"Macrobot": An Automated Segmentation-Based System for Powdery Mildew Disease Quantification.

Stefanie Lück,Friedrich Melchert,Maximilian Lorbeer,Udo Seiffert,Marc Strickert,David Kilias,Andreas Backhaus,Dimitar Douchkov

doi:10.34133/2020/5839856

Abstract

Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a “Macrophenomics facility.” The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations.

Highlights

Cereals, which include wheat, barley, rice, maize, rye, oats, sorghum, and millet, have been the primary component of humans’ diet delivering more than 50% of the world’s daily caloric intake [1]
We have developed a set of methods and equipment and combined them into a “Macrophenomics facility.”
Each leaf segment was extracted to a separate region of interest (ROI)

Summary

Introduction

Cereals, which include wheat, barley, rice, maize, rye, oats, sorghum, and millet, have been the primary component of humans’ diet delivering more than 50% of the world’s daily caloric intake [1]. Precise and sensitive phenotyping is one of the critical requirements for modern breeding and functional genomics studies. The quantitative disease resistance of the plants against pathogens is a typical example of a complex polygenic trait. This type of resistance is usually less efficient than the strong R-genebased resistance, it is a desired trait because of its durability on the field, and in high contrast to the Rgene resistance, it is effective against all races of a particular pathogen and even against different pathogen species. The accessibility of the genomic information for several host and pathogen species greatly facilitates these studies but, on the other hand, introduced an enormous amount of data that needs to be tested and functionally validated. The ability of high throughput becomes an essential requirement for the new systematic phenotyping, and the term “phenomics” was coined to describe this approach

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