Intelligent Approaches in Livestock Farming: Using Deep Learning Models
DOI:
https://doi.org/10.24925/turjaf.v12i11.1959-1967.6861Keywords:
Machine Learning, Deep Learning, Artificial Neural Networks, Intelligent Agriculture, LivestockAbstract
Traditional animal production methods are struggling to cope with increasing population and inadequate resources. Machine learning, which has emerged as a solution to these challenges in animal production, offers various advantages in productivity, health monitoring, and breeding areas in the livestock sector. Machine learning in animal husbandry not only optimizes farm management with its significant advantages but also provides farmers with a powerful tool to achieve sustainability goals. The integration of these technological developments into the livestock sector represents a significant step towards a smarter, more efficient, and sustainable livestock practice in the future. In summary, this review provides a comprehensive exploration of the tangible benefits and innovative opportunities brought to farm animal management by machine learning methods such as deep learning and artificial neural networks. It contributes to the ongoing debate on agricultural sustainability and productivity with insights into advanced health monitoring, optimized feeding practices, and strategic breeding management.
References
Aldridge, M. E., Fearon, J. E., Haynes, B. P., Miller, H. M., Sanford, K. Y., Scott, R. R., ... & Memili, E. (2018). Solutions for grand challenges in goat and Sheep Production. Biotropia, 26(1), 55-64. https://doi.org/10.11598/btb.2019.26.1.944.
Alini, M., Diwan, A. D., Erwin, W. M., Little, C. B., & Melrose, J. (2023). An update on animal models of intervertebral disc degeneration and low back pain: exploring the potential of artificial intelligence to improve research analysis and development of prospective therapeutics. JOR Spine, 6(1), e1230. https://doi.org/ 10.1002/jsp2.1230
Alp, S., & Öz, E. (2019). Makine öğrenmesinde sınıflandırma yöntemleri ve R uygulamaları. Nobel Akademik Yayıncılık.
Alves, T. S., Pinto, M. A., Ventura, P., Neves, C. J., Biron, D. G., Junior, A. C., ... & Rodrigues, P. J. (2020). Automatic detection and classification of honey bee comb cells using deep learning. Computers and Electronics in Agriculture, 170, 105244. https://doi.org/10.1016/j.compag.2020.105244
Bao, J., & Xie, Q. (2022). Artificial intelligence in animal farming: A systematic literature review. Journal of Cleaner Production, 331, 129956. https://doi.org/10.1016/j.jclepro.2021.129956
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, 19(24), 5436. https://doi.org/10.3390/s19245436
Beausoleil, N. J., Blache, D., Stafford, K. J., Mellor, D. J., & Noble, A. D. (2012). Selection for temperament in sheep: domain-general and context-specific traits. Applied Animal Behaviour Science, 139(1-2), 74-85. https://doi.org/10.1016/j.applanim.2012.02.020
Berkaya, S. K., Gunal, E. S., & Gunal, S. (2021). Deep learning-based classification models for beehive monitoring. Ecological Informatics, 64, 101353. https://doi.org/10.1016/j.ecoinf.2021.101353
Bezen, R., Edan, Y., & Halachmi, I. (2020). Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms. Computers and Electronics in Agriculture, 172, 105345. https://doi.org/10.1016/j.compag.2020.105345
Bondi, E., Oh, H., Xu, H., Fang, F., Dilkina, B., & Tambe, M. (2019). AMAS: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, Canada, 2336-2338.
Cihan, P., Gökçe, E., Atakişi, O., Kırmzıgül, A. H., & Erdoğan, H. M. (2021). Prediction of immunoglobulin G in lambs with artificial intelligence methods. Kafkas Universitesi Veteriner Fakultesi Dergisi, 27(1), 21-27. https://doi.org/10.9775/kvfd.2020.24642
Cowton, J., Kyriazakis, I., Plötz, T., & Bacardit, J. (2018). A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors, 18(8), 2521. https://doi.org/10.3390/s18082521
Çayıroğlu, İ. (2015). İleri algoritma analizi - Yapay sinir ağlar. http://www.ibrahimcayiroglu.com/Dokumanlar/IleriAlgoritmaAnalizi/IleriAlgoritmaAnalizi-5.Hafta-YapaySinirAglari.pdf
da Silva, I. N., Spatti, D. H., Flauzino, R. A., Liboni, L. H. B, & Alves, S.F.R. (2016). Artificial Neural Networks: A Practical Course. Springer.
Dac, H. H., Gonzalez Viejo, C., Lipovetzky, N., Tongson, E., Dunshea, F. R., & Fuentes, S. (2022). Livestock identification using deep learning for traceability. Sensors, 22(21), 8256. https://doi.org/10.3390/s22218256
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Gourisaria, M. K., Arora, A., Bilgaiyan, S., & Sahni, M. (2023). Chicken disease multiclass classification using deep learning. Proceedings of International Conference on Information Technology and Applications, 614, 225-238 https://doi.org/10.1049/cmu2.12338
Guo, Y., Aggrey, S. E., Wang, P., Oladeinde, A., & Chai, L. (2022). Monitoring behaviors of broiler chickens at different ages with deep learning. Animals, 12(23), 3390. https://doi.org/10.3390/ani12233390
Gültekin, N. & Doğan, A. (2023). Makine öğrenimi yöntemleriyle bazaltlarda tek eksenli Sıkışma Dayanımının Değerlendirilmesi ve Performanslarının Karşılaştırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 11(2), 1059-1074. https://doi.org/10.29130/dubited.1173624
Han, S., Fuentes, A., Yoon, S., Jeong, Y., Kim, H., & Park, D. S. (2023). Deep learning-based multi-cattle tracking in crowded livestock farming using video. Computers and Electronics in Agriculture, 212, 108044. https://doi.org/10.1016/j.compag.2023.108044
Hodgkin, A. L. & Huxley, A. F. (1952). The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. The Journal of Physiology. 116, 497-506. https://doi.org/10.1113/jphysiol.1952.sp004719
Jiménez, D., Delerce, S., Dorado, H., Cock, J., Muñoz, L. A., Agamez, A., & Jarvis, A. (2019). A scalable scheme to implement data-driven agriculture for small-scale farmers. Global Food Security, 23, 256-266. https://doi.org/10.1016/j.gfs.2019.08.004
Kaur J., & Gupta N. (2020) Bipolar sigmoid algorithm for designing constructive neural network. International Journal on Emerging Technologies, 11(2), 991-996.
Khanikar, D., Phookan, A., & Gogoi, A. (2022, June 23). Artificial Neural Networks: An Introduction and Application in Animal Breeding and Production: A Review. Agriculturals Reviews. https://arccjournals.com/journal/agricultural-reviews/R-2421
Kongsilp, P., Taetragool, U., & Duangphakdee, O. (2024). Individual honey bee tracking in a beehive environment using deep learning and Kalman filter. Scientific Reports, 14(1), 1061. https://doi.org/10.1038/s41598-023-44718-y
Krogh, A. (2008). What are artificial neural networks?. Nature biotechnology, 26(2), 195-197. https://doi.org/10.1038/nbt1386.
Kröse B. & Smagt, P. V. D. (1996). An introduction to neural networks. The University of Amsterdam.
Kubat, M. (2021). An Introduction to Machine Learning. Springer. https://doi.org/10.1007/978-3-319-63913-0
Lasekan, A., Bakar, F. A., & Hashim, D. (2013). Potential of chicken by products as sources of useful biological resources. Waste Management, 33(3), 552-565. https://doi.org/10.1016/j.wasman.2012.08.001
Lee, H. J., Lee, J. H., Gondro, C., Koh, Y. J., & Lee, S. H. (2023). deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle. Genetics Selection Evolution, 55(1), 56. https://doi.org/10.1186/s12711-023-00825-y
Liu, H. W., Chen, C. H., Tsai, Y. C., Hsieh, K. W., & Lin, H. T. (2021). Identifying images of dead chickens with a chicken removal system integrated with a deep learning algorithm. Sensors, 21(11), 3579. https://doi.org/10.3390/s21113579
Lösel, P. D., Monchanin, C., Lebrun, R., Jayme, A., Relle, J. J., Devaud, J. M., ... & Lihoreau, M. (2023). Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning. PLoS Computational Biology, 19(10), e1011529. https://doi.org/10.1371/journal.pcbi.1011529
Mathison, B. A., Kohan, J. L., Walker, J. F., Smith, R. B., Ardon, O., & Couturier, M. R. (2020). Detection of intestinal protozoa in trichrome-stained stool specimens by use of a deep convolutional neural network. Journal of Clinical Microbiology, 58(6), 10-1128. https://doi.org/10.1128/JCM.02053-19
Mishra, S., & Sharma, S. K. (2023). Advanced contribution of IoT in agricultural production for the development of smart livestock environments. Internet of Things, 22(4), 100724. https://doi.org/10.1016/j.iot.2023.100724
Morris, S. T. (2017). Overview of sheep production systems. In D. Ferguson, C. Lee & A. Fisher (Eds.), Advances in Sheep Welfare (pp. 19-35). Woodhead Publishing.
Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and precision livestock farming (PLF): an up to date overview across animal productions. Sensors, 22(12), 4319. https://doi.org/10.3390/s22124319
Nayeri, S., Sargolzaei, M., & Tulpan, D. (2019). A review of traditional and machine learning methods applied to animal breeding. Animal Health Research Reviews, 20(1), 31-46. https://doi.org/10.1017/S1466252319000148
Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29, 100367. https://doi.org/10.1016/j.sbsr.2020.100367
Okut, H., Gianola, D., Rosa, G. J. M. & Weigel, K. A. (2011). Prediction of body mass index in mice using dense molecular markers and a regularized neural network. Genetics Research 93(3),189-201. http://dx.doi.org/10.1017/S0016672310000662
Oyibo, P., Jujjavarapu, S., Meulah, B., Agbana, T., Braakman, I., van Diepen, A., ... & Diehl, J. C. (2022). Schistoscope: an automated microscope with artificial intelligence for detection of Schistosoma haematobium eggs in resource-limited settings. Micromachines, 13(5), 643. https://doi.org/10.3390/mi13050643
Öztemel, E. (2012). Yapay sinir ağları. Papatya Yayıncılık.
Parrish, K. (2018). Deep learning vs. machine learning: What’s the difference between the two. https://www.digitaltrends.com/cool-tech/deep-learning-vs-machine-learning-explained/2/
Phythian, C. J., Michalopoulou, E., Jones, P. H., Winter, A. C., Clarkson, M. J., Stubbings, L. A., Grove-White, D., Cripps, P. J., & Duncan, J. S. (2011). Validating indicators of sheep welfare through a consensus of expert opinion. Animal : an international journal of animal bioscience, 5(6), 943–952. https://doi.org/10.1017/S1751731110002594
Richert, W. (2013). Building machine learning systems with Python. Packt Publishing.
Rojas-Downing, M.M., Nejadhashemi, A.P., Harrigan, T. & Woznicki, S.A. (2017). Climate change and livestock: Impacts, adaptation, and mitigation. Elsevier.
Sağıroğlu, Ş., Beşdok E., & Erler, M. (2003). Mühendislikte yapay zekâ uygulamaları - yapay sinir ağları. Ufuk Kitap Yayıncılık.
Strömbom, D., & King, A. J. (2018). Robot collection and transport of objects: A biomimetic process. Frontiers in Robotics and AI, 5, 48. https://doi.org/10.3389/frobt.2018.00048
Sun, S., Wei, L., Chen, Z., Chai, Y., Wang, S., & Sun, R. (2024). Nondestructive estimation method of live chicken leg weight based on deep learning. Poultry Science, 103477. https://doi.org/10.1016/j.psj.2024.103477
Terlouw, E. M. C., Arnould, C., Aupérin, B., Berri, C., Le Bihan-Duval, E., Deiss, V., ... & Mounier, L. (2008). Pre-slaughter conditions, animal stress and welfare: current status and possible future research. Animal, 2(10), 1501-1517. https://doi.org/10.1017/S1751731108002723
Yavuz, S., & Deveci, M. (2012). İstatiksel normalizasyon tekniklerinin yapay sinir ağin performansina etkisi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (40), 167-187.
Yaxley, K. J., Joiner, K. F., & Abbass, H. (2021). Drone approach parameters leading to lower stress sheep flocking and movement: sky shepherding. Scientific reports, 11(1), 7803. https://doi.org/10.1038/s41598-021-87453-y
Voudiotis, G., Moraiti, A., & Kontogiannis, S. (2022). Deep learning beehive monitoring system for early detection of the Varroa mite. Signals, 3(3), 506-523. https://doi.org/10.3390/signals3030030
Zingaro, M., Salvatori, V., Vielmi, L., & Boitani, L. (2018). Are the livestock guarding dogs where they are supposed to be?. Applied Animal Behaviour Science, 198, 89-94. http://dx.doi.org/10.1016/j.applanim.2017.10.002
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
