The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images
Open accessAbstract
Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A soil-adjusted vegetation index (SAVI)
Huete, A. R. (1988) · Remote Sensing of Environment · doi:10.1016/0034-4257(88)90106-x · Cited by 7,948
Monitoring Vegetation Systems in the Great Plains with Erts
Rouse, J. W., Haas, R. H., Schell, J., & Deering, D. W. (1973) · NASA STI Repository (National Aeronautics and Space Administration) · Cited by 6,835
‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications
Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012) · Geomorphology · doi:10.1016/j.geomorph.2012.08.021 · Cited by 4,044
Using the satellite-derived NDVI to assess ecological responses to environmental change
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J., & Stenseth, N. C. (2005) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2005.05.011 · Cited by 3,584
Use of a green channel in remote sensing of global vegetation from EOS-MODIS
Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996) · Remote Sensing of Environment · doi:10.1016/s0034-4257(96)00072-7 · Cited by 3,253
Unmanned aerial systems for photogrammetry and remote sensing: A review
Colomina, I., & Molina, P. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.02.013 · Cited by 2,954
Mulla, D. J. (2013) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2012.08.009 · Cited by 1,982
UAV for 3D mapping applications: a review
Nex, F., & Remondino, F. (2014) · Applied Geomatics · doi:10.1007/s12518-013-0120-x · Cited by 1,878
Self-citationZarco-Tejada, P. J., González-Dugo, V., & Berni, J. A. J. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.10.007 · Cited by 915
State of the art in high density image matching
Remondino, F., Spera, M. G., Nocerino, E., Menna, F., & Nex, F. (2014) · The Photogrammetric Record · doi:10.1111/phor.12063 · Cited by 628
Self-citation
Cited by
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges
Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., & Guizani, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909530 · Cited by 2,529
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
Agricultural remote sensing big data: Management and applications
Huang, Y., CHEN, Z.-X., YU, T., HUANG, X.-Z., & GU, X.-F. (2018) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(17)61859-8 · Cited by 448
Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research
Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., Rajan, N., Rouze, G., Morgan, C. L. S., Neely, H. L., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0159781 · Cited by 334
What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?
Hunt, E. R., & Daughtry, C. S. T. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1410300 · Cited by 325
Metadata
- Authors
- Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2015-04-02
- Volume
- 7
- Issue
- 4
- Pages
- 4026-4047
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2069209512
- Cited by
- 769
- References
- 24
- FWCI
- 33.80
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. https://app.cochat.ai/papers/10.3390/rs70404026
- The paper
- Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sensing. https://doi.org/10.3390/rs70404026
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.
