Biodiversity is declining due to the impact of human activities. However, public awareness of the biodiversity crisis is low, particularly for plants, creating a barrier to engage with conservation programs. In this perspective, we show how citizen science and mobile apps can be used as educational tools to raise awareness about plant biodiversity among students and the general public. We examine the outcomes of three Bachelor of Science activities as well as two informal education initiatives. We discuss the potential of these approaches as educational and outreach tools. Our results show that citizen science and mobile apps are excellent tools for engaging society in biodiversity conservation and environmental issues.

Citizen science data are increasingly used for ecological research, biodiversity conservation and monitoring. However, these data often present significant analytical challenges due to uneven recording efforts by citizen scientists. Biases caused by intra‐annual differences in levels of recording activity can be particularly severe, hindering the use of citizen science data in research areas such as population dynamics and phenology. Therefore, understanding the temporal patterns and drivers of recording activity by citizen scientists is essential. In this study, we provide a detailed assessment of how weather and calendar‐related factors relate to levels of biodiversity recording activity by citizen scientists at a daily resolution. To perform this, we analyse the recording patterns for six tree species in the Iberian Peninsula, which maintain a consistent appearance throughout the year. Observation data were collected from iNaturalist, a leading platform for citizen science data. We used boosted regression trees (BRT) to compare observed recording activity patterns with those expected by chance. Our analysis included a comprehensive set of explanatory variables, such as the day of the week, the month, holidays, temperature, accumulated precipitation, wind intensity and snow depth. The BRT models demonstrated good predictive performance, with the correlation between predicted and observed patterns of recording activity (left out of model training) ranging from 0.55 to 0.91, depending on the species. The day of the week, month of the year, and daily temperature consistently emerged as the most important predictors. Recording activity was higher on weekends, to some extent on Fridays and during the spring months. Extreme low and high temperatures were generally associated with lower recording activity, although there were exceptions. Precipitation and wind speed had relatively lower importance but remained relevant, with increased precipitation and wind intensity typically associated with reduced recording activity. In contrast, public holidays and accumulated snow demonstrated minimal to negligible importance. Our findings show that citizen scientists record more frequently on weekends, during mild weather and in spring. By addressing these non‐random patterns in recording activity, we can maximise the utility of citizen‐collected data for research and applied purposes. Read the free Plain Language Summary for this article on the Journal blog.