![]() Of particular significance to this study, Google® researchers developed a competition-winning network in 2014 called GoogLeNet that successfully classifies images depicting English language nouns from the ImageNet database. CNNs have been successfully applied to many biological classification problems including the classification of leaf images for species identification and the detection of different diseases and stresses. Recent advances in deep learning CNNs have brought their performance to levels that rival human observers for correctly classifying labeled images. For a description of CNNs and recent advances in machine vision the reader is directed to review articles on this topic. Ĭlassification algorithms are an area of machine vision that has experienced tremendous growth over the past decade with the development of convolutional neural networks (CNNs), a form of artificial intelligence that is loosely based on the neural architecture of animal visual systems. Image analysis approaches range from processes that result in pixel counting metrics, as in the above cases, to algorithms for detection of complex structures. Applied to plant disease quantification, image capture approaches have included batch imaging with a smartphone, flatbed scanner, or multispectral imager, among other devices. At its simplest, machine vision involves image capture and image analysis, both of which can be automated for higher throughput. In particular, machine vision approaches have enabled rapid progress in trait analysis under controlled conditions, including the analysis of quantitative trait loci for host resistance. Phenomics is revolutionizing plant phenotyping with high-throughput, objective disease assessment. In addition, new CNNs could be readily developed for phenotyping within diverse pathosystems or for diverse traits amenable to leaf disk assays. 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. This live-imaging approach was nondestructive, and a repeated measures time course of infection showed differentiation among susceptible, moderate, and resistant samples. For an independent image set the CNN was in agreement with human experts for 89.3% to 91.7% of subimages. ![]() necator hyphae in approximately 800 subimages per leaf disk as an assessment of severity, with a training validation accuracy of 94.3%. A convolutional neural network (CNN) based on GoogLeNet determined the presence or absence of E. Each image pixel represented 1.44 μm 2 of the leaf disk. 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. 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. ![]() ![]() Powdery mildews present specific challenges to phenotyping systems that are based on imaging.
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