Maß Virginia, Alirezazadeh Pendar, Seidl-Schulz Johannes, Leipnitz Matthias, Fritzsche Eric, Ibraheem Rasheed Ali Adam, Geyer Martin, Pflanz Michael, Reim Stefanie
Leibniz Institute for Agricultural Engineering and Bioeconomy, Department Agromechatronics, Potsdam, Germany.
geo-konzept, Gesellschaft für Umweltplanungssyteme mbH, Adelschlag, Germany.
Data Brief. 2025 Jan 3;58:111271. doi: 10.1016/j.dib.2025.111271. eCollection 2025 Feb.
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 () as model pathogen. Pear rust is widespread in orchards and causes conspicuous, clearly visible, yellow to orange-colored disease symptoms. In this paper, we provide a dataset with expert-annotated high-resolution RGB images with pear rust symptoms. For data collection, ten UAV-flight campaigns were realized between 2021 and 2023 under various weather conditions and with different flight parameters in the experimental orchard of the Julius Kühn-Institute for Breeding Research on Fruit Crops in Dresden-Pillnitz (Germany). 1394 images were captured of different pear genotypes, including varieties, wild species and progeny from breeding. The dataset contains manually labelled images with a size of 768 × 768 pixels of leaves infected with pear rust at different stages of development, labelled as class GYMNSA, as well as background images without symptoms. Each leaf with pear rust symptoms was annotated with the drawing method by two points (bounding boxes) using the Computer Vision Annotation Tool (CVAT, v1.1.0) [1] and presented in YOLO 1.1 file format (.txt files). A total of 584 annotated images and 162 background images, organized into a training and validation set, are included in the GYMNSA dataset. This GYMNSA dataset can be used as a resource for researchers and developers working on drone-based plant disease monitoring systems.
对水果遗传资源的病原体抗性进行评估是水果育种后续选择的重要基础。对特定性状进行遗传分析和表型分析都是重要工具，并在评估过程中提供决策数据。然而，植物的表型分析通常是“手工”进行的，仍然是水果育种和水果种植中的瓶颈。开发一种基于数字和无人机（无人驾驶飞机）的表型分析方法，用于评估果园中特定基因型对疾病的易感性或抗性，将显著提高植物育种的效率。在此框架下，以欧洲梨锈病（）为模型病原体，开发了一种基于无人机监测果园病原体的工作流程。梨锈病在果园中广泛存在，会导致明显可见的黄色至橙色病害症状。在本文中，我们提供了一个数据集，其中包含带有梨锈病症状的专家注释高分辨率RGB图像。为了收集数据，2021年至2023年期间，在德国德累斯顿-皮尔尼采的尤利乌斯·库恩水果作物育种研究所的实验果园中，在各种天气条件下并采用不同飞行参数进行了十次无人机飞行作业。拍摄了1394张不同梨基因型的图像，包括品种、野生种和育种后代。该数据集包含手动标注的图像，图像大小为768×768像素，为处于不同发育阶段感染梨锈病的叶片，标注为GYMNSA类别，以及无病害症状的背景图像。每张带有梨锈病症状的叶片都使用计算机视觉标注工具（CVAT，v1.1.0）[1]通过两点（边界框）绘图方法进行标注，并以YOLO 1.1文件格式（.txt文件）呈现。GYMNSA数据集中总共包含584张标注图像和162张背景图像，它们被组织成一个训练集和一个验证集。这个GYMNSA数据集可作为研究人员和开发人员开发基于无人机的植物病害监测系统的资源。
