future of artificial intelligence in monitoring animal identification, health, and behaviour. Animals 2022; 12(7): 3210.
14. Costa LS, Pereira DF, Bueno LGF, Pandorfi H. Some aspects of chicken behavior and welfare. Braz J Poultry Sci 2012; 14(3):159-64.
15. Çakmakçı C. Dijital hayvancılıkta yapay zekâ ve insansız hava araçları: derin öğrenme ve bilgisayarlı görme ile dağlık ve engebeli arazide kıl keçisi tespiti, takibi ve sayımı. Turk J Agric Food Sci Technol 2024; 12(7):1162-73.
16. Çakmakçı C, Turan M, Çakmakçı Y, Assis Ferraz P, Bülbüller F, Dalga S, Olcar B, Şireli HD. Akıllı hayvancılık teknolojileri: yapay zekâ destekli hayvan izleme çözümleri. In: Demirel AF, Yılmaz O, Orunç Kılınç Ö, eds. Uygulamalı Bilimlerde Güncel Çalışmalar -I. Ankara: İKSAD, 2023; pp.23-35.
17. Demir C, Aydın A. Büyükbaş hayvancılıkta görüntü işleme ile sağlık ve refah tespiti. Lapseki MYO Uyg Araşt Derg 2021; 2(4): 1-15.
18. Dolecheck K, Bewley J. Pre-investment considerations for precision dairy farming technologies. University of Kentucky; 2013.
19. Fuentes A, Yoon S, Park J, Park DS. Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information. Comput Electron Agric 2020; 177:105627.
20. Fuentes S, Gonzalez Viejo C, Tongson E, Lipovetzky N, Dunshea FR. Biometric physiological responses from dairy cows measured by visible remote sensing are good predictors of milk productivity and quality through artificial intelligence. Sensors 2021; 21(20):6785.
21. Gezici M, Ünay E, Üstün K, Coşkun Mİ. Hayvancılık i̇şletmelerinde teknoloji kullanımı ve ekonomik verimlilik. Ziraat Mühendisliği 2023; (377): 26-32.
22. Gültekin R. Avrupa Birliği sınırda karbon düzenlemesi ve Türkiye açısından bir değerlendirme. Balkan Near East J Soc Sci (BNEJSS) 2022; 8.
23. Günlü A, Barıt B. Dijital teknolojiler ve teknoloji yoğun üretimin hayvancılık sektörüne etkileri. In: Bulut Z, ed. Türkiye’de Hayvancılığın Gelişimi: Sorun Alanları, Fırsatlar ve Politika Öngörüleri. Ankara: Nobel, 2025; pp.335-56.
24. Hogeveen H, Huijps K, Lam T. Economic aspects of mastitis: new developments. N Z Vet J 2011; 59(1):16-23.
25. Jachowski DS, Slotow R, Millspaugh JJ. Good virtual fences make good neighbors: opportunities for conservation. Anim Conserv 2014; 17(3): 187-96.
26. Kahraman M, Yılmaz H. Sürdürülebilir hayvancılıkta yenilikçi teknolojilerin kullanımı. Turk J Sci Eng 2024; 6(1): 64-71.
27. Kakani V, Nguyen VH, Kumar BP, Kim H, Pasupuleti VR. A critical review on computer vision and artificial intelligence in food industry. J Agric Food Res 2020; 2:100033.
28. Kaya E, Örs A. Süt çiftliklerinde hassas tarım teknolojileri. 2. Uluslararası Tarım, Gıda ve Gastronomi Kongresi; 2-5 Eylül 2015; Diyarbakır.
29. Kopuzlu S. Hayvancılık işletmelerinde sürü yönetimi, otomasyon ve yapay zeka uygulamaları. Uluslar Gıda Tar Hay Bil Derg 2023; 3(2): 75-86.
30. Kumar D, Jakhar SD. Artificial intelligence in animal surveillance and conservation. Impact Artif Intell Organ Transform 2022; 10:73-85.
31. Kumar S, Singh SK. Cattle recognition: a new frontier in visual animal biometrics research. Proc Natl Acad Sci India Sect A Phys Sci 2019; 90(4): 689-708.
32. Kumari M, Dhawal K. Application of artificial intelligence (AI) in animal husbandry. Vigyan Varta 2021; 2(2): 27-9.
33. Lencioni GC, de Sousa RV, de Souza Sardinha EJ, Correa RR, Zanella AJ. Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling. PLoS One 2021; 16(10): e0258672.
34. Li G, Huang Y, Chen Z, Chesser GD, Purswell JL, Linhoss J, Zhao Y. Practices and applications of convolutional neural network-based computer vision systems in animal farming: a review. Sensors 2021; 21(4): 1492.
35. Mayo LM, Silvia WJ, Ray DL, Jones BW, Stone AE, Tsai IC, Clark JD, Bewley JM, Heersche Jr, G. Automated estrous detection using multiple commercial precision dairy monitoring technologies in synchronized dairy cows. J Dairy Sci 2019; 102.3: 2645-56.
36. Mazrier H, Tal S, Aizinbud E, Bargai U. A field investigation of the use of the pedometer for the early detection of lameness in cattle. Can Vet J 2006; 47(9): 883-6.
37. McSweeney D, O’Brien B, Coughlan NE, Férard A, Ivanov S, Halton P, Umstatter C. Virtual fencing without visual cues: design, difficulties of implementation, and associated dairy cow behaviour. Comput Electron Agric 2020; 176(9):105613.
38. Morstatter B. Using artificial intelligence (AI) in dairy farms: examples and opportunities. Appl Anim Sci 2023; 39:200-8.
39. Nabwire S, Suh HK, Kim MS, Baek I, Cho BK. Application of artificial intelligence in phenomics: a review. Sensors 2021; 21(13): 4555.
40. Neethirajan S, Tuteja SK, Huang ST, Kelton D. Recent advancement in biosensors technology for animal and livestock health management. Biosens Bioelectron 2017; 98(13):398-407.
41. Neethirajan S, Reimert I, Kemp B. Measuring farm animal emotions: sensor-based approaches. Sensors 2021; 21(2): 452.
42. Odintsov Vaintrub M, Levit H, Chincarini M, Fusaro I, Giammarco M, Vignola G. Precision livestock farming, automats and new technologies: possible applications in extensive dairy sheep farming. Anim 2021; 15(3):100143.
43. Porto JVA, Rezende FPC, Astolfi G, Weber VAdM, Pache MCB, Pistori H. Automatic counting of cattle with Faster R-CNN on UAV images. Proc Int Conf Comput Agric 2021;1-6.
44. Richeson JT, Lawrence TE, White BJ. Using advanced technologies to quantify beef cattle behavior. Transl Anim Sci 2018; 2(22):223-9.
45. Sarwar F, Griffin A, Rehman SU, Pasang T. Detecting sheep in UAV images. Comput Electron Agric 2021; 187(8):106219.
46. Shanahan M, Crosby M, Beyret B, Cheke L. Artificial intelligence and the common sense of animals. Anim Behav Cogn 2020; 24(11): 862-72.
47. Shu H, Wang W, Guo L, Bindelle J. Recent advances on early detection of heat strain in dairy cows using animal-based indicators: a review. Animals 2021; 11(4): 980.
48. Shu H, Li Y, Fang T, Xing M, Sun F, Chen X, Bindelle J, Wang W, Guo L. Evaluation of the best region for measuring eye temperature in dairy cows exposed to heat stress. Front Vet Sci 2022; 9:857777.
49. Silanikove N. Effects of heat stress on the welfare of extensively managed domestic ruminants. Livest Prod Sci 2000; 67(1-2): 1-18.
50. Sjaastad OV, Hove K, Sand O. Physiology of Domestic Animals. Oslo: Scandinavian Veterinary Press; 2003; p.507-27.
51. Song X, Bokkers EAM, van der Tol PPJ, Groot Koerkamp PWG, van Mourik S. Automated body weight prediction of dairy cows using 3-dimensional vision. J Dairy Sci 2018; 101(5): 4448-59.
52. Soriani N, Trevisi E, Calamari L. Relationships between rumination time, metabolic conditions, and health status in dairy cows during the transition period. J Anim Sci 2012; 90(12): 4544-54.
53. Sprecher DE, Hostetler DE, Kaneene JB. A lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance. Theriogenology 1997; 47(6): 1179-87.
54. Stampa E, Zander K, Hamm U. Insights into German consumers’ perceptions of virtual fencing ingrassland-based beef and dairy systems: recommendations for communication. Animals 2020; 10(12): 2227.
55. Sun Z, Samarasinghe S, Jago J. Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks. J Dairy Res 2010; 77(2): 168-75.
56. Tuvay NH, Ermetin O. Yapay zeka teknolojilerinin hayvancılıkta kullanımı. Hay Üret 2023; 64(1): 48-58.
57. Uzmay C, Kaya İ, Tömek B. Süt sığırcılığında hassas sürü yönetim uygulamaları. J Anim Product 2010; 51(2): 50-8.
58. Wang S, Jiang H, Qiao Y, Jiang S, Sun HL. The research progress of vision-based artificial intelligence in smart pig farming. Sensors 2022; 22(17):6302.
59. Wolc A, Barczak E, Wójtowski J, Slósarz P, Szwaczkowski T. Genetic parameters of body weight in sheep estimated via random regression and multi-trait animal models. Small Rumin Res 2011; 100(9):15-8.
60. Yaman H, Sungur O, Dulupçu MA. Dünyada tarım ve hayvancılığın dönüşümü: teknolojiye dayalı uygulamalar ve devrimler. Tarım Ekonomisi Derg 2021; 27(1):128-30.
61. Yurtseven A. Tarihi süreç içerisinde bitki ve hayvanların evcilleştirilmesinin sosyo-ekonomik etkileri. Dumlupınar Univ İİBF Derg 2023; 12(3):105-11.