The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
A systematic literature review on the use of machine learning in precision livestock farming
Open accessAbstract
Abstract This article presents a systematic literature review of recent works on the use of machine learning (ML) in precision livestock farming (PLF), focusing on two areas of interest: grazing and animal health. This review: (i) highlights opportunities for ML in the livestock sector; (ii) shows the current sensors, software and techniques for data analysis; (iii) details the increasing openness of data sources. It was found that the use of ML in PLF is in a stage of development and has several research challenges. Examples of such challenges are: (i) to develop hybrid models for diagnosis and prescription as a tool for the prevention and control of animal diseases; (ii) to bring together the grazing and animal health issues; (iii) to give autonomy to PLF using autonomous cycles of data analysis tasks and meta-learning; and (iv) to bring together soil and pasture variables because, for both, animal health and animal grazing, the variables used are only behavioral and environmental.
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Cites
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210
2015 ASABE Annual International Meeting
(2015) · doi:10.13031/aim.2015 · Cited by 533
BERCKMANS, D. (2014) · Revue Scientifique et Technique de l'OIE · doi:10.20506/rst.33.1.2273 · Cited by 431
Smart poultry management: Smart sensors, big data, and the internet of things
Astill, J., Dara, R. A., Fraser, E. D. G., Roberts, B., & Sharif, S. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105291 · Cited by 316
Overgaauw, P. A. M., Vinke, C. M., van Hagen, M. A. E., & Lipman, L. J. A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17113789 · Cited by 301
Methodology for systematic literature review applied to engineering and education
Torres-Carrion, P. V., Gonzalez-Gonzalez, C. S., Aciar, S., & Rodriguez-Morales, G. (2018) · 2018 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon.2018.8363388 · Cited by 275
Vázquez Diosdado, J. A., Barker, Z. E., Hodges, H. R., Amory, J. R., Croft, D. P., Bell, N. J., & Codling, E. A. (2015) · Animal Biotelemetry · doi:10.1186/s40317-015-0045-8 · Cited by 258
Precision livestock farming: an international review of scientific and commercial aspects
Banhazi, T. M., Lehr, H., Black, J. L., Crabtree, H., Schofield, P., Tscharke, M., & Berckmans, D. (2012) · University of Southern Queensland ePrints (University of Southern Queensland) · Cited by 244
Cited by
Precision Agriculture for Crop and Livestock Farming—Brief Review
Monteiro, A., Santos, S., & Gonçalves, P. (2021) · Animals · doi:10.3390/ani11082345 · Cited by 396
Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100416 · Cited by 245
Metadata
- Authors
- García, R., Aguilar, J., Toro, M., Pinto, A., & Rodríguez, P.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 179
- Pages
- 105826
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W3094525182
- Cited by
- 292
- References
- 50
- FWCI
- 3.41
- Type
- journal article
- Language
- en
- Open access
- bronze
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- García, R., Aguilar, J., Toro, M., Pinto, A., & Rodríguez, P. (2020). A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2020.105826
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
