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Proceedings Paper

Monitoring of maize chlorophyll content based on multispectral vegetation indices
Author(s): Hong Sun; Minzan Li; Lihua Zheng; Yane Zhang; Yajing Zhang
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Paper Abstract

In order to estimate the nutrient status of maize, the multi-spectral image was used to monitor the chlorophyll content in the field. The experiments were conducted under three different fertilizer treatments (High, Normal and Low). A multispectral CCD camera was used to collect ground-based images of maize canopy in green (G, 520~600nm), red (R, 630~690nm) and near-infrared (NIR, 760~900nm) band. Leaves of maize were randomly sampled to detect the chlorophyll content by UV-Vis spectrophotometer. The images were processed following image preprocessing, canopy segmentation and parameter calculation: Firstly, the median filtering was used to improve the visual contrast of image. Secondly, the leaves of maize canopy were segmented in NIR image. Thirdly, the average gray value (GIA, RIA and NIRIA) and the vegetation indices (DVI, RVI, NDVI, et al.) widely used in remote sensing were calculated. A new vegetation index, combination of normalized difference vegetation index (CNDVI), was developed. After the correlation analysis between image parameter and chlorophyll content, six parameters (GIA, RIA, NIRIA, GRVI, GNDVI and CNDVI) were selected to estimate chlorophyll content at shooting and trumpet stages respectively. The results of MLR predicting models showed that the R2 was 0.88 and the adjust R2 was 0.64 at shooting stage; the R2 was 0.77 and the adjust R2 was 0.31 at trumpet stage. It was indicated that vegetation indices derived from multispectral image could be used to monitor the chlorophyll content. It provided a feasible method for the chlorophyll content detection.

Paper Details

Date Published: 9 November 2012
PDF: 10 pages
Proc. SPIE 8527, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV, 852711 (9 November 2012); doi: 10.1117/12.977414
Show Author Affiliations
Hong Sun, China Agricultural Univ. (China)
Minzan Li, China Agricultural Univ. (China)
Lihua Zheng, China Agricultural Univ. (China)
Yane Zhang, China Agricultural Univ. (China)
Yajing Zhang, Chinese Society for Agricultural Machinery (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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