PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data

Abstract

Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the carbon and energy cycles of the Earth system. However, existing trait maps remain limited by the high cost and sparse geographic coverage of field-based measurements. Citizen science initiatives offer a largely untapped resource to overcome these limitations, with over 50 million geotagged plant photographs worldwide capturing valuable visual information on plant morphology and physiology. In this study, we introduce PlantTraitNet, a multi-modal, multi-task uncertainty-aware deep learning framework that predicts four key plant traits (plant height, leaf area, specific leaf area, and nitrogen content) from citizen science photos using weak supervision. By aggregating individual trait predictions across space, we generate global maps of trait distributions. We validate these maps against independent vegetation survey data (sPlotOpen) and benchmark them against leading global trait products. Our results show that PlantTraitNet consistently outperforms existing trait maps across all evaluated traits, demonstrating that citizen science imagery, when integrated with computer vision and geospatial AI, enables not only scalable but also more accurate global trait mapping. This approach offers a powerful new pathway for ecological research and Earth system modeling.

Plant phenotyping relevance

植物画像から複数の形態・生理形質を推定する深層学習フレームワークを開発し、独立データで検証・既存手法と比較しており、表現型取得・推定手法が研究の中心である。

Code and data availability

The paper's first block explicitly advertises authors' public code (github.com/GeoSense-Freiburg/PlantTraitNet) and a machine-learning-ready trait dataset (huggingface.co/datasets/ayushi3536/PlantTraitNet), which would qualify as paper-specific public assets. However, neither URL appears in the allowed_urls list, and I

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