From data to decisions: a paradigm shift in fruit agriculture through the integration of multi-omics, modern phenotyping, and cutting-edge bioinformatic tools.

Abstract

Fruit agriculture is undergoing a profound transformation driven by multi-omics, high-throughput phenotyping, and machine learning-driven bioinformatics. However, we demonstrate that this technological revolution has paradoxically created a 'valley of death' where most of genomic discoveries fail to reach farmers' fields. While we can now identify beneficial alleles in days and edit genomes in weeks, it still takes 10 years and 14,5 million euros to deliver a single improved cultivar to European markets - the same timeline as 30 years ago. This review exposes how data abundance has shifted, not eliminated, the fundamental bottlenecks in fruit crop improvement. We critically assess how these tools reshape genetic and metabolic diversity, emphasizing both their transformative promises and structural limitations. We highlight three persistent gaps: the challenge of integrating heterogeneous multi-omics datasets, the phenotyping bottleneck for complex traits, and the tension between innovation and biodiversity conservation. By framing fruit breeding as a "data-to-decisions" challenge, we outline the systemic changes needed for sustainable, resilient, and high-quality fruit production.

Plant phenotyping relevance

果樹育種におけるハイスループット表現型解析とデータ統合の課題を批判的にレビューしており、表現型解析が中心的な方法論テーマの一つである。

Code and data availability

This is a review article on multi-omics and phenotyping in fruit agriculture. The only paper-specific materials are four small supplementary DataSheet docx files, which are not described as containing phenotype datasets, images, analysis code, or models; no availability or deposit language for such assets appears inthe

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