Improving Berry Uniformity in Grape (Vitis vinifera): Trait-Based Evaluation and Selection Perspectives

Abstract

This study explores the conceptual framework and evaluation methods of grape berry uniformity, elucidating its multidimensional nature arising from the coordinated contributions of berry size, shape, and cluster structure. Quantitative evaluation approaches based on the coefficient of variation, composite multi-trait indices, and high-throughput phenotyping technologies are systematically summarized. On this basis, key factors influencing berry uniformity are further analyzed, including genetic background, pollination and fertilization processes, berry developmental dynamics, plant growth regulator treatments, and water-nutrient environmental conditions. Integrating breeding strategies with production practices, a framework for improving berry uniformity is proposed, centered on “multi-trait selection, marker-assisted selection, and cultivation regulation.” Meanwhile, with the advancement of machine vision, high-throughput phenotyping, and multi-source data integration technologies, the evaluation of berry uniformity is shifting toward automation, precision, and intelligence. However, challenges remain in the standardization of evaluation systems, elucidation of molecular mechanisms, and integration of multi-source data. Future research directions toward data-driven precision improvement are discussed. This study aims to provide theoretical foundations and technical support for enhancing the quality and standardized production of table grapes.

Plant phenotyping relevance

ブドウ果実の均一性を対象に、評価指標、高スループットフェノタイピング、機械ビジョンによる自動評価を体系的に扱うレビューであり、フェノタイピング手法が中心です。

Code and data availability

This is a review article on grape berry uniformity evaluation. It summarizes phenotyping methods (CV, composite indices, BAT/CAT, Mask R-CNN, SAM) and reproduces a figure adopted from prior work (Torres-Lomas et al., 2024), but presents no paper-specific phenotype datasets, images, code, models, or supplements with any

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