Article

Imaging plants dynamics in heterogenic environments

Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich, Leo-Brandt-Straße, 52425 Jülich, Germany.
Current Opinion in Biotechnology (Impact Factor: 8.04). 01/2012; 23(2):227-35. DOI: 10.1016/j.copbio.2011.12.010
Source: PubMed

ABSTRACT Noninvasive imaging sensors and computer vision approaches are key technologies to quantify plant structure, physiological status, and performance. Today, imaging sensors exploit a wide range of the electromagnetic spectrum, and they can be deployed to measure a growing number of traits, also in heterogenic environments. Recent advances include the possibility to acquire high-resolution spectra by imaging spectroscopy and classify signatures that might be informative of plant development, nutrition, health, and disease. Three-dimensional (3D) reconstruction of surfaces and volume is of particular interest, enabling functional and mechanistic analyses. While taking pictures is relatively easy, quantitative interpretation often remains challenging and requires integrating knowledge of sensor physics, image analysis, and complex traits characterizing plant phenotypes.

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    • "The large payoff of these measurements justifies enduring effort for improving these traits. To widen the spectrum of relevant traits under field conditions, recent advances in the development and application of novel noninvasive sensors (Fiorani et al., 2012; Araus & Cairns, 2014) could significantly contribute by providing higher precision in screening or by decreasing screening and selection efforts. However , these promising methodologies still need considerable improvement to become valuable tools to support breeding programmes, by identifying novel and relevant traits, establishing robust sensors, close monitoring of the environment linked to the development of predictive models. "
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    • "However, to date, the most promising methods for diagnosis of rust disease symptoms in wheat involve hyperspectral measurements of the reflected radiation and further process through different approaches such as neural networks (Moshou et al., 2004) or the formulation of vegetation indices (Franke et al., 2005; Ashourloo et al., 2014). However these methods are implicitly expensive, requiring either a spectroradiometer or a multispectral or hyperspectral camera, and to date, besides some exceptions (Moshou et al., 2004), they have been mostly applied at the leaf (rather than at the canopy) level (Fiorani et al., 2012). As an alternative, the use of conventional digital images to derive green vegetation indices to predict yield and resistance to biotic stresses (caused by pests and diseases) has been reported in recent years (Diéguez-Uribeondo et al., 2003; Graeff et al., 2006; Mirik et al., 2006). "
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    • "This is a major advantage in studying localized stress responses such as those caused by plant diseases with longer incubation periods, where a leaf can exhibit both irregularly infected and apparently healthy areas (Baker 2008; Rolfe and Scholes 2010). Chlorophyll a fluorescence imaging may be used to quantify the effects of foliar diseases on photosynthesis as a non-invasive, non-destructive and highly sensitive probe (Schreiber et al. 1986; Baker 2008; Rolfe and Scholes 2010; Fiorani et al. 2012; Mahlein et al. 2012). Chlorophyll a fluorescence imaging is based on the property that light energy absorbed by chlorophyll molecules in photosystem II (PSII) can either be re-emitted as a detectable fluorescence used for photosynthesis (photochemical quenching, q p ) or lost as heat (non-photochemical quenching, NPQ) (Maxwell and Johnson 2000). "
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