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: In this review, various applications of Near Infrared Hyperspectral Imaging (NIR-HSI) in agriculture and in the quality control of agro-food products are presented. NIR-HSI is an emerging technique that combines classical NIR spectroscopy and imaging techniques in order to simultaneously obtain spectral and spatial information from a field or a sample. The technique is non-destructive, non-polluting, fast and relatively inexpensive per analysis. Currently, its applications in agriculture range from vegetation mapping, crop disease, stress and yield detection to component identification in plants and impurity detection. There is growing interest in HSI for the safety and quality assessment of agro-food products. The applications have been classified from the level of satellite images to the macroscopic, if not, molecular level.Applied Spectroscopy Reviews 01/2013; 48:142. · 2.92 Impact Factor
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ABSTRACT: With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to lowinput agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun. Expected final online publication date for the Annual Review of Plant Biology Volume 64 is April 29, 2013. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.Annual Review of Plant Biology 02/2013; · 18.71 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; · 6.56 Impact Factor