Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences

Abstract

As an important method for crop phenotype quantification, three-dimensional (3D) reconstruction is of critical importance for exploring the phenotypic characteristics of crops. In this study, maize seedlings were subjected to 3D reconstruction based on the imaging technology, and their phenotypic characters were analyzed. In the first stage, a multi-view image sequence was acquired via an RGB camera and video frame extraction method, followed by 3D reconstruction of maize based on structure from motion algorithm. Next, the original point cloud data of maize were preprocessed through Euclidean clustering algorithm, color filtering algorithm and point cloud voxel filtering algorithm to obtain a point cloud model of maize. In the second stage, the phenotypic parameters in the development process of maize seedlings were analyzed, and the maize plant height, leaf length, relative leaf area and leaf width measured through point cloud were compared with the corresponding manually measured values, and the two were highly correlated, with the coefficient of determination ( R 2 ) of 0.991, 0.989, 0.926 and 0.963, respectively. In addition, the errors generated between the two were also analyzed, and results reflected that the proposed method was capable of rapid, accurate and nondestructive extraction. In the third stage, maize stem leaves were segmented and identified through the region growing segmentation algorithm, and the expected segmentation effect was achieved. In general, the proposed method could accurately construct the 3D morphology of maize plants, segment maize leaves, and nondestructively and accurately extract the phenotypic parameters of maize plants, thus providing a data support for the research on maize phenotypes.

Plant phenotyping relevance

多視点画像からの3D再構成、点群処理、葉分割を開発し、草丈・葉長・葉面積・葉幅を手動測定と比較検証しており、植物表現型取得法が研究の中心である。

Code and data availability

The paper describes maize seedling multi-view image sequences, point clouds, and phenotype measurements, but provides no public repository, code, or dataset deposit. The Data Availability Statement only says raw data will be made available by the authors on request, so any qualifying asset requires contacting the.

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