Abstract: In order to explore the rapid detection method of field maizecanopy′s chlorophyll index.A 2-CCD multi-spectral image monitoring system was used to collect multi-spectral images of maize canopy in the field, and SPAD index of each sample was measured to show the chlorophyll content index.The collected RGB (red, green, and blue) and NIR (near-infrared)images were processed by median filtering algorithm to eliminate the noise, and then HSI color model was used to segment the image of maize canopy from background.The average gray level of R, G, B and NIR bands were extracted from the processed images, and RVI, NDVI and other vegetation indexes were calculated based on those average gray levels.The correlation between the 12 parameters and chlorophyll content were analyzed, and a variety of combinations of image detection parameters were discussed, and multiple linear regression models (MLR) for chlorophyll content were established.The results showed that there is an obvious negative correlation between the average gray level of red, green, blue bands and the chlorophyll content, correlation coefficients are -0.73, -0.71 and -0.71, the correlation coefficients between the NDVI, MSAVI2, RVI and the chlorophyll content was 0.83, 0.81 and -0.81, separately, higher than other vegetation indexes.According to the results of correlation analysis, the parameters including R, G, B, NDVI, MSAVI2 and RVI were used to establish MLR models for chlorophyll content index, which is more sufficient than the models based on separate parameters, and the calibration determination coefficient r2 is 0.79, and validation determination coefficient r2 is 0.71.Research provides a support for the nondestructive detection of chlorophyll content atmaize jointing stage.