Soybean bacterial blight disease is a threat to soybean production. Multispectral technology has shown good potential in detecting this disease and can overcome the limitations of traditional methods. The aim of this study was to perform field monitoring of the dynamics of this disease in Northeast China in 2022. The correlation between the soybean chlorophyll content index (CCI) and disease grade was obtained using artificial inoculation of the pathogen. The correlation between the soybean CCI, disease grade, green normalized difference vegetation index (GNDVI), and soybean yield was analyzed using a drone-mounted spectrometer platform for image acquisition and preprocessing. The soybean CCI was negatively correlated with the disease grade. The GNDVI declined with disease progression, which allowed for an indirect determination of the disease grade. The soybean yield loss was significant at disease grade 4 for soybean bacterial blight disease. The random forest regression model was more accurate than the regression model in estimating the yield based on the GNDVI. Therefore, the GNDVI could be used to survey the disease class and estimate the yield using the random forest model. This study provides support for field trials of drone-mounted multispectral equipment. This surveillance approach holds the potential to bring about precision plant protection in the future.