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

Abstract

<jats:p>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<jats:italic> Erysiphe necator</jats:italic> on thousands of grape leaf disk samples for genetic analysis, here we developed automated imaging and analysis methods for<jats:italic> E. necator</jats:italic> 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 <jats:italic>μ</jats:italic>m<jats:sup>2</jats:sup> of the leaf disk. A convolutional neural network (CNN) based on GoogLeNet determined the presence or absence of<jats:italic> E. necator</jats:italic> 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<jats:italic> E. necator</jats:italic>. In addition, new CNNs could be readily developed for phenotyping within diverse pathosystems or for diverse traits amenable to leaf disk assays.</jats:p>

Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves

“…The previous section presented successful results for the classification of the presence of powdery mildew in foliar disks containing melon leaves. The obtained performances are similar to the recently published work on the classification of powdery mildew in two [11] or three classes [12] as presented in the related work section. It is to be noticed that the closest related method of [12] is applied to another crop but in a similar in vitro imaging conditions protocol.…”

“…An accuracy of 87% was reported to classify "healthy", "infected" and "severely" diseased bunches. In another work, a machine vision based phenotyping system was developed to assess the severity of grapevine powdery mildew [12]. The system is based on a high-resolution camera and a long working distance macro-focusing lens.…”

“…Also, acquisition time is important as the global objective is to implement a high-throughput phenotyping system. Unlike microscope-based images of [12] that catches only a single foliar disk at a time, we acquire 9 foliar disks in the same snapshot. The previous works have improved disease detection accuracy by investing in the imaging system (hyper-spectral camera and microscope).…”

An FPGA-Based Hardware/Software Design Using Binarized Neural Networks for Agricultural Applications: A Case Study

“…Based on segmented regions, feature subsets were used to detect whether the plant leaf is diseased or not. Bierman et al [12] adopted both SVM and artificial neural networks (ANN) classifiers training based on 18 colors and texture features, and fused the results of the two classifier. Their experiments showed a recognition accuracy of 100% for both downy and powdery mildew using the ensemble classifier.…”

“…Their experiments showed a recognition accuracy of 100% for both downy and powdery mildew using the ensemble classifier. The above methods [10]- [12] could provide high-accurate crop pest and disease recognition; however, these recognition methods were not applied to a real growth environment.…”