A newly developed method to extract the optimal hyperspectral feature for monitoring leaf biomass in wheat
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T.C.E. Cheng | Yan Zhu | Min Jia | Weixing Cao | Yongchao Tian | Chen Zhou | Wei Li | Kangkang Wang | Xia Yao | T. Cheng | Yongchao Tian | W. Cao | Yan Zhu | Min Jia | Wei Li | Kangkang Wang | Xia Yao | Chen Zhou
[1] M. C. U. Araújo,et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .
[2] J. Clevers. The use of imaging spectrometry for agricultural applications , 1999 .
[3] J Kolar,et al. Use of genetic algorithms with multivariate regression for determination of gelatine in historic papers based on FT-IR and NIR spectral data. , 2010, Talanta.
[4] José Crossa,et al. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. , 2010, Genetics research.
[5] Jiewen Zhao,et al. Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm. , 2015, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[6] Limin Yang,et al. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .
[7] Vincent Leemans,et al. Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting , 2003 .
[8] Anil K. Jain,et al. Artificial neural networks for feature extraction and multivariate data projection , 1995, IEEE Trans. Neural Networks.
[9] On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms. , 2016, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[10] Chunjiang Zhao,et al. Optimization of Informative Spectral Variables for the Quantification of EGCG in Green Tea Using Fourier Transform Near-Infrared (FT-NIR) Spectroscopy and Multivariate Calibration , 2011, Applied spectroscopy.
[11] Osval A. Montesinos-López,et al. Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture , 2018, Plant Methods.
[12] G. de los Campos,et al. Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data , 2017, Plant Methods.
[13] José Crossa,et al. Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data , 2017, Plant Methods.
[14] R. M. Hoffer,et al. Biomass estimation on grazed and ungrazed rangelands using spectral indices , 1998 .
[15] R. Waring,et al. The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations , 1994 .
[16] Riccardo Leardi,et al. Genetic Algorithms as a Tool for Wavelength Selection in Multivariate Calibration , 1995 .
[17] Yu Huang,et al. Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration , 2015, Remote. Sens..
[18] Priyakant Sinha,et al. Review of the use of remote sensing for biomass estimation to support renewable energy generation , 2015 .
[19] Benoit Rivard,et al. The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data , 2008, Sensors.
[20] A. Qin,et al. Development of a critical nitrogen dilution curve based on leaf dry matter for summer maize , 2017 .
[21] J. Schjoerring,et al. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .
[22] Zou Xiaobo,et al. Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.
[23] Lorenzo Bruzzone,et al. Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[24] W. Verhoef,et al. PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .