Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection

The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans ( Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evalu...

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Vydané v:	Scientific reports Ročník 9; číslo 1; s. 7610
Hlavní autori:	Sakoda, Kazuma, Watanabe, Tomoya, Sukemura, Shun, Kobayashi, Shunzo, Nagasaki, Yuichi, Tanaka, Yu, Shiraiwa, Tatsuhiko
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 20.05.2019 Nature Publishing Group
Predmet:	631/449/1734 631/449/1870 631/449/2124 Algorithms Alleles Breeding - methods Deep Learning Genetic diversity Genetic Variation - genetics Genotype Glycine max Glycine max - genetics High-Throughput Screening Assays - methods Humanities and Social Sciences multidisciplinary Photosynthesis Photosynthesis - genetics Plant Leaves - genetics Plant Stomata - genetics Population genetics Science Science (multidisciplinary) Sensors Soybeans Stomata
ISSN:	2045-2322, 2045-2322
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Abstract	The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans ( Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R 2 reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm −2 among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs.
AbstractList	The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans (Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R2 reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm−2 among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs. The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans ( Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R 2 reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm −2 among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs. The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans (Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R2 reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm-2 among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs.The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans (Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R2 reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm-2 among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs. The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans (Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs.
ArticleNumber	7610
Author	Tanaka, Yu Shiraiwa, Tatsuhiko Watanabe, Tomoya Sakoda, Kazuma Sukemura, Shun Kobayashi, Shunzo Nagasaki, Yuichi
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Cites_doi	10.2135/cropsci2007.12.0707 10.1093/treephys/19.1.47 10.1093/jexbot/52.355.369 10.1016/j.copbio.2011.12.012 10.1093/aob/mcl253 10.1111/j.1365-3040.2005.01493.x 10.2135/cropsci2016.02.0122 10.1016/j.ab.2014.12.007 10.2135/cropsci2010.02.0058 10.2135/cropsci1975.0011183X001500030008x 10.3390/s17092022 10.3389/fpls.2016.01419 10.1007/978-3-319-46448-0_2 10.1109/TPAMI.2015.2389824 10.1038/nmeth.2089 10.1270/jsbbs.61.566 10.1073/pnas.0904209106 10.1111/j.1469-8137.1995.tb03059.x 10.2135/cropsci1998.0011183X003800060011x 10.1093/dnares/dsx043 10.1109/ICCV.2015.169 10.1109/CVPR.2014.81 10.1109/CVPR.2016.91 10.1109/ICCVW.2017.241
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References	Tanaka, Shiraiwa, Nakajima, Nakazaki (CR5) 2008; 48 Liu, Anguelov, Erhan, Szegedy, Reed, Fu, Berg (CR15) 2016 He, Zhang, Ren, Sun (CR10) 2015; 37 Franks, Beerling (CR6) 2009; 106 CR19 Ohsumi (CR4) 2007; 99 Kundu, Tigerstedt (CR22) 1998; 19 Lawson, Kramer, Raines (CR1) 2012; 23 Fuentes, Yoon, Kim, Park (CR14) 2017; 17 CR13 CR12 Long, Zhu, Naidu, Ort (CR2) 2006; 29 CR11 Mohanty, Hughes, Salathe (CR18) 2016; 7 Ciha, Brun (CR20) 1975; 15 Schneider, Rasband, Eliceiri. (CR16) 2012; 9 Ceulemans, Praet, Jiang (CR21) 2017; 131 Sakoda, Tanaka, Long, Shiraiwa (CR24) 2016; 56 Watanabe (CR25) 2018; 25 Grishagin (CR17) 2015; 473 CR9 Hultine, Marshall (CR8) 2001; 52 Fischer (CR3) 1998; 38 Tanaka, Fujii, Shiraiwa (CR7) 2010; 50 Kaga (CR23) 2012; 61 SK Kundu (44127_CR22) 1998; 19 K Sakoda (44127_CR24) 2016; 56 PJ Franks (44127_CR6) 2009; 106 A Ohsumi (44127_CR4) 2007; 99 R Ceulemans (44127_CR21) 2017; 131 A Fuentes (44127_CR14) 2017; 17 T Lawson (44127_CR1) 2012; 23 Wei Liu (44127_CR15) 2016 SP Long (44127_CR2) 2006; 29 Y Tanaka (44127_CR5) 2008; 48 Y Tanaka (44127_CR7) 2010; 50 44127_CR19 44127_CR9 K He (44127_CR10) 2015; 37 S Watanabe (44127_CR25) 2018; 25 KR Hultine (44127_CR8) 2001; 52 CA Schneider (44127_CR16) 2012; 9 AJ Ciha (44127_CR20) 1975; 15 44127_CR13 44127_CR12 44127_CR11 IV Grishagin (44127_CR17) 2015; 473 K Kaga (44127_CR23) 2012; 61 SP Mohanty (44127_CR18) 2016; 7 RA Fischer (44127_CR3) 1998; 38
References_xml	– volume: 48 start-page: 1925 year: 2008 end-page: 1932 ident: CR5 article-title: Leaf gas exchange activity in soybean as related to leaf traits and stem growth habit publication-title: Crop Sci. doi: 10.2135/cropsci2007.12.0707 – volume: 19 start-page: 47 year: 1998 end-page: 52 ident: CR22 article-title: Variation in net photosynthesis, stomatal characteristics, leaf area and whole-plant phytomass production among ten provenances of neem (Azadirachta indica) publication-title: Tree Physiol. doi: 10.1093/treephys/19.1.47 – volume: 52 start-page: 369 year: 2001 end-page: 373 ident: CR8 article-title: A comparison of three methods for determining the stomatal density of pine needles publication-title: J. Exp. Bot. doi: 10.1093/jexbot/52.355.369 – ident: CR12 – volume: 23 start-page: 215 year: 2012 end-page: 220 ident: CR1 article-title: Improving yield by exploiting mechanisms underlying natural variation of photosynthesis publication-title: Curr. Opin. Biotech. doi: 10.1016/j.copbio.2011.12.012 – volume: 99 start-page: 265 year: 2007 end-page: 273 ident: CR4 article-title: A model explaining genotypic and ontogenetic variation of leaf photosynthetic rate in rice (Oryza sativa) based on leaf nitrogen content and stomatal conductance publication-title: Ann. Bot-London doi: 10.1093/aob/mcl253 – volume: 29 start-page: 315 year: 2006 end-page: 330 ident: CR2 article-title: Can improvement in photosynthesis increase crop yields? publication-title: Plant Cell Environ. doi: 10.1111/j.1365-3040.2005.01493.x – volume: 56 start-page: 2731 year: 2016 end-page: 2741 ident: CR24 article-title: Genetic and physiological diversity in the leaf photosynthetic capacity of soybean publication-title: Crop Sci. doi: 10.2135/cropsci2016.02.0122 – volume: 473 start-page: 63 year: 2015 end-page: 65 ident: CR17 article-title: Automatic cell counting with ImageJ publication-title: Anal. Biochem. doi: 10.1016/j.ab.2014.12.007 – volume: 50 start-page: 2525 year: 2010 end-page: 2532 ident: CR7 article-title: Variability of leaf morphology and stomatal conductance in soybean [Glycine max (L.) Merr.] cultivars publication-title: Crop Sci doi: 10.2135/cropsci2010.02.0058 – ident: CR19 – volume: 15 start-page: 309 year: 1975 end-page: 313 ident: CR20 article-title: Stomatal size and frequency in soybean publication-title: Crop Sci. doi: 10.2135/cropsci1975.0011183X001500030008x – volume: 17 start-page: 2022 year: 2017 ident: CR14 article-title: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition publication-title: Sensors doi: 10.3390/s17092022 – volume: 7 start-page: 1419 year: 2016 ident: CR18 article-title: Using deep learning for image-based plant disease detection publication-title: Fron. Plant Sci. doi: 10.3389/fpls.2016.01419 – start-page: 21 year: 2016 end-page: 37 ident: CR15 article-title: SSD: Single Shot MultiBox Detector publication-title: Computer Vision – ECCV 2016 doi: 10.1007/978-3-319-46448-0_2 – volume: 37 start-page: 1904 year: 2015 end-page: 1916 ident: CR10 article-title: Spatial pyramid pooling in deep convolutional networks for visual recognition publication-title: IEEE T. Pattern Anal. doi: 10.1109/TPAMI.2015.2389824 – ident: CR13 – volume: 9 start-page: 671 year: 2012 end-page: 675 ident: CR16 article-title: NIH Image to ImageJ: 25 years of image analysis publication-title: Nat. Methods doi: 10.1038/nmeth.2089 – ident: CR11 – ident: CR9 – volume: 61 start-page: 566 year: 2012 end-page: 592 ident: CR23 article-title: Evaluation of soybean germplasm conserved in NIAS Genebank and development of mini core collections publication-title: Breeding Sci. doi: 10.1270/jsbbs.61.566 – volume: 106 start-page: 10343 year: 2009 end-page: 10347 ident: CR6 article-title: Maximum leaf conductance driven by CO effects on stomatal size and density over geologic time publication-title: P. Natl. Acad. Sci. USA doi: 10.1073/pnas.0904209106 – volume: 131 start-page: 99 year: 2017 end-page: 107 ident: CR21 article-title: Effects of CO2 enrichment, leaf position and clone on stomatal index and epidermal cell density in poplar ( ) publication-title: New Phytol. doi: 10.1111/j.1469-8137.1995.tb03059.x – volume: 38 start-page: 1467 year: 1998 end-page: 1475 ident: CR3 article-title: Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies publication-title: Crop Sci. doi: 10.2135/cropsci1998.0011183X003800060011x – volume: 25 start-page: 123 year: 2018 end-page: 136 ident: CR25 article-title: Development of a high-density linkage map and chromosome segment substitution lines for Japanese soybean cultivar Enrei publication-title: DNA Res. doi: 10.1093/dnares/dsx043 – volume: 37 start-page: 1904 year: 2015 ident: 44127_CR10 publication-title: IEEE T. Pattern Anal. doi: 10.1109/TPAMI.2015.2389824 – volume: 38 start-page: 1467 year: 1998 ident: 44127_CR3 publication-title: Crop Sci. doi: 10.2135/cropsci1998.0011183X003800060011x – volume: 25 start-page: 123 year: 2018 ident: 44127_CR25 publication-title: DNA Res. doi: 10.1093/dnares/dsx043 – start-page: 21 volume-title: Computer Vision – ECCV 2016 year: 2016 ident: 44127_CR15 doi: 10.1007/978-3-319-46448-0_2 – volume: 15 start-page: 309 year: 1975 ident: 44127_CR20 publication-title: Crop Sci. doi: 10.2135/cropsci1975.0011183X001500030008x – ident: 44127_CR11 doi: 10.1109/ICCV.2015.169 – ident: 44127_CR9 doi: 10.1109/CVPR.2014.81 – volume: 9 start-page: 671 year: 2012 ident: 44127_CR16 publication-title: Nat. Methods doi: 10.1038/nmeth.2089 – ident: 44127_CR13 doi: 10.1109/CVPR.2016.91 – volume: 19 start-page: 47 year: 1998 ident: 44127_CR22 publication-title: Tree Physiol. doi: 10.1093/treephys/19.1.47 – volume: 131 start-page: 99 year: 2017 ident: 44127_CR21 publication-title: New Phytol. doi: 10.1111/j.1469-8137.1995.tb03059.x – volume: 473 start-page: 63 year: 2015 ident: 44127_CR17 publication-title: Anal. Biochem. doi: 10.1016/j.ab.2014.12.007 – volume: 17 start-page: 2022 year: 2017 ident: 44127_CR14 publication-title: Sensors doi: 10.3390/s17092022 – volume: 7 start-page: 1419 year: 2016 ident: 44127_CR18 publication-title: Fron. Plant Sci. doi: 10.3389/fpls.2016.01419 – volume: 99 start-page: 265 year: 2007 ident: 44127_CR4 publication-title: Ann. Bot-London doi: 10.1093/aob/mcl253 – volume: 52 start-page: 369 year: 2001 ident: 44127_CR8 publication-title: J. Exp. Bot. doi: 10.1093/jexbot/52.355.369 – volume: 56 start-page: 2731 year: 2016 ident: 44127_CR24 publication-title: Crop Sci. doi: 10.2135/cropsci2016.02.0122 – volume: 29 start-page: 315 year: 2006 ident: 44127_CR2 publication-title: Plant Cell Environ. doi: 10.1111/j.1365-3040.2005.01493.x – volume: 48 start-page: 1925 year: 2008 ident: 44127_CR5 publication-title: Crop Sci. doi: 10.2135/cropsci2007.12.0707 – volume: 50 start-page: 2525 year: 2010 ident: 44127_CR7 publication-title: Crop Sci doi: 10.2135/cropsci2010.02.0058 – ident: 44127_CR19 doi: 10.1109/ICCVW.2017.241 – volume: 61 start-page: 566 year: 2012 ident: 44127_CR23 publication-title: Breeding Sci. doi: 10.1270/jsbbs.61.566 – volume: 23 start-page: 215 year: 2012 ident: 44127_CR1 publication-title: Curr. Opin. Biotech. doi: 10.1016/j.copbio.2011.12.012 – volume: 106 start-page: 10343 year: 2009 ident: 44127_CR6 publication-title: P. Natl. Acad. Sci. USA doi: 10.1073/pnas.0904209106 – ident: 44127_CR12
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Snippet	The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans ( Glycine max (L.) Merr). In a conventional SD evaluation,... The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans (Glycine max (L.) Merr). In a conventional SD evaluation,...
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SubjectTerms	631/449/1734 631/449/1870 631/449/2124 Algorithms Alleles Breeding - methods Deep Learning Genetic diversity Genetic Variation - genetics Genotype Glycine max Glycine max - genetics High-Throughput Screening Assays - methods Humanities and Social Sciences multidisciplinary Photosynthesis Photosynthesis - genetics Plant Leaves - genetics Plant Stomata - genetics Population genetics Science Science (multidisciplinary) Sensors Soybeans Stomata
Title	Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection
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