William Weaver
Premise: Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time-consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant datasets.
Methods: LeafMachine2 was trained on 494,766 manually prepared and expert-reviewed annotations from 5597 herbarium images obtained from 288 institutions, representing 2663 species and employs object detection and segmentation algorithms to isolate individual leaves and petioles. Our landmarking network identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Archival processing algorithms prepare labels for optical character recognition and interpretation, while reproductive organs a
Results: LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types.
William Weaver