GYMNSA dataset

Annotated image dataset with different stages of European pear rust in orchards for UAV-based automatic symptom detection. The evaluation of fruit genetic resources regarding a resistance to pathogens is an essential basis for subsequent selection in fruit breeding. Both genetic analysis and phenotyping of defined traits are important tools and provide decision data in the evaluation process. However, the phenotyping of plants is often carried out "by hand" and remains the bottleneck in fruit breeding and fruit growing. The development of a digital and UAV (unmanned aerial vehicle)-based phenotyping method for the assessment of genotype-specific susceptibility or resistance against diseases in orchards would significantly increase the efficiency of plant breeding. In this framework, a workflow for drone-based monitoring of pathogens in orchards was developed using the European pear rust (Gymnosporangium sabinae) as model pathogen. We provide a dataset with expert-annotated high-resolution RGB images with pear rust symptoms. The UAV images present different pear genotypes, including varieties, wild species and progeny from breeding. The dataset contains manually labelled images with a size of 768 x 768 pixels of leaves infected with pear rust at different stages of development, labelled as class GYMNSA, as well as background images without symptoms. A total of 584 annotated images and 162 background images, organized into a training and validation set, are included in the GYMNSA dataset. This dataset can be used as a resource for researchers and developers working on drone-based plant disease monitoring systems.面向无人机自动病害症状检测的果园欧洲梨锈病不同发病阶段标注图像数据集。 对果树遗传资源开展抗病性评价，是果树育种后续选育工作的核心基础。针对特定性状开展遗传分析与表型鉴定，是评价流程中的关键技术手段，可为决策提供数据支撑。然而传统植物表型鉴定多采用人工方式，已成为果树育种与产业生产的瓶颈环节。开发基于数字化技术与无人机（Unmanned Aerial Vehicle, UAV）的表型鉴定方法，用于评价果园内不同基因型对病害的感病性或抗病性，可显著提升植物育种效率。 基于此研究框架，本团队以欧洲梨锈病（Gymnosporangium sabinae）为模式病原菌，开发了一套面向果园病原菌监测的无人机作业流程。本数据集包含经专家标注的高分辨率RGB病害图像，涵盖梨锈病症状样本。该数据集的无人机图像涵盖了多种梨基因型材料，包括栽培品种、野生种以及育种后代群体。数据集包含两类手动标注的768×768像素图像：一类为处于不同发病阶段的梨锈病感染叶片，标注类别为GYMNSA；另一类为无病害症状的背景图像。GYMNSA数据集共计包含584张标注图像与162张背景图像，并已划分为训练集与验证集。本数据集可作为从事无人机植物病害监测系统研发的科研人员与开发者的重要支撑资源。