UAV Phenotyping Data for Megathyrsus maximus Yield and Height Prediction

A study by Guilherme Francio Niederauer, published on figshare in April 2026, optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population. It examines how ground sampling distance, environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height, applying machine learning and mixed model analyses.

Use Cases

• Predicting green and dry matter yield based on pixel count and Haralick's entropy features.
• Estimating canopy height using machine learning models trained on UAV-derived digital traits.
• Evaluating genotype selection efficiency by comparing rankings from digital traits with conventional yield measurements.
• Assessing the heritability and environmental susceptibility of various vegetative indices and canopy height.

Strengths

• Demonstrates strong predictive power for yield with machine learning models achieving correlations greater than 0.80.
• Reports high broad-sense heritability (0.7 to 0.87) for key yield traits, pixel count, and entropy.
• Identifies an optimal ground sampling distance range of 0.5–1.0 cm for UAV image acquisition.
• Shows 80% correspondence between top genotypes ranked by pixel count and conventional dry matter yield.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is contained within a 16.8 MB PDF file, which may require extraction of underlying data.

Provenance

Source

figshare

Collection Method

UAV-based image acquisition and analysis of a Megathyrsus maximus biparental population.

Time Range

Harvest dates were examined, but specific temporal coverage is not provided.

Freshness

Last updated 2026-04-22 05:46:04; freshness should be verified.

Geography

Environment 2 is mentioned, but specific geographic location is not provided.