High-precision tobacco phenotype extraction based on 3D point clouds

Abstract

As an economically important crop, tobacco requires the precise extraction of phenotypic characterization data, which is crucial for breeding, cultivation practices, physiological research, and industrial applications. However, there is currently a lack of automated algorithms for extracting key basic phenotypic traits such as plant height, leaf number, leaf area, and stem-leaf angle. In this study, we developed a set of computational methods for fully automated extraction of these phenotypic features from 3D tobacco point cloud data. Specifically, our pipeline includes: (1) preprocessing the 3D point cloud data, involving operations such as downsampling, denoising, normal vector estimation, and coordinate transformation; (2) integrating a graph neural network with a region-growing algorithm to segment leaves, stems, and other organs, and refining the segmentation results to address the challenge of overlapping leaves; and (3) calculating fundamental phenotypic attributes including plant height, leaf count, leaf area, and stem-leaf angle based on the segmentation output. Additionally, to address potential gaps in the scanned point cloud, we implemented perforation detection and repair operations. The effectiveness and accuracy of the proposed algorithm were validated through mathematical model simulations. Distinct from traditional statistical discriminative methods, this approach provides a novel framework for the precise extraction of tobacco phenotypic data.

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