PlantMove: A tool for quantifying motion fields of plant movements from point cloud time series

The mechanisms involved in organ motions are central to our understanding of how plants develop and respond to environmental stimuli such as light quality, gravity, and water availability throughout time. Recent studies have shown that motions in plants such as circadian rhythms and growth patterns, can be recorded and quantified from time series of terrestrial laser scans (TLS). However, most works monitored the changes of certain functional traits such as height and volume to detect and analyze structural dynamics. A generic method for retrieving fine-scale three-dimensional (3D) motion fields of plant structural movements is still missing. We present PlantMove, a new fully automatic tool to quantify 3D motion fields of plant structural movements with varied magnitudes using TLS point clouds acquired over different time periods. The method uses spatio-temporal point cloud registration embedded in a progressive and coarse-to-fine framework, enabling an efficient processing of large datasets with complex structures. PlantMove was first demonstrated on synthetic plant datasets, displaying millimeter to centimeter level accuracy of retrieved motion fields. In addition, PlantMove was used to assess circadian rhythms on a birch tree from TLS data acquired over the course of one night with about one-hour time intervals, and growth patterns on an English oak from a four-year TLS survey. PlantMove can help to better monitor plant phenotypic plasticity with fine level of details, and can contribute to improve our understanding in plant dynamics across various spatial and temporal scales.

Automated summary

This study presents PlantMove, a fully automatic tool for quantifying 3D motion fields of plant structural movements using TLS point clouds. The method demonstrates high accuracy and efficiency in handling large datasets with complex structures. It has been successfully applied to synthetic plant datasets as well as real TLS data to detect circadian rhythms and growth patterns in plants.