Global declines in insect pollinators have triggered the need for standardised, scalable methods to monitor pollinator populations. Automated photomonitoring and machine-learning-based identification are emerging as promising non-lethal approaches for large-scale insect observation, but their effectiveness requires reliable and consistent monitoring designs. Artificial flowers offer a solution to limitations associated with natural flowers, including variability in floral availability, lack of standardisation, and wind-induced noise in visual data, yet their performance under real field conditions remains largely untested.

Here, we developed 3D-printed artificial flowers, using Malva multiflora (Malvaceae) as a model species, incorporating increasingly complex sensory cues and rewards. We evaluated their performance by comparing insect pollinator species richness and visitation rates in artificial and co-occurring natural flowers using visual observations across 16 field sites in Southern Spain. Although artificial flowers received four times fewer species and visits per flower than natural flowers, they nonetheless accounted for around 50% of all recorded species. Variation in sensory cues among artificial flower treatments had little influence on attractiveness. Importantly, pollinator richness and visitation rates on artificial and natural flowers were strongly positively correlated, indicating that artificial flowers can provide reliable relative indicators of pollinator diversity and activity. Smaller-bodied bee species and those with warmer climatic affinities were more likely to visit artificial flowers, suggesting trait-based differences in exploratory behaviour or floral discrimination.

Our findings demonstrate the potential of 3D-printed artificial flowers, showing that, while not direct substitutes for natural flowers, they can function as effective, reproducible, and scalable tools for standardised pollinator monitoring.