Breeding apple cultivars with durable genetic resistance is a potential long-term solution to fire blight, a devastating bacterial disease caused by Erwinia amylovora. However, phenotyping resistance/susceptibility to fire blight is challenging due to E. amylovora strain virulence, differential host × strain interactions, quantitative host resistance, environmental influences on disease, and impacts of tree vigor on susceptibility. Inheritance of resistance/susceptibility to fire blight is complex and phenotypic information alone is insufficient to guide breeding decisions targeting resistance. Several quantitative trait loci (QTLs) associated with resistance/susceptibility to fire blight have been detected throughout the apple genome. Most resistance alleles at fire blight QTLs have been identified in wild Malus germplasm with poor fruit quality, which limits their breeding utility. Several QTLs have been identified in populations derived from cultivars and reduced-susceptibility alleles have been characterized in multiple important breeding parents. Although resistance to fire blight is an attractive target for DNA-informed breeding, relatively few trait-predictive DNA tests for breeding relevant fire blight QTLs are available. Here we discuss (1) considerations and challenges associated with phenotyping resistance/susceptibility to fire blight; (2) sources of resistance that have been identified for use as parents; and (3) our perspective on short and long-term strategies to breed apple cultivars with durable resistance to fire blight with emphasis on the potential contributions of reduced susceptibility alleles to achieve this goal.

Over the decades in the US, the introduction of rootstocks with precocity, stress tolerance, and dwarfing has increased significantly to improve the advancement in modern orchard systems for high production of tree fruits. In pear, it is difficult to establish modern high-density orchard systems due to the lack of appropriate vigor-controlling rootstocks. The measurement of traits using unmanned aerial vehicle (UAV) sensing techniques can help in identifying rootstocks suitable for higher-density plantings. The overall goal of this study is to optimize UAV flight parameters (sensor angles and direction) and preprocessing approaches to identify ideal flying parameters for data extraction and achieving maximum accuracy. In this study, five UAV missions were conducted to acquire high-resolution RGB imagery at different sensor inclination angles (90°, 65°, and 45°) and directions (forward and backward) from the pear rootstock breeding plot located at a research orchard belonging to the Washington State University (WSU) Tree Fruit Research and Extension Center in Wenatchee, WA, USA. The study evaluated the tree height and canopy volume extracted from four different integrated datasets and validated the accuracy with the ground reference data (n = 504). The results indicated that the 3D point cloud precisely measured the traits (0.89 < r < 0.92) compared to 2D datasets (0.51 < r < 0.75), especially with 95th percentile height measure. The integration of data acquired at different angles could be used to estimate the tree height and canopy volume. The integration of sensor angles during UAV flight is therefore critical for improving the accuracy of extracting architecture to account for varying tree characteristics and orchard settings and may be useful to further precision orchard management.