Next-Generation Crop Breeding: Harnessing Genomics, Phenomics and Machine Learning: A Review

Abstract

Global food security requires crop improvement strategies that can respond to population growth, climate variability and increasing constraints on agricultural resources. Conventional plant breeding has contributed substantially to crop productivity, yet long selection cycles and dependence on extensive field evaluation can limit the rate of genetic gain. This review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding, with emphasis on their combined contribution to selection accuracy and breeding efficiency. Key genomic approaches discussed include whole-genome sequencing, reference and pan-genome resources, genome-wide association studies, genomic selection and CRISPR-Cas-based genome editing. The review also examines high-throughput phenotyping platforms, including controlled-environment systems, ground-based robots, UAV-based remote sensing and root phenotyping tools. Machine learning approaches, ranging from random forest and support vector machines to convolutional neural networks, recurrent networks, transformers and explainable artificial intelligence, are considered in relation to genomic prediction, image analysis and breeding decision support. Multi-omics integration, data management, FAIR principles and an integrated genomics-phenomics-ML breeding pipeline are reviewed as enabling components for practical deployment. Crop-specific examples from wheat, rice, maize, soybean and legumes illustrate the potential and constraints of these technologies. The review further identifies key challenges, including phenotyping bottlenecks, genotype-environment interaction, data governance, model interpretability and regulatory uncertainty.

Plant phenotyping relevance

植物フェノタイピング手法を中心に、ハイスループット計測プラットフォーム、画像解析、機械学習、UAV・ロボット・根系計測などをレビューしているため。

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