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Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology

Stien Mertens (UGent) , Lennart Verbraeken (UGent) , Heike Sprenger (UGent) , Kirin Demuynck (UGent) , Katrien Maleux (UGent) , Bernard Cannoot (UGent) , Jolien De Block (UGent) , Steven Maere (UGent) , Hilde Nelissen (UGent) , Gustavo Bonaventure, et al.
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Abstract
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.
Keywords
Plant Science, automated phenotyping platform, hyperspectral, phenotyping, drought, physiology, maize, proximal sensing

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MLA
Mertens, Stien, et al. “Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology.” FRONTIERS IN PLANT SCIENCE, vol. 12, 2021, doi:10.3389/fpls.2021.640914.
APA
Mertens, S., Verbraeken, L., Sprenger, H., Demuynck, K., Maleux, K., Cannoot, B., … Wuyts, N. (2021). Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology. FRONTIERS IN PLANT SCIENCE, 12. https://doi.org/10.3389/fpls.2021.640914
Chicago author-date
Mertens, Stien, Lennart Verbraeken, Heike Sprenger, Kirin Demuynck, Katrien Maleux, Bernard Cannoot, Jolien De Block, et al. 2021. “Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology.” FRONTIERS IN PLANT SCIENCE 12. https://doi.org/10.3389/fpls.2021.640914.
Chicago author-date (all authors)
Mertens, Stien, 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, and Nathalie Wuyts. 2021. “Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology.” FRONTIERS IN PLANT SCIENCE 12. doi:10.3389/fpls.2021.640914.
Vancouver
1.
Mertens S, Verbraeken L, Sprenger H, Demuynck K, Maleux K, Cannoot B, et al. Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology. FRONTIERS IN PLANT SCIENCE. 2021;12.
IEEE
[1]
S. Mertens et al., “Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology,” FRONTIERS IN PLANT SCIENCE, vol. 12, 2021.
@article{8703804,
  abstract     = {{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.}},
  articleno    = {{640914}},
  author       = {{Mertens, Stien and Verbraeken, Lennart and Sprenger, Heike and Demuynck, Kirin and Maleux, Katrien and Cannoot, Bernard and De Block, Jolien and Maere, Steven and Nelissen, Hilde and Bonaventure, Gustavo and Crafts-Brandner, Steven J. and Vogel, Jonathan T. and Bruce, Wesley and Inzé, Dirk and Wuyts, Nathalie}},
  issn         = {{1664-462X}},
  journal      = {{FRONTIERS IN PLANT SCIENCE}},
  keywords     = {{Plant Science,automated phenotyping platform,hyperspectral,phenotyping,drought,physiology,maize,proximal sensing}},
  language     = {{eng}},
  pages        = {{18}},
  title        = {{Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology}},
  url          = {{http://dx.doi.org/10.3389/fpls.2021.640914}},
  volume       = {{12}},
  year         = {{2021}},
}

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