UAV Remote Sensing for Drought-Adaptive Sesame Breeding: Flight-Altitude Benchmarking, Predictive Modelling, and Composite Stress Tolerance Indexing

Abstract

Early-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments. A DJI Phantom 4 Multispectral UAV was flown at 40, 80, and 120 m above ground level (AGL) over 588 M2 genotypes under full irrigation (ENV1) and terminal drought (ENV2; irrigation withheld from reproductive onset) on four dates (July–September 2025). Structure-from-motion canopy height models were compared with ground measurements, and four spectral reflectance indices—Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), and Leaf Chlorophyll Index (LCI)—were derived from 40 m imagery. Ordinary least squares (OLS), Random Forest, and Gradient Boosting were evaluated under leave-one-genotype-out (LOGO), leave-one-environment-out (LOEO), and leave-one-date-out (LODO) cross-validation; genotypic repeatability was quantified by intraclass correlation (ICC), and drought performance was ranked by a composite Stress Tolerance Index (STI) validated against an independent breeder assessment. The 40 m altitude gave the highest height accuracy (R2 = 0.812 in ENV1; 0.663 in ENV2). LOGO accuracy (R2 ≈ 0.83) fell to R2 ≈ 0.55 under LODO—the operationally relevant figure for a new phenological stage—and the full structural–spectral OLS model collapsed (R2 = −0.203) where tree ensembles remained stable. Spectral-index repeatability was up to ~2-fold higher under stress (ICC(3,4) > 0.84). The composite STI flagged 38 elite genotypes (7.6% of 498); 10 of its top 30 were confirmed in the breeder’s 48-best selection from all 588 rows—a 4.1-fold enrichment over chance (hypergeometric p = 4.5 × 10−5).

Plant phenotyping relevance

UAV画像から草冠高・スペクトル形質を抽出し、飛行高度、予測モデル、再現性、交差検証を体系的にベンチマークしているため、植物表現型取得法が中心である。

Code and data availability

The supplied blocks describe UAV multispectral phenotyping of sesame M2 rows, canopy height extraction, SRI computation, predictive modelling, and composite STI ranking, but contain no public phenotype/trait dataset, imagery deposit, author analysis code, or trained model with an availability statement or URL. Analyses

No evidence-backed public reproduction asset is currently recorded.