This dataset is a snapshot of our GitHub repository and other data which accompanies our published paper: Feldman, A., Wang, H.,Fukano, Y., Kato, Y., Ninomiya, S., and Guo, W. (2021). EasyDCP: an affordable, high-throughput tool to measure plant phenotypic traits in 3D. Methods in Ecology and Evolution. Abstract High-throughput 3D phenotyping is a rapidly emerging field that has widespread application for measurement of individual plants. Despite this, high-throughput plant phenotyping is rarely used in ecological studies due to financial and logistical limitations. We introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data. Here we give instructions for the imaging setup and the required hardware, which is minimal and do-it-yourself, and introduce the functionality and workflow of EasyDCP. We compared the performance of EasyDCP against a high-end commercial laser scanner for the acquisition of plant height and projected leaf area. Both tools had strong correlations with ground truth measurement, and plant height measurements were more accurate using EasyDCP (plant height: EasyDCP r2 = 0.96, Laser r2 = 0.86; projected leaf area: EasyDCP r2 = 0.96, Laser r2 = 0.96). EasyDCP is an open-source software tool to measure phenotypic traits of container plants with high throughput and low labor and financial costs. Usage Notes Download and extract "EasyDCP_Data.zip" anywhere on your pc, e.g.: D:\EasyDCP_Data\ Follow all steps in "EasyDCP-GitHub-repository-master\EasyDCP-master\docs\installation.md" to ensure EasyDCP is setup properly. To generate 3D point cloud (.ply) files using EasyDCP_Creation Navigate to "EasyDCP-GitHub-repository-master/EasyDCP-master/easydcp/creation/" Open "params.ini" in text editor and modify the root_folder variable (line 4) to match the location of the EasyDCP_Data folder. e.g., if you extracted to D:\EasyDCP_Data\, root_folder = D:\EasyDCP_Data\Performance_test\0_Image_acquisition\ Note that there are no quotes. Run "creation-win.bat" (Windows) or "creation-mac.sh" (Mac/Linux) and wait for completion. .ply and .pdf files will be outputted in each subfolder in "Performance test\0_Image_acquisition". You can compare these with the files in "1_EasyDCP_Creation\". Note they will not be identical because Agisoft Metashape generates slightly different results every time, but they should be very similar. To analyze the 3D point cloud (.ply) files using EasyDCP_Analysis: Navigate to "EasyDCP-GitHub-repository-master\EasyDCP-master\example\" Open "analysis.py" in text editor and modify the plot_path variable (line 19) to match the location of the EasyDCP_Data folder. e.g., if you extracted to D:\EasyDCP_Data\, plot_path = 'D:/EasyDCP_Data/Performance_test/1_EasyDCP_Creation/' Note that there are quotes and the path uses forward slashes. Using Anaconda, open a terminal and navigate to your EasyDCP folder. e.g. if you extracted the archive to "D:\EasyDCP\": d: cd EasyDCP_Data\EasyDCP-GitHub-repository-master\EasyDCP-master Execute the following line: python example\analysis.py See "EasyDCP-GitHub-repository-master\EasyDCP-master\docs\installation.md" for more details. output folders will be created in the "EasyDCP-master\" folder. One folder will be created for each input .ply file, and one folder starting with "data_out" can be found. These folders contain output data: the folders for each .ply contain individual segmented and classified point clouds, visualization .png of each plant and traits. The "data_out" folder contains .png visualizations of segmentation step and a .csv containing all individual plant phenotypic traits. You can check all these files against the files in "Performance test\2_EasyDCP_Analysis\" and expect them to be nearly identical. Check more details here: https://github.com/UTokyo-FieldPhenomics-Lab/EasyDCP

1. A folder called Performance_test, containing folders for: All three steps of the performance test (Image acquisition, EasyDCP_Creation, and EasyDCP_Analysis) and PlantEye reference data. a. 0_Image_acquisition - Source images b. 1_EasyDCP_Creation - Output .ply point cloud and .pdf reports c. 2_EasyDCP_Analysis - Output files (.ply, .png, .csv) d. PlantEye (reference data) - .ply point cloud and .csv files 2. R files used to create the graph figures in paper (.R and .csv), including ground truth data 3. Scale bar data files a. EasyDCP_Scalebar_data.zip – Data and python code used to generate Figure 4a. b. Scalebar_orthoimage.zip – Not used in the paper; the intermediary files used when creating scalebar_data 4. Snapshot of he GitHub repository at the time of writing the paper.