Share Email Print

Proceedings Paper

Winter wheat nutrition diagnosis under different N treatments based on multispectral images and remote sensing
Author(s): Ruijiao Zhao; Minzan Li; Shuqiang Li; Yongjun Ding
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In order to rapidly and accurately acquire winter wheat growing information and nitrogen content, a non-destructive testing method was developed combined with multi-spectral imaging technique and remote sensing technology to research wheat growing and nutrition status. Firstly, a 2-CCD multi-spectral image collecting platform was developed to acquire visible image and NIR image synchronously, meanwhile, the canopy spectral reflectance and the nitrogen content of wheat leaves were measured and analyzed to research the characteristics of the canopy spectral reflectance. Secondly, using calibration panels the experiential linear calibration model was established between image gray value and spectral reflectance. Thirdly, NIR image was processed to segment wheat canopy from soil and then gray value of wheat leaves was achieved by image processing of Red, Green, and Blue channels. Finally, the gray value of wheat leaves was transformed into spectral reflectance by aforementioned experiential linear model, and the vegetation index were calculated and analyzed to research the winter wheat growing and nitrogen content status. Experiment results showed that it was reasonable to diagnose nitrogen content of winter wheat based on multi-spectral imaging system and experiential linear model. There existed remarkable correlation between vegetation index (NDVI, GNDVI) and nitrogen content of winter wheat, and the correlation coefficients (R2 ) were 0.633 and 0.6.

Paper Details

Date Published: 16 November 2010
PDF: 6 pages
Proc. SPIE 7857, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III, 78571G (16 November 2010); doi: 10.1117/12.866213
Show Author Affiliations
Ruijiao Zhao, China Agricultural Univ. (China)
Minzan Li, China Agricultural Univ. (China)
Shuqiang Li, China Agricultural Univ. (China)
Yongjun Ding, China Agricultural Univ. (China)

Published in SPIE Proceedings Vol. 7857:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III
Allen M. Larar; Hyo-Sang Chung; Makoto Suzuki, Editor(s)

© SPIE. Terms of Use
Back to Top