Deep learning and citizen science enable automated plant trait predictions from photographs.

Abstract

Plant functional traits ('traits') are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth's plant functional diversity.

Plant phenotyping relevance

市民科学画像とCNNを用いて植物機能形質を自動推定する計算手法を開発・評価しており、形質取得が研究の中心である。

Code and data availability

The paper's Data/Code availability statements provide public figshare deposits with the trained CNN ensemble models and global trait maps (10.6084/m9.figshare.13312040), raw data tables with image download links and mean trait values (10.6084/m9.figshare.14410379), the iNaturalist raw image dataset via GBIF (10.15468/3