Expanding phenological insights: automated phenostage annotation with community science plant images.

ABSTRACT: Plant phenology plays a pivotal role in understanding the interactions between plants and their environment. Despite increasing interest in plant phenology research, documenting their spatial and temporal variability at large spatial scales remains a challenge for many species and a variety of phenostages. The use of plant identification apps results in a vast repository of plant occurrence records spanning large spatial and temporal scales. As these observations are usually accompanied by images, they could potentially be a rich source of fine-grained large scale phenological information. However, manually annotating phenological stages is time intensive, necessitating efficient automated approaches. In this study, we developed a machine learning-based workflow to automatically classify p

SUBMITTER: Katal N 

PROVIDER: S-EPMC12479636 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature