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Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time.

Neilson EH, Edwards AM, Blomstedt CK, Berger B, Møller BL, Gleadow RM - J. Exp. Bot. (2015)

Bottom Line: Plant architectural instead of architecture elements were determined using imaging and found to correlate with an improved tolerance to stress, for example diurnal leaf curling and leaf area index.Analysis of colour images revealed that leaf 'greenness' correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content.It is shown that imaging sorghum using a high-throughput system can accurately identify and differentiate between growth and specific phenotypic traits.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, Monash University, Clayton 3800, Australia Plant Biochemistry Laboratory, Department of Plant and Environmental Sciences, University of Copenhagen, 40 Thorvaldsensvej, DK-1871 Frederiksberg C, Copenhagen, Denmark.

No MeSH data available.


Example of image processing methods and derivation of geometric parameters. First, the foreground and background are separated in the raw image, as taken from above (A), using a nearest-neighbour colour classification, resulting in a binary image (B). Thereafter, the object (highlighted green; C) undergoes geometric measurements, such as calliper length (black line), convex hull (red line), and minimum enclosing circle (blue line). Compactness and surface coverage are measures of leaf coverage and are the ratio of object area (green) and convex hull or minimum enclosing circle area, respectively.
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Figure 1: Example of image processing methods and derivation of geometric parameters. First, the foreground and background are separated in the raw image, as taken from above (A), using a nearest-neighbour colour classification, resulting in a binary image (B). Thereafter, the object (highlighted green; C) undergoes geometric measurements, such as calliper length (black line), convex hull (red line), and minimum enclosing circle (blue line). Compactness and surface coverage are measures of leaf coverage and are the ratio of object area (green) and convex hull or minimum enclosing circle area, respectively.

Mentions: To estimate the degree of leaf rolling in the drought experiment, plants were imaged in the late afternoon (rolled leaves) and at pre-dawn the following day (unfolded leaves), 45 d after planting. Additional types of automated imaging analysis included the geometric parameters of convex hull (the smallest possible mathematically solved perimeter that envelopes the imaged plant), compactness (the ratio of leaf area per convex hull area), calliper length (the longest dimension of the canopy when viewed from above), circumference (the minimum circle that can enclose the plant), and surface coverage (the ratio of leaf area to the area of the minimum enclosing circle calculated from the top view image) (Fig. 1). Compactness and convex hull measure the degree of leaf area coverage, analogous to the agronomic measure of LAI. The degree of radial symmetry (eccentricity) was also determined.


Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time.

Neilson EH, Edwards AM, Blomstedt CK, Berger B, Møller BL, Gleadow RM - J. Exp. Bot. (2015)

Example of image processing methods and derivation of geometric parameters. First, the foreground and background are separated in the raw image, as taken from above (A), using a nearest-neighbour colour classification, resulting in a binary image (B). Thereafter, the object (highlighted green; C) undergoes geometric measurements, such as calliper length (black line), convex hull (red line), and minimum enclosing circle (blue line). Compactness and surface coverage are measures of leaf coverage and are the ratio of object area (green) and convex hull or minimum enclosing circle area, respectively.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
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getmorefigures.php?uid=PMC4378625&req=5

Figure 1: Example of image processing methods and derivation of geometric parameters. First, the foreground and background are separated in the raw image, as taken from above (A), using a nearest-neighbour colour classification, resulting in a binary image (B). Thereafter, the object (highlighted green; C) undergoes geometric measurements, such as calliper length (black line), convex hull (red line), and minimum enclosing circle (blue line). Compactness and surface coverage are measures of leaf coverage and are the ratio of object area (green) and convex hull or minimum enclosing circle area, respectively.
Mentions: To estimate the degree of leaf rolling in the drought experiment, plants were imaged in the late afternoon (rolled leaves) and at pre-dawn the following day (unfolded leaves), 45 d after planting. Additional types of automated imaging analysis included the geometric parameters of convex hull (the smallest possible mathematically solved perimeter that envelopes the imaged plant), compactness (the ratio of leaf area per convex hull area), calliper length (the longest dimension of the canopy when viewed from above), circumference (the minimum circle that can enclose the plant), and surface coverage (the ratio of leaf area to the area of the minimum enclosing circle calculated from the top view image) (Fig. 1). Compactness and convex hull measure the degree of leaf area coverage, analogous to the agronomic measure of LAI. The degree of radial symmetry (eccentricity) was also determined.

Bottom Line: Plant architectural instead of architecture elements were determined using imaging and found to correlate with an improved tolerance to stress, for example diurnal leaf curling and leaf area index.Analysis of colour images revealed that leaf 'greenness' correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content.It is shown that imaging sorghum using a high-throughput system can accurately identify and differentiate between growth and specific phenotypic traits.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, Monash University, Clayton 3800, Australia Plant Biochemistry Laboratory, Department of Plant and Environmental Sciences, University of Copenhagen, 40 Thorvaldsensvej, DK-1871 Frederiksberg C, Copenhagen, Denmark.

No MeSH data available.