Abstract: Abstract: The development of instruments for monitoring and diagnosing crop growth quickly and non-destructively obtain crop growth information, which is very helpful for the production and management of crop fields. Aimed at the problems with the existing two-band instrument used for crop growth monitoring and diagnosis, such as relying on a single vegetation index and low accuracy of growth index retrieval, this study developed a portable three-band instrument for crop-growth monitoring and diagnosis CGMD303 (Crop-Growth Monitoring and Diagnosis, CGMD). The CGMD303 instrument consisted of a multi-spectral crop growth sensor, processor system, sensor holder, level, shielded cable, and other components. Multi-spectral crop sensors were divided into upward light sensors and downward light sensors in structure. The upward light sensor could receive solar radiation information in the 660, 730, and 815 nm bands; the downward light sensor consisted of three detector lenses, which were used to detect the characteristic wavelengths of 660, 730, and 815 nm, respectively. The radiation information would be processed after being converted to electrical signals through the photoelectric detector. To test the monitoring performance of CGMD303 on rice growth, rice field experiments were conducted from July 2018 to September 2018 at the demonstration base of the National Engineering and Technology Center for Information Agriculture in Rugao City, Jiangsu Province, China (32°27′N, 120°77′E), and 2 varieties (Liangyou 728 and Huaidao NO.5), 3 nitrogen levels (0, 150 and 360 kg/hm2) and 2 planting density levels (30 cm×15 cm and 50 cm×15 cm) were set in the rice experiments. The canopy spectral reflectance of 660, 730, and 815 nm was obtained at the jointing stage, booting stage, and heading stage of rice and 2 new three-band vegetation indices were constructed. Fitting results of the vegetation indices obtained by CGMD303 and the commercial instrument ASD FieldSpec HandHeld2 showed a good linear correlation, indicating that CGMD303 effectively obtained rice canopy reflectance. Two three-band vegetation indices obtained by CGMD303 and rice growth parameters were fitted to construct rice growth monitoring models. The highest coefficient of determination values of the three-band vegetation indices and leaf area index, leaf dry weight, leaf nitrogen content, and leaf nitrogen accumulation of indica rice were 0.90, 0.74, 0.70, 0.79, respectively, and the relative root mean square error was 0.12, 0.17, 0.11, 0.22, respectively; the highest coefficient of determination values of the three-band vegetation indices and corresponding growth indices of Japonica rice were 0.74, 0.62, 0.38, 0.59, respectively, and the relative root mean square error was 0.21, 0.21, 0.16, 0.31, respectively. The prediction accuracy of the rice growth monitoring models based on CGMD303 for each growth parameters was tested and the coefficient of determination of leaf area index, leaf dry weight, leaf nitrogen content, and leaf nitrogen accumulation of rice were 0.85, 0.72, 0.45, 0.68, respectively, and the relative root mean square error were 0.21, 0.32, 0.13, 0.39, respectively. Verification results showed that CGMD303 could accurately predict leaf area index, biomass, and nitrogen indices of rice. CGMD303 had the advantages of accurate and stable data acquisition, simple operation, high-cost performance, etc. It was suitable for field operations and had high application potential.