Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean

Vollmann, J.; Walter, Hildrun; Sato, Takashi; Schweiger, Peter

doi:10.1016/j.compag.2010.11.003

Cited by 92 publications

(63 citation statements)

References 29 publications

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“…NIRS spectra were collected in two replications per sample using a Bruker Matrix-I Fourier transform NIRS instrument (Bruker, Ettlingen, Germany) with a RT-PbS detector. Seed oil, protein and sucrose content were predicted in g kg -1 on a dry matter basis using calibrations described elsewhere (Sato et al 2012;Vollmann et al 2011). For measuring Cd concentration, seed samples were homogenized followed by wet mineralization using microwave digestion with HNO 3 ?…”

Section: Pot Experimentsmentioning

confidence: 99%

Soybean cadmium concentration: validation of a QTL affecting seed cadmium accumulation for improved food safety

et al. 2014

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Soybean can accumulate considerable amounts of cadmium depending on soil conditions. As low cadmium concentration of soybean is of great interest in soy food production, a major quantitative trait locus described earlier for controlling cadmium accumulation in the seed was utilized to classify early maturity genotypes with respect to cadmium uptake and to verify their cadmium accumulation level. Marker-selected soybean genotypes were then tested for cadmium accumulation in a pot experiment with three cadmium levels and at three field locations in Austria. Out of 48 soybean genotypes, 19 exhibited the microsatellite marker allele associated with low cadmium accumulation. In the pot experiment, seed cadmium concentration was below 0.15 mg kg -1 in low cadmium accumulating genotypes while it reached over 0.30 mg kg -1 in genotypes carrying the high cadmium allele. In field experiments, cadmium concentration was in the range between 0.03 and 0.16 mg kg -1 , and both the cadmium accumulation locus as well as the experimental locations had a significant influence on seed cadmium. Marker-assisted selection of soybean genotypes for low cadmium accumulation could thus contribute to improved food safety which is particularly important for environments with unknown cadmium status of soil.

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Section: Pot Experimentsmentioning

confidence: 99%

Soybean cadmium concentration: validation of a QTL affecting seed cadmium accumulation for improved food safety

et al. 2014

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“…The color index value analyzed from the plant image is chosen for the building of a model to evaluate plant characteristics. This method proved that leaf color image R, G (red, green channel of the image), and other transformation indices have a highly significant relationship with chlorophyll content, nitrogen concentration and other plant status [4,5]. Chlorophyll fluorescence is emitted while plants photosynthesize.…”

Section: Introductionmentioning

confidence: 99%

Relation of leaf image, chlorophyll fluorescence, reflectance and SPAD in rice and barley

Fang

2013

2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

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Remote sensing tools such as SPAD, digital image analysis, reflectance and chlorophyll fluorescence have different advantages and limitations in evaluating plant physical ecology. It is very important to select the best one for the measurement. Few papers were found studying on the relationship between various remote sensing methods. Two experiments, one in 2009 and the other one in 2010, were conducted to study the relationship between these characteristics at a leaf scale. Three rice (M17, M15, Xiushui 09) and three barley (Hua 30, Zhepi 33, Zhexiu 12) were selected in our glasshouse experiment. The results showed that there was a highly significant relationship between R (red channel of the leaf image), G (green channel of the image), R550 (reflectance at 550 nm), photochemical reflectance index (PRI), and SPAD-502 readings (SPAD) for rice and barley. Close linear correlation was found between leaf chlorophyll maximal fluorescence (F m ), ratio of variable to maximal chlorophyll fluorescence (F v /F m ) and SPAD reading for rice. However, no significant relationship between F m , F v /F m and SPAD reading was found in barley. Linear and Logarithmic equations could be used to describe the relationship of R, G, R550, PRI, F m , F v /F m and SPAD reading. It suggests that leaf image analysis has a stronger relationship with SPAD than reflectance or leaf chlorophyll fluorescence across the two species of rice and barley.

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“…Working on lentil seed germination, he concluded that quantitative changes in RedGreen-Blue (RGB) colour component density may be considered as markers of the start of germination and can be used in classifying sub-samples and maintaining high germination quality in aged seed samples represents a nondestructive method in seed testing and sorting. Digital image analysis is a non-destructive technique increasingly utilized in phenotyping various parameters of plant health status such as chlorophyll content (Dutta Gupta et al 2013), nitrogen content (Vollmann et al 2011), and disease status (Porob et al 2017). Seed banks contain enormous amounts of germplasm, which is only partially characterized.…”

Section: Introductionmentioning

confidence: 99%

Analysis of variability and phylogeny in pisum (Pisum spp.) using digital phenotyping and morphological traits

Gatti¹,

Guindón²,

Bermejo³

et al. 2017

Aust J Crop Sci

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Plant phenotyping links genomics with plant ecophysiology and agronomy. It is usually performed by non-destructive, automated and image-based technology and generates information for efficient and searchable digital characterization of crop that can be performed during routine, periodical regeneration of accessions in germplasm collections. In the present work, ninety-two accessions of Pisum from different species and subspecies were studied during 2015 and 2016. Size and colour traits were measured using digital images from a Samsung CLX 3300 scanner and analysed with appropriate software; also seed weight, plant height and days to flowering were measured. Highly significant differences between accessions and species and subspecies for all these traits were found. When distances among species and subspecies are calculated, P. sativum subsp. sativum showed the greatest distance with P. fulvum (8.02) followed by P. abyssinicum (7.13); while the smallest distance was found between P. fulvum and P. sativum subsp. transcaucasicum (3.16). A Neighbour-joining tree with a cofenetic r of 0.985 was obtained. Seed and pod characteristics as colour parameters and size, obtained by digital phenotyping, have proved to be suitable markers for genetic diversity evaluation and they are useful in evolutionary analysis, allowing the discrimination of the main wild and cultivated species in the genus Pisum.Keywords: Pisum, seed coat colour, origin, variability, phenotyping, legumes. Abbreviations: a_and b-colour coordinates from the CieLab system of colour, C_ calibre of grains in centimetres, CR_ coincidence rate, CV%_ variation coefficient, DF_ days until 50% of plants flowering, L_ psychometric index of lightness from the CieLab system of colour; Max_ maximum , MD%_ mean difference percentage , Min_ minimum, PD_ pod diameter in centimetres, PH_ plant height in centimetres, PL_ pod length in centimetres, RBIP_ retrotransposon-based insertion polymorphisms markers, RGB_ red, green and blue, SD_ standard deviation , SSAP_ sequence-specific amplification polymorphism markers, VD%_ variance difference percentage, VR%_ variable rate of coefficient of variance, W100S_ weight of 100 seeds in grams.

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Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean

Cited by 92 publications

References 29 publications

Soybean cadmium concentration: validation of a QTL affecting seed cadmium accumulation for improved food safety

Soybean cadmium concentration: validation of a QTL affecting seed cadmium accumulation for improved food safety

Relation of leaf image, chlorophyll fluorescence, reflectance and SPAD in rice and barley

Analysis of variability and phylogeny in pisum (Pisum spp.) using digital phenotyping and morphological traits

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