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.