The widespread decline in biodiversity and abundance of pollinator insects is expected to provoke cascading effects on food security and jeopardize ecosystem services crucial for many crops and wild plants. Pollinator monitoring is a crucial element in preventing further decline of pollinators, to which computer vision approaches can make essential contributions. To facilitate research in such approaches, we present a dataset for pollinator detection with accurate annotations. We develop the dataset with an iterative semi-automatic annotation approach, which leverages YOLO to assist with human annotation. We quantify the impact of multiple levels of errors in annotations on training and report the increase in mAP of 28.7% at the final iteration when compared to the manual annotations. Our dataset encompasses pollinator detection for honeybees and bumblebees across various flower treatments over multiple days. Our dataset facilitates the development of deep learning-based methods for automatic large-scale pollinator detection under various real-world field conditions, as well as adjacent computer vision tasks such as small object detection and label correction.
Chong et al. (Tue,) studied this question.