Abstract: Abstract: Crop spectrum characteristic analysis plays an important role on identification of crops, production estimation, condition monitoring, and nutrition diagnosis crop management. In order to predict the nutrient content of crop non-destructively and quickly, a reflectance spectrum detection system for winter wheat was developed to measure the reflectance of 350-820 nm. The system has three parts: optical sensor, data storage and transmission module and the controller. The optical system was developed based on the Ocean Optics STS-VIS sensor. The sensor was controlled by an enhanced electronic device to obtain reflected light with a high-sensitivity 1 024 pixel linear CCD array detector. The spectral information collection and analysis software was developed on Windows 7 platform, using PHP. Software included three modules: acquisition parameters, acquisition control and data management. After the controller and sensor connected successfully, users should optimize the system parameters, such as the integral time, the scan average times and boxcar width. Then it needed to choose the collection function, DN value. Finally, the reflectance data could be analyzed and stored. In order to test the performance of the spectrum analyzer, calibration experiment was carried out. A gray calibration board with four different gray gradations was involved. The correlation was analyzed between the spectral reflectance measured by spectrum analyzer and ASD Field Spec Hand Held 2. The result showed that the average correlation coefficient value was 0.94. it also showed that the developed spectrum analyzer was worked with good stability under different light conditions. The system was used to collect 350-820 nm (visible-near infrared) reflectance of winter wheat at heading stage in the field to detect the chlorophyll of winter wheat. In order to decrease the noise influence, canopy reflectance spectra curves were pre-processed by 1-order differential method. And the data smoothing was conducted by Bior Nr.Nd biorthogonal wavelet packet analysis method. After that, Monte Carlo sampling method was used to remove 5 outliers. And 8 sensitive wavelengths (719、572、562、605、795、527、705、514 nm) were chosen using Random frog algorithm. In order to indicate the effective of preprocessing method, the chlorophyll content detecting PLSR (partial least squares regression) model was established based on original reflectance spectra. The modeling determination coefficient was 0.70 and predictive determination coefficient was 0.10. Meanwhile, the revised chlorophyll content detecting model was established. The involved data was pre-processed after 1-order differential and wavelet packet decomposition. The modeling determination coefficient was 0.69. The root mean square error of model was 1.364 8. And predictive determination coefficient was 0.52. The root mean square error of prediction was 1.839 7. The modling results showed the background interference and noise of wheat canopy reflectance were removed effectively. The system based on miniature spectrometer could estimate the chlorophyll of wheat leaves and help to diagnose the crop nutrition of winter wheat at heading stage.