Application of deep learning in crop research: From genomics to phenomics

Abstract

Abstract Deep learning, as a pivotal branch of machine learning, has demonstrated remarkable potential in advancing crop science by effectively integrating genomics and phenomics. This review systematically outlines the application of diverse deep learning architectures—such as convolutional neural networks, recurrent neural networks, and transformers—across key crop genomic tasks, including gene expression prediction, alternative splicing analysis, cis ‐regulatory element identification, epigenomic profiling, and genome‐based trait prediction. In phenomics, these models facilitate high‐throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground‐based imagery, supporting yield forecasting, disease diagnosis, and stress response monitoring. We critically evaluate the performance and limitations of each model type across tasks, considering trade‐offs between complexity, accuracy, and interpretability, to offer practical guidance for crop researchers. Additionally, the review addresses major challenges in deploying deep learning—such as data scarcity, model transparency, and computational demands—and proposes future pathways to enhance model generalizability, multimodal data integration, and applications in intelligent breeding and sustainable agriculture.

Plant phenotyping relevance

作物フェノミクスにおける深層学習による画像からの形質抽出を中心的にレビューしており、フェノタイピング手法の方法論的整理に該当する。

Code and data availability

This is a review article surveying deep learning applications in crop genomics and phenomics. It reports no original plant-phenotyping measurements, datasets, images, or author analysis code. All repositories and URLs mentioned (e.g., yield prediction, disease detection, TFBS models) belong to cited prior studies, not,

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