Leveraging Citizen Science and Machine Learning for Plant Phenology Monitoring

Plant phenology investigates the timing of critical events in a plant's life cycle, encompassing budburst, flowering, fruiting, and senescence, with their significance rooted in their responsiveness to environmental conditions. Despite the growing interest in phenology, challenges persist in documenting these processes due to their extensive spatial and temporal scales.

While global phenological networks traditionally collect data at the individual scale, a concern is arising regarding the declining number of phenological observers, prompting questions about the future of these datasets. Simultaneously, the surge in plant identification apps among citizens has yielded a substantial volume of plant occurrence records, accompanied by plant images, spanning diverse temporal and spatial scales.

In this study, we explore the viability of utilizing opportunistically captured plant observations gathered through a plant identification app to determine the onset of flowering. Additionally, we investigate how citizen science-based phenological monitoring can be enhanced by incorporating images generated by the app. To achieve this, we developed a machine learning-based workflow enabling the automatic annotation of thousands of images into specific phenological stages. Beyond examining the onset of flowering, our established methodology allows for the exploration of other phenological stages, such as budburst or fruiting, on a large scale.

Subsequently, we compare these opportunistic phenological records with systematically collected data from phenological networks. This approach not only streamlines image annotation but also augments the usefulness of citizen science data for phenological monitoring purposes.