A Novel Drought-Resistance Index Balancing Foxtail Millet Yield and Quality and Its Prediction Based on UAV Multimodal Data

Abstract

Drought stress severely limits foxtail millet yield and quality, yet current drought-resistance indices are exclusively yield-oriented and ignore grain-filling quality. Our two-year (2024–2025) experiments with 24–48 varieties revealed that yield and blighted grain rate (BGR) are partially decoupled (e.g., Zhangzagu 18: yield 2307 kg/ha, BGR 0.444; Zhonggu 19: yield 1622 kg/ha, BGR 0.280). We therefore constructed the Yield–Quality Synergy Index (YQSI = DYI − BGR), which penalizes varieties with poor grain filling. The YQSI tied for first place with DYI in comprehensive screening performance and achieved the highest inter-annual stability (Spearman ρ = 0.823, Jaccard = 0.438, composite score = 1.261). Sensitivity analysis confirmed robustness of the equal-weight formula across a 4-fold range of quality-penalty weights. Six strongly drought-resistant germplasms with balanced yield and quality were identified. Using UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening. Feature-importance analysis highlighted NDVI, WDRVI, and red-edge texture as key predictors. This study provides a quality-constrained drought-resistance evaluation framework and demonstrates the potential of UAV-based high-throughput phenotyping for foxtail millet breeding.

Plant phenotyping relevance

UAVのRGB・マルチスペクトル・熱赤外データから干ばつ耐性指標を予測する高スループット表現型解析手法が研究の中心であり、モデル性能も評価している。

Code and data availability

The supplied article blocks describe UAV multimodal phenotyping, a YQSI drought-resistance index, and a Random Forest prediction model, but contain no data availability statement, public dataset deposit, or author code/model release with a URL. No paper-specific public asset is identified.

No evidence-backed public reproduction asset is currently recorded.