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Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat

文献类型： 外文期刊

作者： Li, Wenrui 1 ; Wu, Sheng 2 ; Wen, Weiliang 2 ; Lu, Xianju 2 ; Liu, Haishen 2 ; Zhang, Minggang 2 ; Xiao, Pengliang 2 ; Guo, Xinyu 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.Northwest A&F Univ, Coll Informat Engn, Xinong Rd, Xianyang 712100, Shaanxi, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China

关键词： 3D reconstruction; plant morphology; point cloud segmentation; Wheat

期刊名称：AOB PLANTS （ 影响因子：2.6； 五年影响因子：3.0 ）

ISSN： 2041-2851

年卷期： 2024 年 16 卷 2 期

页码：

收录情况： SCI

摘要： It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP50 is 0.665. The R2 values for extracted leaf length, leaf width, leaf attachment height, stem leaf angle, tiller length, and spike length were 0.97, 0.80, 1.00, 0.95, 0.99, and 0.95, respectively. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants, providing strong technical support for research in fields such as agricultural production optimization and genetic breeding. We propose a point cloud data-driven 3D wheat reconstruction method, which achieves plant-scale 3D structure reconstruction and wheat morphological parameterization. First, we used the MVS-Pheno platform to reconstruct the 3D point cloud of wheat and then performed instance segmentation of the wheat organ point cloud based on the deep learning method. On this basis, we automatically reconstructed the 3D structure of the leaves and tillers and extracted the wheat morphological parameters. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants.

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