Precision procedures and the application of artificial intelligence in cattle breeding with special reference to the identification of beef cattle

Authors

  • Bence Tarr Magyar Agrár- és Élettudományi Egyetem, Műszaki Tudományok Intézet, Szent István Campus, 2100 Gödöllő, Páter Károly u. 1.
  • Balázs Katona Magyar Agrár- és Élettudományi Egyetem, Állattenyésztési Tudományok Intézet
  • István Szabó Magyar Agrár- és Élettudományi Egyetem, Műszaki Tudományok Intézet, Szent István Campus, 2100 Gödöllő, Páter Károly u. 1.
  • János Tőzsér Magyar Agrár- és Élettudományi Egyetem, Állattenyésztési Tudományok Intézet

DOI:

https://doi.org/10.17205/SZIE.AWETH.2022.1.051

Keywords:

image capturing, individual identification, artificial intelligence, neural network

Abstract

Artificial Intelligence (AI) has become an important tool for optimising breeding processes in several areas of animal production. In this thesis, we have presented examples from the literature, mainly for the identification and counting of cattle. The individual identification of animals, the monitoring of their behaviour and the control of their movements support a number of conclusions from both an animal welfare and a veterinary point of view. Automation of the processing of captured images has also become essential. This process is supported by Artificial Intelligence. Deep learning and neural networks are excellent tools for segmenting images and processing their content based on different features. Convolutional neural networks are specifically powerful for such tasks and we have seen that further developments of these networks (e.g. Faster R-CNN) allow even more efficient image analysis procedures. Processing animal images can be a major step forward for automatic analysis and identification of livestock.

References

Alföldi L., Tarr Z., Tőzsér J. (2020): Digitális mikroklíma mérés a tejtermelő farmon. Animal Welfare Etológia és tartástechnika 16: 2 pp. 94–109., 16 p. https://doi.org/10.17205/SZIE.AWETH.2020.2.094

Barbedo, J. G. A., Koenigkan, L. V., Santos, T. T., Santos, P. M. (2019): A Study on the Detection of Cattle in UAV Images Using Deep Learning. Sensors 2019, 19, 5436. https://doi.org/10.3390/s19245436

Barbedo, J., Koenigkan, L., Santos, T., Santos, P. (2019). A Study on the Detection of Cattle in UAV Images Using Deep Learning. Sensors. 19. 5436. https://doi.org/10.3390/s19245436

Barriuso, A. L., Villarrubia González G., De Paz J. F., Lozano Á., Bajo J. (2018): Combination of Multi-Agent Systems and Wireless Sensor Networks for the Monitoring of Cattle. Sensors (Basel). 18(1):108. PMID: 29301310; PMCID: PMC5795335. https://doi.org/10.3390/s18010108

Beibei Xu, Wensheng Wang, Greg Falzon, Paul Kwan, Leifeng Guo, Zhiguo Sun & Chunlei Li (2020) Livestock classification and counting in quadcopter aerial images using Mask R-CNN, International Journal of Remote Sensing, 41:21, 8121–8142, https://doi.org/10.1080/01431161.2020.1734245

Hollósi D. (2017) (szerk: Milics G.): Adatalapú döntések a 2020 utáni finanszírozásban. Precíziós Gazdálkodás, Adat, Információ, Haszon. Budapest, Agroinform és NAK, 26. p ISBN: 978-963-12-8921-3

Kühl, H. S., Burghardt, T. (2013): Animal biometrics: quantifying and detecting phenotypic appearance. Trends Ecol Evol 28(7):432–441. https://doi.org/10.1016/j.tree.2013.02.013

Kumar, Santosh, Singh, Sanjay (2017): Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm. Multimedia Tools and Applications. 76. 1–30. https://doi.org/10.1007/s11042-016-4181-9

Linko, S. (1998): Expert systems-what can they do for the food industry? Trends in Food Science and Technology 9: 3–12. https://doi.org/10.1016/S0924-2244(97)00002-2

Russel, S. Norvig, P. (2021): Artificial Intelligence: A Modern Approach, Global Edition, Pearson Education Limited, London, 1170 p.

Tóth, L., Kovács, L., Póti, P., Alföldi, L., Tarr, Z., Szenci, O., Tőzsér, J. (2019): Korszerű információ technika (IT) a tejelő szarvasmarha tartásban. Állattenyésztés és takarmányozás, 68. 3. 253. p. http://real-j.mtak.hu/16047/3/att_2019_03.pdf

Weber, Fabricio de Lima, Weber, Vanessa Aparecida de Moraes, Menezes, Geazy Vilharva, Oliveira Junior, Adair da Silva, Alves, Daniela Arestides, de Oliveira, Marcus Vinicius Morais, Matsubara, Edson T (2020): Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks. Computers and Electronics in Agriculture,08., 175. 10. https://doi.org/10.1016/j.compag.2020.105548

Yongliang Qiao, Daobilige Su, HeKong, Salah Sukkarieh, Sabrina Lomax, Cameron Clark (2019): Individual Cattle Identification Using a Deep Learning Based Framewor, IFAC-PapersOnLine, Volume 52, Issue 30, Pages 318–323 https://doi.org/10.1016/j.ifacol.2019.12.558

Published

2022-06-30

Issue

Section

Cikk szövege

How to Cite

Precision procedures and the application of artificial intelligence in cattle breeding with special reference to the identification of beef cattle. (2022). Animal Welfare, Ethology and Housing Systems (AWETH), 18(1), 51-63. https://doi.org/10.17205/SZIE.AWETH.2022.1.051

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