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Items: 1 to 20 of 179

1.

A High-Throughput, Field-Based Phenotyping Technology for Tall Biomass Crops.

Salas Fernandez MG, Bao Y, Tang L, Schnable PS.

Plant Physiol. 2017 Aug;174(4):2008-2022. doi: 10.1104/pp.17.00707. Epub 2017 Jun 15.

2.

Semiautomated Feature Extraction from RGB Images for Sorghum Panicle Architecture GWAS.

Zhou Y, Srinivasan S, Mirnezami SV, Kusmec A, Fu Q, Attigala L, Salas Fernandez MG, Ganapathysubramanian B, Schnable PS.

Plant Physiol. 2019 Jan;179(1):24-37. doi: 10.1104/pp.18.00974. Epub 2018 Nov 2.

3.

Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies.

Wang X, Singh D, Marla S, Morris G, Poland J.

Plant Methods. 2018 Jul 4;14:53. doi: 10.1186/s13007-018-0324-5. eCollection 2018.

4.

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 Apr;66(7):1817-32. doi: 10.1093/jxb/eru526. Epub 2015 Feb 19.

5.

Genetic analysis of vegetative branching in sorghum.

Kong W, Guo H, Goff VH, Lee TH, Kim C, Paterson AH.

Theor Appl Genet. 2014 Nov;127(11):2387-403. doi: 10.1007/s00122-014-2384-x. Epub 2014 Aug 28.

PMID:
25163936
6.

Association mapping of brassinosteroid candidate genes and plant architecture in a diverse panel of Sorghum bicolor.

Mantilla Perez MB, Zhao J, Yin Y, Hu J, Salas Fernandez MG.

Theor Appl Genet. 2014 Dec;127(12):2645-62. doi: 10.1007/s00122-014-2405-9. Epub 2014 Oct 19.

PMID:
25326721
7.

Identification of QTLs for eight agronomically important traits using an ultra-high-density map based on SNPs generated from high-throughput sequencing in sorghum under contrasting photoperiods.

Zou G, Zhai G, Feng Q, Yan S, Wang A, Zhao Q, Shao J, Zhang Z, Zou J, Han B, Tao Y.

J Exp Bot. 2012 Sep;63(15):5451-62. doi: 10.1093/jxb/ers205. Epub 2012 Aug 1.

PMID:
22859680
8.

Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley.

Rueda-Ayala VP, Peña JM, Höglind M, Bengochea-Guevara JM, Andújar D.

Sensors (Basel). 2019 Jan 28;19(3). pii: E535. doi: 10.3390/s19030535.

9.

Energy sorghum--a genetic model for the design of C4 grass bioenergy crops.

Mullet J, Morishige D, McCormick R, Truong S, Hilley J, McKinley B, Anderson R, Olson SN, Rooney W.

J Exp Bot. 2014 Jul;65(13):3479-89. doi: 10.1093/jxb/eru229. Epub 2014 Jun 22. Review.

PMID:
24958898
10.

Crop 3D-a LiDAR based platform for 3D high-throughput crop phenotyping.

Guo Q, Wu F, Pang S, Zhao X, Chen L, Liu J, Xue B, Xu G, Li L, Jing H, Chu C.

Sci China Life Sci. 2018 Mar;61(3):328-339. doi: 10.1007/s11427-017-9056-0. Epub 2017 Dec 6.

PMID:
28616808
11.

Dissecting repulsion linkage in the dwarfing gene Dw3 region for sorghum plant height provides insights into heterosis.

Li X, Li X, Fridman E, Tesso TT, Yu J.

Proc Natl Acad Sci U S A. 2015 Sep 22;112(38):11823-8. doi: 10.1073/pnas.1509229112. Epub 2015 Sep 8.

12.

High-throughput genomics in sorghum: from whole-genome resequencing to a SNP screening array.

Bekele WA, Wieckhorst S, Friedt W, Snowdon RJ.

Plant Biotechnol J. 2013 Dec;11(9):1112-25. doi: 10.1111/pbi.12106. Epub 2013 Aug 7.

13.

A novel mesh processing based technique for 3D plant analysis.

Paproki A, Sirault X, Berry S, Furbank R, Fripp J.

BMC Plant Biol. 2012 May 3;12:63. doi: 10.1186/1471-2229-12-63.

14.

Predicting plant biomass accumulation from image-derived parameters.

Chen D, Shi R, Pape JM, Neumann K, Arend D, Graner A, Chen M, Klukas C.

Gigascience. 2018 Feb 1;7(2). doi: 10.1093/gigascience/giy001.

15.

Stability and genetic control of morphological, biomass and biofuel traits under temperate maritime and continental conditions in sweet sorghum (Sorghum bicolour).

Mocoeur A, Zhang YM, Liu ZQ, Shen X, Zhang LM, Rasmussen SK, Jing HC.

Theor Appl Genet. 2015 Sep;128(9):1685-701. doi: 10.1007/s00122-015-2538-5. Epub 2015 May 16.

PMID:
25982132
16.

Increased Power To Dissect Adaptive Traits in Global Sorghum Diversity Using a Nested Association Mapping Population.

Bouchet S, Olatoye MO, Marla SR, Perumal R, Tesso T, Yu J, Tuinstra M, Morris GP.

Genetics. 2017 Jun;206(2):573-585. doi: 10.1534/genetics.116.198499.

17.

Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level.

Rose JC, Paulus S, Kuhlmann H.

Sensors (Basel). 2015 Apr 24;15(5):9651-65. doi: 10.3390/s150509651.

18.

High throughput phenotyping of morpho-anatomical stem properties using X-ray computed tomography in sorghum.

Gomez FE, Carvalho G Jr, Shi F, Muliana AH, Rooney WL.

Plant Methods. 2018 Jul 13;14:59. doi: 10.1186/s13007-018-0326-3. eCollection 2018.

19.

LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget.

Vadez V, Kholová J, Hummel G, Zhokhavets U, Gupta SK, Hash CT.

J Exp Bot. 2015 Sep;66(18):5581-93. doi: 10.1093/jxb/erv251. Epub 2015 Jun 1.

20.

GiNA, an Efficient and High-Throughput Software for Horticultural Phenotyping.

Diaz-Garcia L, Covarrubias-Pazaran G, Schlautman B, Zalapa J.

PLoS One. 2016 Aug 16;11(8):e0160439. doi: 10.1371/journal.pone.0160439. eCollection 2016.

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