Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology
| Autor: | Stien Mertens, Lennart Verbraeken, Heike Sprenger, Kirin Demuynck, Katrien Maleux, Bernard Cannoot, Jolien De Block, Steven Maere, Hilde Nelissen, Gustavo Bonaventure, Steven J. Crafts-Brandner, Jonathan T. Vogel, Wesley Bruce, Dirk Inzé, Nathalie Wuyts |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
automated phenotyping platform phenotyping Vapour Pressure Deficit Sensing applications Physiology Plant Science drought lcsh:Plant culture maize 01 natural sciences 03 medical and health sciences ddc:570 Partial least squares regression lcsh:SB1-1110 030304 developmental biology Transpiration Original Research proximal sensing 0303 health sciences fungi Hyperspectral imaging Biology and Life Sciences food and beverages Plant phenotyping Reflectivity hyperspectral Temporal resolution physiology Environmental science 010606 plant biology & botany |
| Zdroj: | Frontiers in Plant Science Frontiers in Functional Plant Ecology 12, 640914 (2021). doi:10.3389/fpls.2021.640914 FRONTIERS IN PLANT SCIENCE Frontiers in Plant Science, Vol 12 (2021) |
| ISSN: | 1664-462X |
| Popis: | Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution. |
| Databáze: | OpenAIRE |
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