Whether the chlorophyll 3D distribution of crop is obtained accurately really attracts attention of scientific research and production field, such as crop nutrition, cultivation and breeding. In this study, the research object is the rice plant. The transformed ordinary SLR camera with different near infrared filters was used to acquire the multispectral images of rice plant in multi-view. Five kinds of vegetation indexes were calculated by combination image based on different bands and different channels. Then the optimal rice plant chlorophyll (SPAD value) prediction model was built between vegetation index and measured SPAD value. The research results showed that the prediction model with the quadratic function between GNDVI vegetation index and measured SPAD value can analyze chlorophyll content (SPAD value) of rice plant well, R2=0.758, RMSE=1.532. The GNDVI vegetation index was constructed by the R channel of near-infrared 760nm band and the G channel of visible light band. On this basis, the rice 3D model with texture information was built by multi-angle imaging 3D modeling method. Meanwhile, the optimal prediction model was applied to the integrated texture map of rice, and then the chlorophyll 3D distribution of rice was obtained. So rapid nondestructive detection of rice growth condition and chlorophyll nutrient situation can be realized.