Annotated image dataset of fire blight symptoms for object detection in orchards

The monitoring of plant diseases in nurseries, breeding farms and orchards is essential for maintaining plant health. Fire blight (<i>Erwinia amylovora</i>) is still one of the most dangerous diseases in fruit production, as it can spread epidemically and cause enormous economic damage. All measures are therefore aimed at preventing the spread of the pathogen in the orchard and containing an infection at an early stage [1-6]. Efficiency in plant disease control benefits from the development of a digital monitoring system if the spatial and temporal resolution of disease monitoring in orchards can be increased [7]. In this context, a digital disease monitoring system for fire blight based on RGB images was developed for orchards. Between 2021 and 2024, data was collected on nine dates under different weather conditions and with different cameras. The data source locations in Germany were the experimental orchard of the Julius Kühn Institute (JKI), Institute of Plant Protection in Fruit Crops and Viticulture in Dossenheim, the experimental greenhouse of the Julius Kühn Institute for Resistance Research and Stress Tolerance in Quedlinburg and the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden-Pillnitz. The RGB images were taken on different apple genotypes after artificial inoculation with <i>Erwinia amylovora</i>, including cultivars, wild species and progeny from breeding. The presented ERWIAM dataset contains manually labelled RGB images with a size of 1280 × 1280 pixels of fire blight infected shoots, flowers and leaves in different stages of development as well as background images without symptoms. In addition, symptoms of other plant diseases were acquired and integrated into the ERWIAM dataset as a separate class. Each fire blight symptom was annotated with the Computer Vision Annotation Tool (CVAT [8]) using 2-point annotations (bounding boxes) and presented in YOLO 1.1 format (.txt files). The dataset contains a total of 1611 annotated images and 87 background images. This dataset can be used as a resource for researchers and developers working on digital systems for plant disease monitoring.