Precision procedures and the application of artificial intelligence in cattle breeding with special reference to the identification of beef cattle
DOI:
https://doi.org/10.17205/SZIE.AWETH.2022.1.051Keywords:
image capturing, individual identification, artificial intelligence, neural networkAbstract
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
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Tarr Bence, Katona Balázs, Szabó István, Tőzsér János
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.