The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring
Open accessAbstract
Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.
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Cites
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Tester, M., & Langridge, P. (2010) · Science · doi:10.1126/science.1183700 · Cited by 2,322
Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications
Murchie, E. H., & Lawson, T. (2013) · Journal of Experimental Botany · doi:10.1093/jxb/ert208 · Cited by 2,195
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
A Review of Imaging Techniques for Plant Phenotyping
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Field-based phenomics for plant genetics research
White, J. W., Andrade-Sanchez, P., Gore, M. A., Bronson, K. F., Coffelt, T. A., Conley, M. M., Feldmann, K. A., French, A. N., Heun, J. T., Hunsaker, D. J., et al. (2012) · Field Crops Research · doi:10.1016/j.fcr.2012.04.003 · Cited by 575
Cited by
Review: application of artificial intelligence in phenomics
Nabwire, S., Suh, H.-K., Kim, M. S., Baek, I., & Cho, B.-K. (2021) · Sensors · doi:10.3390/s21134363 · Cited by 91
The Future is Bright for Evolutionary Morphology and Biomechanics in the Era of Big Data
Muñoz, M. M., & Price, S. A. (2019) · Integrative and Comparative Biology · doi:10.1093/icb/icz121 · Cited by 39
Metadata
- Authors
- Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., & Hawkesford, M. J.
- Venue
- Functional Plant Biology
- Publisher
- CSIRO Publishing
- Published
- 2016-11-02
- Volume
- 44
- Issue
- 1
- Pages
- 143-153
- ISSN
- 1445-4408 (ISSN-L)
- 1445-4416
- OpenAlex
- W2518417964
- Cited by
- 382
- References
- 30
- FWCI
- 27.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., & Hawkesford, M. J. (2016). Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Functional Plant Biology. https://doi.org/10.1071/fp16163
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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.
