AUTHOR=Li Yuchao , Liu Jingyan , Zhang Bo , Wang Yonggang , Yao Jingfa , Zhang Xuejing , Fan Baojiang , Li Xudong , Hai Yan , Fan Xiaofei TITLE=Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences JOURNAL=Frontiers in Plant Science VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.974339 DOI=10.3389/fpls.2022.974339 ISSN=1664-462X 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 (R2) 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.