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[Objectives] Aiming at the problem that the vegetation index in the early stage of wheat growth is easily disturbed by soil background, a wheat canopy vegetation index measurement method based on narrow band spectral image analysis was proposed. [Methods] A narrow-band spectral image acquisition device with multi-lens structure was constructed to obtain 656 and 770 nm narrow-band spectral images of field wheat in real time. Simple linear iterative clustering (SLIC) and visual geometry group network 16 (VGG16) full convolution neural network were used for super-pixel clustering and classification of wheat near-infrared narrow-band spectral images. Using intersection over union Qseg, comprehensive evaluation index (F value) and Precision as segmentation accuracy evaluation indexes, the performance difference between traditional threshold segmentation method and the proposed method to remove soil background interference was analyzed. The sunlight free whiteboard calibration method was used for the narrow-band spectral image after removing the soil background to calculate the vegetation index. The calculated vegetation index took the measured data of GreenSeeker RT200 as a reference to qualitatively and quantitatively evaluate the performance of the proposed method to remove the soil background interference. [Results]The images of 12 wheat varieties, two nitrogen application levels and 24 planting areas were collected. The average value of Qseg, Precision, F were 90.41%, 80.82% and 72.73%, respectively. The segmentation performance was better than the traditional threshold segmentation method. For the same test field, the maximum variation coefficient, average value and standard deviation of normalized difference vegetation index (NDVI) of each area measured by GreenSeeker RT200 were 47.12%, 33.61% and 10.17% respectively, while that of NDVI of each area measured by the proposed method were 18.59%, 9.61% and 3.88%, respectively. Moreover, the vegetation index extracted by this method had a high correlation with the measurement results of GreenSeeker RT200 after the closure stage of wheat in the test plot, and the determination coefficient was 0.895 9. Hence, the calculated NDVI could effectively characterize the nutritional status of wheat varieties under different fertilization levels. [Conclusions] The proposed method can complete the canopy extraction and vegetation index measurement of wheat narrow-band spectral images under complex soil background and field illumination change, and can provide a reference for the optimal design of crop canopy reflectance spectrometer with multi-lens structure. [ABSTRACT FROM AUTHOR]