The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review
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
Food Security: The Challenge of Feeding 9 Billion People
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010) · Science · doi:10.1126/science.1185383 · Cited by 11,943
Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo
Baker, N. R. (2008) · Annual Review of Plant Biology · doi:10.1146/annurev.arplant.59.032607.092759 · Cited by 4,639
Wang, W., Vinocur, B., & Altman, A. (2003) · Planta · doi:10.1007/s00425-003-1105-5 · Cited by 3,678
The application of small unmanned aerial systems for precision agriculture: a review
Zhang, C., & Kovacs, J. M. (2012) · Precision Agriculture · doi:10.1007/s11119-012-9274-5 · Cited by 2,012
Field high-throughput phenotyping: the new crop breeding frontier
Araus, J. L., & Cairns, J. E. (2014) · Trends in Plant Science · doi:10.1016/j.tplants.2013.09.008 · Cited by 1,817 · cited 11×
“The integrated system can provide (i) better access to the field, (ii) high-resolution data (1–2 cm depending on flying altitude), (iii) timely data collection (even under cloudy conditions), (iv) quick evaluations of the field growth conditions, (v) simultaneous image acquisition, (vi) self-automated flights for moni…”
BackgroundInfluentialPhenomics – technologies to relieve the phenotyping bottleneck
Furbank, R. T., & Tester, M. (2011) · Trends in Plant Science · doi:10.1016/j.tplants.2011.09.005 · Cited by 1,675
DeVierno, A., Thorn, B. K., & Carrano, A. L. (2013) · DigitalCommons - Fairfield (Fairfield University) · Cited by 1,457
Hyperspectral imaging – an emerging process analytical tool for food quality and safety control
GOWEN, A., ODONNELL, C., CULLEN, P., DOWNEY, G., & FRIAS, J. (2007) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2007.06.001 · Cited by 1,412
A review of advanced techniques for detecting plant diseases
Sankaran, S., Mishra, A., Ehsani, R. J., & Davis, C. E. (2010) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2010.02.007 · Cited by 1,372
Self-citationBerni, J., Zarco-Tejada, P. J., Suarez, L., & Fereres, E. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.2010457 · Cited by 1,308
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
Holman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., & Hawkesford, M. (2016) · Remote Sensing · doi:10.3390/rs8121031 · Cited by 398
Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring
Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., & Hawkesford, M. J. (2016) · Functional Plant Biology · doi:10.1071/fp16163 · Cited by 382
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
Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle
Duan, T., Chapman, S. C., Guo, Y., & Zheng, B. (2017) · Field Crops Research · doi:10.1016/j.fcr.2017.05.025 · Cited by 282
Metadata
- Authors
- Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., Miklas, P. N., Carter, A. H., Pumphrey, M. O., Knowles, N. R., & Pavek, M. J.
- Venue
- European Journal of Agronomy
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 70
- Pages
- 112-123
- ISSN
- 1161-0301 (ISSN-L)
- OpenAlex
- W1462825729
- Cited by
- 507
- References
- 81
- FWCI
- 31.18
- Type
- journal article
- Language
- en
- Open access
- closed
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: Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. https://app.cochat.ai/papers/10.1016/j.eja.2015.07.004
- The paper
- Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., Miklas, P. N., Carter, A. H., Pumphrey, M. O., Knowles, N. R., & Pavek, M. J. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy. https://doi.org/10.1016/j.eja.2015.07.004
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.
