“Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification

Author:

Lück Stefanie1ORCID,Strickert Marc2,Lorbeer Maximilian3,Melchert Friedrich4ORCID,Backhaus Andreas4ORCID,Kilias David4,Seiffert Udo4ORCID,Douchkov Dimitar1ORCID

Affiliation:

1. Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Correnstr. 3, 06466 Seeland, Germany

2. Physics Institute II, University of Giessen, Heinrich-Buff-Ring 16, 35392 Giessen, Germany

3. Julius Kühn Institute for National and International Plant Health, Messeweg 11/12, 38104 Braunschweig, Germany

4. Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany

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.

Funder

Bundesministerium für Bildung und Forschung

Publisher

American Association for the Advancement of Science (AAAS)

Subject

General Medicine

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