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Assessment of Vegetation Indices Derived by UAV Imagery for Durum Wheat Phenotyping under a Water Limited and Heat Stressed Mediterranean Environment
Open accessAbstract
There is growing interest for using Spectral Vegetation Indices (SVI) derived by Unmanned Aerial Vehicle (UAV) imagery as a fast and cost-efficient tool for plant phenotyping. The development of such tools is of paramount importance to continue progress through plant breeding, especially in the Mediterranean basin, where climate change is expected to further increase yield uncertainty. In the present study, Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Green Normalized Difference Vegetation Index (GNDVI) derived from UAV imagery were calculated for two consecutive years in a set of twenty durum wheat varieties grown under a water limited and heat stressed environment. Statistically significant differences between genotypes were observed for SVIs. GNDVI explained more variability than NDVI and SR, when recorded at booting. GNDVI was significantly correlated with grain yield when recorded at booting and anthesis during the 1st and 2nd year, respectively, while NDVI was correlated to grain yield when recorded at booting, but only for the 1st year. These results suggest that GNDVI has a better discriminating efficiency and can be a better predictor of yield when recorded at early reproductive stages. The predictive ability of SVIs was affected by plant phenology. Correlations of grain yield with SVIs were stronger as the correlations of SVIs with heading were weaker or not significant. NDVIs recorded at the experimental site were significantly correlated with grain yield of the same set of genotypes grown in other environments. Both positive and negative correlations were observed indicating that the environmental conditions during grain filling can affect the sign of the correlations. These findings highlight the potential use of SVIs derived by UAV imagery for durum wheat phenotyping under low yielding Mediterranean conditions.
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“Even more, today remote and proximal sensing allows also determining plant biomass, nitrogen content, and nitrogen uptake, by that providing the basis for management decisions (Kyratzis et al., 2017).”
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Metadata
- Authors
- Kyratzis, A. C., Skarlatos, D. P., Menexes, G. C., Vamvakousis, V. F., & Katsiotis, A.
- Affiliations
- Cyprus University of Technology (CY)
- Corresponding author
- Kyratzis, Angelos C.
- Katsiotis, Andreas
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2017-06-26
- Volume
- 8
- Pages
- 1114-1114
- ISSN
- 1664-462X (ISSN-L)
- PMC
- PMC5483459
- OpenAlex
- W2704880239
- Cited by
- 134
- References
- 57
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 13 · 2023: 16 · 2022: 22 · 2021: 23 · 2020: 15 · 2019: 13 · 2018: 3 · 2017: 3
- FWCI
- 7.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Plant Science (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Leaf Properties and Growth Measurement
- Keywords
- durum wheat, vegetation indices, UAV imagery, NDVI, GNDVI, simple ratio, grain yield prediction, plant phenotyping, water stress, heat stress, Mediterranean climate, gene-environment interaction, plant phenology, grain filling, heading
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