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: 7.12). 01/2012; 23(2):227-35. DOI: 10.1016/j.copbio.2011.12.010
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 great deal of progress in developing these technologies over the past 40 to 50 years and introducing them to agriculture and plant disease detection is impressive (Brenchley 1964; Jackson andWallen 1975;Seelan et al. 2003;Nilsson 1995;West et al. 2003). Due to advances in precision agriculture and plant phenotyping, new and specific solutions for plant and crop science have been developed (Cobb et al. 2013;Fiorani et al. 2012;Furbank and Tester 2011;Steddom et al. 2005). The most successful sensors currently are being used for noninvasive evaluation of crop nutrition status in the field. "
ABSTRACT: Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multi-scale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Non-destructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.
- "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). "
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.
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