Soybean UAV Phenotyping and Yield Data for Drought Tolerance Evaluation, 2023-2025

85 soybean genotypes from multiple U.S. breeding programs were evaluated under irrigated and rainfed conditions from 2023 to 2025. Unmanned Aerial Vehicle (UAV)-based multispectral imagery captured vegetation indices at the R4/R5 growth stage, which showed significant correlations with yield under rainfed conditions. The dataset, authored by Popat Shivaji Pawar and shared under a CC-BY-4.0 license, compares these quantitative UAV metrics against traditional visual canopy wilting scores.

Use Cases

• Compare UAV-derived vegetation indices against visual wilting scores for drought stress assessment.
• Train machine learning models to predict soybean grain yield based on multispectral canopy data.
• Identify drought-tolerant soybean genotypes using unsupervised clustering of UAV phenotyping data.
• Validate the relationship between specific vegetation indices (e.g., NDVI, GNDVI) and yield retention under rainfed conditions.

Strengths

• Includes data from 85 soybean genotypes evaluated over three consecutive years (2023-2025).
• UAV-derived indices showed significant correlations with yield (r = 0.22 to 0.45, p<0.05) under rainfed conditions.
• Clustering based on UAV data revealed consistent yield differences between groups across all three seasons.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data collection is geographically limited to a single location in Stuttgart, Arkansas.

Provenance

Source

figshare

Collection Method

UAV-based high-throughput phenotyping and visual scoring of soybean genotypes under field conditions.

Time Range

2023 to 2025

Freshness

Last updated 2026-06-02 05:40:33; freshness should be verified.

Geography

Stuttgart, Arkansas, United States