The twospotted spider mite, Tetranychus urticae Koch, is among the most economically important pests in strawberries (Fragaria spp.). As T. urticae feeds, it ingests mesophyll cells that contain pigments essential for physiologic function and alters radiant energy use of the leaf tissue, severely compromising plant health and productivity. In our study, diffuse reflectance spectroscopy in the visible and near infrared (VNIR) portions of the spectrum was used to identify specific spectral regions altered by T. urticae feeding and to quantitatively assess T. urticae density. During the 2006–2007 growing season, 80 strawberry leaflets with varying levels of T. urticae infestation were collected. Spectral classification of both mite density (continuous) and mite density class (categorical) were developed. Spider mite density classes were low infestation (0–20 mites/leaflet), moderate infestation (20–50 mites/leaflet), and high infestation (≥50 mites/leaflet). Continuous spectral prediction for leaf infestation was developed using partial least squares (PLS) regression. Classification trees were used to train spectra to categorical levels of infestation. Both models were calibrated with 67% of the samples, and accuracy was evaluated using the remaining 33%. Categorical validation accuracy was 81%, with odds ratios for correctly predicting extreme categories (low and high) of 33 and 47.7, respectively. Continuous validation efficiency was also high, with an r² between predicted and observed of 0.85 and a root-mean-squared error (RMSE) of 12.2 mites per leaf. Developing a spectral pest monitoring system would provide a diagnostic tool allowing early and effective intervention for precision management of T. urticae in strawberry.