Prediction of fruit shapes in F1 progenies of chili peppers (Capsicum annuum) based on parental image data using elliptic Fourier analysis

Abstract

Fruit shape significantly impacts the quality and market value of chili peppers (Capsicum annuum). However, predicting their fruit shapes in F₁ hybrids remains challenging, often relying on skilled breeders. This study aimed to clarify the potential of elliptic Fourier descriptors (EFDs) to predict fruit shape of F₁ progeny in chili peppers based on parental data. Using images of 291 accessions (132 inbred and 159 F₁ from 20 parental inbreds), EFDs were extracted to reconstruct shape contours. The initial prediction method, PP_mid, used midpoint EFDs of the parents, achieving accuracies comparable to genomic methods. To improve accuracy, a new method, PP_δ, was developed. PP_δ incorporates dominance effects observed in F₁ progeny, yielding significantly better predictions. Over 80% of F₁ accessions showed improved accuracy with PP_δ, and the predicted contours aligned closely with real shapes. Cross-validation confirmed the reproducibility of PP_δ predictions. These findings suggest that combining parental EFDs with dominance effect ratios enables accurate fruit shape predictions without genetic data. This is the first study demonstrating EFD applicability in F₁ hybrid breeding for fruit shape, offering a promising tool for developing innovative breeding techniques in chili peppers.