Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object Recognition

TL;DR

This paper develops a computer vision system using YOLO models to detect and monitor bees in images, aiding pollinator conservation efforts amid climate change and food security concerns.

Contribution

It introduces a new bee dataset and compares YOLOv5 models for accuracy and real-time detection, integrating explainable AI for stakeholder use.

Findings

YOLOv5m achieves highest recognition accuracy.

YOLOv5s offers optimal real-time detection with 5.1ms per frame.

The system provides timestamped reports and visualizations for non-technical users.

Abstract

In an era of rapid climate change and its adverse effects on food production, technological intervention to monitor pollinator conservation is of paramount importance for environmental monitoring and conservation for global food security. The survival of the human species depends on the conservation of pollinators. This article explores the use of Computer Vision and Object Recognition to autonomously track and report bee behaviour from images. A novel dataset of 9664 images containing bees is extracted from video streams and annotated with bounding boxes. With training, validation and testing sets (6722, 1915, and 997 images, respectively), the results of the COCO-based YOLO model fine-tuning approaches show that YOLOv5m is the most effective approach in terms of recognition accuracy. However, YOLOv5s was shown to be the most optimal for real-time bee detection with an average processing and inference time of 5.1ms per video frame at the cost of slightly lower ability. The trained model is then packaged within an explainable AI interface, which converts detection events into timestamped reports and charts, with the aim of facilitating use by non-technical users such as expert stakeholders from the apiculture industry towards informing responsible consumption and production.