Building a hyperspectral library of common bean diseases.
2024
RIBEIRO, W. R. | ALMEIDA, A. DE S. | FERREIRA, A. L. | SILVA, R. M. | ALVES, T. M. | GERALDINE, A. M. | SILVA, J. F. A. E | PINHEIRO, P. V. | LOBO JUNIOR, M. | WILLIAM RAFAEL RIBEIRO, UNIVERSIDADE FEDERAL DE GOIÁS; ANDRESSA DE SOUZA ALMEIDA, UNIVERSIDADE FEDERAL DE GOIÁS; AMANDA LOPES FERREIRA, UNIVERSIDADE FEDERAL DE GOIÁS; ROMULO MOREIRA SILVA, INSTITUTO FEDERAL GOIANO; TAVVS MICAEL ALVES, INSTITUTO FEDERAL GOIANO; ALAERSON MAIA GERALDINE, INSTITUTO FEDERAL GOIANO; JOSE FRANCISCO ARRUDA E SILVA, CNPAF; PATRICIA VALLE PINHEIRO, CNPAF; MURILLO LOBO JUNIOR, CNPAF.
Sensors attached to unmanned aerial vehicles can detect diseased plants before they become symptomatic, according to differences in their reflectance. Despite the advances in hardware and methods for detecting infections with remote sensing, using these tools to scout common bean diseases is still incipient. The objectives of this study were: 1) to evaluate the effectiveness of a hyperspectral sensor in the diagnosis of common bean diseases; 2) verify differences in the spectral signatures in different genotypes with healthy or infected plants; and 3) to investigate if mulch used in no-tillage can influence the spectral signatures.
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