Imaging plants dynamics in heterogenic environments.
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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ABSTRACT: Background Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device. Results The working principle of the HyperART system relies on the upward redirection of transmitted and reflected light (range of 400 to 2500 nm) of a plant sample towards two line scanners. By using both the reflectance and transmittance image, an image of leaf absorption can be calculated. The comparison with the dynamically high-resolution ASD FieldSpec data showed good correlation, underlying the accuracy of the HyperART system. Our experiments showed that variation in both leaf chlorophyll content of four different crop species, due to different fertilization regimes during growth, and fungal symptoms on sugar beet leaves could be accurately estimated and monitored. The use of leaf reflectance and transmittance, as well as their sum (by which the non-absorbed radiation is calculated) obtained by the HyperART system gave considerably improved results in classification of Cercospora leaf spot disease and determination of chlorophyll content. Conclusions The HyperART system offers the possibility for non-invasive and accurate mapping of leaf transmittance and absorption, significantly expanding the applicability of reflectance, based on mapping spectroscopy, in plant sciences. Therefore, the HyperART system may be readily employed for non-invasive determination of the spatio-temporal dynamics of various plant properties.Plant Methods 01/2015; · 2.59 Impact Factor
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ABSTRACT: The large numbers of samples processed in breeding and biodiversity programmes require the development of efficient methods for the nondestructive evaluation of basic seed properties. Near-infrared spectroscopy is the state-of-the-art solution for this analytical demand, but it also has some limitations. Here, we present a novel, rapid, accurate procedure based on time domain-nuclear magnetic resonance (TD-NMR), designed to simultaneously quantify a number of basic seed traits without any seed destruction. Using a low-field, benchtop 1H-NMR instrument, the procedure gives a high-accuracy measurement of oil content (R2 = 0.98), carbohydrate content (R2 = 0.99), water content (R2 = 0.98) and both fresh and dry weight of seeds/grains (R2 = 0.99). The method requires a minimum of ~20 mg biomass per sample and thus enables to screen individual, intact seeds. When combined with an automated sample delivery system, a throughput of ~1400 samples per day is achievable. The procedure has been trialled as a proof of concept on cereal grains (collection of ~3000 accessions of Avena spp. curated at the IPK genebank). A mathematical multitrait selection approach has been designed to simplify the selection of outlying (most contrasting) accessions. To provide deeper insights into storage oil topology, some oat accessions were further analysed by three-dimensional seed modelling and lipid imaging. We conclude that the novel TD-NMR-based screening tool opens perspectives for breeding and plant biology in general.Plant Biotechnology Journal 09/2014; · 6.28 Impact Factor
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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; · 7.39 Impact Factor