Measurement of Maize Leaf Phenotypic Parameters Based on 3D Point Cloud.

Abstract

Plant height (PH), leaf width (LW), and leaf angle (LA) are critical phenotypic parameters in maize that reliably indicate plant growth status, lodging resistance, and yield potential. While various lidar-based methods have been developed for acquiring these parameters, existing approaches face limitations, including low automation, prolonged measurement duration, and weak environmental interference resistance. This study proposes a novel estimation method for maize PH, LW, and LA based on point cloud projection. The methodology comprises four key stages. First, 3D point cloud data of maize plants are acquired during middle-late growth stages using lidar sensors. Second, a Gaussian mixture model (GMM) is employed for point cloud registration to enhance plant morphological features, resulting in spliced maize point clouds. Third, filtering techniques remove background noise and weeds, followed by a combined point cloud projection and Euclidean clustering approach for stem-leaf segmentation. Finally, PH is determined by calculating vertical distance from plant apex to base, LW is measured through linear fitting of leaf midveins with perpendicular line intersections on projected contours, and LA is derived from plant skeleton diagrams constructed via linear fitting to identify stem apex, stem-leaf junctions, and midrib points. Field validation demonstrated that the method achieves 99%, 86%, and 97% accuracy for PH, LW, and LA estimation, respectively, enabling rapid automated measurement during critical growth phases and providing an efficient solution for maize cultivation automation.

Plant phenotyping relevance

トウモロコシの草丈・葉幅・葉角という植物形質を、LiDAR点群から自動抽出・推定する新規手法を開発し、圃場で精度検証しているため、フェノタイピング手法が研究の中心です。

Code and data availability

The paper describes maize point cloud collection (VLP-16 lidar) and a MATLAB/C++ analysis pipeline, but the Data Availability Statement only says data are contained within the article; no public dataset, code repository, or model deposit is provided. All URLs in the reference list are cited prior works, not paper-asset

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