UAV-based digital phenotyping of European pear rust in genetic resources

Phenotyping of selected traits is an important prerequisite for evaluating genetic resources and providing resistant genotypes for subsequent fruit breeding. In order to establish a high-throughput method for more objective and accurate phenotyping in the future, the aim of our study was to develop a UVA-based digital phenotyping method in the field. European pear rust (Gymnosporangium sabinae) was selected as a model pathogen for this purpose because without pesticide application it is widely distributed in pear orchards and shows conspicuous yellow-orange disease symptoms. In 2021 and 2022, 705 images showing symptoms of European pear rust were taken in the experimental field of the Julius Kühn Institute in Dresden-Pillnitz and the symptoms were labeled using the Computer Vision Annotation Tool (CVAT). Model training was performed based on four pre-trained YOLOv5 algorithms that use an object detector approach and allow unique identification of each symptom in an image. Accurate localization of disease symptoms within the orchard was enabled by a novel photogrammetry approach on georeferenced image data. For subsequent quantification of disease symptoms per genotype, the number of infected leaves was related to the total volume of the tree. In the future, this digital phenotyping system will provide a high-throughput method for evaluating European pear rust in pear genetic resources.