Convenient and reliable crop nutrition diagnosis methods is basis of scientific crop fertilizer management and the core of the precision agriculture, and chlorophyll content is an important index of crop nitrogen nutrition content. In this research, the research object was rice leaf, and visible image and the center wavelength of 650, 680, 720, 760, 850 and 950 nm near infrared image were captured by transformed ordinary camera and filters. Then the relative reflectance values of different wave band were acquired. After regression analysis with visible-band and near-infrared band combined, the high precision and stable models were selected. Compared with the three imaging channels of camera, the correlation between chlorophyll content (SPAD value) and R channel was higher than B, G channels. Results showed that in the comparison of vegetation indexes, GVI can best reflect growth status of crops, and 760 nm has become the best near-infrared band in SPAD prediction. The model prediction accuracy R2 of the least squares support vector machine method combined with multiple vegetation index was 0.831 4, while ideal result had been achieved. Meanwhile, hyperspectral image of rice leaf was captured by hyperspectral imager. Compared the two imaging modalities, the multi factor prediction model based on vegetation index has the same precision. Experiments proved that consumer-grade near infrared camera could gain similar estimation result of chlorophyll content as hyperspectral imager.