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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    • "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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    • "It is important that the outputs from these imaging platforms are bench-marked against traditional measures, so that they can be integrated with the wealth of phenotypic data already accumulated over many years of crop breeding. This requires that the models be experimentally verified for particular crops and a range of environmental conditions (Furbank and Tester, 2011; Fiorani et al., 2012). Measurement of plant growth has traditionally been labour intensive, for example destructively harvesting plants at specific time points to provide intermittent quantification of certain parameters such as total biomass or leaf area. "
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    ABSTRACT: The use of high-throughput phenotyping systems and non-destructive imaging is widely regarded as a key technology allowing scientists and breeders to develop crops with the ability to perform well under diverse environmental conditions. However, many of these phenotyping studies have been optimized using the model plant Arabidopsis thaliana. In this study, The Plant Accelerator(®) at The University of Adelaide, Australia, was used to investigate the growth and phenotypic response of the important cereal crop, Sorghum bicolor L. Moench and related hybrids to water-limited conditions and different levels of fertilizer. Imaging in different spectral ranges was used to monitor plant composition, chlorophyll, and moisture content. Phenotypic image analysis accurately measured plant biomass. The data set obtained enabled the responses of the different sorghum varieties to the experimental treatments to be differentiated and modelled. Plant architectural instead of architecture elements were determined using imaging and found to correlate with an improved tolerance to stress, for example diurnal leaf curling and leaf area index. Analysis of colour images revealed that leaf 'greenness' correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content. It is shown that imaging sorghum using a high-throughput system can accurately identify and differentiate between growth and specific phenotypic traits. R scripts for robust, parsimonious models are provided to allow other users of phenomic imaging systems to extract useful data readily, and thus relieve a bottleneck in phenotypic screening of multiple genotypes of key crop plants. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.
    Journal of Experimental Botany 02/2015; 66(7). DOI:10.1093/jxb/eru526 · 5.79 Impact Factor
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    • "Current efforts to understand the structure of crop root systems have already led to a number of imaging solutions (Lobet et al., 2013) that are able to extract root architecture traits under various conditions (Fiorani et al., 2012), including laboratory conditions (de Dorlodot et al., 2007) in which plants are often grown in pots or glass containers (Zeng et al., 2008; Armengaud et al., 2009; Le Bot et al., 2010; Clark et al., 2011; Lobet et al., 2011; Naeem et al., 2011; Galkovskyi et al., 2012). In the case of pots, expensive magnetic resonance imaging technologies represent one noninvasive approach to capture highresolution details of root architecture (Schulz et al., 2013), similar to the capabilities of x-ray microcomputed tomography (mCT) systems. "
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    ABSTRACT: Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap and diversity of root components. Our imaging solution combines a field imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 days. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait-estimation pipeline under field conditions.
    Plant physiology 09/2014; DOI:10.1104/pp.114.243519 · 7.39 Impact Factor
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