Insights on Multi-Spectral Vegetation Indices Derived From Uav-Based High-Throughput Phenotyping for Indirect Selection in Tropical Wheat Breeding

Abstract

Abstract High-throughput phenotyping (HTP) approaches are potentially useful for the accurate and efficient evaluation and selection of superior genotypes, leveraging high genetic gains. Vegetation indices are of particular interest because they allow indirect selection. Considering the lack of information regarding high-throughput phenotyping approaches in tropical wheat breeding, this study aimed to (i) determine the best stages to carry out image acquisition for applying multi-spectral vegetation indices; (ii) evaluate the heritability and accuracy of multi-spectral vegetation indices; (iii) understand the relationships between vegetation indices and target agronomic traits; and (iv) evaluate the efficiency of indirect selection via UAV-based high-throughput phenotyping. A diversity panel of 49 tropical wheat cultivars was evaluated during the 2022 winter season. Weekly flight campaigns were performed to further build multi-spectral vegetation indices, which were then analyzed together with four target agronomic traits. Mixed model analyses were performed to estimate genetic parameters and predict genetic values, which were subjected to correlation analysis. Additionally, factor analysis was applied, and the factorial scores were used in an indirect selection strategy (indirect via HTP). This strategy was compared to three alternative strategies: direct via grain yield, direct via days to heading, and the multi-trait genotype-ideotype distance index. The results indicate that vegetation indices are suitable for indirect selection strategies and highly efficient for the indirect selection of grain yield and cycle. The findings of this study will help decision making regarding the use of these approaches in Brazilian public wheat breeding programs.