In order to meet the needs of rapid detection of crop growth and guide variable management, a portable crop chlorophyll detector was developed based on the selection and optimization of characteristic wavelength of crop chlorophyll spectral response. Firstly, the reflectance spectra of field leaves were collected by ASD spectrometers, and the true chlorophyll content of leaves was extracted to screen the wavelength of chlorophyll sensitive response based on hyperspectral reflectance. The Monre Carlo uninformative variables elimination (MC-UVE) algorithm was used to select 10~100 characteristic wavelengths, which showed that the optimal chlorophyll content detection ability was achieved when 50 characteristic wavelengths were used.Secondly, the AS7265x spectral sensor was selected to cover 50 wavelength positions screened in 12 intervals with FWHM (full width at half maximum) of 20nm. The chlorophyll detector was designed to include modules such as sensor, main controller, display and control, and realize the functions of collection, processing, display and storage of reflected spectral data of crop canopy.Sensor reflectivity calibration and field application tests were carried out, based on the reflectivity of 12 bandwidths, a partial least squares regression detection model of chlorophyll content was constructed, and the coefficient of determination of the verification set was 0.628. The normalized difference red edge index (NDRE: 730nm, 900nm) and the green normalized difference vegetative index (GNDVI: 535nm, 900nm) were further combined, and the accuracy of the detection model was improved to be 0.69. By embedding the model into the system, the rapid detection of chlorophyll content in the field was realized, which provided technical support for the efficient analysis of crop growth.