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Hayvancılık İşletmelerinde Sürü Yönetimi, Otomasyon ve Yapay Zeka Uygulamaları

Year 2023, Volume: 3 Issue: 2, 75 - 86, 30.09.2023

Sinan Kopuzlu

Abstract

Hayvancılık işletmelerinde sürü yönetimi karmaşık olması, teknik bilgi gerektiren, hayvan sağlık ve refahını gözeten, kalite ve ürün güvenliği sağlayan, işçi verimliliği ve sağlığını gözeten, elde edilen farklı verileri kendi mantığı çerçevesinde iyi değerlendirmek zorunda kalan ve isabetli kararlar alınmasında profesyonel bir yaklaşım gerektiren işletme yönetimidir. Bu nedenle hayvancılık işletmelerinde sürü yönetim sistemleri, otomasyon ve yapay zeka uygulamaları zaman içerisinde kullanılmaya başlanmıştır. Bu uygulamalar kısa ve uzun vadede üretimin devamlılığı ve karlılığı açısından sonlanmayan bir döngünün olması sebebiyle çok büyük bir öneme sahiptirler. Bu makalede hayvancılık işletmelerinde geçmişten günümüze kadar geliştirilen ve kullanılan ileri teknolojilerden sürü yönetim sistemleri, otomasyon ve yapay zeka uygulamalarını ve faydalarını tanıtılmaktır.

Keywords

Hayvancılık sürü yönetimi otomasyon yapay zeka

Thanks

Makale konusunda çalışmaları olan araştırmacılara teşekkür ederim

References

  • Bazarbaeva, A.Kh., Popp, V.A. & Nardin, D.S. (2016). Electronic Scientific and Methodological Journal of the Omsk State Agrarian Uiversity, 1 (4): 71.
  • Bello, R.W., Mohamed, A.S.A. & Talib, A.Z. (2021). Contour Extraction of Individual Cattle From an Image Using Enhanced Mask R-CNN Instance Segmentation Method. IEEE Access 9: 56984-57000.
  • Bergfeld, U. (2006). Precision Dairy Farming – ein Schlagwort oder Basis zukunftsfähiger Milchproduktion? http://www.smul.sachsen.de/de /wu/Landwirtschaft/lfl/inhalt/download/Vortrag_BL S_Fachtag_6_12_2006.pdf (Erişim tarihi: 18/08/2023).
  • Bewley, J. (2008). Precision dairy farming: What is it and when does it pay? Proc. Kentucky Dairy Conference, 14-18.
  • Bewley, J. (2012). How precision dairy technologies can change your world. In Penn State dairy Cattle Nutrition Workshop, 65-74.
  • Carolan, M. (2020). Automated agrifood futures: Robotics, labor and the distributive politics of digital agriculture. Journal of Peasant Studies, 47: 184-207.
  • De Koning, K. (2010). Automatic milking - Common practice on dairy farms. In Proc. First North Am. Conf. Precision Dairy Management, Toronto, Canada, 52-67.
  • Dhanya, V.G., Subeesh, A., Kushwaha, N.L., Vishwakarma, D.K., Kumar, T.N., Ritika, G. & Singh, A.N. (2022). Deep learning based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture, 6: 211-229.
  • Doluschitz, R. (2003). Precision agriculture-Applications, economic considerations, experiences and perspectives. EFITA 2003, Conference, 5-9 July 2003, Debrecen, Hungary, 541-546.
  • Dwenger, R.H., Cappella, E., Perez, E., Baayen, M. & Muller, E. (1992). Application of a computerized herd management & productıon control program in Costa Rica. XXV. American Association of Bovine Pratictitioners Conference Proceeding. 3: 42-44.
  • Frost, A.R., Schofield, C.P., Beaulah, S.A., Mottram, T.T., Lines, J.A. & Wathes, C.M. (1997). A review of livestock monitoring and the need for integrated systems. Computers and Electronics in Agriculture. 17: 139-159.
  • Fuentes, S., Viejo, C.G., Cullen, B., Tongson, E., Chauhan, S.S. & Dunshea, F.R. (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, 2975: 1-11.
  • Fuentes, S., Viejo, C.G., Tongson, E., Lipovetzky, N. & Dunshea, F.R. (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, 6844: 1-16.
  • Fuentes, S., Viejo, C.G., Tongson, E. & Dunshea, F.R. (2022). The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence. Animal Health Research Reviews, 23: 59-71.
  • Göncü, S. (2023). Süt sığırcılığında sürü yönetimi. https://www.ruminantbesleme.com/2018/08/15/sut-sigirciliginda-suru-yonetimi/ (Erişim tarihi: 18.08.2023).
  • Hogeveen, H., Noordhuizen-Stassen, E.N., Schreinemakers, J.F. & Brand, A. (1991). Development of an integrated knowledge-based system for management support on dairy farms. Journal of Dairy Science, 74: 4377-4384.
  • Hogeveen, H. & Steeneveld, W. (2013). Integrating it all: Making it work and pay at the farm. Precision Dairy Conference. Rochester, MN., 113-121 pp.
  • Işık, A.H., Alakuş, F. & Eskicioğlu, Ö.F. (2021). Hayvancılıkta robotik sistemler ve yapay zekâ uygulamaları. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9 (2021): 370-382.
  • Kanjilal, D., Singh, D., Reddy, R. & Mathew, J. (2014). Smart Farm: Extending Automation To The Farm Level. International Journal of Scientific & Technology Research, 3 (7): 109-113 pp.
  • Kaya, İ., Uzmay, C. & Kaya, A. (1994). Süt sığırcılığında bilgisayara dayalı sürü yönetimi. Tarımda Bilgisayar Uygulamaları Sempozyumu, 5-7 Ekim 1994, İzmir, 156-161 s.
  • Kim, S.W. & Kim S.M. (1995). Turing-computability and artificial intelligence: Godel’s incompleteness results. Kybernetes, 24, 6.
  • Knijn, H., Taweel, H. Van Wichen, H, Wulfse, B.J. & Vonder, M. (2014). Smart dairy farming program in the Netherlands. Precision Dairy conference and expo, Rochester, MN., 141-142 pp.
  • Kumar, S., Singh, S.K., Singh, R. & Singh, A.K. (2017). Recognition of cattle using face images. Animal Biometrics., 79-110.
  • Leonardi, S. (2014). Internet of Things (IoT) and Dairy Farm Automation. Chapter 1. Graduate School of Veterinary Sciences for Animal Health And Food Safety, Università degli Studi di Milano, 41-48 p.
  • Luening, R. (1996). Teknik Süt Sığırcılığı Rehberi: Süt sığırcılığı işletmelerinin yönetimi. Ed. Önal, A.G, Çev. Türkyılmaz, K., Adnan Menderes Üniversitesi Yayınları, No: 29, Aydın.
  • Moraga, C., Trillas, E. & Guadarrama, S. (2003). Multiple-Valued Logic and Artificial Intelligence Fundamentals of Fuzzy Control Revisited. Artificial Intelligence Review, 20 (3-4): 169-197 p.
  • Nardin, D.S. & Malinina, A.I. (2015). Electronic Scientific and Methodological Journal of the Omsk State Agrarian University, 3 (3): 51 p.
  • Neethirajan, S. & Kemp, B. (2021). Digital Livestock Farming. Sensing and Bio-Sensing Research, 32: 1-12.
  • Neethirajan, S. (2022). Affective State Recognition in Livestock-Artificial Intelligence Approaches. Animals, 12, 759: 1-24.
  • Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S. & Clark, C. (2019). Individual cattle identification using a deep learning based framework. IFAC-Pap. 52: 318-323.
  • Rutten, C.J., Velthuis, A.G.J., Steeneveld, W. & Hogeveen, H. (2013). Sensors to support health management on dairy farms. Journal Dairy Science. 96: 1928-1952.
  • Scerri, P., Pynadath, D.V. & Tambe, M. (2002). Towards adjustable autonomy for the real world. Journal of Artificial Intelligence Research, 17: 171-228.
  • Schulze, C., Spilke, J. & Lehner, W. (2007). Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks. Computers and Electronics in Agriculture, 59: 39-55.
  • Shen, W., Hu, H., Dai, B., Wei, X., Sun, J., Jiang, L. & Sun, Y. (2020). Individual identification of dairy cows based on convolutional neural networks. Multimedia Tools and Applications, 79: 14711-14724.
  • Steeneveld, W., Van der Gaag, L.C., Ouweltjes, W., Mollenhorst, H. & Hogeveen, H. (2010). Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems, 93 (6): 2559-68.
  • Svennersten-Sjaunja, K.M., & Pettersson, G. (2008). Pros and cons of automatic milking in Europe. Journal of Animal Science, 86: 37-46.
  • Titovskii, S., Titovskaia, N., Titovskaya, T., & Alekseeva, E. (2023). Automated system for assessing the structure of a dairy cattle herd. VIII International Conference on Advanced Agritechnologies, Environmental Engineering and Sustainable Development (Agritech-VIII 2023), E3S Web of Conferences, 390: 03016.
  • Uzmay, C., Kaya, İ. & Tömek, B. 2010. Süt Sığırcılığında Hassas Sürü Yönetim Uygulamaları. Hayvansal Üretim, 51 (2): 50-58.
  • Xiao, J., Liu, G., Wang, K. & Si, Y. (2022). Cow identification in free-stall barns based on an improved Mask R CNN and an SVM. Computers and Electronics in Agriculture, 194: 106738. https://doi. org/10.1016/j.compag.2022.106738.

Year 2023, Volume: 3 Issue: 2, 75 - 86, 30.09.2023

Sinan Kopuzlu

Abstract

References

  • Bazarbaeva, A.Kh., Popp, V.A. & Nardin, D.S. (2016). Electronic Scientific and Methodological Journal of the Omsk State Agrarian Uiversity, 1 (4): 71.
  • Bello, R.W., Mohamed, A.S.A. & Talib, A.Z. (2021). Contour Extraction of Individual Cattle From an Image Using Enhanced Mask R-CNN Instance Segmentation Method. IEEE Access 9: 56984-57000.
  • Bergfeld, U. (2006). Precision Dairy Farming – ein Schlagwort oder Basis zukunftsfähiger Milchproduktion? http://www.smul.sachsen.de/de /wu/Landwirtschaft/lfl/inhalt/download/Vortrag_BL S_Fachtag_6_12_2006.pdf (Erişim tarihi: 18/08/2023).
  • Bewley, J. (2008). Precision dairy farming: What is it and when does it pay? Proc. Kentucky Dairy Conference, 14-18.
  • Bewley, J. (2012). How precision dairy technologies can change your world. In Penn State dairy Cattle Nutrition Workshop, 65-74.
  • Carolan, M. (2020). Automated agrifood futures: Robotics, labor and the distributive politics of digital agriculture. Journal of Peasant Studies, 47: 184-207.
  • De Koning, K. (2010). Automatic milking - Common practice on dairy farms. In Proc. First North Am. Conf. Precision Dairy Management, Toronto, Canada, 52-67.
  • Dhanya, V.G., Subeesh, A., Kushwaha, N.L., Vishwakarma, D.K., Kumar, T.N., Ritika, G. & Singh, A.N. (2022). Deep learning based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture, 6: 211-229.
  • Doluschitz, R. (2003). Precision agriculture-Applications, economic considerations, experiences and perspectives. EFITA 2003, Conference, 5-9 July 2003, Debrecen, Hungary, 541-546.
  • Dwenger, R.H., Cappella, E., Perez, E., Baayen, M. & Muller, E. (1992). Application of a computerized herd management & productıon control program in Costa Rica. XXV. American Association of Bovine Pratictitioners Conference Proceeding. 3: 42-44.
  • Frost, A.R., Schofield, C.P., Beaulah, S.A., Mottram, T.T., Lines, J.A. & Wathes, C.M. (1997). A review of livestock monitoring and the need for integrated systems. Computers and Electronics in Agriculture. 17: 139-159.
  • Fuentes, S., Viejo, C.G., Cullen, B., Tongson, E., Chauhan, S.S. & Dunshea, F.R. (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, 2975: 1-11.
  • Fuentes, S., Viejo, C.G., Tongson, E., Lipovetzky, N. & Dunshea, F.R. (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, 6844: 1-16.
  • Fuentes, S., Viejo, C.G., Tongson, E. & Dunshea, F.R. (2022). The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence. Animal Health Research Reviews, 23: 59-71.
  • Göncü, S. (2023). Süt sığırcılığında sürü yönetimi. https://www.ruminantbesleme.com/2018/08/15/sut-sigirciliginda-suru-yonetimi/ (Erişim tarihi: 18.08.2023).
  • Hogeveen, H., Noordhuizen-Stassen, E.N., Schreinemakers, J.F. & Brand, A. (1991). Development of an integrated knowledge-based system for management support on dairy farms. Journal of Dairy Science, 74: 4377-4384.
  • Hogeveen, H. & Steeneveld, W. (2013). Integrating it all: Making it work and pay at the farm. Precision Dairy Conference. Rochester, MN., 113-121 pp.
  • Işık, A.H., Alakuş, F. & Eskicioğlu, Ö.F. (2021). Hayvancılıkta robotik sistemler ve yapay zekâ uygulamaları. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9 (2021): 370-382.
  • Kanjilal, D., Singh, D., Reddy, R. & Mathew, J. (2014). Smart Farm: Extending Automation To The Farm Level. International Journal of Scientific & Technology Research, 3 (7): 109-113 pp.
  • Kaya, İ., Uzmay, C. & Kaya, A. (1994). Süt sığırcılığında bilgisayara dayalı sürü yönetimi. Tarımda Bilgisayar Uygulamaları Sempozyumu, 5-7 Ekim 1994, İzmir, 156-161 s.
  • Kim, S.W. & Kim S.M. (1995). Turing-computability and artificial intelligence: Godel’s incompleteness results. Kybernetes, 24, 6.
  • Knijn, H., Taweel, H. Van Wichen, H, Wulfse, B.J. & Vonder, M. (2014). Smart dairy farming program in the Netherlands. Precision Dairy conference and expo, Rochester, MN., 141-142 pp.
  • Kumar, S., Singh, S.K., Singh, R. & Singh, A.K. (2017). Recognition of cattle using face images. Animal Biometrics., 79-110.
  • Leonardi, S. (2014). Internet of Things (IoT) and Dairy Farm Automation. Chapter 1. Graduate School of Veterinary Sciences for Animal Health And Food Safety, Università degli Studi di Milano, 41-48 p.
  • Luening, R. (1996). Teknik Süt Sığırcılığı Rehberi: Süt sığırcılığı işletmelerinin yönetimi. Ed. Önal, A.G, Çev. Türkyılmaz, K., Adnan Menderes Üniversitesi Yayınları, No: 29, Aydın.
  • Moraga, C., Trillas, E. & Guadarrama, S. (2003). Multiple-Valued Logic and Artificial Intelligence Fundamentals of Fuzzy Control Revisited. Artificial Intelligence Review, 20 (3-4): 169-197 p.
  • Nardin, D.S. & Malinina, A.I. (2015). Electronic Scientific and Methodological Journal of the Omsk State Agrarian University, 3 (3): 51 p.
  • Neethirajan, S. & Kemp, B. (2021). Digital Livestock Farming. Sensing and Bio-Sensing Research, 32: 1-12.
  • Neethirajan, S. (2022). Affective State Recognition in Livestock-Artificial Intelligence Approaches. Animals, 12, 759: 1-24.
  • Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S. & Clark, C. (2019). Individual cattle identification using a deep learning based framework. IFAC-Pap. 52: 318-323.
  • Rutten, C.J., Velthuis, A.G.J., Steeneveld, W. & Hogeveen, H. (2013). Sensors to support health management on dairy farms. Journal Dairy Science. 96: 1928-1952.
  • Scerri, P., Pynadath, D.V. & Tambe, M. (2002). Towards adjustable autonomy for the real world. Journal of Artificial Intelligence Research, 17: 171-228.
  • Schulze, C., Spilke, J. & Lehner, W. (2007). Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks. Computers and Electronics in Agriculture, 59: 39-55.
  • Shen, W., Hu, H., Dai, B., Wei, X., Sun, J., Jiang, L. & Sun, Y. (2020). Individual identification of dairy cows based on convolutional neural networks. Multimedia Tools and Applications, 79: 14711-14724.
  • Steeneveld, W., Van der Gaag, L.C., Ouweltjes, W., Mollenhorst, H. & Hogeveen, H. (2010). Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems, 93 (6): 2559-68.
  • Svennersten-Sjaunja, K.M., & Pettersson, G. (2008). Pros and cons of automatic milking in Europe. Journal of Animal Science, 86: 37-46.
  • Titovskii, S., Titovskaia, N., Titovskaya, T., & Alekseeva, E. (2023). Automated system for assessing the structure of a dairy cattle herd. VIII International Conference on Advanced Agritechnologies, Environmental Engineering and Sustainable Development (Agritech-VIII 2023), E3S Web of Conferences, 390: 03016.
  • Uzmay, C., Kaya, İ. & Tömek, B. 2010. Süt Sığırcılığında Hassas Sürü Yönetim Uygulamaları. Hayvansal Üretim, 51 (2): 50-58.
  • Xiao, J., Liu, G., Wang, K. & Si, Y. (2022). Cow identification in free-stall barns based on an improved Mask R CNN and an SVM. Computers and Electronics in Agriculture, 194: 106738. https://doi. org/10.1016/j.compag.2022.106738.
There are 39 citations in total.

Details

Primary Language Turkish
Subjects Zootechny (Other)
Journal Section Reviews
Authors

Sinan Kopuzlu 0000-0002-1582-3929

Publication Date September 30, 2023
Submission Date August 28, 2023
Published in Issue Year 2023 Volume: 3 Issue: 2

Cite

APA Kopuzlu, S. (2023). Hayvancılık İşletmelerinde Sürü Yönetimi, Otomasyon ve Yapay Zeka Uygulamaları. Uluslararası Gıda Tarım Ve Hayvan Bilimleri Dergisi, 3(2), 75-86.
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