Abstract: The real-time, fast, non-destructive and quantitative monitoring of leaf area index (LAI) is critical for precise regulation population quality of double cropping rice production. The objective of this study was to test the accuracy and adaptability of crop growth monitoring and diagnosis apparatus (CGMD) in double cropping rice of different plant types growth index monitoring and application, and to establish the leaf area index (LAI) monitoring model of double cropping rice based on CGMD. Field experiments were conducted in Jiangxi China in 2016 and 2017, including different plant type cultivars and nitrogen application rates. The differential vegetation index (DVI), normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were measured at tillering stage, jointing stage, booting stage, heading stage and filling stage with two spectrometers, i.e., CGMD (a passive multispectral spectrometer containing 810 and 720 nm wavelengths) and analytical spectral devices (ASD, a passive hyper-spectral spectrometer containing 325 to 1 075 nm wavelengths). Vegetation indexes change characteristics were compared between CGMD and ASD, and their quantitative relationships were analyzed. The LAI monitoring models for compact and loose plant type cultivars of double cropping rice were established based on CGMD from field experimental dataset in 2016 and then validated using field experimental dataset in 2017. The results showed that the LAI, DVI, NDVI and RVI of different plant type cultivars were increased with increasing nitrogen application rate at different growth stages. All of them showed a “low-high-low” trend with double cropping rice development progress. The determination coefficient (R2) of DVI, NDVI and RVI based on CGMD and ASD were 0.959-0.968, 0.961-0.966 and 0.957-0.959, respectively. This indicated that vegetation indexes based on CGMD and ASD was highly consistent, and the CGMD could be used to replace expensive ASD to measure NDVI, DVI and RVI. The prediction effect of LAI monitoring model at single growth stage based on CGMD vegetation indexes was better than that in the whole stage, and the prediction effect of LAI monitoring model in the loose plant type cultivar based on CGMD vegetation indexes was better than that in the compact plant type cultivar. The linear equation based on DVICGMD could be used to estimate LAI with the R2 in the range of 0.857-0.903, and the correlation coefficient (r), root mean square error (RMSE) and relation root mean square error (RRMSE) of model validation in the range of 0.950-0.984, 0.18-0.43 and 3.95%-9.40%, respectively. The exponential equation based on NDVICGMD could be used to estimate LAI with the R2 in the range of 0.831-0.884, and the r, RMSE and RRMSE of model validation in the range of 0.906-0.967, 0.24-0.38 and 5.73%-9.16%, respectively. The power function equation based on RVICGMD could be used to estimate LAI with the R2 in the range of 0.830-0.881, and the r, RMSE and RRMSE of model validation in the range of 0.905-0.954, 0.25-0.56 and 7.37%-9.99%, respectively. Compared with the normal manual sampling method, using the CGMD can real-time and non-destructive monitoring the LAI dynamic change of double cropping rice. The CGMD could be used to replace SunScan (an expensive plant canopy analyzer used to measure LAI) to measure LAI of double cropping rice, which has a potential to be widely applied for precise regulation of LAI and high yield cultivation in double cropping rice production.