Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from UAV in the Coast of Peru

• Autores: Saravia Navarro, David, Salazar Coronel, Wilian, Valqui Valqui, Lamberto, Quille Mamani, Javier Alvaro, Porras Jorge, Zenaida Rossana, Corredor Arizapana, Flor Anita, Barboza Castillo, Elgar, Vásquez Pérez, Héctor Vladimir, Casas Diaz, Andrés V., Arbizu Berrocal, Carlos Irvin
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2200
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2200; https://doi.org/10.3390/agronomy12112630
• Nivel de acceso: acceso abierto
• Materia: Vegetation indices; Precision farming; Hybrid; Phenotyping; Remote sensing; https://purl.org/pe-repo/ocde/ford#4.01.06; Precision agricultura; Zea mays; Agricultura de precisión; Fenotipado; Teledetección

Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability for the farmer’s economy. In this study, we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using vegetation indices (VIs). A total of 10 VIs (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. Highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA showed clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.