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 predictsfour 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.

翻译：全球尺度的植物性状（如叶片氮含量或植株高度）分布图对于理解生态系统过程（包括地球系统的碳和能量循环）至关重要。然而，现有的性状分布图仍受限于实地测量成本高昂和地理覆盖稀疏。公民科学倡议为克服这些限制提供了一个尚未被充分开发的资源，全球范围内超过5000万张带有地理标记的植物照片捕捉了关于植物形态和生理的宝贵视觉信息。在本研究中，我们提出了PlantTraitNet，这是一个多模态、多任务、不确定性感知的深度学习框架，它利用弱监督从公民科学照片中预测四种关键植物性状（植株高度、叶面积、比叶面积和氮含量）。通过在空间上聚合个体性状预测，我们生成了性状分布的全球地图。我们利用独立的植被调查数据（sPlotOpen）对这些地图进行了验证，并与领先的全球性状产品进行了基准比较。我们的结果表明，PlantTraitNet在所有评估的性状上均持续优于现有的性状分布图，这表明公民科学图像与计算机视觉和地理空间人工智能相结合，不仅能够实现可扩展的全球性状制图，而且能获得更高的精度。该方法为生态研究和地球系统建模提供了一条强大的新途径。