Grapevine downy mildew is the most serious grape disease worldwide. Early detection of this disease can achieve early control so that quality and yield are improved. A test method based on multicolor fluorescence images (MFI) on grape leaves and a Support Vector Machine (SVM) model was proposed in the current study. Multicolor fluorescence imaging was performed on 145 inoculated leaves and 145 healthy leaves from the backside at six consecutive DPI (Days Post Innoculation). 16 fluorescence parameters (F440, F520, F690, F740 and their respective ratios) were obtained. Based on the image variation of four independent fluorescence wavelengths as DPI proceeding, single-factor ANOVA and correlation analysis were conducted. Four wavelengths of F520, F690, F440/F740 and F690/F740 were best selected with stronger detection ability of early infection and low correlation. For better detection, an SVM model was constructed with all four features. The results showed that the four basic bands F440, F520, F690, F740 and their ratios had the ability to detect early infection of grapevine downy mildew. F440 and F520 were more sensitive to the infection than F690 and F740. Start from 2 DPI, the area of the lesion could be highlighted in the fluorescence images of F440 and F520, at which the fluorescence intensity of the inoculated leaves was significantly higher than that of healthy leaves (p<0.01), and the difference increased with the increase of DPI (p<0.000 1). At F690 and F740 bands, the fluorescence intensity of inoculated leaves decreased gradually with the increase of DPI, and there was no significant difference between inoculated and healthy leaves from 1DPI to 3DPI. At 4DPI, inoculated leaves’ fluorescence intensity was significantly lower than that of heathy leaves (p<0.05) and the difference increased at 5DPI and 6DPI(p<0.01). The fluorescence parameters of healthy leaves changed little. F440 was the most susceptible to interference with the maximum coefficient of variation among the four bands, while F520 was more stable with the least coefficient of variation. With the increase of DPI, the detection accuracy of SVM model for distinguishing healthy and inoculated leaves was gradually improved, at 1DPI, the accuracy of SVM with multi-features was 65.6%, the accuracy of 3DPI achieved 82.2%, and the average accuracy was 84.6% in the whole experimental period (6 d), which was better than the best threshold method (F520 with 61.1% at 1DPI, 78.9% at 3DPI and 80.0% in the whole experimental period). In conclusion, the MFI technology with SVM model can achieve the early detection of downy mildew before the onset of symptoms, which provides a theoretical basis and proof for the development of portable equipment for early diagnosis of grape downy mildew.