Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data.

ABSTRACT: Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based match

SUBMITTER: Rodriguez-Sanchez J 

PROVIDER: S-EPMC11325728 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature