AMB_2024v14n5

Animal Molecular Breeding 2024, Vol.14, No.5, 335-344 http://animalscipublisher.com/index.php/amb 343 Eckelkamp E., 2019, Invited review: current state of wearable precision dairy technologies in disease detection, Applied Animal Science, 35(2): 209-220. https://doi.org/10.15232/AAS.2018-01801 Elavarasan D., Vincent D., Sharma V., Zomaya A., and Srinivasan K., 2018, Forecasting yield by integrating agrarian factors and machine learning models: a survey, Comput. Electron. Agric., 155: 257-282. https://doi.org/10.1016/j.compag.2018.10.024 García R., Aguilar J., Aguilar J., Toro M., Pinto Á., and Rodríguez P., 2020, A systematic literature review on the use of machine learning in precision livestock farming, Comput. Electron. Agric., 179: 105826. https://doi.org/10.1016/j.compag.2020.105826 Gengler N., 2019, Symposium review: Challenges and opportunities for evaluating and using the genetic potential of dairy cattle in the new era of sensor data from automation, Journal of Dairy Science, 102(6): 5756-5763. https://doi.org/10.3168/jds.2018-15711 PMid:30904300 Halachmi I., and Guarino M., 2016, Editorial: Precision livestock farming: a 'per animal' approach using advanced monitoring technologies, Animal : An International Journal of Animal Bioscience, 10(9) 1482-1483. https://doi.org/10.1017/S1751731116001142 PMid:27534883 Kolipaka V., 2020, Predictive analytics using cross media features in precision farming, International Journal of Speech Technology, 23: 57-69. https://doi.org/10.1007/s10772-020-09669-z Koltes J., 2021, 51 opportunities to apply and learn from deep phenotyping in dairy cattle, Journal of Animal Science, 99(3): 30-31. https://doi.org/10.1093/jas/skab235.051 Lasser J., Matzhold C., Egger-Danner C., Fuerst-Waltl B., Steininger F., Wittek T., and Klimek P., 2021, Integrating diverse data sources to predict disease risk in dairy cattle-a machine learning approach, Journal of Animal Science, 99(11): 294. https://doi.org/10.1093/jas/skab294 Li J., Chen D., Qi X., Li Z., Huang Y., Morris D., and Tan X., 2023, Label-efficient learning in agriculture: a comprehensive review, ArXiv, 215: 108412. https://doi.org/10.48550/arXiv.2305.14691 Liu N., Qi J., An X., and Wang Y., 2023, A review on information technologies applicable to precision dairy farming: focus on behavior, health monitoring, and the precise feeding of dairy cows, Agriculture, 13(10): 1858. https://doi.org/10.3390/agriculture13101858 Lokhorst C., Mol R., and Kamphuis C., 2019, Invited review: big data in precision dairy farming, Animal, 13: 1519-1528. https://doi.org/10.1017/S1751731118003439 PMid:30630546 PMCid:PMC6581964 Oehm A., Springer A., Jordan D., Strube C., Knubben-Schweizer G., Jensen K., and Zablotski Y., 2022, A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cows, PLoS ONE, 17(7): e0271413. https://doi.org/10.1371/journal.pone.0271413 PMid:35816512 PMCid:PMC9273072 Sharma A., Jain A., Gupta P., and Chowdary V., 2021, Machine learning applications for precision agriculture: a comprehensive review, IEEE Access, 9: 4843-4873. https://doi.org/10.1109/ACCESS.2020.3048415 Sharma R., Kamble S., Gunasekaran A., Kumar V., and Kumar A., 2020, A systematic literature review on machine learning applications for sustainable agriculture supply chain performance, Comput. Oper. Res., 119: 104926. https://doi.org/10.1016/j.cor.2020.104926 Shine P., and Murphy M., 2021, Over 20 years of machine learning applications on dairy farms: a comprehensive mapping study, Sensors (Basel, Switzerland), 22(1): 52. https://doi.org/10.3390/s22010052 PMid:35009593 PMCid:PMC8747441 Slob N., Catal C., and Kassahun A., 2020, Application of machine learning to improve dairy farm management: a systematic literature review, Preventive Veterinary Medicine, 187: 105237. https://doi.org/10.1016/j.prevetmed.2020.105237 PMid:33418514 Yang B., 2024, Enhancing dairy cow milk fat synthesis genes with CRISPR-Cas9 technology to increase dairy product yield, International Journal of Molecular Veterinary Research, 14(1): 9-16. https://doi.org/10.5376/ijmvr.2024.14.0002 Yousefi D., Rafie A., Al-Haddad S., and Azrad S., 2022, A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles, IEEE Access, 10: 80071-80091. https://doi.org/10.1109/access.2022.3194507

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