Development of a Distance-Adaptive Gaussian Fitting Method for Scheimpflug LiDAR-Based Plant Phenotyping

Abstract

Lidar has emerged as a pivotal technique within the booming field of plant phenotyping, which has seen significant advancements in recent years. Beyond the conventional LiDAR systems that determine distance based on time-of-flight principles, Scheimpflug LiDAR, an emerging technique proposed within the past decade, has also expanded its field to plant phenotyping. However, early applications of Scheimpflug LiDAR were predominantly focused on aerosol detection, where stringent requirements for range resolution were not paramount. In this paper, a detailed description of a Scheimpflug LiDAR designed for plant phenotyping is proposed. Furthermore, to ensure high-precision scanning of plant targets, a distance-adaptive Gaussian fitting methodology is proposed to improve the spatial precision from 0.1781 m to 0.044 m at 10 m, compared with the traditional maximum method. The results indicate that the point cloud data acquired through our method yield more precise phenotyping outcomes, such as diameter at breast height (DBH) and plant height. This paves the way for further application of the Scheimpflug LiDAR on growth stages monitoring and precision agriculture.

Code and data availability

The paper's SLiDAR point cloud data and phenotyping measurements (tea plant and rhododendron scans) are not publicly deposited; the Data Availability Statement says data are available only upon request, and no author code or dataset URL is provided.

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