Cauliflower Plant and Curd Growth Traits from High-Throughput Phenotyping

RGB images of cauliflower from seedling to harvest were collected and processed using instance segmentation models. The dataset contains automatically extracted traits such as plant canopy width, leaf area, and curd diameter, with high consistency to manual measurements (R² > 0.90). It was created by Saichuan Cheng and last updated on 2026-05-13.

Use Cases

• Training instance segmentation models for plant and curd detection based on RGB imagery.
• Analyzing growth dynamics of leaf area and curd area using fitted Richards and Sine models.
• Classifying cauliflower germplasms into growth types via cluster analysis of high-throughput phenotyping data.
• Developing platforms for automated crop growth monitoring and phenotypic analysis.

Strengths

• Segmentation model achieved a mask mAP50 of 99.4% for plants and curds in sparse images.
• Automatically extracted traits showed high consistency with manual measurements (R² > 0.90).
• Analysis covers the entire growth period from seedling to harvest.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Collection Method

RGB images were collected and processed through systematic screening of instance segmentation models.

Time Range

Covers the entire growth period from seedling to harvest.

Freshness

Last updated 2026-05-13 05:56:28; freshness should be verified.