請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/71524
標題: Estimation of leaf nitrogen content using artificial neural network with cross-learning scheme and significant wavelengths
作者: Chen, C.T.
Chen, S.
Hsieh, K.W.
Yang, H.C.
Hsiao, S.
Yang, I.C.
關鍵字: artificial neural network
cross-learning
nitrogen content
reflectance
spectra
infrared reflectance spectroscopy
winter-wheat
vegetation
airborne
foliage
grass
nir
期刊/報告no:: Transactions of the Asabe, Volume 50, Issue 1, Page(s) 295-301.
摘要: Reflectance from crops provides spectral information for non-destructive monitoring of their nutrition status. In order to develop a multi-spectral imaging system for remote sensing of the nitrogen content of crops, the significant wavelengths and calibration models were carefully evaluated in this study. The significant wavelengths in full band (4002500 nm) and a selected band (450-950 nm), which is suitable for silicon CCD cameras, were investigated. In this article, significant wavelengths for estimating nitrogen content of cabbage seedling leaves were first determined by SMLR (stepwise multi-linear regression) analysis. A proposed ANN (artificial neural network) model with cross-learning scheme (ANN-CL) was further developed to increase the prediction accuracy. To comply with the design of a practical multi-spectral imaging system using silicon CCD cameras and commercially available bandpass filters, an ANN-CL model with four inputs of spectral absorbance at 490, 570, 600, and 680 nm was developed. The calibration results (rc = 0.93, SEC = 0.873%, and SEV = 0.96001o) reduced the SEVabout 15% when compared with the SMLR method withfour wavelengths (SEV = 1.099%). In addition, the results were comparable to that of SMLR with seven wavelengths (rc = 0.94, SEC = 0.806010, and SEV = 0.993%) in the full band. These results indicated that the ANN model with cross-learning using spectral information at 490, 570, 600, and 680 nm could be used to develop a practical remote sensing system to predict nitrogen content of cabbage seedlings.
URI: http://hdl.handle.net/11455/71524
ISSN: 0001-2351
顯示於類別:期刊論文

文件中的檔案:
沒有與此文件相關的檔案。


在 DSpace 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。