Cherry Tree Disease Detection Dataset

Description

1. Introduction This cherry tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of cherry trees, including stress analysis and prediction. An orchard of cherry trees is considered in the area of Western Macedonia, where 577 cherry trees were recorded in a full crop season starting from Jul. 2021 to Jul. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos. Two agronomist experts annotated the dataset by identifying a stress, which in this case is a common disease in cherry trees known as Armillaria [1][2]. 2. Citation Please cite the following papers when using this dataset: C. Chaschatzis, C. Karaiskou, E. Mouratidis, E. Karagiannis, and P. Sarigiannidis, “Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning”, Drones, vol. 6, no. 1, 2022. P. Radoglou-Grammatikis, P. Sarigiannidis, T. Lagkas, & I. Moscholios, “A compilation of UAV applications for precision agriculture,” Computer Networks, vol. 172, no. 107148, 2020. A. Lytos, T. Lagkas, P. Sarigiannidis, M. Zervakis, & G. Livanos, “Towards smart farming: Systems, frameworks and exploitation of multiple sources,” Computer Networks, vol. 172, no. 107147, 2020. 3. Cherry tree mapping In this dataset, an orchard of cherry trees is considered in the area of Western Macedonia, where 577 cherry trees were recorded in a full crop season starting from Jul. 2021 to Jul. 2022. The tree mapping within the orchard is depicted in Fig. 1. (please refer to the ReadMe file), where each circle represents a cherry tree. Labels on the circles (green, red etc) will be elaborated in the following Sections. The five time periods, where the orchard was recorded are: 8th of Jul. 2021, 16th of Sep. 2021, 3rd of Nov. 2021, 26th of May 2022, and 13th of Jul. 2022, providing data to a full year of life cycle. 4. Dataset Modalities The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos. Two agronomist experts annotated the dataset by identifying a stress, which in this case is a common disease in cherry trees known as Armillaria [1][2]. In particular, the following modalities are featured in the dataset: Ground RGB images Ground multispectral images UAV/Aerial images (RGB, multispectral, and NDVI). These modalities represent the cherry tree cultivation in many levels. Each modality describes the same object (cherry tree) within the dataset, i.e., for each tree within. For example, Fig. 2 (please refer to the ReadMe file) show RGB images, Fig. 3 (please refer to the ReadMe file) illustrates multispectral images, and Fig. 4 (please refer to the ReadMe file) provides UAV images. All images show the same cherry trees under three (RGB, multispectral, and UAV) aspects. 5. Dataset Collection & Annotation This dataset was annotated by two agronomist experts in terms of disease stage (Armillaria). In particular, they annotated each cherry tree, one by one, in four levels of disease stage: Healthy: the cherry tree is completely healthy; Stage1: Armillaria is present in light form in the cherry tree; Stage2: Armillaria is present in advanced form; Stage3: the cherry tree is killed due to Armillaria. The annotation process was considered by each one of the underlying modalities (RGB, multispectral and UAV/aerial). 5.1 Image Collection The image collection is depicted in the following image (please refer to the ReadMe file) in terms of the three modalities (aerial / Unmanned Aerial Vehicle (UAV) images, ground RGB images/photos, and ground multispectral images/photos). 5.2 Dataset Overview The dataset overview is depicted in Table 1 (please refer to the ReadMe file). 6. Structure and Format 6.1 Dataset Structure The provided dataset has the following structure (please refer to the ReadMe file). 6.2 Guide to edit the *.tif files The Aerial/UAV images contain images obtained from the UAV camera in the .tif format. To open these images, you will need the QGIS or other relevant program, or load them by using the corresponding python libraries. Please follow the steps below: Open QGIS Locate the browser window in QGIS Navigate to the folder that contains the images and select all the images in the layer. Once you have selected the images, select Add Layer to Project, and the selected image will be added to your map. For accessing the Image data with the OpenCV python library the following code example is provided (please refer to the ReadMe file). 7. Acknowledgment This work was co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: Τ1EDK-04759). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 957406 (TERMINET). References [1] Devkota, P.; Iezzoni, A.; Gasic, K.; Reighard, G.; Hammerschmidt, R. Evaluation of the susceptibility of Prunus rootstock genotypes to Armillaria and Desarmillaria species. Eur. J. Plant Pathol. 2020, 158, 177–193. [2] Devkota, P.; Hammerschmidt, R. “The infection process of Armillaria mellea and Armillaria solidipes”. Physiol. Mol. Plant Pathol. 2020, 112, 101543.

Resources

Name Format Description Link
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7144071
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7144071
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7144071
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7144071
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7144071
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7144071
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7144071
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7144071

Tags

  • sensors
  • artificial-intelligence
  • iot
  • image-processing
  • precision-agriculture
  • machine-learning

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