BEEHIVE: a public dataset of Apis mellifera images to empower honeybee monitoring research
Description
The BEEHIVE dataset has been created for Precision Agriculture, Measurement Science, and Entomology research specifically dealing with Apis mellifera (common honeybee) image analysis. The contents of the dataset include data acquired from two cameras located at different observation points: the "Frame" dataset, acquired with a camera placed inside a frame of the beehive and depicting very close-range images of the bees (potentially a few with Varroa destructor mites on their backs), and the "Bottom" dataset, acquired with a camera positioned at the bottom of the beehive. In this case, a metallic grid partially occludes the view. The two datasets are already subdivided into training, validation, and test sub-datasets following a 70%-20%-10% splitting protocol. The "Frame" dataset includes 1.440 training images, 411 validation images, and 206 test images. The "Bottom" dataset includes 1044 training images, 303 validation images, and 147 test images. Each dataset includes annotations obtained in RoboFlow for the task of object detection, considering two classes: "bee" and "blurred_bee" for the "Frame" dataset, "bee" and "occluded_bee" for the "Bottom" dataset. The data is valuable for the field of Precision Agriculture, Entomology, Measurement Science, and Computer Vision, especially for the tasks of bees' monitoring, counting, and detection of potential parasites by training image-based Deep Learning models. It is also useful as a reference dataset for benchmarking models. If you use this dataset for your work, please cite the related papers: - https://doi.org/10.3390/s24165270
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Funding
European Union, FSE-REACT-EU, PON "Research and Innovation 2014-2020", D.M. 1062/202
46-G-13219-3