At the IEEE IT2026 conference in Žabljak, researchers from the University of Donja Gorica presented PollenTrace, an innovative project combining Artificial Intelligence and High Performance Computing (HPC) to enhance honey authenticity verification. Traditional pollen analysis (melissopalynology), while reliable, is time-consuming and dependent on expert knowledge. PollenTrace addresses this limitation by developing a large-scale microscopy dataset and an AI-driven detection pipeline capable of automatically identifying pollen grains in honey samples.
The project is building a dataset of over 33,000 high-resolution microscopy images derived from more than 1,100 biological samples collected across Montenegro, enabling the development of robust and scalable AI models. As a proof of concept, a deep learning model based on YOLOv11 was trained on annotated microscopy images, achieving 84% precision and 88% recall, demonstrating strong potential for automated pollen detection and future large-scale deployment.
HPC resources played a key role in enabling efficient model training and handling of high-resolution image datasets, highlighting the importance of national HPC infrastructure—such as that provided through NCC Montenegro -in supporting advanced AI applications in agri-food systems. This is also cross-project collaboration.
PollenTrace represents a step forward toward digital, scalable, and reproducible food authenticity verification, with strong potential to support laboratories, regulatory bodies, and industry in ensuring product quality and consumer trust. PollenTrace is supported as a PoC project by the Innovation Fund of Montenegro.