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Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer.

Yang W, Guo Z, Huang C, Wang K, Jiang N, Feng H, Chen G, Liu Q, Xiong L - J. Exp. Bot. (2015)

Bottom Line: Nine associated loci contained known leaf-related genes, such as Nal1 for controlling the leaf width.In addition, a total of 73, 123, and 177 new loci were detected for traits associated with leaf size, colour, and shape, respectively.In summary, after evaluating the performance with a large number of rice accessions, the combination of GWAS and high-throughput leaf phenotyping (HLS) has proven a valuable strategy to identify the genetic loci controlling rice leaf traits.

View Article: PubMed Central - PubMed

Affiliation: National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, PR China College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, PR China.

No MeSH data available.


The primary procedure for the image analysis. (a, b) The distorted images in RGB colour space of two adjoining images. (c, e) The distorted partially enlarged images. (d, f) The corrected partially enlarged images. (g, h) The corrected images related to a and b. (i, j) The segmented images of the two adjoining images. (k, l) After image cropping, the cut partial image in the previous image and remaining leaf portion in the next image. (m) The mosaicking image. (n) The partially enlarged image with impurities. (o) The binary leaf image without impurities. (p, q) The binary image of a green (or green-2, green-3, green-4) leaf with impurities. (r, s) After setting the intersection, the binary image of a green (or green-2, green-3, green-4) leaf without impurities.
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Figure 2: The primary procedure for the image analysis. (a, b) The distorted images in RGB colour space of two adjoining images. (c, e) The distorted partially enlarged images. (d, f) The corrected partially enlarged images. (g, h) The corrected images related to a and b. (i, j) The segmented images of the two adjoining images. (k, l) After image cropping, the cut partial image in the previous image and remaining leaf portion in the next image. (m) The mosaicking image. (n) The partially enlarged image with impurities. (o) The binary leaf image without impurities. (p, q) The binary image of a green (or green-2, green-3, green-4) leaf with impurities. (r, s) After setting the intersection, the binary image of a green (or green-2, green-3, green-4) leaf without impurities.

Mentions: The colour line-scan camera consists of three monochrome chips, which are fixed in parallel to trap the red, green, and blue (RGB) spectra, respectively. Thus, the original colour image is distorted in RGB colour space (Fig. 2c, e). After the three greyscale images (RGB components) are extracted using a predefined value, the greyscale images of the green and blue components are shifted up a level with the position of the red greyscale image. Finally, the three greyscale images are merged back together to form a corrected RGB colour image (Fig. 2d, f). The source code for image correction is introduced in Supplementary Notes.


Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer.

Yang W, Guo Z, Huang C, Wang K, Jiang N, Feng H, Chen G, Liu Q, Xiong L - J. Exp. Bot. (2015)

The primary procedure for the image analysis. (a, b) The distorted images in RGB colour space of two adjoining images. (c, e) The distorted partially enlarged images. (d, f) The corrected partially enlarged images. (g, h) The corrected images related to a and b. (i, j) The segmented images of the two adjoining images. (k, l) After image cropping, the cut partial image in the previous image and remaining leaf portion in the next image. (m) The mosaicking image. (n) The partially enlarged image with impurities. (o) The binary leaf image without impurities. (p, q) The binary image of a green (or green-2, green-3, green-4) leaf with impurities. (r, s) After setting the intersection, the binary image of a green (or green-2, green-3, green-4) leaf without impurities.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4585412&req=5

Figure 2: The primary procedure for the image analysis. (a, b) The distorted images in RGB colour space of two adjoining images. (c, e) The distorted partially enlarged images. (d, f) The corrected partially enlarged images. (g, h) The corrected images related to a and b. (i, j) The segmented images of the two adjoining images. (k, l) After image cropping, the cut partial image in the previous image and remaining leaf portion in the next image. (m) The mosaicking image. (n) The partially enlarged image with impurities. (o) The binary leaf image without impurities. (p, q) The binary image of a green (or green-2, green-3, green-4) leaf with impurities. (r, s) After setting the intersection, the binary image of a green (or green-2, green-3, green-4) leaf without impurities.
Mentions: The colour line-scan camera consists of three monochrome chips, which are fixed in parallel to trap the red, green, and blue (RGB) spectra, respectively. Thus, the original colour image is distorted in RGB colour space (Fig. 2c, e). After the three greyscale images (RGB components) are extracted using a predefined value, the greyscale images of the green and blue components are shifted up a level with the position of the red greyscale image. Finally, the three greyscale images are merged back together to form a corrected RGB colour image (Fig. 2d, f). The source code for image correction is introduced in Supplementary Notes.

Bottom Line: Nine associated loci contained known leaf-related genes, such as Nal1 for controlling the leaf width.In addition, a total of 73, 123, and 177 new loci were detected for traits associated with leaf size, colour, and shape, respectively.In summary, after evaluating the performance with a large number of rice accessions, the combination of GWAS and high-throughput leaf phenotyping (HLS) has proven a valuable strategy to identify the genetic loci controlling rice leaf traits.

View Article: PubMed Central - PubMed

Affiliation: National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, PR China College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, PR China.

No MeSH data available.