[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2625":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":32,"doi":33,"paper":34,"created_at":49},2625,"Multi-omics and artificial intelligence for climate-resilient and nutrient-enriched food crops: advances, applications and future perspectives","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1931230","Climate change has intensified both abiotic and biotic stresses, threatening global crop productivity, nutritional quality, and agricultural sustainability. These challenges have accelerated the adoption of advanced multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, together with artificial intelligence (AI) to accelerate the development of climate-resilient and nutrient-enriched crops. This review provides an integrated view of the recent advances in AI-enabled multi-omics, genome editing and precision agriculture for climate-resilient and nutrient-rich crops along with emerging trends, current challenges, and future research priorities. Machine learning-enhanced genomic selection approaches such as convolutional neural network (CNN)-based models and transformer models enhance the accuracy of predictions for complex stress-tolerant and nutritional traits. AI-assisted unmanned aerial vehicle (UAV) remote sensing allows measurement of traits at an unprecedented scale and with high-throughput and quantitative phenotyping. AI-driven multi-omics integration approaches, such as pan-omics pipelines, decipher abiotic stress tolerance regulatory networks. The AI-assisted CRISPR-Cas9 genome editing systems have accelerated crop biofortification outcomes by improving target prioritization and guide RNA design. Genome-editing studies have reported enhanced iron and zinc bioavailability in experimental wheat lines, reduced phytic acid content in soybean, and enhanced vitamin and amino acid content in cassava and maize, highlighting the potential of AI-assisted genome-editing approaches for crop biofortification. AI-enabled precision agriculture platforms use AI together with satellite data and Internet of Things (IoT) sensors and predictive modeling to enhance crop management while decreasing environmental resource usage. This review examines data standardization and model interpretability together with access and regulations while it presents a future roadmap for achieving 2050 food security targets. By integrating multi-omics, artificial intelligence, genome engineering, and precision agriculture into a unified framework, this review identifies future research directions and key knowledge gaps for the development of climate-resilient and nutrient-rich crops.","气候变化加剧了非生物与生物胁迫，对全球作物生产力、营养品质和农业可持续性构成威胁。这些挑战加速了先进多组学技术（包括基因组学、转录组学、蛋白质组学和代谢组学）以及人工智能（AI）的应用，以加快气候韧性及营养强化作物的开发。本综述综合阐述了AI赋能的多组学、基因组编辑和精准农业在气候韧性及营养丰富作物方面的最新进展，以及新兴趋势、当前挑战和未来研究重点。机器学习增强的基因组选择方法，如基于卷积神经网络（CNN）的模型和Transformer模型，提高了对复杂耐逆性和营养性状预测的准确性。AI辅助的无人机（UAV）遥感能够以前所未有的规模进行高通量定量表型测量。AI驱动的多组学整合方法，如泛组学流程，可解析非生物胁迫耐受调控网络。AI辅助的CRISPR-Cas9基因组编辑系统通过改进靶标优先级排序和引导RNA设计，加速了作物生物强化成果。基因组编辑研究报告了实验小麦品系中铁和锌生物利用度的提高、大豆中植酸含量的降低，以及木薯和玉米中维生素和氨基酸含量的增强，凸显了AI辅助基因组编辑方法在作物生物强化方面的潜力。AI赋能的精准农业平台利用AI结合卫星数据、物联网（IoT）传感器和预测建模，在减少环境资源使用的同时增强作物管理。本综述审视了数据标准化和模型可解释性以及可及性和监管问题，同时提出了实现2050年粮食安全目标的未来路线图。通过将多组学、人工智能、基因组工程和精准农业整合为统一框架，本综述确定了开发气候韧性及营养丰富作物的未来研究方向和关键知识空白。",null,"Frontiers in Sustainable Food Systems","2026-09-16T00:00:00Z","论文",10,false,86,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,19,14,9,1,"核心期刊综述，系统梳理AI驱动多组学、基因编辑与精准农业在气候韧性及营养强化作物上的进展，方法新颖、结论可靠，对智慧育种与农业AI方向有较高参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"农业人工智能","精准农业","基因编辑","气候韧性","智慧育种","多组学",0,"10.3389\u002Ffsufs.2026.1931230",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":41,"direction":47,"ingested_from":48},"W7213241547",[37,39],{"name":38,"orcid":9},"Surasreeta Paul",{"name":40,"orcid":9},"Sandeep Singh Rana",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"综述AI赋能多组学、基因编辑与精准农业，培育气候韧性与营养强化作物。","整合基因组、转录组、蛋白与代谢组学，结合CNN、Transformer、UAV遥","AI辅助多组学与基因编辑可提升耐逆与营养性状预测及生物强化效率。","农业人工智能与决策模型","多组学数据标准化与模型可解释性不足，可研究跨物种可迁移的AI决策框架。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:07.496425Z"]