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Journal Abstract Search

259 related items for PubMed ID: 31922609

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  • 2. 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
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  • 5. Selecting informative bands for partial least squares regressions improves their goodness-of-fits to estimate leaf photosynthetic parameters from hyperspectral data.
    Jin J, Wang Q, Song G.
    Photosynth Res; 2022 Jan 15; 151(1):71-82. PubMed ID: 34491493
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  • 7. 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
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  • 8. 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
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  • 9. 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
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  • 10. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression.
    Burnett AC, Anderson J, Davidson KJ, Ely KS, Lamour J, Li Q, Morrison BD, Yang D, Rogers A, Serbin SP.
    J Exp Bot; 2021 Sep 30; 72(18):6175-6189. PubMed ID: 34131723
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  • 11. Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis.
    El-Hendawy S, Al-Suhaibani N, Alotaibi M, Hassan W, Elsayed S, Tahir MU, Mohamed AI, Schmidhalter U.
    Sci Rep; 2019 Nov 11; 9(1):16473. PubMed ID: 31712701
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  • 12. Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning.
    Furbank RT, Silva-Perez V, Evans JR, Condon AG, Estavillo GM, He W, Newman S, Poiré R, Hall A, He Z.
    Plant Methods; 2021 Oct 19; 17(1):108. PubMed ID: 34666801
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  • 13. Using leaf optical properties to detect ozone effects on foliar biochemistry.
    Ainsworth EA, Serbin SP, Skoneczka JA, Townsend PA.
    Photosynth Res; 2014 Feb 19; 119(1-2):65-76. PubMed ID: 23657827
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  • 16. Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt.
    Elmetwalli AH, Tyler AN, Moghanm FS, Alamri SAM, Eid EM, Elsayed S.
    Sensors (Basel); 2021 Jun 06; 21(11):. PubMed ID: 34204099
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  • 18. 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 06; 173(1):614-626. PubMed ID: 28049858
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