Abstract

Popping expansion is the main quality trait determining the commercial value of popcorn maize, yet its evaluation requires destructive grain sampling. We investigated whether multitemporal UAV multispectral and textural features could predict grain yield and popping expansion in a native population of Peruvian popcorn and its five purple-kernel corn hybrids grown in 16 drainage lysimeters (80 subplots) under controlled irrigation in Lima, Peru. Eight UAV flights were conducted between 50 and 117 days after sowing, and 8 vegetation indices plus 5 GLCM texture metrics were extracted from canopy-masked imagery. Six regression algorithms were trained using Sequential Forward Selection (SFS; applied to five of six algorithms) and validated by Leave-One-Lysimeter-Out cross-validation (LOGO). Early grain, grain filling, and maturity were the most informative stages for yield prediction. The best model, obtained at maturity, was SVR-rbf using SCCCI and Homogeneity, reaching R2 = 0.66 and RMSE = 1.23 t ha−1. SCCCI was the most consistently selected predictor across models. By contrast, popping expansion was poorly predicted (R2 = 0.17), indicating that this post-harvest quality trait is only weakly linked to canopy-level spectral information. Multitemporal UAV phenotyping therefore shows promise for non-destructive yield screening, but not for replacing direct popping expansion measurements.

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