Currently, it is possible to use software/tools to produce 3D point cloud for extracting phenotypic traits of the plant from a set of images. However, this process is time-consuming and requires additional knowledge of operations. Here we propose an affordable high-throughput system with consumer-level RGB camera and PC that can analyze large populations of container plants automatically. The first part of this pipeline is using Python code to control API of Agisoft Metashape Professional to produce 3D point cloud data with input of an image set or group of image sets. The point cloud is then passed to a Python package we wrote called "phenotypy" to get phenotypic traits such as plant height, leaf area, etc. The system is affordable because the hardware is a consumer-level RGB camera that can be handheld over container plants. Any PC can run the software and pipeline, though a dedicated GPU is recommended. The pipeline is high-throughput because many container plants can be quickly photographed with an RGB camera, and the pipeline can handle multiple image sets, so large populations of container plants can be efficiently analyzed. This is a scalable solution that can enable researchers to get detailed measurement data with reduced labor and capital costs. In this study, 217 container plants were photographed in under 3 hours and automatically generated point cloud for each image set by the pipeline. Each point cloud was then processed in Python to create a CSV containing phenotypic data. Supplemental data are also produced to aid in checking data quality. Preliminary results show r2 = 0.98 and slope = 0.93 with negligible intercept for height measurement against ground truth from laser scanner. In the future, we plan to expand this system also to support datasets obtained from UAV photos.