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Proceedings Paper

Estimation of tomato leaf nitrogen content using continuum-removal spectroscopy analysis technique
Author(s): Yongjun Ding; Minzan Li; Lihua Zheng; Hong Sun
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Paper Abstract

In quantitative analysis of spectral data, noises and background interference always degrades the accuracy of spectral feature extraction. Continuum-removal analysis enables the isolation of absorption features of interest, thus increasing the coefficients of determination and facilitating the identification of more sensible absorption features. The purpose of this study was to test continuum-removal methodology with Visual-NIR spectral data of tomato leaf. Through analyzing the correlation between continuum-removal spectrum and nitrogen content, 15 characteristics parameters reflected changing tendency of nitrogen content were chosen, which is at 335, 405, 500, 520, 540, 550, 560, 580, 620, 640, 683, 704, 720, 736 and 770 nm. Finally, the variance inflation analysis and stepwise regression method was used to develop the prediction model of the nitrogen content of tomato leaf. The result showed that the predicted model, which used the values of continuum-removal spectrum at 335 and 720nm as input variables, had high predictive ability, with R2 of 0.755. The root mean square errors of prediction using a leave-one-out cross validation method were 0.513. These results suggest that the continuum-removal spectroscopy analysis has better potential to diagnose tomato growth in greenhouse.

Paper Details

Date Published: 9 November 2012
PDF: 7 pages
Proc. SPIE 8527, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV, 852719 (9 November 2012); doi: 10.1117/12.977406
Show Author Affiliations
Yongjun Ding, China Agricultural Univ. (China)
Lanzhou City Univ. (China)
Minzan Li, China Agricultural Univ. (China)
Lihua Zheng, China Agricultural Univ. (China)
Hong Sun, China Agricultural Univ. (China)

Published in SPIE Proceedings Vol. 8527:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV
Allen M. Larar; Hyo-Sang Chung; Makoto Suzuki; Jian-yu Wang, Editor(s)

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