High-throughput phenotyping of leaf discs infected withgrapevine downy mildew using shallow convolutionalneural networks

Abstract

Abstract Objective and standardized recording of disease severity in mapping crosses and breeding lines is a crucial step in characterizing resistance traits utilized in breeding programs and to conduct QTL or GWAS studies. Here we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs. As proof of concept, we used leaf discs inoculated with Plasmopara viticola causing grapevine downy mildew (DM). This oomycete is one of the major grapevine pathogens and has the potential to reduce grape yield dramatically if environmental conditions are favorable. Breeding of DM resistant grapevine cultivars is an approach for a novel and more sustainable viticulture. This involves the evaluation of several thousand inoculated leaf discs from mapping crosses and breeding lines every year. Therefore, we trained a shallow convolutional neural-network (SCNN) for efficient detection of leaf disc segments showing P. viticola sporangiophores. We could illustrate a high and significant correlation with manually scored disease severity used as ground truth data for evaluation of the SCNN performance. Combined with an automated imaging system, this leaf disc-scoring pipeline has the potential to reduce the amount of time during leaf disc phenotyping considerably. The pipeline with all necessary documentation for adaptation to other pathogens is freely available.

Plant phenotyping relevance

ブドウ葉ディスクの病害重症度を自動画像解析・CNNで推定する手法を開発し、手動評価との相関で検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The authors publicly released the complete leaf-disc scoring pipeline (SCNN training scripts, classification pipeline, R plotting scripts, ZenBlue imaging workflow) plus the training image datasets in an open-source GitHub repository, enabling reproduction of this paper's phenotyping measurements and analysis.