Authors
Erin L Grady, Ellen G Denny, Carrie E Seltzer, John Deck, Daijiang Li, Russell Dinnage, Robert P Guralnick
Published in
Applications in plant sciences. Volume 14. Issue 5. Pages e70067. Epub Jun 24, 2026.
Abstract
Plant phenology dictates many aspects of community function and ecosystem dynamics. Yet, global phenology data are still limited, especially in areas lacking monitoring programs. Here we present a new data resource, PhenoVision-Leaf, which extends a computer vision pipeline utilizing iNaturalist digital image vouchers to produce global-scale leaf phenophase data for deciduous woody genera.
We first discuss our implementation of a new human annotation framework for leaf phenology on iNaturalist, aligning with phenophase definitions used by the larger phenology community. We then showcase the use of 165,988 crowdsourced annotated records to train a Vision Transformer model with a two-stage regime to maximize precision across single- and multi-image records. This approach extends PhenoVision from scoring individual images to aggregating at the iNaturalist record level, better aligning with human annotation processes.
Post-hoc validation showed high performance for detecting present green and colored leaves (>98% precision) and reasonable precision for breaking leaf buds (>87% precision). Applying PhenoVision-Leaf to over six million iNaturalist records yielded 5.6 million record-level phenology observations across 6500 species and 57 families, filling geographic and taxonomic gaps.
These data, now accessible through the Phenobase web application, establish a foundation for near real-time monitoring of leaf phenology, supporting global-scale synthesis analyses.
PMID:
42964331
Bibliographic data and abstract were imported from PubMed on 31 Oct 2026.
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