Rice growers are reluctant to physically sample their crops, with many relying on visual estimates of crop nitrogen status to determine mid-season nitrogen topdressing requirements. There is increased interest in using remote sensing to determine how accurately mid-season rice crop nitrogen status (nitrogen concentration and content in shoots) can be predicted. Field-derived hyperspectral reflectance measurements were collected over three seasons to evaluate the optimal wavelengths for the prediction of in-crop nitrogen at panicle initiation. Rice plots were planted and differing nitrogen rates applied to establish a range of crop nitrogen uptake levels at the panicle initiation stage of crop development. Hyperspectral canopy reflectance (350'2500 nm) data were collected from each plot at the panicle initiation growth stage and plant samples were collected at each scanning location. Calibration models were developed for plant nitrogen concentration, dry matter and nitrogen uptake using partial least squares regression and the influential wavelengths used in the nitrogen uptake prediction model identified. When the nitrogen uptake model, which included samples collected in three crop seasons, was used to predict nitrogen uptake in an independent set of samples, r2 was 0.84 and the root-mean-square error of prediction (RMSEP) was 16.5 kg N ha'1. The important wavelengths utilised for predicting nitrogen uptake were determined from the regression coefficients and a new calibration created by multiple linear regression (MLR) using only four wavelengths (738 nm, 1362 nm, 1835 nm and 1859 nm). The MLR calibration using the four wavelengths had a prediction accuracy of r2 = 0.82 and RMSEP = 18.4 kg N ha'1, which shows potential for commercial applications. These findings will encourage the development of a remote sensing method for predicting nitrogen uptake for rice crops using the identified wavelengths in an inexpensive multiband instrument.