Título:	Unmanned aircraft for monitoring elephant grass genotypes in energy biomass production.
Autor:	ANDRADE, R. G. HOTT, M. C. MAGALHAES JUNIOR, W. C. P. de MACHADO, J. C. BORGES, C. A. V.
Afiliación:	RICARDO GUIMARAES ANDRADE, CNPGL; MARCOS CICARINI HOTT, CNPGL; WALTER COELHO P DE MAGALHAES JUNIOR, CNPGL; JUAREZ CAMPOLINA MACHADO, CNPGL; CRISTIANO AMANCIO VIEIRA BORGES, CNPGL.
Año:	2022
Referencia:	International Journal of Advanced Engineering Research and Science, v. 9, n. 1, p. 70-76, 2022.
Descripción:	Elephant grass is a promising plant for economic and sustainable energy production. However, adapted cultivars and efficient strategies for selecting genotypes aimed at energy biomass production is essential. Remote sensing techniques provide spatiotemporal information from plants in an agile, non-destructive and non-invasive way. The present study aimed to use remote sensors onboard an unmanned aerial vehicle (UAV) to monitor elephant grass genotypes and assist in plant phenotyping for energy biomass production. The experimental plots were imaged in the visible and near infrared bands. Imaging was carried out in 66 experimental plots in the José Henrique Bruschi Experimental Field (CEJHB), located in Coronel Pacheco, MG, Brazil. The experiment was arranged in a randomized block design with three replications, and 22 elephant grass genotypes were evaluated. The aggregated index iMAPNDRE was strongly correlated with the dry matter production observed in the field, therefore a method with potential application for estimating the biomass of elephant grass genotypes. Thus, sensors aboard UAV platforms can assist breeders to select the best elephant grass genotypes for energy production.
Thesagro:	Capim Elefante Bioenergia Sensoriamento Remoto
NAL Thesaurus:	Grasses Bioenergy Vegetation index Remote sensing
Palabras clave:	Índice de vegetação UAV Drone Monitoramento
DOI:	https://dx.doi.org/10.22161/ijaers.91.9
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPGL)