Phenotyping of responses in the earliest stages of pathogen growth has required microscopy and observation by human experts, an expensive and slow process that severely restricted throughput. We developed a high-throughput phenotyping system that combined high-resolution, low magnification (4X) optics, a 47 MP 24X36 mm CMOS sensor with a pixel width of 4.2µm, long working distance, consequent maximum depth of field; and paired this with an X-Y-Z robotic stage, image-stacking to create fully-focused composite images, and a pre-trained convolutional neural network. Projecting a 4X image of the 4 µm-wide hyphae of the grape powdery mildew pathogen (Erysiphe necator) onto the camera sensor provided 4-pixel width coverage of the hyphal diameter. The system synoptically captures a fully-focused high-resolution image covering a 1-cm leaf disk in as little as 13 seconds. We named the system Blackbird, after the SR-71 reconnaissance plane. The current throughput rate ranges between 1200 to 2400 samples imaged per 8-hour workday. The assessments are also non-destructive; allowing repeated assessments of samples through multiple time points. Our present convolutional neural network estimates the percentage of infected area per sample at an agreement ratio of 91% compared to human expert assessments. Further QTL analysis showed that this automated phenotyping methodology has higher QTL association than the traditional manual method.