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.