Nitrogen is a necessary and important element for the growth and development of fruit orchards. Timely, accurate and nondestructive monitoring of nitrogen status in fruit orchards would help maintain the fruit quality and efficient production of the orchard, and mitigate the pollution of water resources caused by excessive nitrogen fertilization. This study investigated the capability of hyperspectral imagery for estimating and visualizing the nitrogen content in citrus canopy. Hyperspectral images were obtained for leaf samples in laboratory as well as for the whole canopy in the field with ImSpector V10E (Spectral Imaging Ltd., Oulu, Finland). The spectral datas for each leaf sample were represented by the average spectral data extracted from the selected region of interest (ROI) in the hyperspectral images with the aid of ENVI software. The nitrogen content in each leaf sample was measured by the Dumas combustion method with the rapid N cube (Elementar Analytical, Germany). Simple correlation analysis and the two band vegetation index (TBVI) were then used to develop the spectra data-based nitrogen content prediction models. Results obtained through the formula calculation indicated that the model with the two band vegetation index (TBVI) based on the wavelengths 811 and 856 nm achieved the optimal estimation of nitrogen content in citrus leaves (R2=0.607 1). Furthermore, the canopy image for the identified TBVI was calculated, and the nitrogen content of the canopy was visualized by incorporating the model into the TBVI image. The tender leaves, middle-aged leaves and elder leaves showed distinct nitrogen status from highto low-levels in the canopy image. The results suggested the potential of hyperspectral imagery for the nondestructive detection and diagnosis of nitrogen status in citrus canopy in real time. Different from previous studies focused on nitrogen content prediction at leaf level, this study succeeded in predicting and visualizing the nutrient content of fruit trees at canopy level. This would provide valuable information for the implementation of individual tree-based fertilization schemes in precision orchard management practices.