Pre-visual detection of crop disease is critical for both food and economic security. The sudden death syndrome (SDS) in soybeans, caused by Fusarium virguliforme (Fv), induces 100 million US$ crop loss, per year, in the US alone. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were measured at canopy level over a course of a growing season to assess the capacity of spectral measurements to detect Fv infection by inoculated vs control plots. A dual field-of-view, two-spectrometer (400 to 1630 nm) system named Piccolo Doppio, provided an efficient method for the collection of plant reflectance by eliminating the need for frequent reference standard measurements. This system was used on a tractor to obtain canopy reflectance eight times during the 2016 growing season in Arlington WI. Partial least squares discriminant analysis (PLSDA) was applied to the spectroscopic data to discriminate between Fv-inoculated and control plots. Best performance was during the early reproductive stages, spectrally discriminating Fv infection prior to visual canopy symptoms with total accuracy of classification of 88% for calibration, 79% for cross-validation, and 82% for independent validation. Near infrared (NIR) wavelengths were most sensitive to inoculation status, suggesting that canopy level detection is related to lower biomass influenced as a consequence of disease. Partial least square regression (PLSR) was used on a late-stage canopy level data to predict soybean seed yield, which is typically the metric most desired by growers and breeders, with calibration, cross-validation and independent validation coefficient of determination (R²) values 0.71, 0.59 and 0.62 (p<0.01), respectively, and independent validation root mean square error (RMSE) of 0.31 t ha^-1. Therefore, it is concluded that: early reproductive stages are recommended for canopy spectral detection of soybean root Fv infection prior to canopy symptoms and that late reproductive stages are best for seed yield detection.  SDS is detectable due to physiological effects on foliage, while detection of yield is based on indirect correlations with physiology. This study showed promising results for use of spectral sensing of canopy to identify Fv infection on soybean roots where no canopy symptoms are visually present. It is also concluded that the dual field of view, two spectrometers, tractor mounted system has shown spectral feasibility of operation for precision agriculture research and potential applications