AI驱动的作物育种技术革新与应用

    AI-driven innovation and applications in crop breeding technology

    • 摘要: 全球粮食产量增长速度难以满足人口增长需求,气候变化、耕地退化和环境压力正进一步加剧粮食生产系统的脆弱性。保障粮食安全是维系人类社会稳定与发展的基础命脉,作物育种是实现这一目标的关键技术手段,培育高产、优质、多抗的作物新品种,已成为农业可持续发展和全球粮食供给的核心路径。传统育种技术存在周期长、精准度低、效率有限等问题,难以满足现代化种业发展需求。人工智能(Artificial intelligence,AI)技术的快速迭代为作物育种革新注入全新智能动力,推动育种技术从传统经验育种向精准化、智能化、高效化育种转型。本文系统梳理了作物育种的发展历程,着重介绍AI驱动下全基因组选择、基因精准编辑设计、蛋白质设计和高通量表型鉴定技术的创新突破,以及上述技术给育种流程(种质资源挖掘、基因功能解析、性状定向改良、智能表型评估及智能工厂化育种)带来的智能化动力;剖析了AI在作物育种领域应用过程中面临的挑战与未来发展方向,以期为智能育种技术的创新与产业化应用提供参考。

       

      Abstract: The growth rate of global grain production can no longer meet the demands arising from population expansion. Meanwhile, climate change, cultivated land degradation and environmental stress are further exacerbating the vulnerability of the food production system. Ensuring food security is fundamental to maintain the stability and sustainable development of human society. Crop breeding is the pivotal technical means to achieve this goal. Developing new crop varieties with high yield, superior quality, and multiple resistances have become the core pathway to safeguard agricultural sustainable development and global food supply. However, traditional breeding techniques suffer from long cycles, low accuracy and limited efficiency. which cannot satisfy the development requirements of modern seed industry. The rapid iteration of artificial intelligence (AI) technology has injected new intelligent momentum into the innovation of crop breeding, driving the transformation of breeding technology from traditional experience-based breeding to precise, intelligent and efficient breeding. In this review, we summarized the development of crop breeding, and focused on the innovative breakthroughs of genomic selection, precision genome editing, protein design and high-throughput phenotyping driven by AI. We also further elaborated the intelligent driving effects exerted by these technologies on key breeding links involving germplasm mining, gene function analysis, directional trait improvement, intelligent phenotypic assessment and intelligent factory breeding. Finally, we discussed the challenges and future developmental prospects of AI deployment in crop breeding, aiming to provide a reference for the innovation and industrial application of intelligent breeding technologies.

       

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