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.
Highlights
One of the major challenges in plant biology and crop research is elucidating the link between genome, physiological processes, plant trait performance, and yield across genotypes and environments
The hyperspectral imaging system consists of two pushbroom line scanner spectrographs (VNIR and SWIR) mounted on a motorized linear stage (1.5 m in length) in a dedicated cabin that is equipped with a white reference surface, halogen lighting frames that move alongside the cameras, and a lift with rotating platform, which positions the plant at the optimal distance from the top-view cameras and at the level of the white reference surface
Due to the high resolution of proximal hyperspectral imaging, the influence of geometry was very prominent as can be seen in the top-view brightness image of a maize plant (Figure 1B), where the distichous leaf organization causes brightness variations between and within leaves
Summary
One of the major challenges in plant biology and crop research is elucidating the link between genome, physiological processes, plant trait performance, and yield across genotypes and environments This requires combining vast amounts of genotypic data with corresponding phenotypic measurements (Yang et al, 2014; Clauw et al, 2016). To facilitate the collection of phenotypic measurements, high-throughput phenotyping platforms have been developed to automate data collection on a large number of plants (Granier et al, 2006; Busemeyer et al, 2013; Virlet et al, 2017). More recent studies on the other hand have utilized hyperspectral imaging for close-range phenotyping on in-door automated platforms, allowing the monitoring of spatial and temporal variations in traits that were previously inaccessible (Ge et al, 2016; Moghimi et al, 2018; Thomas et al, 2018)
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