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

Sensor-based phenotyping of above-ground plant-pathogen interactions

Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.

Mining for New Sources of Resistance to Powdery Mildew in Genetic Resources of Winter Wheat

Genetic pathogen control is an economical and sustainable alternative to the use of chemicals. In order to breed resistant varieties, information about potentially unused genetic resistance mechanisms is of high value. We phenotyped 8,316 genotypes of the winter wheat collection of the German Federal ex situ gene bank for Agricultural and Horticultural Crops, Germany, for resistance to powdery mildew (PM), Blumeria graminis f. sp. tritici, one of the most important biotrophic pathogens in wheat. To achieve this, we used a semi-automatic phenotyping facility to perform high-throughput detached leaf assays. This data set, combined with genotyping-by-sequencing (GBS) marker data, was used to perform a genome-wide association study (GWAS). Alleles of significantly associated markers were compared with SNP profiles of 171 widely grown wheat varieties in Germany to identify currently unexploited resistance conferring genes. We also used the Chinese Spring reference genome annotation and various domain prediction algorithms to perform a domain enrichment analysis and produced a list of candidate genes for further investigation. We identified 51 significantly associated regions. In most of these, the susceptible allele was fixed in the tested commonly grown wheat varieties. Eleven of these were located on chromosomes for which no resistance conferring genes have been previously reported. In addition to enrichment of leucine-rich repeats (LRR), we saw enrichment of several domain types so far not reported as relevant to PM resistance, thus, indicating potentially novel candidate genes for the disease resistance research and prebreeding in wheat.

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

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.