Multivariate analysis and image-based phenotyping of cayenne fruit traits in selection and diversity mapping of multiple F1 cross lines

Abstract

The phenomenon of fluctuating chili prices can be resolved in stages, one of which is through multiple crosses. However, this cross requires precise methods in the evaluation and selection process, especially regarding fruit characteristics. Image-based phenotyping 4.0 approaches can increase the potential precision of such evaluation genotypes, especially when this approach is combined with multivariate analysis. Therefore, both methods are needed to evaluate and select these cayenne multiple crosses. This research aims to identify the effectiveness of multivariate analysis and image-based explanatory characteristics of fruit phenotypes and to select multiple crosses that can continue to the F2 generation. The research was designed with a randomized complete block design of ten F1 multiple cross-genotypes and four check varieties. Each genotype was repeated three times, so there were 42 experimental units. Based on the results, multivariate was considered adequate in determining image explanatory characters based on fruit phenotype and genotype mapping of the population diversity of multiple crosses of cayenne pepper. The characteristics of fruit height, fruit area, and fruit Intden are image-based explanatory characters that can map the completeness of cayenne pepper fruit between multiple crosses well. This indicates that image-based phenotyping and multivariate analysis can provide more detailed image information of the potential of cayenne fruit from multiple crosses than just based on fruit weight. Therefore, both approaches are recommended for analyzing cayenne paper fruit potential, especially for multiple crosses. In addition, three crosses (MC4, MC8, and MC9) are optimal for the next generation to be recommended and continued.

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