Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data
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...
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000034 |
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| author | Virginia Maß Pendar Alirezazadeh Johannes Seidl-Schulz Matthias Leipnitz Eric Fritzsche Rasheed Ali Adam Ibraheem Martin Geyer Michael Pflanz Stefanie Reim |
| author_facet | Virginia Maß Pendar Alirezazadeh Johannes Seidl-Schulz Matthias Leipnitz Eric Fritzsche Rasheed Ali Adam Ibraheem Martin Geyer Michael Pflanz Stefanie Reim |
| author_sort | Virginia Maß |
| collection | DOAJ |
| description | 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. 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. |
| format | Article |
| id | doaj-art-66b20d8ffeb44d3db4665c4d33986043 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-66b20d8ffeb44d3db4665c4d339860432025-01-31T05:11:46ZengElsevierData in Brief2352-34092025-02-0158111271Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley DataVirginia Maß0Pendar Alirezazadeh1Johannes Seidl-Schulz2Matthias Leipnitz3Eric Fritzsche4Rasheed Ali Adam Ibraheem5Martin Geyer6Michael Pflanz7Stefanie Reim8Corresponding author.; Leibniz Institute for Agricultural Engineering and Bioeconomy1, Department Agromechatronics, Potsdam, GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy1, Department Agromechatronics, Potsdam, Germanygeo-konzept, Gesellschaft für Umweltplanungssyteme mbH, Adelschlag, Germanygeo-konzept, Gesellschaft für Umweltplanungssyteme mbH, Adelschlag, GermanyJulius Kühn-Institute, Federal Research Centre for Cultivated Plants2, Institute for Breeding Research on Fruit Crops, Dresden, GermanyJulius Kühn-Institute, Federal Research Centre for Cultivated Plants2, Institute for Breeding Research on Fruit Crops, Dresden, GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy1, Department Agromechatronics, Potsdam, GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy1, Department Agromechatronics, Potsdam, GermanyJulius Kühn-Institute, Federal Research Centre for Cultivated Plants2, Institute for Breeding Research on Fruit Crops, Dresden, GermanyThe 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. 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.http://www.sciencedirect.com/science/article/pii/S2352340925000034YoloGymnosporangium sabinaePhenotypingDrone monitoringMachine learningObject detection |
| spellingShingle | Virginia Maß Pendar Alirezazadeh Johannes Seidl-Schulz Matthias Leipnitz Eric Fritzsche Rasheed Ali Adam Ibraheem Martin Geyer Michael Pflanz Stefanie Reim Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data Data in Brief Yolo Gymnosporangium sabinae Phenotyping Drone monitoring Machine learning Object detection |
| title | Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data |
| title_full | Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data |
| title_fullStr | Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data |
| title_full_unstemmed | Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data |
| title_short | Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data |
| title_sort | annotated image dataset with different stages of european pear rust for uav based automated symptom detection in orchardsmendeley data |
| topic | Yolo Gymnosporangium sabinae Phenotyping Drone monitoring Machine learning Object detection |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000034 |
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