Evaluation of vegetation indices from aerial images in soybean breeding

• Ano de defesa: 2020
• Autor(a) principal: Vianna, Mariana Silva
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Glicine max; Glycine max; Fenotipagem de alto rendimento; High throughput phenotyping; Imagens RGB; Índices de vegetação; Melhoramento de plantas; Plant breeding; RGB images; Vegetation index
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11137/tde-12012021-102452/

High throughput phenotyping (HTP) is an emerging tool that allows access and identifies simple and complex quantitative traits, accelerating genetic discoveries, and selection. Vegetation indices have been using to detect variation in the crop field, demonstrating correlation with several traits of crop performance. Thus, this study had the main goal to estimate vegetation indices and their correlation with agronomic traits in different soybean populations using RGB images derived from an unmanned aerial vehicle (UAV). Were conducted three experiments in the 2018/2019 season: RIL-C (stink bugs control), RIL-N (Without stink bugs control), and LQ (Without fertilization, soil correction, and stink bugs control) aiming to evaluate genetic resistance to the stink bug complex in soybean lineages. The genotypes were evaluated based on the following traits: Number the days to maturity (NDM), agronomic value (AV), Lodging (LOD), Plant height maturity (PHM), and grain yield (GY). A UAV system with an RGB camera coupled was used to acquire aerial photography flight over the field during the R5 stage. Was estimated the Red Green Blue Vegetation Index (RGBVI), Gren Leaf Index (GLI), Visible Atmospheric Resistant Index (VARI), Triangular Greenness Index (TGI), Normalized Green Red Difference Index (NGRDI), and canopy from the orthomosaic. Linear mixed models were used to estimate the variance of each trait using the likelihood ratio test, and the principal component analysis (PCA) was performed using the Best Linear Unbiased Predictions (BLUPs) to verify the multivariate pattern among genotypes. The results showed significant genotypic effects for the majority of the traits evaluated. High broad-sense heritability of the traits can be observed. The principal component analysis revealed that the genotypes had more agronomic performance in the experiment with control of stink bugs, also, showed a strong correlation between the traits GY and PHM, and independence between the traits LOD and NDM. There were significant correlations among the agronomic traits, vegetation indices, and canopy, that can be used for indirect selection and joint selections of the traits of the best lineages in the breeding pipeline.