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A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction

文献类型： 外文期刊

作者： Wu, Sheng 1 ; Wen, Weiliang 1 ; Gou, Wenbo 2 ; Lu, Xianju 2 ; Zhang, Wenqi 2 ; Zheng, Chenxi 2 ; Xiang, Zhiwei 2 ; Chen, Liping 3 ; Guo, Xinyu 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing, Peoples R China

4.Jiangsu Univ, Coll Agr Engn, Zhenjiang, Peoples R China

关键词： MVS-Pheno; multi-view stereo reconstruction; three-dimensional point cloud; phenotyping platform; wheat

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Plant phenotyping is essential in plant breeding and management. High-throughput data acquisition and automatic phenotypes extraction are common concerns in plant phenotyping. Despite the development of phenotyping platforms and the realization of high-throughput three-dimensional (3D) data acquisition in tall plants, such as maize, handling small-size plants with complex structural features remains a challenge. This study developed a miniaturized shoot phenotyping platform MVS-Pheno V2 focusing on low plant shoots. The platform is an improvement of MVS-Pheno V1 and was developed based on multi-view stereo 3D reconstruction. It has the following four components: Hardware, wireless communication and control, data acquisition system, and data processing system. The hardware sets the rotation on top of the platform, separating plants to be static while rotating. A novel local network was established to realize wireless communication and control; thus, preventing cable twining. The data processing system was developed to calibrate point clouds and extract phenotypes, including plant height, leaf area, projected area, shoot volume, and compactness. This study used three cultivars of wheat shoots at four growth stages to test the performance of the platform. The mean absolute percentage error of point cloud calibration was 0.585%. The squared correlation coefficient R-2 was 0.9991, 0.9949, and 0.9693 for plant height, leaf length, and leaf width, respectively. The root mean squared error (RMSE) was 0.6996, 0.4531, and 0.1174 cm for plant height, leaf length, and leaf width. The MVS-Pheno V2 platform provides an alternative solution for high-throughput phenotyping of low individual plants and is especially suitable for shoot architecture-related plant breeding and management studies.

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