The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Review of Imaging Techniques for Plant Phenotyping
Open accessAbstract
Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.
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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
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 2×
“The ground station controls the unmanned aerial platforms by radio link, transmitting the position, altitude and status [40,41].”
BackgroundMontes, J. M., Technow, F., Dhillon, B. S., Mauch, F., & Melchinger, A. E. (2011) · Field Crops Research · doi:10.1016/j.fcr.2010.12.017 · Cited by 144
Cited by
Seed priming: state of the art and new perspectives
Paparella, S., Araújo, S. S., Rossi, G., Wijayasinghe, M., Carbonera, D., & Balestrazzi, A. (2015) · Plant Cell Reports · doi:10.1007/s00299-015-1784-y · Cited by 940
Buschmann, A. H., Camus, C., Infante, J., Neori, A., Israel, Á., Hernández-González, M. C., Pereda, S. V., Gomez-Pinchetti, J. L., Golberg, A., Tadmor-Shalev, N., et al. (2017) · European Journal of Phycology · doi:10.1080/09670262.2017.1365175 · Cited by 767
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
Close range hyperspectral imaging of plants: A review
Mishra, P., Asaari, M. S. M., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2017.09.009 · Cited by 319
Ear density estimation from high resolution RGB imagery using deep learning technique
Madec, S., Jin, X., Lu, H., De Solan, B., Liu, S., Duyme, F., Heritier, E., & Baret, F. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.10.013 · Cited by 312
Metadata
- Authors
- Li, L., Zhang, Q., & Huang, D.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2014-10-24
- Volume
- 14
- Issue
- 11
- Pages
- 20078-20111
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W1998686312
- Cited by
- 1,094
- References
- 3
- FWCI
- 41.39
- Type
- journal article
- Language
- en
- Open access
- gold
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