Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning.

ABSTRACT: The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding, agricultural production, and diverse research applications. Nevertheless, the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion. This drawback obstructed the accurate extraction of phenotypic parameters. Hence, this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques. The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network. The findings

SUBMITTER: Chen H 

PROVIDER: S-EPMC10795496 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature