BuzzSet v1.0: A Dataset for Pollinator Detection in Field Conditions

• URL: http://arxiv.org/abs/2508.19762v4
• Date: Tue, 09 Sep 2025 14:02:24 GMT
• Title: BuzzSet v1.0: A Dataset for Pollinator Detection in Field Conditions
• Authors: Ahmed Emam, Mohamed Elbassiouny, Julius Miller, Patrick Donworth, Sabine Seidel, Ribana Roscher,
• Abstract summary: We present a large-scale dataset of high-resolution pollinator images collected under real field conditions.<n>BuzzSet contains 7,856 manually verified images with more than 8,000 annotated instances across three classes: honeybees, bumblebees, and unidentified insects.<n>The model achieves strong classification accuracy with F1 scores of 0.94 and 0.92 for honeybees and bumblebees, with minimal confusion between these categories.
• Score: 4.696111119794421
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Pollinator insects such as honeybees and bumblebees are vital to global food production and ecosystem stability, yet their populations are declining due to anthropogenic and environmental stressors. Scalable, automated monitoring in agricultural environments remains an open challenge due to the difficulty of detecting small, fast-moving, and often camouflaged insects. To address this, we present BuzzSet v1.0, a large-scale dataset of high-resolution pollinator images collected under real field conditions. BuzzSet contains 7,856 manually verified images with more than 8,000 annotated instances across three classes: honeybees, bumblebees, and unidentified insects. Initial annotations were produced using a YOLOv12 model trained on external data and refined through human verification with open-source tools. All images were preprocessed into 256 x 256 tiles to improve the detection of small insects. We provide baselines using the RF-DETR transformer-based object detector. The model achieves strong classification accuracy with F1 scores of 0.94 and 0.92 for honeybees and bumblebees, with minimal confusion between these categories. The unidentified class remains more difficult due to label ambiguity and fewer samples, yet still contributes insights for robustness evaluation. Overall detection performance (mAP at 0.50 of 0.559) illustrates the challenging nature of the dataset and its potential to drive advances in small object detection under realistic ecological conditions. Future work focuses on expanding the dataset to version 2.0 with additional annotations and evaluating further detection strategies. BuzzSet establishes a benchmark for ecological computer vision, with the primary challenge being reliable detection of insects frequently camouflaged within natural vegetation, highlighting an open problem for future research.

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