Rapid acquisition and mapping of normalized difference vegetation index (NDVI) values is essential for a timely evaluation of nitrogen use efficiency (NUE) in sustainable agriculture. The zonal nutrient management techniques for sustainable agriculture require timely and efficient NDVI data collection and analysis capabilities, – a need that is still rarely addressed. However, as with the use of manned aircraft, the primary limitation of established unmanned aerial vehicle (UAV) imaging technologies is the limited time window for large-area data collection. Furthermore, as the spatial resolution increases, the post-processing time generally becomes longer. To overcome problems associated with the need for rapid data acquisition and post-processing, we have developed a UAV-based sensor system called the Accurate and Speed Scanner (AS-Scanner). The data acquisition accuracy and NDVI mapping speed of the system were assessed. According to the results of a ground-level experiment, the mean absolute error (MAE) between the reflectance data of the AS-Scanner and the corresponding data of the ASD FieldSpec 3 spectrometer is less than 1.11 %. Furthermore, results of a plot-level experiment suggest that the NDVI values of a raw point cloud from AS-Scanner-based UAV platform (AS-NDVI) and UAV-mounted MicaSense RedEdge camera (REG-NDVI) are highly correlated (0.54<R2<0.57, p<0.01). In a map-level experiment, the interpolated, 15 m×15 m pixel size AS-NDVI values were in good agreement with the REG-NDVIs over a 4.01 hectare (40100 m2) rice field (0.55<R2<0.65, p<0.01). The average processing times of the inverse distance weighted (IDW, 0.45 s), kriging linear (KL, 0.73 s), and nearest neighbor (NN, 1.05 s) interpolations of the AS-Scanner data prove that it is possible to efficiently create NDVI maps using data from the sensor. Besides, the effect of the varying solar irradiance values on the AS-NDVIs was analyzed under varied illumination conditions. Moreover, the AS-NDVIs were also used to assess the NUE in a paddy rice field at the panicle stage. The results show that nutrients can be effectively assessed using AS-Scanner data. Despite some disadvantages such as the potential of NDVI value saturation over dense canopies, the AS-Scanner provides a method for quickly obtaining reasonable spatial resolution crop information for agricultural nutrient monitoring and management. This also offers the potential to reduce environmental pressure which may be caused by untimely estimation of NUE.