A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew

Powdery mildews present specific challenges to phenotyping systems that are based on imaging. Having previously developed low-throughput, quantitative microscopy approaches for phenotyping resistance to Erysiphe necator on thousands of grape leaf disk samples for genetic analysis, here we developed automated imaging and analysis methods for E. necator severity on leaf disks. By pairing a 46-megapixel CMOS sensor camera, a long-working distance lens providing 3.5× magnification, X-Y sample positioning, and Z-axis focusing movement, the system captured 78% of the area of a 1-cm diameter leaf disk in 3 to 10 focus-stacked images within 13.5 to 26 seconds. Each image pixel represented 1.44 μm² of the leaf disk. A convolutional neural network (CNN) based on GoogLeNet determined the presence or absence of E. necator hyphae in approximately 800 subimages per leaf disk as an assessment of severity, with a training validation accuracy of 94.3%. For an independent image set the CNN was in agreement with human experts for 89.3% to 91.7% of subimages. This live-imaging approach was nondestructive, and a repeated measures time course of infection showed differentiation among susceptible, moderate, and resistant samples. Processing over one thousand samples per day with good accuracy, the system can assess host resistance, chemical or biological efficacy, or other phenotypic responses of grapevine to E. necator. In addition, new CNNs could be readily developed for phenotyping within diverse pathosystems or for diverse traits amenable to leaf disk assays.