EasyDCP: 3D Plant Phenotypic Traits from Photogrammetry

A 2021 dataset from The University of Tokyo accompanies the EasyDCP Python package for 3D plant phenotyping. It likely contains 3D point clouds and derived trait measurements for container plants, generated using a do-it-yourself photogrammetry setup. The data was used to validate EasyDCP against a commercial laser scanner, showing strong correlations for plant height and projected leaf area.

Use Cases

• Measuring plant height from 3D reconstructions based on the described photogrammetry workflow.
• Calculating projected leaf area from point cloud data as validated in the performance test.
• Developing or benchmarking 3D phenotyping pipelines for container plants using the provided open-source tool and data.
• Studying the accuracy of low-cost photogrammetry versus commercial laser scanners for ecological trait measurement.

Strengths

• Validation against a commercial laser scanner showed strong correlation (r² = 0.96 for plant height and projected leaf area using EasyDCP).
• The associated tool is open-source and designed for high-throughput with low financial cost, as described.
• Includes detailed usage notes and a performance test dataset for comparison.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• The description suggests results from the photogrammetry software may vary slightly between runs.

Provenance

Source

The University of Tokyo, accompanying the published paper 'EasyDCP: an affordable, high-throughput tool to measure plant phenotypic traits in 3D'.

Collection Method

Likely generated using the described EasyDCP photogrammetry pipeline on images of container plants.

Time Range

2021