The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Phenomics – technologies to relieve the phenotyping bottleneck
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Citations
Cites
Mechanisms of Salinity Tolerance
Munns, R., & Tester, M. (2008) · Annual Review of Plant Biology · doi:10.1146/annurev.arplant.59.032607.092911 · Cited by 13,792
Self-citationElevated CO2 effects on plant carbon, nitrogen, and water relations: six important lessons from FACE
Leakey, A. D. B., Ainsworth, E. A., Bernacchi, C. J., Rogers, A., Long, S. P., & Ort, D. R. (2009) · Journal of Experimental Botany · doi:10.1093/jxb/erp096 · Cited by 1,678
Fischer, R. A., Rees, D., Sayre, K. D., Lu, Z. ‐., Condon, A. G., & Saavedra, A. L. (1998) · Crop Science · doi:10.2135/cropsci1998.0011183x003800060011x · Cited by 887
Models for navigating biological complexity in breeding improved crop plants
Hammer, G., Cooper, M., Tardieu, F., Welch, S., Walsh, B., van Eeuwijk, F., Chapman, S., & Podlich, D. (2006) · Trends in Plant Science · doi:10.1016/j.tplants.2006.10.006 · Cited by 415
Factors affecting CO 2 assimilation, leaf injury and growth in salt-stressed durum wheat
James, R. A., Rivelli, A. R., Munns, R., & Caemmerer, S. V. (2002) · Functional Plant Biology · doi:10.1071/fp02069 · Cited by 330
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
Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction
Cooper, M., Messina, C. D., Podlich, D., Totir, L. R., Baumgarten, A., Hausmann, N. J., Wright, D., & Graham, G. (2014) · Crop & Pasture Science · doi:10.1071/cp14007 · Cited by 411
Fabris, M., Abbriano, R. M., Pernice, M., Sutherland, D. L., Commault, A. S., Hall, C. C., Labeeuw, L., McCauley, J. I., Kuzhiuparambil, U., Ray, P., et al. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00279 · Cited by 382
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Jiang, Y., & Li, C. (2020) · Plant Phenomics · doi:10.34133/2020/4152816 · Cited by 380
PlantCV v2: Image analysis software for high-throughput plant phenotyping
Gehan, M. A., Fahlgren, N., Abbasi, A., Berry, J. C., Callen, S. T., Chavez, L., Doust, A. N., Feldman, M. J., Gilbert, K. B., Hodge, J. G., et al. (2017) · PeerJ · doi:10.7717/peerj.4088 · Cited by 365
Metadata
- Authors
- Furbank, R. T., & Tester, M.
- Venue
- Trends in Plant Science
- Publisher
- Elsevier BV
- Published
- 2011-12
- Volume
- 16
- Issue
- 12
- Pages
- 635-644
- ISSN
- 1360-1385 (ISSN-L)
- OpenAlex
- W2086330580
- Cited by
- 1,675
- References
- 5
- FWCI
- 4.85
- 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.
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- CoChat. (2026). Citation check: Phenomics – technologies to relieve the phenotyping bottleneck. https://app.cochat.ai/papers/10.1016/j.tplants.2011.09.005
- The paper
- Furbank, R. T., & Tester, M. (2011). Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science. https://doi.org/10.1016/j.tplants.2011.09.005
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.
