German Centre for Integrative Biodiversity Research (iDiv)

Tools for rapid measurement of visitation networks

Pollination is critical to many ecosystem services, such as the maintenance of biodiversity and human nutrition. Addressing how plant-pollinator interactions change across environmental gradients requires quantifying pollinator visitation. However, we are limited by the available tools.

Ecologists typically observe pollinators in the field, record the species identity of the easily distinguished individuals, and collect with a net the remaining individuals so that they can be later identified to species in the lab. This is labor and time intensive, and thus prevents the massive data collection that is necessary to achieve a general, predictive understanding of how pollination responds to global change.

Therefore, we want to use machine learning methods and technology to automatize ecological data collection on plant-pollinator interactions.

A first step is to develop and test a camera system capable of capturing clear images of visiting insects on flowers. This can be as simple as using a phone or digital camera for time-lapse photography with enough battery supply, capturing images every 1-2 seconds. We will explore with such a tool (Fig .1) in the summer of 2021 with the goal of capturing insect images on flowers. The pollinators from the collected images will be identified by entomologists down to lowest possible taxonomic level (order > family > genus > species).

The second part of the project is about using the captured images and images from existing online databases (like iNaturalist and Observation) for training state of the art Convolutional Neural Networks (CNN) with the goal of insect identification down to the lowest possible taxonomic level (Fig. 2).

Finally, the developed CNN models together with a tailored camera system will be used for pollinator identification in field studies aimed at quantifying plant-pollinator networks. We also want to derive abundance data about each pollinator. We will focus on certain families from four orders of pollinators: Coleoptera, Diptera, Hymenoptera and Lepidoptera.

Our goal is not to develop an app, but a tool that can help us obtain insect identity and abundance data about plant-pollinator interactions.


How can you contribute?

If you have images of pollinators and are willing to share them with us, feel free to contact Valentin Ștefan at 

We also welcome your thoughts, ideas and collaboration.


Scientific investigators

Prof. Tiffany Knight 

MSc. Valentin Ștefan 



Steen, R. (2017). Diel activity, frequency and visit duration of pollinators in focal plants: in situ automatic camera monitoring and data processing. Methods in Ecology and Evolution, 8(2), 203-213.

Boussioux, L., Giro-Larraz, T., Guille-Escuret, C., Cherti, M., & Kégl, B. (2019). InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification. arXiv preprint arXiv:1906.11898.


August 2021

Our project PAI - Pollination artificial intelligence is one of the 17 funded projects selected by the Helmholtz AI.

This sets our collaboration with the team of Prof. Hannes Taubenböck, City and Society.

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