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

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.

Quantitative Trait Loci Mapping of Adult Plant and Seedling Resistance to Stripe Rust (Puccinia striiformis Westend.) in a Multiparent Advanced Generation Intercross Wheat Population

“…All RILs, the parental lines, and the susceptible standard “Akteur” were evaluated for resistance at the seedling stage in a detached leaf assay (Lück et al, 2020 ). Seedlings were grown in 77-cell propagation trays with mixed potting soil (Gebr.…”

Efficiency of a Seedling Phenotyping Strategy to Support European Wheat Breeding Focusing on Leaf Rust Resistance

“…The relation between leaf size and pustule size resulted in the rated percentage of infected leaf area [28]. A more detailed description of the experimental setup can be found elsewhere [23].…”

“…Therefore, the development of pioneering phenotyping strategies is requested to generate highly precise data in a time-and resource-saving manner [20]. Some strategies fulfilling these issues have already been published on the basis of greenhouse trials [23,24]. For instance, Lueck et al (2020) [23] reported a highly promising approach by generating detached leaf assays of seedlings screened by a robotic-and computer-based, high-throughput system (further denoted as Macrobot platform).…”

“…Some strategies fulfilling these issues have already been published on the basis of greenhouse trials [23,24]. For instance, Lueck et al (2020) [23] reported a highly promising approach by generating detached leaf assays of seedlings screened by a robotic-and computer-based, high-throughput system (further denoted as Macrobot platform). This methodology allows a fast resistance quantification for a large number of single plants using steadily updated pathogen populations or observing in parallel different diseases or isolates within separated experiments.…”

High-Throughput Phenotyping of Leaf Discs Infected with Grapevine Downy Mildew Using Shallow Convolutional Neural Networks

“…For reviews on this topic see [6,7,28,29]. For visually easily detectable diseases located on the leaf surface like powdery mildew several deep learning image segmentation approaches have been published in recent years [24,30,31].…”