Protocolo de processamento de imagens RGB aéreas para identificação de genótipos potenciais de milho em fase vegetativa

Barbara Nascimento Santos; Nartênia Susane Costa Aragão; Mário Sérgio Rodrigues Barreto; Henrique Rocha Azevedo Santos; Jacilene Francisca Souza Santos; José Jairo Florentino Cordeiro Junior; Gustavo Hugo Ferreira de Oliveira

Authors

Barbara Nascimento Santos Federal University of Sergipe (UFS) https://orcid.org/0000-0002-1689-7399
Nartênia Susane Costa Aragão Federal University of Sergipe (UFS) https://orcid.org/0000-0003-3409-3236
Mário Sérgio Rodrigues Barreto Federal University of Sergipe (UFS) https://orcid.org/0000-0002-4037-4328
Henrique Rocha Azevedo Santos Federal University of Sergipe (UFS) https://orcid.org/0000-0001-6940-022X
Jacilene Francisca Souza Santos Federal University of Sergipe (UFS) https://orcid.org/0000-0001-5109-3663
José Jairo Florentino Cordeiro Junior Federal University of Sergipe (UFS) https://orcid.org/0000-0002-1138-8309
Gustavo Hugo Ferreira de Oliveira Federal University of Sergipe (UFS) https://orcid.org/0000-0002-3839-6261

Keywords:

Plant breeding, Vegetation indices, Plant breeding. Vegetation indices. Zea mays L.

Abstract

Abstract: The aim was to evaluate an RGB image for identify grain yield potential in maize before physiological maturity in a semi-arid region. A randomized complete block design with two replications was employed, to access 50 maize genotypes. Two drone flights were conducted at different time points, specifically 27 and 46 days after planting (DAP), at flight heights of 40, 60, and 80 meters. A total of 29 vegetation indices were used in the analysis. Analysis of variance revealed genetic variability among the genotypes, enabling the selection of promising materials. By assessing the repeatability of vegetation indices, the optimal flight date was determined. Temporal BLUP (Best Linear Unbiased Prediction) allowed for the categorization of materials as high or low performers, considering the mean grain yield, and identified the most productive materials during the vegetative phenological stage of the crop. It is recommended, to conduct flights at 27 DAP at an height of 80 meters. The TGI (Triangular Greenness Index) and Green indices proved to be indicative of early predictions of material productivity. It is suggested to maintain the experimental area free from biotic and abiotic interferences and to conduct additional flights, thus optimizing aerial image phenotyping.

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Author Biographies

Barbara Nascimento Santos, Federal University of Sergipe (UFS)

Master's student in Agriculture and Biodiversity at the Federal University of Sergipe – UFS. Email: barbaranascimento2804@gmail.com. ORCID: https://orcid.org/0000-0002-1689-7399.

Nartênia Susane Costa Aragão, Federal University of Sergipe (UFS)

Student of Agricultural Engineering at the Federal University of Sergipe – UFS. Email: nartenia.aragao@gmail.com. ORCID: https://orcid.org/0000-0003-3409-3236.

Mário Sérgio Rodrigues Barreto, Federal University of Sergipe (UFS)

Student of Agricultural Engineering at the Federal University of Sergipe – UFS. Email: mzs.esc@gmail.com. ORCID: https://orcid.org/0000-0002-4037-4328.

Henrique Rocha Azevedo Santos, Federal University of Sergipe (UFS)

Student of Agricultural Engineering at the Federal University of Sergipe – UFS. Email: rique999@academico.ufs.br.

Jacilene Francisca Souza Santos, Federal University of Sergipe (UFS)

PhD student in Agriculture and Biodiversity at the Federal University of Sergipe – UFS. Email: jacilenesantos_14@hotmail.com.

José Jairo Florentino Cordeiro Junior, Federal University of Sergipe (UFS)

PhD in Agricultural Engineering. Professor at the Federal University of Sergipe – UFS. Email: jairofcordeiro@academico.ufs.br. ORCID: https://orcid.org/0000-0002-1138-8309.

Gustavo Hugo Ferreira de Oliveira, Federal University of Sergipe (UFS)

PhD in Agronomy. Professor at the Federal University of Sergipe – UFS. Email: gustavooliveira@academico.ufs.br. ORCID: https://orcid.org/0000-0002-3839-6261.

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