178 related articles for article (PubMed ID: 34131723)
1. 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; 72(18):6175-6189. PubMed ID: 34131723
[TBL] [Abstract][Full Text] [Related]
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; 231():111176. PubMed ID: 31534277
[TBL] [Abstract][Full Text] [Related]
3. Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information.
Song G; Wang Q; Jin J
J Plant Physiol; 2022 Dec; 279():153831. PubMed ID: 36252398
[TBL] [Abstract][Full Text] [Related]
4. Predicting leaf traits across functional groups using reflectance spectroscopy.
Kothari S; Beauchamp-Rioux R; Blanchard F; Crofts AL; Girard A; Guilbeault-Mayers X; Hacker PW; Pardo J; Schweiger AK; Demers-Thibeault S; Bruneau A; Coops NC; Kalacska M; Vellend M; Laliberté E
New Phytol; 2023 Apr; 238(2):549-566. PubMed ID: 36746189
[TBL] [Abstract][Full Text] [Related]
5. Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression.
Fu P; Meacham-Hensold K; Guan K; Wu J; Bernacchi C
Plant Cell Environ; 2020 May; 43(5):1241-1258. PubMed ID: 31922609
[TBL] [Abstract][Full Text] [Related]
6. 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; 72(2):341-354. PubMed ID: 32937655
[TBL] [Abstract][Full Text] [Related]
7. 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; 17(1):108. PubMed ID: 34666801
[TBL] [Abstract][Full Text] [Related]
8. Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice.
Das B; Manohara KK; Mahajan GR; Sahoo RN
Spectrochim Acta A Mol Biomol Spectrosc; 2020 Mar; 229():117983. PubMed ID: 31896051
[TBL] [Abstract][Full Text] [Related]
9. 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; 15():66. PubMed ID: 31391863
[TBL] [Abstract][Full Text] [Related]
10. 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
[TBL] [Abstract][Full Text] [Related]
11. 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; 151(1):71-82. PubMed ID: 34491493
[TBL] [Abstract][Full Text] [Related]
12. 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; 232(1):134-147. PubMed ID: 34165791
[TBL] [Abstract][Full Text] [Related]
13. Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms.
Fu P; Meacham-Hensold K; Guan K; Bernacchi CJ
Front Plant Sci; 2019; 10():730. PubMed ID: 31214235
[TBL] [Abstract][Full Text] [Related]
14. 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; 74(14):4050-4062. PubMed ID: 37018460
[TBL] [Abstract][Full Text] [Related]
15. High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion.
Zhang H; Ge Y; Xie X; Atefi A; Wijewardane NK; Thapa S
Plant Methods; 2022 May; 18(1):60. PubMed ID: 35505350
[TBL] [Abstract][Full Text] [Related]
16. Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements.
Chavana-Bryant C; Malhi Y; Wu J; Asner GP; Anastasiou A; Enquist BJ; Cosio Caravasi EG; Doughty CE; Saleska SR; Martin RE; Gerard FF
New Phytol; 2017 May; 214(3):1049-1063. PubMed ID: 26877108
[TBL] [Abstract][Full Text] [Related]
17. A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach.
Falcioni R; Antunes WC; Demattê JAM; Nanni MR
Sensors (Basel); 2023 Apr; 23(8):. PubMed ID: 37112184
[TBL] [Abstract][Full Text] [Related]
18. Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset.
Ji F; Li F; Hao D; Shiklomanov AN; Yang X; Townsend PA; Dashti H; Nakaji T; Kovach KR; Liu H; Luo M; Chen M
New Phytol; 2024 Jul; 243(1):111-131. PubMed ID: 38708434
[TBL] [Abstract][Full Text] [Related]
19. From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance.
Serbin SP; Wu J; Ely KS; Kruger EL; Townsend PA; Meng R; Wolfe BT; Chlus A; Wang Z; Rogers A
New Phytol; 2019 Dec; 224(4):1557-1568. PubMed ID: 31418863
[TBL] [Abstract][Full Text] [Related]
20. Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees.
Chavana-Bryant C; Malhi Y; Anastasiou A; Enquist BJ; Cosio EG; Keenan TF; Gerard FF
Sci Total Environ; 2019 May; 666():1301-1315. PubMed ID: 30970495
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]