1. Utilizing Visible Band Vegetation Indices from Unmanned Aerial Vehicle Images for Maize Phenotyping
- Author
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Guilherme Gonçalves Coswosk, Vivane Mirian Lanhellas Gonçalves, Valter Jário de Lima, Guilherme Augusto Rodrigues de Souza, Antônio Teixeira do Amaral Junior, Messias Gonzaga Pereira, Evandro Chaves de Oliveira, Jhean Torres Leite, Samuel Henrique Kamphorst, Uéliton Alves de Oliveira, Jocarla Ambrosim Crevelari, Késia Dias dos Santos, Frederico César Ribeiro Marques, and Eliemar Campostrini
- Subjects
geoprocessing ,remote sensing ,photogrammetry ,high-throughput phenotyping ,applied statistics ,precision agriculture ,Science - Abstract
Recent advancements in high-throughput phenotyping have led to the use of drones with RGB sensors for evaluating plant traits. This study explored the relationships between vegetation indices (VIs) with grain yield and morphoagronomic and physiological traits in maize genotypes. Eight maize hybrids, including those from the UENF breeding program and commercial varieties, were evaluated using a randomized block design with four replications. VIs were obtained at various stages using drones and Pix4D Mapper 4.7.5 software. Analysis revealed significant differences in morphoagronomic traits and photosynthetic capacity. At 119 days after planting (DAP), the RGB vegetation index VARI showed a significant correlation (r = 0.99) with grain yield. VARI also correlated with female flowering (r = −0.87), plant height (r = −0.79), 100-grain weight (r = −0.77), and anthocyanin concentration (r = −0.86). PCA showed a clear separation between local and commercial hybrids, explaining 46.7% of variance at 91 DAP, 52.3% at 98 DAP, 64.2% at 112 DAP, and 66.1% at 119 DAP. This study highlights the utility of VIs in maize phenotyping and genotype selection during advanced reproductive stages.
- Published
- 2024
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