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

• Published in: Frontiers in Plant Science
• Main Authors: Sheng Wu, Weiliang Wen, Wenbo Gou, Xianju Lu, Wenqi Zhang, Chenxi Zheng, Zhiwei Xiang, Liping Chen, Xinyu Guo
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2022-08-01
• Subjects: MVS-Pheno; multi-view stereo reconstruction; three-dimensional point cloud; phenotyping platform; wheat
• Online Access: https://www.frontiersin.org/articles/10.3389/fpls.2022.897746/full

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 R2 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.