Three-dimensional fruit growth analysis clarifies developmental mechanisms underlying complex shape diversity in persimmon fruit

Abstract

How fruit size and shape are determined is of research interest in agriculture and developmental biology. Fruit typically exhibits three-dimensional structures with genotype-dependent geometric features. Although minor developmental variations have been recognized, little research has fully visualized and measured these variations throughout fruit growth. In this study, a high-resolution 3D scanner was used to investigate the fruit development of 51 persimmon (Diospyros kaki) cultivars with various complex shapes. We obtained 2,380 3D fruit models that fully represented fruit appearance, and enabled precise and automated measurements of unique geometric features throughout fruit development. The 3D fruit model analysis identified key stages that determined the shape attributes at maturity. Typically, genetic diversity in vertical groove development was found, and such grooves can be filled by tissue expansion in the carpal fusion zone during fruit development. Furthermore, transcriptome analysis of fruit tissues from groove/non-groove tissues revealed gene co-expression networks that were highly associated with groove depth variation. The presence ofYABBYhomologs was most closely associated with groove depth and indicated the possibility that this pathway is a key molecular contributor to vertical groove depth variation. These results demonstrate the validity of fruit 3D growth analysis, which is a powerful tool for identifying the developmental mechanisms of fruit shape variation and the molecular basis of this diversity.

Advancing Mixed Reality Digital Twins Through 3D Reconstruction of Fresh Produce

“…Villordon et al [14] utilized a low-cost 3D scanner to measure sweet potato storage root surface area and volume, crucial for processing product recovery. Kusumi et al [15] employed a highresolution 3D scanner to analyze persimmon fruit growth, identifying key stages that influence shape attributes. Su et al [16] combined hyperspectral imaging and deep learning to determine strawberry maturity degree and soluble solids content.…”