Aerial hyperspectral remote sensing imagery was collected on three dates over three plots of corn. The imagery had a spatial resolution of 1 m and a spectral resolution of 3 nm between 471 nm and 828 nm. A machine vision corn plant population sensing system was also used to map every row of corn within the three plots, and a complete inventory of corn plants was generated as a rich ground reference dataset for remote sensing image analysis. A multiple linear regression analysis was performed to estimate corn plant stand density using reflectance in combinations of three wavebands, and R ² s of up to 0.82 were found. Estimates of corn plant stand density were best when using imagery collected at the later vegetative growth stage. Quantization effects due to row width complicated corn plant stand density estimates at 2 m spatial resolution, and better estimations were typically seen at resolutions of 6 m and 10 m. For the best-case scenarios, the first predictor variable in the regression model typically fell in the blue reflectance region (473 to 492 nm). The second predictor variable was typically in the longer green and shorter red wavelengths (584 to 635 nm), and reflectance for the third predictor variable was typically at the red edge (729 nm) or in the near-infrared region. Because results for the second and third predictor variables tended to straddle between important regions of typical vegetative reflectance spectra, it is expected that multiple linear regressions using a greater number of bands would improve the distinction between important spectral ranges for estimating corn plant stand density.