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Système d'information stratégique et agriculture (serveur d'exploration) - Checkpoint (PubMed)

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List of bibliographic references

Number of relevant bibliographic references: 9.
Ident.Authors (with country if any)Title
000128 (2014) Tiezhu Shi [République populaire de Chine] ; Huizeng Liu ; Junjie Wang ; Yiyun Chen ; Teng Fei ; Guofeng WuMonitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants.
000237 (2010) Jing-Cheng Zhang [République populaire de Chine] ; Jian-Yuan Li ; Gui-Jun Yang ; Wen-Jiang Huang ; Ju-Hua Luo ; Ji-Hua Wang[Monitoring of winter wheat stripe rust based on the spectral knowledge base for TM images].
000240 (2010) Zhan-Yu Liu [République populaire de Chine] ; Jing-Jing Shi ; Da-Cheng Wang ; Jing-Feng Huang[Discrimination and spectral response characteristic of stress leaves infected by rice Aphelenchoides besseyi Christie].
000268 (2009) Fu-Min Wang [République populaire de Chine] ; Jing-Feng Huang ; Xiu-Zhen Wang[Normalized difference ratio pigment index for estimating chlorophyll and cartenoid contents of in leaves of rice].
000298 (2008) Fu-Min Wang [République populaire de Chine] ; Jing-Feng Huang ; Jun-Feng Xu ; Xiu-Zhen Wang[Wavebands selection for rice information extraction based on spectral bands inter-correlation].
000303 (2008) Zhan-Yu Liu [République populaire de Chine] ; Jing-Feng Huang ; Rong-Xiang Tao ; Hong-Zhi Zhang[Estimating the severity of rice brown spot disease based on principal component analysis and radial basis function neural network].
000322 (2008) Fu-Min Wang [République populaire de Chine] ; Jing-Feng Huang ; Xiu-Zhen WangIdentification of optimal hyperspectral bands for estimation of rice biophysical parameters.
000336 (2007) Fu-Min Wang [République populaire de Chine] ; Jing-Feng Huang ; Yan-Lin Tang ; Xiu-Zhen Wang[Estimation of rice LAI by using NDVI at different spectral bandwidths].
000348 (2007) Zhan-Yu Liu [République populaire de Chine] ; Jing-Feng Huang ; Jing-Jing Shi ; Rong-Xiang Tao ; Wan Zhou ; Li-Li ZhangCharacterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression.

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