Traditional methods of obtaining chlorophyll content of corn leaves require grinding corn leaves, which would be harmful to corn plant and time-consuming.Besides, it's difficult to be integrated in the modem precision agriculture technology system.This research aimed at exploring the VIS/NIR (Visible Spectrum/Near Infrared) reflectance spectral characteristics of corn leaves,and establishing a high-precision model to predict nitrogen nutrient content for these leaves,providing technical support for the fine management of com.Firstly, the relationship between chlorophyll content and response spectrum of different growth stages of corn in field was studied by using spectral analysis technique.The results showed the absolute value of the spectral reflectance was larger at jointing and booting stage.It means that the sensitive period for detecting the chlorophyll content of corn was jointing and booting stage.Then, samples were collected from corn plants in Beijing Shangzhuang experimental field of CAU (China Agricultural University) during the period of jointing and booting separately.Eighty corn plants were selected randomly from different regions.Then a main leaf of each target plant was selected and a representative part of every leaf were marked, and the leaves from the representative part were considered as one sample.In the end, 80 samples of corn plants were collected, and the visible and near infrared spectral reflectance were measured using ASD spectrograph and vehicle detection platform of crop nitrogen nutrition based on CropSpec.At the same time, the chlorophyll content for each sample was detected using spectrophotometry in the laboratory.So many spectrographs as ASD can only be used for a point on the measurement, the measurement area is limited, and during the process of measurement they should be stable over the crop canopy.Devices like this are human-consuming and not easy to be integrated into the real-time detection system.Whilethe vehicle detection platform of crop nitrogen nutrition based on CropSpecis by scanning crops to obtain nitrogen content, record and build a prescription map tosupport the scientific production decision-making.So it can predict the content of chlorophyll in the process of moving.By comparison, the predicted results of the two have a high consistency.And the detected results of the vehicle spectrum detection platform also have a high consistencywith the result of spectrophotometry in the laboratory.It means that the vehicle spectrum detection platformprovides a feasible method to measure the chlorophyll content of corn more efficiently.In the process of data processing,abnormal samples was discriminated and eliminated by using 3 times standard deviation criterion.Principal component analysis (PCA) was performed to overcome the effect of multiple linear of spectral variables.In order to predict the content of chlorophyll in corn, the multiple linear regression model and partial least square regression model were established by selecting 550 650 766 and 850 nm four bands respectively.The results show that the partial least squares regression model is more accurate than the multiple linear regression model.This research can be used to evaluate the nutritional status of corn plants and support the fine fertilizer application.