Data from: High-throughput phenotyping for the prediction and quantification of flower-related traits in sugarcane

This dataset provides the comprehensive primary data and metadata
supporting the research article: "High-Throughput Phenotyping for the
Prediction and Quantification of Flower-Related Traits in Sugarcane."
It integrates traditional agronomic field assessments with digital metrics
derived from high-throughput phenotyping (HTP) using Unmanned Aerial
Vehicles (UAVs). The data is structured to facilitate the development and
validation of Machine Learning (ML) models for both classification and
regression tasks in plant breeding. The dataset includes: HTP
Features: Raw values for all vegetation indices (e.g., ExG) and structural
metrics (Canopy Cover, Plant Height, and Volume) extracted from RGB
orthomosaics across multiple time points; and Ground Truth Field
Data: Complete manual measurements of flower-related traits, including
Days to Flag Leaf (DTFL), Days to Flowering (DTF), and flowering
intensity.