Wildflower Monitoring with Expert-annotated Images and Flowering Phenology

Understanding biodiversity trends is essential for preservation policy planning, and advanced computer vision solutions now enable large-scale automated monitoring for many biodiversity use cases. Wildflower monitoring, in particular, presents unique challenges.Visual similarities in shape and color may exist between different species, while flowers within a species may have significant visual differences. Moreover, flowers follow a growth cycle and look distinctly different over the year, while different species flower at different times of the year. Having access to flowering phenology, more accurate predictions may be made.We propose a novel multi-modal wildflower monitoring task to better identify species, levering both expert-annotated wildflower images and flowering phenology estimates.Moreover, we benchmark several state-of-the-art models using two groups of common wildflower species that have high inter-class similarity, and show that this multi-modal approach significantly outperforms image-only baselines. With this work, we aim to encourage the development of standards for automated wildflower monitoring as a step towards bending the curve of biodiversity loss.The data and the code are publicly available https://georgianagmanolache.github.io/wildflowerpower/