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. 2024 Aug 10;56:110826. doi: 10.1016/j.dib.2024.110826. eCollection 2024 Oct.
The monitoring of plant diseases in nurseries, breeding farms and orchards is essential for maintaining plant health. Fire blight () 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 , 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.
监测苗圃、养殖场和果园中的植物病害对于维持植物健康至关重要。火疫病（ ）仍是水果生产中最危险的病害之一，因为它会呈流行性传播并造成巨大的经济损失。因此，所有措施都旨在防止病原体在果园中传播，并在早期控制感染[1 - 6]。如果能够提高果园病害监测的空间和时间分辨率，那么数字监测系统的开发将有助于提高植物病害防治的效率[7]。在此背景下，针对果园开发了一种基于RGB图像的火疫病数字监测系统。在2021年至2024年期间，于九个不同日期、在不同天气条件下使用不同相机收集了数据。德国的数据来源地点包括位于多森海姆的尤利乌斯·库恩研究所（JKI）水果作物和葡萄栽培植物保护研究所的实验果园、位于奎德林堡的尤利乌斯·库恩抗逆性研究与胁迫耐受性研究所的实验温室，以及位于德累斯顿 - 皮尔尼采的JKI水果作物育种研究实验果园。RGB图像是在人工接种 后的不同苹果基因型上拍摄的，包括栽培品种、野生种和育种后代。所呈现的ERWIAM数据集包含手动标注的RGB图像，图像大小为1280×1280像素，涵盖了火疫病感染的嫩枝、花朵和处于不同发育阶段的叶片，以及无病症的背景图像。此外，还获取了其他植物病害的症状，并作为单独类别整合到ERWIAM数据集中。使用计算机视觉标注工具（CVAT [8]）通过两点标注（边界框）对每个火疫病症状进行标注，并以YOLO 1.1格式（.txt文件）呈现。该数据集总共包含1611张标注图像和87张背景图像。此数据集可作为研究人员和开发人员致力于植物病害监测数字系统的资源。
