Machine Vision for Seed Phenomics

Gustin, Jeffery L.; Settles, A. Mark

doi:10.1002/9781118525524.ch13

Cited by 4 publications

(4 citation statements)

References 111 publications

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“…X-ray microcomputed tomography (μCT) is a feasible approach to measure the density of individual kernels . μCT utilizes X-ray attenuation from multiple radiograph “slices” of a sample to reconstruct a three-dimensional (3D) representation of the structure.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Maize (Zea mays) Kernel Density and Volume Using Microcomputed Tomography and Single-Kernel Near-Infrared Spectroscopy

Gustin

Jackson

Williams

et al. 2013

J. Agric. Food Chem.

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Maize kernel density affects milling quality of the grain. Kernel density of bulk samples can be predicted by near-infrared reflectance (NIR) spectroscopy, but no accurate method to measure individual kernel density has been reported. This study demonstrates that individual kernel density and volume are accurately measured using X-ray microcomputed tomography (μCT). Kernel density was significantly correlated with kernel volume, air space within the kernel, and protein content. Embryo density and volume did not influence overall kernel density. Partial least-squares (PLS) regression of μCT traits with single-kernel NIR spectra gave stable predictive models for kernel density (R(2) = 0.78, SEP = 0.034 g/cm(3)) and volume (R(2) = 0.86, SEP = 2.88 cm(3)). Density and volume predictions were accurate for data collected over 10 months based on kernel weights calculated from predicted density and volume (R(2) = 0.83, SEP = 24.78 mg). Kernel density was significantly correlated with bulk test weight (r = 0.80), suggesting that selection of dense kernels can translate to improved agronomic performance.

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Section: Introductionmentioning

confidence: 99%

Analysis of Maize (Zea mays) Kernel Density and Volume Using Microcomputed Tomography and Single-Kernel Near-Infrared Spectroscopy

Gustin

Jackson

Williams

et al. 2013

J. Agric. Food Chem.

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“…Single kernel NIR spectroscopy, and prediction of kernel traits was performed as described previously (Spielbauer et al ., ; Gustin and Settles, ; Gustin et al ., ) with the following sampling strategy. Fully pollinated mature ears were harvested from each growth regime and 20 kernels were harvested separately from the bottom, middle, and top third of each ear.…”

Section: Methodsmentioning

confidence: 99%

Effects of long‐term exposure to elevated temperature on Zea mays endosperm development during grain fill

et al. 2019

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Summary Cereal yields decrease when grain fill proceeds under conditions of prolonged, moderately elevated temperatures. Endosperm‐endogenous processes alter both rate and duration of dry weight gain, but underlying mechanisms remain unclear. Heat effects could be mediated by either abnormal, premature cessation of storage compound deposition or accelerated implementation of normal development. This study used controlled environments to isolate temperature as the sole environmental variable during Zea mays kernel‐fill, from 12 days after pollination to maturity. Plants subjected to elevated day, elevated night temperatures (38°C day, 28°C night (38/28°C])) or elevated day, normal night (38/17°C), were compared with those from controls grown under normal day and night conditions (28/17°C). Progression of change over time in endosperm tissue was followed to dissect contributions at multiple levels, including transcriptome, metabolome, enzyme activities, product accumulation, and tissue ultrastructure. Integrated analyses indicated that the normal developmental program of endosperm is fully executed under prolonged high‐temperature conditions, but at a faster rate. Accelerated development was observed when both day and night temperatures were elevated, but not when daytime temperature alone was increased. Although transcripts for most components of glycolysis and respiration were either upregulated or minimally affected, elevated temperatures decreased abundance of mRNAs related to biosynthesis of starch and storage proteins. Further analysis of 20 central‐metabolic enzymes revealed six activities that were reduced under high‐temperature conditions, indicating candidate roles in the observed reduction of grain dry weight. Nonetheless, a striking overall resilience of grain filling in the face of elevated temperatures can be attributed to acceleration of normal endosperm development.

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“…11 For single seed analysis, Xray microcomputed tomography (μCT) can be used to image and reconstruct a three-dimensional (3D) representation of individual seeds. 10 Imaging for μCT is completed with a series of radiographic slices along the long axis of the seed. Each pixel within the radiograph indicates X-ray attenuation of the sample, which is directly related to density.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Single seed NIR can also be used to predict physical properties of seeds such as volume and density. , Analytical reference data for volume and density are conventionally collected with a pycnometer, but most gas pycnometers are limited to bulk analysis of seeds . For single seed analysis, X-ray microcomputed tomography (μCT) can be used to image and reconstruct a three-dimensional (3D) representation of individual seeds . Imaging for μCT is completed with a series of radiographic slices along the long axis of the seed.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Single Seed Trait Predictions in Soybean (Glycine max) and Robust Calibration Model Transfer with Near-Infrared Reflectance Spectroscopy

Hacisalihoglu

Gustin

Louisma

et al. 2016

J. Agric. Food Chem.

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Single seed near-infrared reflectance (NIR) spectroscopy predicts soybean (Glycine max) seed quality traits of moisture, oil, and protein. We tested the accuracy of transferring calibrations between different single seed NIR analyzers of the same design by collecting NIR spectra and analytical trait data for globally diverse soybean germplasm. X-ray microcomputed tomography (μCT) was used to collect seed density and shape traits to enhance the number of soybean traits that can be predicted from single seed NIR. Partial least-squares (PLS) regression gave accurate predictive models for oil, weight, volume, protein, and maximal cross-sectional area of the seed. PLS models for width, length, and density were not predictive. Although principal component analysis (PCA) of the NIR spectra showed that black seed coat color had significant signal, excluding black seeds from the calibrations did not impact model accuracies. Calibrations for oil and protein developed in this study as well as earlier calibrations for a separate NIR analyzer of the same design were used to test the ability to transfer PLS regressions between platforms. PLS models built from data collected on one NIR analyzer had minimal differences in accuracy when applied to spectra collected from a sister device. Model transfer was more robust when spectra were trimmed from 910 to 1679 nm to 955-1635 nm due to divergence of edge wavelengths between the two devices. The ability to transfer calibrations between similar single seed NIR spectrometers facilitates broader adoption of this high-throughput, nondestructive, seed phenotyping technology.

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Machine Vision for Seed Phenomics

Cited by 4 publications

References 111 publications

Analysis of Maize (Zea mays) Kernel Density and Volume Using Microcomputed Tomography and Single-Kernel Near-Infrared Spectroscopy

Analysis of Maize (Zea mays) Kernel Density and Volume Using Microcomputed Tomography and Single-Kernel Near-Infrared Spectroscopy

Effects of long‐term exposure to elevated temperature on Zea mays endosperm development during grain fill

Enhanced Single Seed Trait Predictions in Soybean (Glycine max) and Robust Calibration Model Transfer with Near-Infrared Reflectance Spectroscopy

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