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AgriEngineering
Volume 8
Issue 6
10.3390/agriengineering8060209
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Predicting Grain Yield and Popping Expansion in Native Peruvian Popcorn and Purple-Kernel Hybrids Using Multitemporal Unmanned Aerial Vehicle-Derived Multispectral and Textural Indices

by
Elias Huanuqueño-Coca
1,
José Huanuqueño-Murillo
2,
Roxana Peña-Amaro
2,
David Quispe-Tito
2,
Lena Cruz-Villacorta
3,
Indira Betalleluz-Pallardel
4,
Javier Quille-Mamani
5,* and
Lia Ramos-Fernández
2,*
1
Department of Fitotecnia, Universidad Nacional Agraria La Molina, Lima 15024, Peru
2
Department of Water Resources, Universidad Nacional Agraria La Molina, Lima 15024, Peru
3
Doctoral Program in Engineering and Environmental Sciences, Department of Territorial Planning, Universidad Nacional Agraria La Molina, Lima 15024, Peru
4
Department of Food Engineering and Agricultural Products, Universidad Nacional Agraria La Molina, Lima 15024, Peru
5
Geo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
*
Authors to whom correspondence should be addressed.
AgriEngineering 2026, 8(6), 209; https://doi.org/10.3390/agriengineering8060209
Submission received: 3 April 2026 / Revised: 17 May 2026 / Accepted: 26 May 2026 / Published: 27 May 2026
(This article belongs to the Special Issue The Application of Remote Sensing for Agricultural Monitoring)
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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.
Keywords: UAV; GLCM texture; machine learning; grain yield; popping expansion; purple maize; phenology UAV; GLCM texture; machine learning; grain yield; popping expansion; purple maize; phenology
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MDPI and ACS Style

Huanuqueño-Coca, E.; Huanuqueño-Murillo, J.; Peña-Amaro, R.; Quispe-Tito, D.; Cruz-Villacorta, L.; Betalleluz-Pallardel, I.; Quille-Mamani, J.; Ramos-Fernández, L. Predicting Grain Yield and Popping Expansion in Native Peruvian Popcorn and Purple-Kernel Hybrids Using Multitemporal Unmanned Aerial Vehicle-Derived Multispectral and Textural Indices. AgriEngineering 2026, 8, 209. https://doi.org/10.3390/agriengineering8060209

AMA Style

Huanuqueño-Coca E, Huanuqueño-Murillo J, Peña-Amaro R, Quispe-Tito D, Cruz-Villacorta L, Betalleluz-Pallardel I, Quille-Mamani J, Ramos-Fernández L. Predicting Grain Yield and Popping Expansion in Native Peruvian Popcorn and Purple-Kernel Hybrids Using Multitemporal Unmanned Aerial Vehicle-Derived Multispectral and Textural Indices. AgriEngineering. 2026; 8(6):209. https://doi.org/10.3390/agriengineering8060209

Chicago/Turabian Style

Huanuqueño-Coca, Elias, José Huanuqueño-Murillo, Roxana Peña-Amaro, David Quispe-Tito, Lena Cruz-Villacorta, Indira Betalleluz-Pallardel, Javier Quille-Mamani, and Lia Ramos-Fernández. 2026. "Predicting Grain Yield and Popping Expansion in Native Peruvian Popcorn and Purple-Kernel Hybrids Using Multitemporal Unmanned Aerial Vehicle-Derived Multispectral and Textural Indices" AgriEngineering 8, no. 6: 209. https://doi.org/10.3390/agriengineering8060209

APA Style

Huanuqueño-Coca, E., Huanuqueño-Murillo, J., Peña-Amaro, R., Quispe-Tito, D., Cruz-Villacorta, L., Betalleluz-Pallardel, I., Quille-Mamani, J., & Ramos-Fernández, L. (2026). Predicting Grain Yield and Popping Expansion in Native Peruvian Popcorn and Purple-Kernel Hybrids Using Multitemporal Unmanned Aerial Vehicle-Derived Multispectral and Textural Indices. AgriEngineering, 8(6), 209. https://doi.org/10.3390/agriengineering8060209

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