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. "
    New Phytologist 08/2015; 207(4):950-952. DOI:10.1111/nph.13529 · 7.67 Impact Factor
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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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    ABSTRACT: Establishing low-cost methods for stripe (yellow) rust (Puccinia striiformis f. sp. tritici) phenotyping is paramount to maintain the breeding pipeline in wheat. Twelve winter wheat genotypes were grown to test rust resistance and yield performance. Physiological traits, including leaf chlorophyll content (Chl), net photosynthesis rate (Pn), stomatal conductance (gs), transpiration rate (E) and canopy temperature depression (CTD), together with diverse color components derived from Red, Green and Blue (RGB) images, were measured at different crop stages. Grain yield (GY) and grain yield loss index (GYLI) were assessed through comparison with the previous normal planting year. Genotypes exhibited a wide range of resistance to yellow rust, with GYLI values ranging from about −3% for the more resistant (Zhoumai 22) to 89% for the most susceptible (Lankao 298) genotypes. Moreover yellow rust reduced Chl and to a lesser extent, Pn, while traits related to water status were lower (gs) or not affected (E and CTD). The color parameters Green Fraction, Greener Fraction, Hue, a and u measured during grain filling were much better correlated with GY and GYLI (r2 ranging between 74% and 81%) than the set of photosynthetic and transpirative traits (Chl, Pn, gs, E, CTD) measurements in the same stage. Conventional digital imaging appears to be a potentially affordable approach for high-throughput phenotyping of yellow rust resistance.
    Computers and Electronics in Agriculture 08/2015; 116:20-29. DOI:10.1016/j.compag.2015.05.017 · 1.49 Impact Factor
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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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    ABSTRACT: Coffee is the most traded commodity in the world, and Brazil is its largest producer. Coffee leaf rust, caused by the biotrophic fungus Hemileia vastatrix, is the most important coffee disease, reducing coffee yield by 35–50%. This study aimed to use the ratio of variable and maximum fluorescence of dark-adapted tissue (Fv/Fm) as a parameter to differentiate presymptomatic tissue from healthy tissue during disease development in plants sprayed with pyraclostrobin and epoxiconazole after 4 days postinoculation. Visual severity was considered as an indicative of apparent disease and true severity as an indicative of both apparent and non-apparent disease. There was a significant linear relationship between the areas of true severity and visual severity, and for each additional unit in the visual severity, there was an increase of 1.53 units on the true severity. For the epoxiconazole and pyraclostrobin treatments, coffee leaf rust symptoms decreased according to both visual and Fv/Fm images. Pustules on the leaves sprayed with epoxiconazole were smaller in size than those on the leaves of non-sprayed plants but bigger than those sprayed with pyraclostrobin. The reduction in Fv/Fm values at the pustule epicentres present on the leaves of plants sprayed with epoxiconazole, and pyraclostrobin was greater than those of the non-sprayed plants. This finding was expected and reflects the importance of these fungicides in prohibiting the progress of coffee leaf rust. The photosynthetic capacity of Coffea arabica was affected by H. vastatrix infection, and the Fv/Fm parameter was able to show this effect before the visual symptoms were noticed.
    Journal of Phytopathology 04/2015; DOI:10.1111/jph.12399 · 0.82 Impact Factor
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