BEEHIVE: a public dataset of Apis mellifera images to empower honeybee monitoring research
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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
BEEHIVE数据集专为精准农业、计量科学及昆虫学研究构建，聚焦西方蜜蜂（Apis mellifera，俗称普通蜜蜂）的图像分析相关研究。该数据集包含由两个不同观测点的相机采集的多组数据：其一为“Frame”数据集，由安装在蜂箱巢框内的相机采集，拍摄蜜蜂的近距离特写图像（部分个体背部可能携带狄斯瓦螨（Varroa destructor））；其二为“Bottom”数据集，由安装在蜂箱底部的相机采集，此时金属网格会部分遮挡拍摄视野。 两个数据集均按照70%-20%-10%的划分协议，被划分为训练集、验证集与测试子集。其中“Frame”数据集包含1440张训练图像、411张验证图像及206张测试图像；“Bottom”数据集包含1044张训练图像、303张验证图像及147张测试图像。两类数据集均附带在RoboFlow平台上完成标注的目标检测任务标注集，标注类别存在差异：“Frame”数据集的标注类别为“蜜蜂”与“模糊蜜蜂”；“Bottom”数据集的标注类别为“蜜蜂”与“遮挡蜜蜂”。 该数据集可应用于精准农业、昆虫学、计量科学与计算机视觉领域，尤其适用于通过训练基于图像的深度学习模型，实现蜜蜂监测、计数及潜在寄生虫检测等任务，同时也可作为基准测试模型的参考数据集。 若您的研究工作使用该数据集，请引用以下相关论文：https://doi.org/10.3390/s24165270
