The DOI matches its Crossref record; 124 of the 124 references it cites were found in a registry or an index.
What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?
Open accessAbstract
Remote sensing from unmanned aircraft systems (UAS) was expected to be an important new technology to assist farmers with precision agriculture, especially crop nutrient management. There are three advantages using UAS platforms compared to manned aircraft platforms with the same sensor for precision agriculture: (1) smaller ground sample distances, (2) incident light sensors for image calibration, and (3) canopy height models created from structure-from-motion point clouds. These developments hold promise for future data products. In order to better match vendor capabilities with farmer requirements, we classify applications into three general niches: (1) scouting for problems, (2) monitoring to prevent yield losses, and (3) planning crop management operations. The three different niches have different requirements for sensor calibration and have different costs of operation. Planning crop management operations may have the most environmental and economic benefits. However, a USDA Economic Research Report showed that only about 20% of farmers in the USA have adopted variable rate applicators; so, most farmers in the USA may not have the technology to benefit from management plans. In the near-term, monitoring to prevent yield losses from weeds, insects, and diseases may provide the most economic and environmental benefits, but the costs for data acquisition need to be reduced.
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Cites
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Cited by
Precision farming at the nexus of agricultural production and the environment
Finger, R., Swinton, S. M., El Benni, N., & Walter, A. (2019) · Annual Review of Resource Economics · doi:10.1146/annurev-resource-100518-093929 · Cited by 539
Lajoie-O'Malley, A., Bronson, K., van der Burg, S., & Klerkx, L. (2020) · Ecosystem Services · doi:10.1016/j.ecoser.2020.101183 · Cited by 390
Metadata
- Authors
- Hunt, E. R., & Daughtry, C. S. T.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2017-12-03
- Volume
- 39
- Issue
- 15-16
- Pages
- 5345-5376
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2772481104
- Cited by
- 325
- References
- 124
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.