LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens

Abstract

Premise Obtaining phenotypic data from herbarium specimens can provide important insights into plant evolution and ecology but requires significant manual effort and time. Here, we present LeafMachine, an application designed to autonomously measure leaves from digitized herbarium specimens or leaf images using an ensemble of machine learning algorithms. Methods and Results We trained LeafMachine on 2685 randomly sampled specimens from 138 herbaria and evaluated its performance on specimens spanning 20 diverse families and varying widely in resolution, quality, and layout. LeafMachine successfully extracted at least one leaf measurement from 82.0% and 60.8% of high‐ and low‐resolution images, respectively. Of the unmeasured specimens, only 0.9% and 2.1% of high‐ and low‐resolution images, respectively, were visually judged to have measurable leaves. Conclusions This flexible autonomous tool has the potential to vastly increase available trait information from herbarium specimens, and inform a multitude of evolutionary and ecological studies.

Keywords

• computer vision
• herbarium digitization
• LeafMachine
• leaf morphology
• machine learning