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

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

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

2026
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Overview
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).