On the value of the Kullback–Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands

Benoit, Landry; Benoit, Romain; Belin, Étienne; Vadaine, Rodolphe; Demilly, Didier; Chapeau‐Blondeau, François; Rousseau, D.

doi:10.1007/s00138-015-0717-7

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…5, option 6), however, would need only pick and place, which is much faster. To monitor different traits of interest beyond morphological ones, additional sensors could be implemented: near-infrared spectroscopy to analyze the chemical content of seeds (Agelet and Hurburgh, 2014); chlorophyll fluorescence to score seed maturity and performance (Jalink et al, 1998); spectral imaging to classify common wheat (Triticum aestivum) and durum wheat (Triticum durum; Benoit et al, 2016); low-field NMR to measure both solid and liquid parts of a seed, as demonstrated for growing bean (Phaseolus vulgaris) pods (Windt and Blümler, 2015), or chemical components, such as lipids, carbohydrates, and proteins (Rolletschek et al, 2015); or x-ray CT to image internal seed structures, allowing, for example, the detection of internal defects of seeds (Stuppy et al, 2003;Belin et al, 2011;Yamauchi et al, 2012;Verboven et al, 2013). Further developments of phenoSeeder can be followed at www.phenoseeder.de.…”

Section: Discussionmentioning

confidence: 99%

phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds

et al. 2016

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The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently roundshaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.

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Section: Discussionmentioning

confidence: 99%

phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds

et al. 2016

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“…Although the developed system may be automated, their implementation on seed applications could be not straightforward if the relevant wavelengths have not been carefully selected or when the calibration procedure has not been precisely conducted [ 18 ]. Consequently, the procedure of selecting the ‘ optimal ’ wavelength is considered the most important step in developing and establishing an a high-throughput system that has the capability of performing the inspection and quality control decision in real time [ 111 ].…”

Section: Real-time Systems For High-throughput Applicationsmentioning

confidence: 99%

Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview

ElMasry

Mandour

Al-Rejaie

et al. 2019

Sensors

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137

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As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.

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“…This requires the description of a communication channel with at least an input and an output characterized by their statistics expressed on a definite (discrete or continuous) alphabet of symbols together with a noise model. The modeling of imaging problems in the framework of information theory is an active research topic and was recently applied for instance to spectral reduction [5,6], observation scale optimization [7], image visualization [8,9] and multimodality imaging [10,11].…”

Section: Introductionmentioning

confidence: 99%

Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke

Giacalone

Frindel

Grenier

et al. 2017

Entropy

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Abstract:In longitudinal medical studies, multicomponent images of the tissues, acquired at a given stage of a disease, are used to provide information on the fate of the tissues. We propose a quantification of the predictive value of multicomponent images using information theory. To this end, we revisit the predictive information introduced for monodimensional time series and extend it to multicomponent images. The interest of this theoretical approach is illustrated on multicomponent magnetic resonance images acquired on stroke patients at acute and late stages, for which we propose an original and realistic model of noise together with a spatial encoding for the images. We address therefrom very practical questions such as the impact of noise on the predictability, the optimal choice of an observation scale and the predictability gain brought by the addition of imaging components.

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On the value of the Kullback–Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands

Cited by 10 publications

References 40 publications

phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds

phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds

Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview

Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke

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