The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping
Open accessAbstract
The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the future. This review evaluates the role of proximal remote sensing buggies for field-based phenotyping with a particular focus on the application of currently available sensor technology for large-scale field phenotyping. To illustrate the potential for the development of high throughput phenotyping techniques, a case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer. The development of such techniques for routine deployment in commercial-scale breeding and pre-breeding operations will require a multidisciplinary approach to leverage the recent technological advances realised in computer science, image analysis, proximal remote sensing and robotics.
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Cites
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Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
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BackgroundField high-throughput phenotyping: the new crop breeding frontier
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A multisite managed environment facility for targeted trait and germplasm phenotyping
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Cited by
Remote sensing for agricultural applications: A meta-review
Weiss, M., Jacob, F., & Duveiller, G. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111402 · Cited by 1,877
Machine Learning for High-Throughput Stress Phenotyping in Plants
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Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review
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Metadata
- Authors
- Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., & Furbank, R.
- Venue
- Agronomy
- Publisher
- MDPI AG
- Published
- 2014-07-10
- Volume
- 4
- Issue
- 3
- Pages
- 349-379
- ISSN
- 2073-4395 (ISSN-L)
- OpenAlex
- W2027563724
- Cited by
- 384
- References
- 4
- FWCI
- 24.33
- Type
- journal article
- Language
- en
- Open access
- gold
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- Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., & Furbank, R. (2014). Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping. Agronomy. https://doi.org/10.3390/agronomy4030349
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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.
