Knowledge of pollen shed dynamics in and around seed production fields is critical for ensuring a high yield of genetically pure corn seed. Recently, changes in canopy reflectance using hyperspectral reflectance have been associated with tassel emergence, which is known to precede pollen shed in a predictable manner. Practical application of this remote sensing technology, however, requires a simple and reliable method to evaluate changes in spectral images associated with the onset of tassel emergence and pollen shed. In this study, several numerical methods were investigated for estimating percentage of plants with visible tassels (VT) and percentage of plants that initiated pollen shed (IPS) from remotely sensed hyperspectral reflectance data (397 to 902 nm). Correlation analysis identified regions of the spectra that were associated with tassel emergence and anthesis (i.e., 50% of plants shedding pollen). No single band, however, generated correlations greater than 0.40 for either VT or IPS. Classification using an artificial neural network (ANN) was predictive, correctly classifying 83.5% and 88.3% of the VT and IPS data, respectively. The extensive preprocessing necessary and the "black box" nature of ANNs, however, rendered analysis of spectral regions difficult using this method. Partial least squares (PLS) analysis yielded models with high predictive capability (R2 of 0.80 for VT and 0.79 for IPS). The PLS coefficients, however, did not exhibit a spectrally consistent pattern. A novel range operator-enabled genetic algorithm (ROE-GA), designed to consider the shape of the spectra, had similar predictive capabilities to the ANN and PLS, but provided the added advantage of allowing information transfer for increased domain knowledge. The ROE-GA analysis is the preferred method to evaluate hyperspectral reflectance data and associate spectral changes to tassel emergence and the onset of pollen shed in corn on a field scale.