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Journal Abstract Search

257 related items for PubMed ID: 28049858

  • 1. High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance.
    Yendrek CR, Tomaz T, Montes CM, Cao Y, Morse AM, Brown PJ, McIntyre LM, Leakey AD, Ainsworth EA.
    Plant Physiol; 2017 Jan; 173(1):614-626. PubMed ID: 28049858
    [Abstract] [Full Text] [Related]

  • 2. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy.
    Wang S, Guan K, Wang Z, Ainsworth EA, Zheng T, Townsend PA, Li K, Moller C, Wu G, Jiang C.
    J Exp Bot; 2021 Feb 02; 72(2):341-354. PubMed ID: 32937655
    [Abstract] [Full Text] [Related]

  • 3. High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity.
    Meacham-Hensold K, Montes CM, Wu J, Guan K, Fu P, Ainsworth EA, Pederson T, Moore CE, Brown KL, Raines C, Bernacchi CJ.
    Remote Sens Environ; 2019 Sep 15; 231():111176. PubMed ID: 31534277
    [Abstract] [Full Text] [Related]

  • 4. High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population.
    Montes CM, Fox C, Sanz-Sáez Á, Serbin SP, Kumagai E, Krause MD, Xavier A, Specht JE, Beavis WD, Bernacchi CJ, Diers BW, Ainsworth EA.
    Genetics; 2022 May 31; 221(2):. PubMed ID: 35451475
    [Abstract] [Full Text] [Related]

  • 5. Spectral Phenotyping of Physiological and Anatomical Leaf Traits Related with Maize Water Status.
    Cotrozzi L, Peron R, Tuinstra MR, Mickelbart MV, Couture JJ.
    Plant Physiol; 2020 Nov 31; 184(3):1363-1377. PubMed ID: 32907885
    [Abstract] [Full Text] [Related]

  • 6. Using leaf optical properties to detect ozone effects on foliar biochemistry.
    Ainsworth EA, Serbin SP, Skoneczka JA, Townsend PA.
    Photosynth Res; 2014 Feb 31; 119(1-2):65-76. PubMed ID: 23657827
    [Abstract] [Full Text] [Related]

  • 7. Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance.
    Kumagai E, Burroughs CH, Pederson TL, Montes CM, Peng B, Kimm H, Guan K, Ainsworth EA, Bernacchi CJ.
    Plant Cell Environ; 2022 Jan 31; 45(1):80-94. PubMed ID: 34664281
    [Abstract] [Full Text] [Related]

  • 8. A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.
    Wijewardane NK, Zhang H, Yang J, Schnable JC, Schachtman DP, Ge Y.
    J Exp Bot; 2023 Aug 03; 74(14):4050-4062. PubMed ID: 37018460
    [Abstract] [Full Text] [Related]

  • 9. Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types.
    Yan Z, Guo Z, Serbin SP, Song G, Zhao Y, Chen Y, Wu S, Wang J, Wang X, Li J, Wang B, Wu Y, Su Y, Wang H, Rogers A, Liu L, Wu J.
    New Phytol; 2021 Oct 03; 232(1):134-147. PubMed ID: 34165791
    [Abstract] [Full Text] [Related]

  • 10. Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data.
    Barnes ML, Breshears DD, Law DJ, van Leeuwen WJD, Monson RK, Fojtik AC, Barron-Gafford GA, Moore DJP.
    PLoS One; 2017 Oct 03; 12(12):e0189539. PubMed ID: 29281709
    [Abstract] [Full Text] [Related]

  • 11. Predicting photosynthetic capacity in tobacco using shortwave infrared spectral reflectance.
    Sexton T, Sankaran S, Cousins AB.
    J Exp Bot; 2021 May 28; 72(12):4373-4383. PubMed ID: 33735372
    [Abstract] [Full Text] [Related]

  • 12. Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat.
    Silva-Perez V, Molero G, Serbin SP, Condon AG, Reynolds MP, Furbank RT, Evans JR.
    J Exp Bot; 2018 Jan 23; 69(3):483-496. PubMed ID: 29309611
    [Abstract] [Full Text] [Related]

  • 13. High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel.
    Ge Y, Atefi A, Zhang H, Miao C, Ramamurthy RK, Sigmon B, Yang J, Schnable JC.
    Plant Methods; 2019 Jan 23; 15():66. PubMed ID: 31391863
    [Abstract] [Full Text] [Related]

  • 14. Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges.
    Grzybowski M, Wijewardane NK, Atefi A, Ge Y, Schnable JC.
    Plant Commun; 2021 Jul 12; 2(4):100209. PubMed ID: 34327323
    [Abstract] [Full Text] [Related]

  • 15. Excised leaves show limited and species-specific effects on photosynthetic parameters across crop functional types.
    Ferguson JN, Jithesh T, Lawson T, Kromdijk J.
    J Exp Bot; 2023 Nov 21; 74(21):6662-6676. PubMed ID: 37565685
    [Abstract] [Full Text] [Related]

  • 16. Seasonal responses of photosynthetic parameters in maize and sunflower and their relationship with leaf functional traits.
    Miner GL, Bauerle WL.
    Plant Cell Environ; 2019 May 21; 42(5):1561-1574. PubMed ID: 30604429
    [Abstract] [Full Text] [Related]

  • 17. Effect of leaf temperature on the estimation of photosynthetic and other traits of wheat leaves from hyperspectral reflectance.
    Khan HA, Nakamura Y, Furbank RT, Evans JR.
    J Exp Bot; 2021 Feb 24; 72(4):1271-1281. PubMed ID: 33252664
    [Abstract] [Full Text] [Related]

  • 18. Detection of chlorophyll content based on optical properties of maize leaves.
    Pan W, Cheng X, Du R, Zhu X, Guo W.
    Spectrochim Acta A Mol Biomol Spectrosc; 2024 Mar 15; 309():123843. PubMed ID: 38215563
    [Abstract] [Full Text] [Related]

  • 19. Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging.
    Meacham-Hensold K, Fu P, Wu J, Serbin S, Montes CM, Ainsworth E, Guan K, Dracup E, Pederson T, Driever S, Bernacchi C.
    J Exp Bot; 2020 Apr 06; 71(7):2312-2328. PubMed ID: 32092145
    [Abstract] [Full Text] [Related]

  • 20. Impact of anatomical traits of maize (Zea mays L.) leaf as affected by nitrogen supply and leaf age on bundle sheath conductance.
    Retta M, Yin X, van der Putten PE, Cantre D, Berghuijs HN, Ho QT, Verboven P, Struik PC, Nicolaï BM.
    Plant Sci; 2016 Nov 06; 252():205-214. PubMed ID: 27717455
    [Abstract] [Full Text] [Related]

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