Spatial Monitoring and Insect Behavioural Analysis Dataset

Insects are the most important global pollinator of crops and play a key role in maintaining the sustainability of natural ecosystems. Insect pollination monitoring and management are therefore essential for improving crop production and food security. Computer vision-facilitated pollinator monitoring can intensify data collection over what is feasible using manual approaches. We introduce a novel system to facilitate markerless data capture for insect counting, insect motion tracking, behaviour analysis and pollination prediction across large agricultural areas. Our system is comprised of edge computing multi-point video recording, offline automated multi-species insect counting, tracking and behavioural analysis. We implement and test our system on a commercial berry farm to demonstrate its capabilities. 

This dataset contains movement tracks for four insect varieties and flowers recorded across nine monitoring stations within polytunnels in a strawberry farm. Insect tracks include details on flower visits, recorded locations and sighted times. Insect varieties included in the dataset are honeybees (1805 tracks), Syrphidae (85 tracks), Lepidoptera (100 tracks) and Vespids (345 tracks). This upload also consists of software to analyse pollination and visualise insect trajectories. In addition, it contains an annotated dataset of images from the four classes for YOLOv4 object detection model training and testing and a dataset of ten videos used for the system evaluation.