The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
The role of sensors, big data and machine learning in modern animal farming
Open accessAbstract
Ever since man began domesticating animals several thousand years ago, we have always relied on our intuition, collective knowledge, and sensory signals to make effective animal production decisions. So far, this has helped us make significant gains in animal husbandry and farming. Together the growing demand for food and the advancement in sensing technology have the potential to make animal farming more centralized, large scale and efficient. It has the potential to change animal farming as we know it. At a broader level, this paper explores the challenges and opportunities that sensor technologies present in terms of helping animal farmers produce more meat and animal products. More specifically, this paper explores the role of sensors, big data, artificial intelligence and machine learning in helping animal farmers to lower production costs, increase efficiencies, enhance animal welfare and grow more animals per hectare. It also explores the challenges and limitations of technology. The paper reviews various animal farming technology applications to understand its value in helping farmers improve animal health, increase profits and lower environmental footprint.
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Climate change and livestock: Impacts, adaptation, and mitigation
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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
A review of Earth Artificial Intelligence
Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., et al. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105034 · Cited by 271
Metadata
- Authors
- Neethirajan, S.
- Venue
- Sensing and Bio-Sensing Research
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 29
- Pages
- 100367
- ISSN
- 2214-1804 (ISSN-L)
- OpenAlex
- W3041906494
- Cited by
- 375
- References
- 58
- FWCI
- 24.97
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
