The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Remote sensing applications for precision agriculture: A learning community approach
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Citations
Cites
Technical Change and the Rate of Imitation
Mansfield, E. (1961) · Econometrica · doi:10.2307/1911817 · Cited by 1,904
Opportunities and limitations for image-based remote sensing in precision crop management
Moran, M. S., Inoue, Y., & Barnes, E. M. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00045-x · Cited by 921
Macdonald, R. B., & Hall, F. G. (1980) · Science · doi:10.1126/science.208.4445.670 · Cited by 336
Soil Structure and Sustainability
Lal, R. (1991) · Journal of Sustainable Agriculture · doi:10.1300/j064v01n04_06 · Cited by 197
Food Production, Population Growth, and the Environment
Daily, G., Dasgupta, P., Bolin, B., Crosson, P., du Guerny, J., Ehrlich, P., Folke, C., Jansson, A. M., Jansson, B.-O., Kautsky, N., et al. (1998) · Science · doi:10.1126/science.281.5381.1291 · Cited by 173
Cited by
Monitoring plant diseases and pests through remote sensing technology: A review
Zhang, J., Huang, Y., Pu, R., Gonzalez-Moreno, P., Yuan, L., Wu, K., & Huang, W. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.104943 · Cited by 623
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
A survey of deep learning techniques for weed detection from images
Hasan, A. S. M. M., Sohel, F., Diepeveen, D., Laga, H., & Jones, M. G. K. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106067 · Cited by 478
Tey, Y. S., & Brindal, M. (2012) · Precision Agriculture · doi:10.1007/s11119-012-9273-6 · Cited by 473
Precision Agriculture for Crop and Livestock Farming—Brief Review
Monteiro, A., Santos, S., & Gonçalves, P. (2021) · Animals · doi:10.3390/ani11082345 · Cited by 396
Metadata
- Authors
- Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2003-11
- Volume
- 88
- Issue
- 1-2
- Pages
- 157-169
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2056513814
- Cited by
- 428
- References
- 5
- FWCI
- 5.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Remote sensing applications for precision agriculture: A learning community approach. https://app.cochat.ai/papers/10.1016/j.rse.2003.04.007
- The paper
- Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A. (2003). Remote sensing applications for precision agriculture: A learning community approach. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2003.04.007
Data sources
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.
