The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Sensing technologies for precision specialty crop production
Overview
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
An introduction to multisensor data fusion
Hall, D. L., & Llinas, J. (1997) · Proceedings of the IEEE · doi:10.1109/5.554205 · Cited by 2,629
Cited by
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
Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review
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., et al. (2015) · European Journal of Agronomy · doi:10.1016/j.eja.2015.07.004 · Cited by 507
Implementation of drone technology for farm monitoring & pesticide spraying: A review
Hafeez, A., Husain, M. A., Singh, S. P., Chauhan, A., Khan, M. T., Kumar, N., Chauhan, A., & Soni, S. K. (2023) · Information Processing in Agriculture · doi:10.1016/j.inpa.2022.02.002 · Cited by 268
Sensing Soil Properties in the Laboratory, In Situ, and On-Line
Kuang, B., Mahmood, H. S., Quraishi, M. Z., Hoogmoed, W. B., Mouazen, A. M., & van Henten, E. J. (2012) · Advances in Agronomy · doi:10.1016/b978-0-12-394275-3.00003-1 · Cited by 267
Agricultural Robotics: Unmanned Robotic Service Units in Agricultural Tasks
Auat Cheein, F. A., & Carelli, R. (2013) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2013.2252957 · Cited by 231
Metadata
- Authors
- Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., & Li, C.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 74
- Issue
- 1
- Pages
- 2-33
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2084546104
- Cited by
- 530
- References
- 2
- FWCI
- 25.83
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Sensing technologies for precision specialty crop production. https://app.cochat.ai/papers/10.1016/j.compag.2010.08.005
- The paper
- Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., & Li, C. (2010). Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2010.08.005
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.
