Sangjan Worasit, Kick Daniel R, Washburn Jacob D
Plant Genetics Research Unit, United States Department of Agriculture, Agricultural Research Service, Columbia, MO, 65211, USA.
Theor Appl Genet. 2025 Jun 2;138(6):132. doi: 10.1007/s00122-025-04910-2.
Integrating, learning from, and predicting using vast datasets from various scales, platforms, and species is crucial for advancing crop improvement through breeding. Artificial intelligence (AI) is a broad category of methods, many of which have been used in breeding for decades. Recent years have seen an explosion of new AI tools (or old ones at new scales), with exciting applications, both demonstrated and potential, to improve or maybe even revolutionize plant breeding! Example use cases and data types included data mining, phenotyping, monitoring, genetics, multi-omics, environment, management practices, cross-species inference, sustainability, economics, and many others. Improvements in these areas could increase predictive accuracy for plant traits, thereby expediting breeding cycles and optimizing resource management. Aside from improving predictions, AI methods can potentially enhance biological inferences and enable more informed approaches to areas like gene discovery, gene editing, and transformation. At the same time, AI is not going to solve every breeding challenge, and studies so far have shown mixed results depending on the application, dataset, and other factors. AI continues to transform plant breeding, yet its full potential remains unclear, with many possibilities still to be realized. This review explores the transformative potential of AI in plant breeding with a particular focus on its ability to integrate the many diverse streams of data involved. Success in this would open opportunities to improve crop resilience, yield, and sustainability, thus supporting global food security and inspiring the next generation of plant breeding technologies.
整合、借鉴并利用来自不同规模、平台和物种的海量数据集进行预测，对于通过育种推进作物改良至关重要。人工智能（AI）是一类广泛的方法，其中许多已在育种中使用了数十年。近年来，新的人工智能工具（或新规模的旧工具）激增，在改良甚至可能彻底改变植物育种方面有着令人兴奋的已展示和潜在应用！示例用例和数据类型包括数据挖掘、表型分析、监测、遗传学、多组学、环境、管理实践、跨物种推断、可持续性、经济学等等。这些领域的改进可以提高植物性状的预测准确性，从而加快育种周期并优化资源管理。除了改进预测外，人工智能方法还可能增强生物学推断，并为基因发现、基因编辑和转化等领域带来更明智的方法。与此同时，人工智能并不能解决所有育种挑战，到目前为止的研究表明，根据应用、数据集和其他因素，结果喜忧参半。人工智能继续改变着植物育种，但其全部潜力仍不明确，仍有许多可能性有待实现。本综述探讨了人工智能在植物育种中的变革潜力，特别关注其整合众多不同数据流的能力。在此方面取得成功将为提高作物恢复力、产量和可持续性带来机会，从而支持全球粮食安全并激发下一代植物育种技术。
