[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-08-26":3},{"date":4,"title":5,"highlights":6,"content":12,"items":13},"2026-08-26","AI赋能农业：棉芯1.0发布，智慧肥水大模型上线",[7,8,9,10,11],"中国农科院发布全球首个棉花专属大语言模型「棉芯1.0」，整合4万余篇文献，提供科研与棉农双端服务，推动AI+棉花产业融合。","全国肥料双交会发布智慧肥水大模型，集成20年测土配方数据，构建八大智能体；同时发布国内首个农业水肥垂直大模型「奇稷·肥水」。","APEC粮食安全部长级会议在杭州召开，刘国中提出产能提升、创新驱动等四点建议，强调构建亚太粮食安全体系。","湖南农村产权交易中心揭牌，已覆盖58个县市区，累计成交超70亿元，建设五级贯通统一大市场。","科研突破：大黄鱼锥体虫无缺口基因组、小豆T2T基因组、水稻纳米农药、幼畜发酵饲料等成果发布。","本期日报聚焦AI在农业领域的深度应用，包括棉花大模型和智慧肥水模型发布；同时关注APEC粮食安全会议、湖南农村产权交易中心运营等政策动态，以及基因组学、纳米农药等前沿研究进展，覆盖种植、畜牧、水产等多领域。\n\n---\n*本日报内容整理自公开来源，外文资料已译为中文，翻译与摘要仅供参考；引用与决策请以官方原文与正式出版物为准。*",[14,86,128,174,238,283,334,378,408,449,497,544,592,633,699],{"id":15,"title":16,"url":17,"summary":18,"summary_zh":19,"content":20,"source_name":21,"source_url":19,"published_at":22,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":26,"score_detail":27,"sources":34,"tags":36,"primary_topic":43,"secondary_topics":49,"classification":50,"search_phrases":80,"slug":83,"view_count":84,"doi":19,"paper":19,"created_at":85},944,"棉花专属知识大语言模型「棉芯1.0」发布 中国棉花产业迈入AI+科研+生产融合新阶段","https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F26\u002Fcontent_38963850.htm","8月24日中国农业科学院棉花研究所联合西部农业研究中心发布全球首个棉花领域专属知识大语言模型「棉芯1.0」(CottonMind1.0)，采用「网页端+小程序端」双端设计：网页端为科研人员提供云端智研工作台(AI检索、文献加速阅读、知识图谱)，小程序端成为棉农随身「农技专家」(AI问答、拍照诊断、专家咨询)。模型已整合41680篇中外文科研文献、2000余项专利与国家标准、559部专业著作、1806条审定品种信息，覆盖基因功能、性状调控、品种选育、栽培管理、病虫害防控等全维度专业知识，并支持中英多语言服务。项目组负责人杨作仁表示，棉芯1.0打通了科研成果转化「最后一公里」，可服务于政府部门种植规划、防控策略与产业政策制定。",null,"![Image 1](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002FgmwLogo_share.jpg)\n\n点击右上角![Image 2](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Ficonshare.png)微信好友\n\n![Image 3](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Fpengyouquan.png)朋友圈\n\n![Image 4](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Flayershare.png)\n\n点击浏览器下方“![Image 5](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Flineshare.png)”分享微信好友Safari浏览器请点击“![Image 6](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Fshare.png)”按钮\n\n![Image 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棉花专属知识大语言模型“棉芯1.0”发布\n\n来源：[光明网-《光明日报》](https:\u002F\u002Fepaper.gmw.cn\u002Fgmrb\u002Fhtml\u002Fcontent\u002F202608\u002F26\u002Fcontent_23394.html)2026-08-26 05:50\n\n[](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F26\u002Fcontent_38963850.htm)[X](javascript:;)[](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F26\u002Fcontent_38963850.htm)\n\n调查问题加载中，请稍候。\n\n若长时间无响应，请刷新本页面\n\n**光明日报京8月25日电 记者杨舒**从中国农业科学院棉花研究所获悉，24日，该所联合中国农业科学院西部农业研究中心正式发布全球首个棉花领域专属知识大语言模型——“棉芯1.0”（CottonMind1.0），标志着我国棉花产业迈入“AI+科研+生产”深度融合的新阶段。\n\n“棉芯1.0”以“网页端+小程序端”双端设计，分别瞄准科研创新与田间生产两大核心场景。网页端为科研人员提供云端智研工作台，集成AI智能检索、文献加速阅读、知识图谱可视化等功能。小程序端可以成为棉农的随身“农技专家”，提供AI问答、拍照诊断、专家在线咨询等服务，打破了传统农技服务的时空壁垒。此外，“棉芯1.0”全平台支持中文、英文等多语言服务，为国际棉花科研协作搭建了数字化桥梁。\n\n项目组负责人杨作仁介绍，“棉芯1.0”系统已整合全球41680篇中外文科研文献、2000余项专利与国家标准、559部专业著作、1806条审定品种信息，覆盖基因功能、性状调控、品种选育、栽培管理、病虫害防控等全维度专业知识。\n\n“棉芯1.0”的发布，是我国智慧农业建设在经济作物领域的重要突破。它打通了科研成果转化“最后一公里”，构建起“科研创新—技术推广—生产应用—数据反哺”的闭环生态。同时，“棉芯1.0”通过全产业数据归集与大数据分析，可为政府部门制定种植规划、防控策略与产业扶持政策提供科学数据支撑。\n\n《光明日报》（2026年08月26日 08版）\n\n[ 责编：赵靓 ]\n\n阅读剩余全文（）\n\n![Image 15](https:\u002F\u002Fimg.gmw.cn\u002Fcontent_banner\u002Fcontent_840x120_qrcodes.jpg)\n\n## 值班总编推荐\n\n*   [重视电动车治理是对道路交通安全负责](https:\u002F\u002Fguancha.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38963649.htm)\n*   [高校选课季，如何让“金课”广受欢迎？](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961616.htm)\n*   [第二届世界人形机器人运动会:\"智芯\"点亮\"冰丝带\"](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961723.htm)\n\n## 追思\n\n[![Image 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光明导读\n\n*   [社会实践成为高校必修课 如何带来真收获](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961675.htm)\n*   [让网信企业充分释放创新活力](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961587.htm)\n*   [内蒙古通辽构建多能互补现代能源产业体系](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961661.htm)\n*   [湖南援疆医疗队对口支援吐鲁番:\"毛医生\"扎根\"火洲\"](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961656.htm)\n*   [APEC海关手续分委会2026年第二次全体会议举行](https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961590.htm)\n\n## 光明图片\n\n*   [![Image 17](https:\u002F\u002Fimgnews.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260824\u002Ff44d305ea4b62bf6613a14.jpg)](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-08\u002F24\u002Fcontent_38961118.htm)[习近平同约旦国王阿卜杜拉二世会谈](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-08\u002F24\u002Fcontent_38961118.htm)\n*   [![Image 18](https:\u002F\u002Fimgnews.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260825\u002Ff44d305ea4db2bf730c63c.jpg)](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961977.htm)[“救援航母”挺进广西宁明灾区](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961977.htm)\n*   [![Image 19](https:\u002F\u002Fimgnews.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260825\u002Ff44d305ea4db2bf730d93d.jpg)](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961956.htm)[“赛考斯”重回毛乌素沙地](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-08\u002F25\u002Fcontent_38961956.htm)\n\n光明网版权所有\n\n*   [光明日报社概况](https:\u002F\u002Fabout.gmw.cn\u002Fnode_21441.htm)\n*   [关于光明网](https:\u002F\u002Fabout.gmw.cn\u002Faboutus.htm)\n*   [报网动态](https:\u002F\u002Fabout.gmw.cn\u002Fnode_8926.htm)\n*   [联系我们](https:\u002F\u002Fabout.gmw.cn\u002Fnode_46280.htm)\n*   [法律声明](https:\u002F\u002Fabout.gmw.cn\u002Fnode_46279.htm)\n*   [光明网邮箱](https:\u002F\u002Fmail.gmw.cn\u002F)\n*   [网站地图](https:\u002F\u002Fwww.gmw.cn\u002Fmap.htm)\n\n![Image 20](https:\u002F\u002Fchannel-analysis.gmw.cn\u002F1.gif?s=1787702980444&cl=4108&pd=2026-08-26&ci=38963850&r=https:\u002F\u002Fnews.gmw.cn\u002F2026-08\u002F26\u002Fcontent_38963850.htm&at=%E8%B5%B5%E9%9D%93&tt=%E6%A3%89%E8%8A%B1%E4%B8%93%E5%B1%9E%E7%9F%A5%E8%AF%86%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E2%80%9C%E6%A3%89%E8%8A%AF1.0%E2%80%9D%E5%8F%91%E5%B8%83%20_%E5%85%89%E6%98%8E%E7%BD%91)\n\n[](https:\u002F\u002Fwww.facebook.com\u002Fguangmingonline)[](https:\u002F\u002Fwww.facebook.com\u002Fguangmingonline)\n\n[](https:\u002F\u002Ftwitter.com\u002FGuangming_Daily)[](https:\u002F\u002Ftwitter.com\u002FGuangming_Daily)\n\n[](javascript:;)","光明日报 2026-08-26 第8版","2026-08-26T00:00:00Z","报道",10,true,88,{"impact":28,"substance":29,"depth":30,"authority":31,"freshness":24,"relevant":32,"comment":33},25,22,16,15,1,"全球首个棉花专属大模型发布，标志产业迈入AI融合新阶段，信息详实，权威央媒报道，时效性强。",[35],{"name":21,"url":17},[37,38,39,40,41,42],"智慧农业","中国农科院","大语言模型","棉花所","棉芯1.0","经济作物",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":55},"classified",false,"2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":25},[57,63,70,75],{"code":44,"score":58,"supported":25,"title":59,"abstract":61,"tags":62},11,[60],"棉花",[60],[],{"code":64,"score":65,"supported":25,"title":66,"abstract":67,"tags":69},"G1",3,[],[68],"成果转化",[],{"code":71,"score":32,"supported":52,"title":72,"abstract":73,"tags":74},"I1",[],[],[37],{"code":76,"score":32,"supported":52,"title":77,"abstract":78,"tags":79},"I2",[],[],[39],[81,82],"中国农科院 大语言模型 智慧农业 经济作物","中国农科院 大语言模型","中国农科院大语言模型智慧农业经济作物-944",5,"2026-08-26T00:09:29.400381Z",{"id":87,"title":88,"url":89,"summary":90,"summary_zh":19,"content":19,"source_name":91,"source_url":19,"published_at":22,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":26,"score_detail":92,"sources":96,"tags":98,"primary_topic":104,"secondary_topics":109,"classification":110,"search_phrases":123,"slug":126,"view_count":65,"doi":19,"paper":19,"created_at":127},943,"刘国中出席第十一届亚太经合组织粮食安全部长级会议开幕式并致辞","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260826\u002F20260826_1\u002Fnmrb_20260826_13388_1_2092322241576538120.html","新华社杭州8月25日电：第十一届亚太经合组织(APEC)粮食安全部长级会议8月25日在浙江杭州召开，中共中央政治局委员、国务院副总理刘国中出席开幕式并致辞。刘国中提出4点建议：聚焦产能提升、强化创新驱动、畅通贸易循环、坚持普惠包容，共同构建更加稳固的亚太粮食安全体系。本次会议以「建设亚太共同体，促进共同繁荣」为主题，来自APEC经济体、秘书处和有关国际组织代表近200人参会。中方在会议期间重申「以我为主、立足国内、确保产能、适度进口、科技支撑」的粮食安全战略，并与越南、新西兰等多国部长展开双边会谈。","农民日报 2026-08-26 第1版",{"impact":93,"substance":94,"depth":31,"authority":31,"freshness":24,"relevant":32,"comment":95},28,20,"国家级粮食安全会议，政策导向明确，信息权威且时效性强。",[97],{"name":91,"url":89},[99,100,101,102,103],"粮食安全","国际合作","刘国中","APEC","杭州",{"code":105,"slug":106,"name":107,"path":108,"parent_code":48},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":111},{"candidates":112,"agricultural_evidence":25},[113,118],{"code":105,"score":114,"supported":25,"title":115,"abstract":116,"tags":117},12,[99],[99],[99],{"code":119,"score":32,"supported":52,"title":120,"abstract":121,"tags":122},"J3",[],[],[100],[124,125],"国际合作 粮食安全 刘国中 杭州","国际合作 粮食安全","国际合作粮食安全刘国中杭州-943","2026-08-26T00:09:29.372868Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":19,"content":133,"source_name":134,"source_url":19,"published_at":135,"category":136,"cover_url":19,"hotness":24,"is_selected":25,"score":137,"score_detail":138,"sources":141,"tags":143,"primary_topic":150,"secondary_topics":151,"classification":152,"search_phrases":167,"slug":170,"view_count":171,"doi":172,"paper":19,"created_at":173},973,"人工智能与粮食安全还是数字不平等：量化农业生产力、社会包容、可持续生计与负责任土地管理之间的全球权衡","https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull","原文标题: Artificial intelligence for food security or digital inequality? Quantifying the global trade-offs between agricultural productivity, social inclusion, sustainable livelihoods, and responsible land stewardship。作者: Moussa El Jarroudi (University of Liège, SPHERES Research Unit)。作者基于来自112个国家1276项研究的全球证据综合，挑战「技术成功自动带来粮食系统转型」的广泛假设，提出「人工智能粮食安全悖论理论(AIFSP Theory)」新概念框架，认为算法性能、生产力、运营效率的提升往往超过相应的粮食安全、生计韧性、社会公平和环境可持续性收益。结合系统综述方法与分层贝叶斯荟萃分析、网络荟萃分析、结构方程模型、可解释机器学习与至2050年的蒙特卡罗模拟，并开发「人工智能-粮食安全-公平-土地责任指数(AI-FSELR)」多维度框架。结果显示：虽然AI技术显著提升农业生产力(+24.8%)、资源利用效率(+18.9%)与农场收入(+13.8%)，但粮食安全(+8.6%)与社会公平(+5.4%)的收益较小。治理质量、数字可及性、推广服务、可负担性与农民培训成为比算法复杂性更重要的社会成果决定因素。未来情景表明，到2050年包容性和治理驱动的AI路径可使社会效益增加40%以上。","## Abstract\n\nArtificial intelligence (AI) is increasingly recognized as a powerful tool for addressing some of the most pressing challenges facing global agrifood systems, including food insecurity, climate change, resource scarcity, and the growing demand for sustainable agricultural production. However, an important question remains largely overlooked: do technological advances in AI translate into meaningful societal benefits? Drawing on a global evidence synthesis of 1,276 studies conducted across 112 countries, this review challenges the widespread assumption that technological success automatically leads to food-system transformation. We introduce the Artificial Intelligence Food Security Paradox Theory (AIFSP Theory), a novel conceptual framework suggesting that improvements in algorithmic performance, productivity, and operational efficiency often exceed corresponding gains in food security, livelihood resilience, social equity, and environmental sustainability. To examine this proposition, we combined systematic review methods with hierarchical Bayesian meta-analysis, network meta-analysis, structural equation modeling, explainable machine learning, and Monte Carlo simulations extending to 2050. We also developed the Artificial Intelligence–Food Security–Equity–Land Responsibility Index (AI-FSELR), a multidimensional framework designed to evaluate the societal value of agricultural AI beyond conventional technical performance metrics. The results reveal a consistent and previously underexplored pattern. While AI technologies significantly improved agricultural productivity (+24.8%), resource-use efficiency (+18.9%), and farm income (+13.8%), gains in food security (+8.6%) and social equity (+5.4%) were considerably smaller. Governance quality, digital accessibility, extension services, affordability, and farmer training emerged as stronger determinants of societal outcomes than algorithmic sophistication itself. Future scenarios indicate that inclusive and governance-driven AI pathways could increase societal benefits by more than 40% by 2050, whereas expanding digital inequalities may substantially limit these gains. These findings suggest that the future success of agricultural AI will depend not only on technological innovation but also on the institutional, social, and governance conditions that shape its adoption. The AIFSP Theory and AI-FSELR framework provide a new foundation for evaluating AI and guiding more equitable, resilient, and sustainable food-system transformations.\n\n## 1 Introduction\n\nThe rapid emergence of artificial intelligence (AI) is profoundly transforming global agrifood systems and is increasingly recognized as one of the most influential technological developments of the twenty-first century. Recent advances in machine learning, deep learning, computer vision, remote sensing, robotics, digital twins, Internet of Things (IoT) technologies, edge computing, and intelligent decision-support systems are creating unprecedented opportunities to improve agricultural productivity, optimize resource use, enhance climate resilience, and support more adaptive and sustainable land-management strategies ([Liakos et al., 2018](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref52); [Kamilaris and Prenafeta-Boldú, 2018](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref45); [Javaid et al., 2023](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref43); [Paul, 2026](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref62)). These innovations are enabling a transition from reactive and generalized management approaches toward data-driven, site-specific, and predictive agricultural systems capable of responding more effectively to increasingly complex environmental and socio-economic challenges.\n\nThe growing interest in AI-driven agriculture is occurring within a context of mounting global pressures on food systems. According to the United Nations, the global population is projected to reach approximately 9.7 billion people by 2050, significantly increasing the demand for food, water, energy, and natural resources ([Gerland et al., 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref31); [Ritchie et al., 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref72)). Simultaneously, climate change, biodiversity loss, land degradation, water scarcity, geopolitical instability, and market volatility are intensifying pressures on agricultural production systems worldwide ([IPCC, 2023](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref41); [FAO et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref24)). These interconnected challenges threaten not only agricultural productivity but also the resilience, sustainability, and equity of food systems, particularly in vulnerable regions already experiencing chronic food insecurity and environmental stress ([FAO et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref24); [Barrett et al., 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref6)).\n\nAgainst this backdrop, artificial intelligence is increasingly presented as a critical component of future food security and sustainability strategies. AI-powered technologies are now deployed across virtually all segments of agrifood systems, including crop monitoring, disease detection, yield forecasting, precision irrigation, nutrient management, pest surveillance, livestock management, supply-chain optimization, market intelligence, and climate-risk assessment ([Liakos et al., 2018](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref52); [Zhou and Chen, 2023](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref97); [Talaviya et al., 2020](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref85); [Klerkx and Rose, 2020](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref49)). Recent advances in computer vision illustrate the increasing technical sophistication of these applications. YOLO-based architectures have enabled integrated detection, segmentation, growth-stage classification, and real-time identification of horticultural crops, while specialized segmentation models have improved the detection of harvesting targets under challenging greenhouse conditions ([Paul et al., 2024](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref65); [Paul and Machavaram, 2024](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref63)). Similar developments in cotton systems demonstrate the progression from fine-tuned YOLO-based phenological detection to graph-augmented object-detection frameworks for robust maturity assessment ([Verma et al., 2024](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref90); [Verma et al., 2026](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref91)). The ability of AI to process large volumes of heterogeneous data and generate actionable insights in near real time has led many researchers and policymakers to view digital agriculture as a key enabler of sustainable intensification and climate-smart food production ([Birner et al., 2021](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref9); [Rose and Chilvers, 2018](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref74); [Dong et al., 2021](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref17); [Fan and Zhao, 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref20); [de Jong et al., 2021](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref15)).\n\nReflecting this growing momentum, major international organizations—including the Food and Agriculture Organization (FAO), the World Bank, the Organization for Economic Co-operation and Development (OECD), CGIAR, UNESCO, and the United Nations—have increasingly emphasized the strategic role of artificial intelligence in accelerating progress toward multiple Sustainable Development Goals (SDGs), particularly Zero Hunger (SDG 2), Responsible Consumption and Production (SDG 12), Climate Action (SDG 13), and Life on Land (SDG 15) ([FAO, 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref22); [FAO, 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref23); [Afrifa et al., 2026](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref3); [OECD, 2019](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref58); [Fuglie et al., 2020](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref29); [Ben Abdallah et al., 2023](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref7); [Harikrishnan et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref34); [Camaréna, 2020](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref13); [Marvin et al., 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref56); [Tagkopoulos et al., 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref84); [United Nations, 2024](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref87); [Opola et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref60)). These organizations argue that AI-enabled innovations can substantially improve decision-making, optimize resource use, reduce production risks, strengthen climate adaptation, support sustainable land management, and enhance the resilience of agrifood systems. Recent academic assessments further suggest that AI has the potential to facilitate broader food-system transformations through improved forecasting, precision management, environmental monitoring, and adaptive governance mechanisms ([Harikrishnan et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref34); [Kuhl, 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref50); [Agrawal et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref4); [Iqbal et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref42); [Wang et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref92); [Kazanskiy et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref46)). Consequently, artificial intelligence is increasingly viewed not merely as a technological innovation but as a strategic enabler of more sustainable, resilient, and inclusive agrifood futures.\n\nOver the past decade, interest in agricultural AI has grown at an extraordinary pace. Bibliometric studies consistently show a sharp increase in publications addressing machine learning, precision agriculture, digital farming, and AI-enabled crop management since around 2015. This expansion reflects growing investments from governments, research organizations, agri-tech companies, and international development agencies. Today, AI applications span virtually every component of agricultural production and food systems, from crop disease diagnosis, yield prediction, irrigation scheduling, fertilizer optimization, and pest monitoring to livestock management, supply-chain optimization, market intelligence, and climate-risk assessment ([Harikrishnan et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref34); [Agrawal et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref4); [El Jarroudi et al., 2024](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref19)). Reviews by Liakos et al., Javaid et al., and Aijaz et al., and many others have documented substantial improvements in predictive performance, operational efficiency, and resource optimization across a wide range of agricultural contexts ([Liakos et al., 2018](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref52); [Javaid et al., 2023](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref43); [Aijaz et al., 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref5)).\n\nThis technological expansion is also reshaping agricultural automation and Agriculture 4.0 architectures. Vision-guided robotic systems increasingly combine kinematic modeling with AI-based optimization to support autonomous harvesting operations ([Paul and Machavaram, 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref64)), while decentralized blockchain architectures are being explored to strengthen traceability, data exchange, and coordination across agricultural value chains ([Paul, 2026](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref62)). In parallel, generative AI is emerging as a new component of Agriculture 4.0, with potential applications in knowledge support, decision assistance, and context-specific agricultural services, although important challenges related to integration, accessibility, reliability, and governance remain unresolved ([Paul and Machavaram, 2025](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref64)).\n\nDespite these remarkable technological advances, a fundamental question remains surprisingly underexplored: to what extent do improvements in algorithmic performance translate into meaningful societal benefits? The vast majority of studies evaluating agricultural AI continue to focus primarily on technical indicators such as classification accuracy, precision, recall, F1-score, root mean square error, computational efficiency, or predictive performance ([Liakos et al., 2018](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref52); [Kamilaris and Prenafeta-Boldú, 2018](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref45); [Javaid et al., 2023](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref43)). While these metrics are indispensable for assessing the reliability and effectiveness of AI models, they provide only a partial and often incomplete representation of their real-world value.\n\nIndeed, a disease-detection system capable of achieving near-perfect classification accuracy does not automatically improve food security, nor does an irrigation optimization platform that reduces water consumption necessarily enhance farmer livelihoods, strengthen social equity, or promote sustainable land stewardship. As emphasized by recent studies on digital agriculture and responsible innovation, technological efficiency alone cannot guarantee positive societal outcomes because the impacts of AI are mediated by a wide range of institutional, economic, social, and governance factors ([Rose and Chilvers, 2018](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref74); [Rose et al., 2021](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref75); [Klerkx and Begemann, 2020](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref47); [Rose, 2026](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref73); [Ozor et al., 2024](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref61)).\n\nThis challenge is becoming increasingly evident as AI technologies move from experimental settings into real-world agricultural systems. Emerging evidence suggests that the benefits generated by digital innovations are often unevenly distributed across regions, farming systems, and socio-economic groups. Large commercial farms generally possess greater access to digital infrastructure, technical expertise, financial resources, and data services than smallholder farmers, creating the risk that technological progress may reinforce rather than reduce existing inequalities ([Birner et al., 2021](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref9); [Fielke et al., 2020](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref25); [Ingram and Maye, 2020](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref40); [Stein, 2026](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref82); [Abbasi et al., 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref1); [MacPherson et al., 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref54); [Klerkx et al., 2019](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref48); [Mühl and de Oliveira, 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref57); [Lioutas and Charatsari, 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref53); [Lajoie-O'Malley et al., 2020](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref51); [Sun et al., 2024](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref83); [Abdulai et al., 2023](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref2); [Eastwood et al., 2019](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref18)). Similarly, improvements in productivity or resource-use efficiency do not necessarily translate into proportional gains in food security, resilience, or environmental sustainability, particularly when adoption barriers, governance limitations, or market constraints remain unresolved ([Barrett et al., 2022](https:\u002F\u002Ffrontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1916378\u002Ffull#ref6)).\n\nA growing gap is emerging between technological success and societal ","Frontiers in Sustainable Food Systems 2026-08-07","2026-08-07T00:00:00Z","论文",87,{"impact":28,"substance":28,"depth":94,"authority":31,"freshness":139,"relevant":32,"comment":140},2,"全球性研究揭示AI农业技术与社会效益间的显著差距，提出新理论框架，数据详实，对政策制定有重要参考价值，但时效性较低。",[142],{"name":134,"url":131},[99,144,145,146,147,148,149],"AI","数字不平等","全球权衡","112国1276项研究","贝叶斯荟萃分析","AIFSP理论",{"code":105,"slug":106,"name":107,"path":108,"parent_code":48},[],{"status":51,"locked":52,"version":53,"method":54,"evidence":153},{"candidates":154,"agricultural_evidence":25},[155,160],{"code":105,"score":31,"supported":25,"title":156,"abstract":157,"tags":159},[99],[99,158],"food security",[99],{"code":76,"score":161,"supported":25,"title":162,"abstract":163,"tags":166},7,[],[164,165],"机器学习","artificial intelligence",[144],[168,169],"贝叶斯荟萃分析 数字不平等 112国1276项研究 全球权衡","贝叶斯荟萃分析 数字不平等","贝叶斯荟萃分析数字不平等112国1276项研究全球权衡-973",4,"10.3389\u002Ffsufs.2026.1916378\u002Ffull","2026-08-26T00:09:32.429020Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":19,"content":179,"source_name":180,"source_url":19,"published_at":181,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":182,"score_detail":183,"sources":187,"tags":189,"primary_topic":196,"secondary_topics":202,"classification":208,"search_phrases":233,"slug":236,"view_count":65,"doi":19,"paper":19,"created_at":237},945,"第二十七届全国肥料信息交流暨产品交易会发布智慧肥水大模型 奇稷·肥水大模型同步上线","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-08-25\u002Fdoc-inipmxch1919969.shtml","日前在内蒙古呼和浩特举行的第二十七届全国肥料信息交流暨产品交易会上，全国农业技术推广服务中心正式发布智慧肥水大模型，集成20年全国测土配方施肥数据、每年30万户农户施肥调查数据与全国土壤墒情监测数据，构建土壤养分查询、墒情监测预警、农田气象感知、植株营养诊断、肥水智慧推荐等八大智能体。同会上国家农业信息化工程技术研究中心智慧水肥技术团队联合农芯科技(北京)发布国内首个农业水肥管理垂直大模型「奇稷·肥水大模型」，具备问土、探墒、知天、诊苗、巧算、识肥、智报、慧答八大核心功能，填补通用大模型在农田水肥精准管理方面的专业空白。截至2025年底全国水肥一体化技术应用面积达1.88亿亩，其中粮油作物8800万亩。会议搭建水肥科研、生产、推广、应用对接平台，吸引约320家企业参展、超120万人次观展。","在日前于内蒙古呼和浩特举行的第二十七届全国肥料信息交流暨产品交易会上，多名专家齐聚一堂，热议肥水精准高效利用，全面支撑大面积单产提升。\n\n“我国化肥产能大，施肥量也大，一些化肥产品和农业生产需求的匹配度不够，限制了粮食产能的提升。未来通过精准施用肥料，进一步提升效率、促进增产是我们的努力方向。”中国工程院院士、中国农业大学教授张福锁说，当前我国农业生产已进入规模化、机械化、智能化的时代，要大力发展适配机械施肥、水肥一体化和无人机作业的专用肥料，同时充分挖掘有机养分潜力，推动肥料产业绿色低碳转型。\n\n国家农业智能装备工程技术研究中心研究员郑文刚认为，肥水精准高效施用的核心是“测得准、配得准、施得准”，即土壤水分和养分检测快而准、灌溉制度和肥料配方生成精而准、作物水肥实施精而准。他表示，未来要更好地借助AI大模型实现不同场景下的水肥耦合调控，同时加强智慧水肥相关标准制定，加快智能水肥车、施肥无人机等智能装备的推广应用。\n\n全国农业技术推广服务中心数据显示，截至2025年底，全国水肥一体化技术应用面积达到1.88亿亩，其中粮油作物8800万亩。内蒙古通辽、新疆伊犁等地建设玉米百万亩、千万亩水肥一体化“超吨粮田”等典型场景，实现水肥利用效率和粮食单产“双提升”。水肥一体化已经成为玉米、小麦、大豆等粮油作物大面积单产提升的关键举措。\n\n大会上，全国农业技术推广服务中心正式发布了智慧肥水大模型。这个模型基于20年全国测土配方施肥数据、每年30万户农户施肥调查数据以及全国土壤墒情监测数据等构建，集成了土壤养分查询、墒情监测预警、农田气象感知、植株营养诊断、肥水智慧推荐等八大智能体，是水肥提单产的专业工具和智能助手。\n\n据了解，第二十七届全国肥料信息交流暨产品交易会以“精准肥水 增粮增效”为主题，由全国农业技术推广服务中心、中国农业技术推广协会主办。交易会搭建水肥科研、生产、推广、应用等多方对接平台，吸引约320家肥料、节水、农机与智能装备等企业参展，超120万人次线上线下观展。","经济参考报 2026-08-25","2026-08-25T00:00:00Z",86,{"impact":28,"substance":29,"depth":30,"authority":184,"freshness":185,"relevant":32,"comment":186},14,9,"全国性智慧肥水大模型发布，数据详实，影响深远，权威性高，时效性强。",[188],{"name":180,"url":177},[190,191,192,193,194,195],"大模型","水肥一体化","智慧肥水","奇稷","全国农技中心","国家农业信息化工程技术研究中心",{"code":197,"slug":198,"name":199,"path":200,"parent_code":201},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[203],{"code":76,"slug":204,"name":205,"path":206,"parent_code":207},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":51,"locked":52,"version":53,"method":54,"evidence":209},{"candidates":210,"agricultural_evidence":25},[211,218,222,228],{"code":197,"score":212,"supported":25,"title":213,"abstract":215,"tags":217},18,[214],"肥料",[216,214,191],"施肥",[191],{"code":76,"score":114,"supported":25,"title":219,"abstract":220,"tags":221},[190],[190],[190],{"code":223,"score":65,"supported":25,"title":224,"abstract":225,"tags":227},"H2",[],[226],"土壤",[],{"code":71,"score":65,"supported":25,"title":229,"abstract":230,"tags":232},[],[231],"农业信息化",[],[234,235],"国家农业信息化工程技术研究中心 全国农技中心 水肥一体化 智慧肥水","国家农业信息化工程技术研究中心 全国农技中心","国家农业信息化工程技术研究中心全国农技中心水肥一体化智慧肥水-945","2026-08-26T00:09:29.433705Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":19,"content":243,"source_name":244,"source_url":19,"published_at":245,"category":23,"cover_url":19,"hotness":24,"is_selected":52,"score":246,"score_detail":247,"sources":249,"tags":251,"primary_topic":257,"secondary_topics":262,"classification":263,"search_phrases":277,"slug":280,"view_count":281,"doi":19,"paper":19,"created_at":282},963,"福建农科院罗土炎团队：首次完成大黄鱼锥体虫染色体水平无缺口参考基因组组装","https:\u002F\u002Fnew.qq.com\u002Frain\u002Fa\u002F20260819A0BVIR00","近日福建省农业科学院农业质量标准与检测技术研究所科研团队牵头，联合海南大学、福建省水产技术推广总站等单位，在海水养殖病害基础研究领域取得突破性进展，首次完成大黄鱼锥体虫染色体水平无缺口完整基因组组装，填补该寄生病原高质量基因组研究空白。组装获得总长51.04Mb的高质量参考基因组，拆分锚定35条拟染色体，97.96%序列定位至染色体；25条染色体实现端粒至端粒无间断完整组装，识别58组端粒特征重复序列；测序序列回贴率99.99%，基因组完整度BUSCO指标突破99%，共注释10172个蛋白编码基因，重复序列占比50.33%。该研究为我国大黄鱼养殖病害绿色防控筑牢基因数据底座，相关研究论文于2026年7月9日在线发表于国际知名期刊《Scientific Data》。","大黄鱼是我国重要的海水养殖经济鱼类，福建为全国大黄鱼核心养殖产区。近年来，大黄鱼锥体虫病在沿海养殖水域频发，染病鱼体普遍出现贫血、鳃部发白、脾脏肿大、体表出血溃烂等病症，病害暴发极易造成养殖大面积减产。\n\n长期以来，国内外针对鱼类锥体虫的研究仅局限于形态观测、单一基因片段比对等浅层分析，缺乏染色体层级完整基因组资源，严重制约病害致病机理、病原进化、宿主适应等核心科学问题深度攻关。大黄鱼锥体虫35条拟染色体Hi-C互作热图为破解产业发展瓶颈，研究团队整合PacBio HiFi高精度长读长测序、Hi-C染色质构象捕获前沿技术，系统开展大黄鱼锥体虫全基因组解析。本次组装获得总长51.04 Mb的高质量参考基因组，拆分锚定35条拟染色体，97.96%序列定位至染色体；25条染色体实现端粒至端粒无间断完整组装，识别58组端粒特征重复序列。数据校验显示，测序序列回贴率99.99%，基因组完整度BUSCO指标突破99%，共注释10172个蛋白编码基因，重复序列占比50.33%，组装连续性、精准度、完整性均达到国际顶尖标准。\n\n大黄鱼锥体虫染色体水平基因组Circos图依托完整基因组数据，团队开展跨物种比较基因组学解析，厘清大黄鱼锥体虫在锥体虫属的系统演化地位，甄别得到515个显著扩张基因家族。功能富集分析表明，扩张基因集中作用于病原与宿主互作、免疫逃逸、物质代谢、细胞信号传导等关键通路，为揭示寄生虫适应海水养殖环境、诱发鱼类病害的内在分子机制提供关键理论依据。","央广网 2026-08-20","2026-08-20T00:00:00Z",85,{"impact":28,"substance":28,"depth":212,"authority":114,"freshness":84,"relevant":32,"comment":248},"重大科研突破，首次完成大黄鱼锥体虫染色体水平无缺口基因组，对水产养殖病害防控有重要价值，但时效性稍差。",[250],{"name":244,"url":241},[252,253,254,255,256],"福建省农科院","大黄鱼锥体虫","罗土炎","染色体基因组","BUSCO Scientific Data",{"code":258,"slug":259,"name":260,"path":261,"parent_code":201},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":264},{"candidates":265,"agricultural_evidence":25},[266,272],{"code":267,"score":65,"supported":25,"title":268,"abstract":269,"tags":271},"D",[],[270],"水产",[],{"code":258,"score":65,"supported":25,"title":273,"abstract":274,"tags":276},[],[275],"绿色防控",[],[278,279],"大黄鱼锥体虫 染色体基因组 福建省农科院 罗土炎","大黄鱼锥体虫 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Nanobiotechnology》（中科院1区TOP期刊，IF5year=12.3）上发表了题为“Reactive Oxygen Species (ROS)-Responsive Deliveryof Chlorantraniliprole by a Nanoscale Metal-Phenolic Network for SystemicControl of Rice Striped Stem Borer”的研究成果。该工作成功研发了一种基于响应活性氧（ROS）的金属-多酚网络纳米农药递送系统，实现了杀虫剂的精准系统释放，并显著促进水稻根系生长，有效控制二化螟（Chilo suppressalis）的危害。\n\n二化螟是水稻生态系统中危害突出的鳞翅目钻蛀性害虫，对水稻安全生产与粮食安全构成严重威胁。受田间耕作制度变革等因素影响，近年来该虫田间发生程度持续加重。由于二化螟幼虫潜藏于水稻茎秆内部蛀食为害，隐蔽性极强，常规叶面喷雾施药难以抵达害虫取食靶位，药剂有效利用率偏低。氯虫苯甲酰胺悬浮剂长期大剂量粗放施用已诱发二化螟种群产生高水平抗药性，显著制约了该药剂在二化螟绿色精准防控中的应用效能。团队利用单宁酸-铁络合物等材料，成功构建了基于金属-多酚网络的氯虫苯甲酰胺（CAP）纳米递送系统（CAP@TF-PEG）。该载体能够响应害虫取食诱导植物产生的活性氧（ROS）信号，实现包裹农药在受损部位的靶向智能释放，显著增强了其在植株内韧皮部介导的系统传导性能，大幅提高了田间防效和杀虫持效期。其次，通过转录组学（RNA-seq）分析发现，CAP@TF-PEG递送策略不仅维持了CAP的杀虫机制，还进一步强化了其对害虫表皮合成的抑制作用，并显著下调了脂质代谢、跨膜转运及解毒相关的基因，从代谢和分子层面协同破坏了害虫的生理稳态。最后，该纳米制剂展现出了优异的环境与生态兼容性。它不仅对非靶标生物（如斑马鱼、鼠成纤维细胞和人脐静脉内皮细胞）表现出低急性毒性，且有效维持了土壤微生物群落的生态稳定与功能多样性。尤为重要的是，伴随着铁元素和单宁酸的释放，以及植物体内生长素（IAA）信号途径的协同激活，该制剂能显著促进水稻根系的生长发育，实现了“防虫”与“促生”的双重功效。\n\n综上所述，本研究全面展示了基于ROS响应性金属-多酚网络智能递送农药的巨大优势，结合靶向控害与植物生长促进作用，开发了一种安全、高效、生态兼容的多功能新型纳米农药，为新一代以植物健康为导向的农药递送系统的设计提供了重要参考。徐德进副研究员为本文的第一作者，博士研究生周正浩和乔恒为论文并列第一作者，团队负责人谭永安博士（末位通讯）和南京大学洪浩教授为论文共同通讯作者，江苏省农业科学院为第一完成单位。上述研究工作得到了长三角科技创新共同体联合攻关（2025CSJZN01300）和国家水稻产业技术体系（CARS-01）等项目的资助。","江苏省农业科学院 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锚定「十五五」畜牧业高质量发展新局","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F08\u002F25\u002F991026761.html","8月21日至23日第十三届兽医大会暨2026畜牧兽医用品展览会在山东省济南市召开，主题为「提'质'增效，筑牢防疫屏障，'智'启新程，共拓'十五五'新局」，来自全国的动物疫控系统代表、高校科研机构专家、企业代表、兽医行业从业者等近7000人参加。大会设猪、禽、牛羊、马、驴、兔、鸽、水生动物、野生动物等30余个分会场，260多位行业专家参与。国务院参事、农业农村部原副部长于康震介绍了兽药行业的发展新方向(通用生物人工智能、人工智能虚拟细胞、虚拟类器官、类器官芯片、AI辅助实验动物替代等)；中国农业科学院战略研究中心原主任王济民介绍「十五五」期间畜牧业已成为构建多元化食物供给体系、把农业建成现代化大产业的战略产业，并提出实施「AI+畜牧业」建设工程等7大重点任务。","在活动中，国务院参事、农业农村部原副部长于康震介绍了兽药行业的发展新方向，如通用生物人工智能、人工智能虚拟细胞、虚拟类器官、类器官芯片、AI辅助实验动物替代等。\n\n“AI与生物制造技术的融合已从学术探索进入制度化和产业化阶段，为兽药研发突破效率瓶颈、降低成本、提高成功率提供了历史性机遇，中国有望在新一轮医药工业革命中实现从跟跑者到领跑者的跨越，为人类和动物健康事业贡献中国智慧和方案。”于康震表示。\n\n农业农村部食物与营养发展研究所研究员、中国农业科学院战略研究中心原主任王济民介绍，“十五五”期间畜牧业已成为构建多元化食物供给体系、把农业建成现代化大产业的战略产业，居民消费呈现健康化、品牌化、便捷化和多样化升级趋势，我国畜牧业高质量发展须聚焦7大重点任务，包括实施种业振兴攻坚行动，牢牢掌握产业发展主动权；优化政策、生产和区域结构，系统提升供给体系韧性与效率；推进生产方式绿色低碳转型，建设生态友好型畜牧业；筑牢动物疫病防控和生物安全防线，守住产业安全底线；实施“AI+畜牧业”建设工程，驱动产业全方位现代化升级；延伸产业链提升价值链，培育产业高质量发展新动能；统筹利用国内国际资源，推动“走出去”战略。\n\n在此次大会上，还设有猪、禽、牛羊、马、驴、兔、鸽、水生动物、野生动物、兽医实验室检测、减抗替抗、人兽共患病防控、兽医教育等30余个分会场，260多位行业专家倾囊相授，为促进兽医技术交流、兽医用品和兽用产品推广、提高兽医人员业务水平作出贡献。","2026-08-25T07:36:58Z",{"impact":28,"substance":94,"depth":31,"authority":114,"freshness":185,"relevant":32,"comment":457},"全国性兽医大会聚焦AI与畜牧业融合，锚定十五五战略，信息增量足，权威性高，时效性强。",[459],{"name":340,"url":452},[461,462,463,464,465,466],"十五五","畜牧业","多元化食物供给","第十三届兽医大会","AI+畜牧业","同健康",{"code":468,"slug":469,"name":470,"path":471,"parent_code":472},"C5","animal-health","疫病防控与兽药","\u002Ftopic\u002Flivestock\u002Fanimal-health","C",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":475},{"candidates":476,"agricultural_evidence":25},[477,482,487],{"code":472,"score":114,"supported":25,"title":478,"abstract":480,"tags":481},[479],"畜牧",[479],[462],{"code":468,"score":65,"supported":25,"title":483,"abstract":484,"tags":486},[],[485],"兽药",[],{"code":488,"score":32,"supported":52,"title":489,"abstract":490,"tags":491},"J1",[],[],[461],[493,494],"第十三届兽医大会 多元化食物供给 AI+畜牧业 十五五","第十三届兽医大会 多元化食物供给","第十三届兽医大会多元化食物供给AI+畜牧业十五五-952","2026-08-26T00:09:29.696394Z",{"id":498,"title":499,"url":500,"summary":501,"summary_zh":19,"content":502,"source_name":289,"source_url":19,"published_at":290,"category":23,"cover_url":19,"hotness":24,"is_selected":52,"score":503,"score_detail":504,"sources":507,"tags":509,"primary_topic":515,"secondary_topics":520,"classification":521,"search_phrases":539,"slug":542,"view_count":65,"doi":19,"paper":19,"created_at":543},957,"江苏农科院畜牧所：幼畜发酵饲料研发在反刍动物养殖领域取得突破性进展 论文登Nature子刊","https:\u002F\u002Fjaas.ac.cn\u002Fxww\u002Fxsky\u002Fart\u002F2026\u002Fart_82a25dd952cf43d2b8883db00241045a.html","近日江苏省农业科学院畜牧所牛羊遗传改良与健康养殖创新团队在幼畜发酵饲料研发与应用机制研究方面取得重要进展，研究论文在线发表于微生物学领域权威国际期刊《NPJ Biofilms and Microbiomes》(Nature子刊，2026年影响因子11.4)。团队从健康成年青山羊瘤胃液中筛选获得酿酒酵母、地衣芽孢杆菌、粪肠球菌3株适配性优良的原籍功能菌株，构建针对性强、安全性高的羔羊专用复合发酵菌种体系，成功研发出契合羔羊生理需求的专用发酵饲料产品；为期40天的羔羊饲喂试验结果表明，相较于常规饲料组，羔羊生长性能与消化机能得到显著提升。该研究为反刍动物高效健康养殖体系构建提供坚实的理论支撑与可行的实践方案。","amino acid synthesis and ruminal epithelial energy metabolism》，研究系统阐明了发酵饲料助推羔羊早期生长与瘤胃发育的机制，为发酵饲料在幼畜养殖中的规模化应用、反刍动物高效健康养殖体系构建提供了坚实的理论支撑与可行的实践方案。\n\n羔羊培育是反刍动物养殖产业提质增效、节本降耗的核心环节。哺乳期是幼龄反刍动物消化道快速发育、瘤胃反刍功能建立的关键窗口期，饲喂管理水平直接关乎幼畜成活率、后期生长性能及终身饲料利用效率。当前，微生物发酵饲料已广泛应用于畜禽养殖领域，但适配幼龄反刍动物生理特征的专用发酵体系仍存在明显短板。团队结合幼龄反刍动物消化道生理特性与瘤胃微生态特征，从健康成年青山羊瘤胃液中筛选获得酿酒酵母、地衣芽孢杆菌、粪肠球菌3株适配性优良的原籍功能菌株，构建了针对性强、安全性高的羔羊专用复合发酵菌种体系，成功研发出契合羔羊生理需求的专用发酵饲料产品；依托宏转录组测序技术，团队揭示了发酵饲料微生物群落演变的重要规律；为期40天的羔羊饲喂试验结果表明，相较于常规饲料组，饲喂自研专用发酵饲料的羔羊生长性能与消化机能得到显著提升；机制解析显示，该发酵饲料可通过优化瘤胃微生物群落结构、激活瘤胃上皮细胞能量代谢通路，强化机体营养吸收与物质转化能力，显著提升羔羊日增重、饲料转化率等核心生产指标，从根本上解决了常规发酵饲料对幼畜消化适配性不足的行业痛点。\n\n该研究由孔凡林助理研究员与硕士研究生刘丰慧为共同第一作者，孟春花副研究员为通讯作者。王慧利研究员、钱勇研究员、郭亭副研究员为该研究提供了重要指导和技术支持。研究工作得到江苏省农业自主创新资金项目【CX（23）3081】的资助。",80,{"impact":28,"substance":29,"depth":212,"authority":505,"freshness":139,"relevant":32,"comment":506},13,"省级农科院在反刍动物幼畜发酵饲料领域取得突破性进展，论文发表于Nature子刊，具有较高科研价值和产业应用前景，但报道时间较早，时效性不足。",[508],{"name":289,"url":500},[300,510,511,512,513,514],"畜牧所","发酵饲料","NPJ Biofilms","羔羊","瘤胃微生物",{"code":516,"slug":517,"name":518,"path":519,"parent_code":472},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":522},{"candidates":523,"agricultural_evidence":25},[524,528,533],{"code":472,"score":58,"supported":25,"title":525,"abstract":526,"tags":527},[479],[479],[],{"code":516,"score":58,"supported":25,"title":529,"abstract":531,"tags":532},[530],"饲料",[530],[],{"code":534,"score":65,"supported":25,"title":535,"abstract":536,"tags":538},"C2",[],[537],"山羊",[],[540,541],"江苏省农科院 瘤胃微生物 发酵饲料 畜牧所","江苏省农科院 瘤胃微生物","江苏省农科院瘤胃微生物发酵饲料畜牧所-957","2026-08-26T00:09:31.013471Z",{"id":545,"title":546,"url":547,"summary":548,"summary_zh":19,"content":549,"source_name":550,"source_url":19,"published_at":551,"category":136,"cover_url":19,"hotness":24,"is_selected":52,"score":552,"score_detail":553,"sources":555,"tags":557,"primary_topic":563,"secondary_topics":568,"classification":569,"search_phrases":587,"slug":590,"view_count":171,"doi":19,"paper":19,"created_at":591},969,"智慧农业中的智能体：架构、应用与未来挑战","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808","原文标题: Intelligent Agents for Smart Agriculture: Architectures, Applications, and Future Challenges。作者: Wenzheng Tao、Qiwei Sang、Cong Chen、Qirong Mao(江苏大学计算机科学与通信工程学院、南京农业机械化研究所、江苏省大数据泛在感知与智能农业应用工程研究中心)。智能体正逐渐成为智慧农业中一种重要的系统级范式。本综述聚焦于由大语言模型及相关多模态基础模型驱动的现代农业智能体，探讨这一新兴领域如何重塑智慧农业智能系统的组织方式。首先厘清农业智能体的概念边界，将其与传统多智能体系统(MAS)、基于智能体的建模(ABM)、农业基础模型以及静态检索增强问答系统加以区分；随后综合评述它们的架构基础、关键能力、应用场景、部署挑战及未来研究方向。研究发现农业智能体正从孤立的感知、预测与响应生成，迈向对农业任务链中农业知识、动态数据、外部工具与决策流程的目标导向式协同；已开始支撑更具整合性的知识服务、作物监测与诊断、决策支持以及农场级协同管理；但仍面临上下文感知的知识扎根、异构数据与工具集成、长时任务可靠性、多智能体协作稳定性与系统安全等核心挑战。","## 1. Introduction\n\nWith the rapid development of artificial intelligence, large language models, multimodal foundation models, and agent technologies, agricultural intelligent agents are gradually emerging as an important new direction in smart agriculture. Unlike conventional agricultural AI applications that are mainly designed for single tasks such as recognition, prediction, or question answering, agricultural intelligent agents provide a new system-level paradigm for agricultural knowledge understanding, complex task organization, and decision support. In recent years, research on agricultural foundation models has continuously expanded the application scope of foundation models in agricultural knowledge understanding, multimodal information processing, and complex decision support [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B1-agriculture-16-01808),[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B2-agriculture-16-01808)]. Meanwhile, rapid advances in LLM-based agents, particularly in task planning, tool use, memory management, and feedback-driven refinement, have provided an important methodological foundation for agricultural intelligent agents to evolve from “generative responses” toward “task-driven workflow orchestration” [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B3-agriculture-16-01808),[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B4-agriculture-16-01808)]. Driven by these advances, agricultural intelligent agents are expected to promote the transition of smart agriculture from isolated model applications toward task-oriented knowledge organization, tool orchestration, and intelligent decision-making.\n\nThe motivation for writing this paper stems from the urgent need for efficient, intelligent, and sustainable decision-support systems in agricultural production. With global population growth, the intensifying impacts of climate change, and increasingly severe constraints on agricultural resources and the environment, agricultural production systems are facing increasing demands in terms of production efficiency, resource utilization, environmental sustainability, and management complexity [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B5-agriculture-16-01808),[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B6-agriculture-16-01808)]. In this context, agricultural intelligent agents provide a new technical pathway for integrating agricultural domain knowledge, dynamic environmental information, external analytical tools, and management decisions, thereby offering new possibilities for improving the organizational capacity and intelligent service capacity of agricultural production. However, existing studies have mainly focused on agricultural foundation models, task-specific agricultural AI applications, or general-purpose LLM-based agents, while a systematic review of LLM-driven agricultural intelligent agents in smart agriculture remains limited. Accordingly, this paper reviews modern agricultural intelligent agents in the context of smart agriculture, with a particular focus on large language model-driven agricultural intelligent agent systems that are capable of knowledge integration, tool use, task organization, and collaborative decision-making.\n\nThe literature discussed in this review was organized around the research boundary of modern agricultural intelligent agents. Priority was given to studies that connect agricultural tasks with foundation-model-driven agent capabilities, such as task-oriented reasoning, external resource use, workflow organization, or collaborative decision-making. Related studies on traditional agents, agricultural AI models, foundation models, and retrieval-augmented systems were also considered when they helped clarify conceptual boundaries or provide methodological context.\n\nSpecifically, this paper first traces the evolution of the agent concept and defines the core characteristics and research boundaries of agricultural intelligent agents. It then summarizes the basic architectures and key capabilities of modern agents. Subsequently, using agricultural task chains as the main analytical thread, this paper reviews recent research progress on agricultural intelligent agents across representative agricultural scenarios. To avoid treating heterogeneous prototypes as equivalent examples of agentic systems, this review further develops an assessment matrix based on evidence reported in the original studies, distinguishing demonstrated capabilities from dimensions that remain unevaluated. Finally, the paper discusses the key challenges faced by agricultural intelligent agents in real-world agricultural applications and outlines future research directions. This review aims to provide a clear conceptual foundation, technical trajectory, and evidence-based analytical framework for agricultural intelligent agent research, while offering guidance for future development and application in agriculture-related fields.\n\n## 2. Overview of Agents\n\nSince the mid-1990s, agents have been a prominent topic in computer science and artificial intelligence. The core concern of this field is how systems can exhibit autonomy, goal-oriented behavior, and interactive capabilities within a given environment [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B7-agriculture-16-01808)]. Along this line, agent-based computing, software agents, multi-agent systems, and agent-based modeling have developed as related but distinct branches of traditional agent research [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B8-agriculture-16-01808)]. In recent years, with the increasing capabilities of large language models in knowledge-based problem solving, complex reasoning, task planning, and tool use, agent research has been undergoing a new paradigm shift. The focus is gradually expanding from rule-driven mechanisms and interactions within closed environments to intelligent execution systems that use large language models as cognitive cores and are capable of multi-step planning, external tool invocation, process feedback, and dynamic refinement around specific goals [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B3-agriculture-16-01808)].\n\nFor smart agriculture, this paradigm shift is of particular significance. Agricultural tasks are often characterized by the coupling of multi-source information, dynamic external environments, complex knowledge dependencies, and continuous decision-making processes. Standalone models, static knowledge-based question-answering systems, or closed rule-based systems are often insufficient to support complete task chains in real-world agricultural production. Therefore, before discussing agricultural intelligent agents, it is necessary to first review the concept and development of agents, and then further define the core characteristics and research boundaries of agricultural intelligent agents.\n\n### 2.1. Concept and Development of Agents\n\nTraditional agents do not refer to a single technical form, but rather represent a research paradigm that has gradually developed around autonomous behavior, environmental interaction, and coordination in complex systems. From the perspective of research focus, early agent studies can be broadly grouped into three representative directions. The first direction focuses on individual behavior and cognitive mechanisms, including reactive agents that emphasize stimulus–response behaviors in the environment, BDI agents based on the Belief–Desire–Intention architecture, and interface agents designed to assist users in specific tasks [[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B9-agriculture-16-01808),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B10-agriculture-16-01808),[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B11-agriculture-16-01808)]. The second direction concerns Multi-Agent Systems (MAS), which focus on communication, negotiation, cooperation, and distributed decision-making among multiple autonomous entities in a shared environment [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B8-agriculture-16-01808)]. The third direction is Agent-Based Modeling (ABM), which treats agents as simulation units with behavioral rules and analyzes the evolution and emergent phenomena of macroscopic systems by modeling individual decision-making and local interactions [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B12-agriculture-16-01808)]. Therefore, the core value of traditional agents lies mainly in rule-based autonomy, structured decision-making, distributed coordination, and complex system modeling. Their implementation typically depends on manually designed state representations, behavioral logic, and interaction mechanisms.\n\nIn contrast, the recent emergence of LLM-based agents has promoted the evolution of agents from “autonomous units under predefined rules” to “foundation model-driven systems for open-ended task execution”. ReAct organizes reasoning and acting in an interleaved manner, enabling models to continuously advance tasks through a “thought–action–observation” loop and revise subsequent steps according to external feedback [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B13-agriculture-16-01808)]. Toolformer further demonstrates that language models can learn when to call external tools, how to pass parameters, and how to incorporate tool-returned results, thereby overcoming the limitations of completing tasks solely based on parametric knowledge [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B14-agriculture-16-01808)]. Thus, the key difference between modern LLM-based agents and traditional agents is not merely the change of the underlying model, but a transformation in the way intelligence is organized. The former uses large language models or foundation models as cognitive cores and emphasizes open-ended semantic understanding, multi-step task planning, external tool use, and feedback-driven process refinement. The latter, by contrast, relies more heavily on explicit rules, predefined protocols, or simulation mechanisms to achieve autonomy, coordination, or modeling within relatively closed task boundaries. Therefore, the term “agent” discussed in the following sections of this paper mainly follows the meaning of modern LLM-based or foundation model-driven agentic AI.\n\nThe conceptual distinctions among these agent-related paradigms are summarized in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#table_body_display_agriculture-16-01808-t001).\n\n**Table 1.** Conceptual distinctions among agent-related paradigms discussed in this review.\n\n### 2.2. Core Characteristics and Research Boundaries of Agricultural Intelligent Agents\n\nAgricultural intelligent agents in this review are defined in a relatively restrictive sense. They refer to foundation-model-driven systems that can organize agricultural objectives, domain knowledge, dynamic data, external tools, and feedback into a task-oriented process. This definition uses a narrower boundary than the loose use of the term “agent” in some agricultural AI studies. Providing a conversational interface, incorporating a large language model, or retrieving agricultural documents before answer generation is not sufficient for a system to be classified as an agricultural intelligent agent. What matters is whether the system can advance an agricultural task through planning, tool invocation, workflow organization, feedback, verification, or collaborative decision-making.\n\nThis boundary is necessary because several adjacent paradigms are easily conflated in current agricultural AI research. Agricultural foundation models may provide domain knowledge understanding, text generation, or multimodal representation capabilities, but they do not automatically become agent systems if they lack explicit task planning, external resource use, or process-level interaction. Similarly, a RAG-based agricultural question-answering system can improve the evidential basis and domain specificity of responses, but it remains closer to a knowledge-enhanced advisory system if it only completes a “retrieval–generation” pipeline without forming an action chain toward a task objective [[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B15-agriculture-16-01808),[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B16-agriculture-16-01808)]. Traditional agricultural expert systems, MAS, and ABM also differ from the modern agricultural intelligent agents discussed here, because their autonomy is usually grounded in predefined rules, interaction protocols, or simulation assumptions rather than foundation-model-driven reasoning and open-ended tool use.\n\nExisting studies show that agricultural intelligent agents differ substantially in the depth of their agentic mechanisms. ShizishanGPT integrates large language models, search modules, agricultural knowledge graphs, RAG, and agricultural tools, and therefore represents a transition from ordinary agricultural question answering toward tool-augmented agricultural knowledge services [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B17-agriculture-16-01808)]. CottonBot is more directly connected to field-level decision-making because it incorporates soil moisture sensors and weather forecast interfaces into an agentic workflow for cotton irrigation recommendation [[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B18-agriculture-16-01808)]. AgriWorld provides a stronger form of agenticity by allowing LLM agents to call geospatial queries, remote-sensing analysis, crop simulation, and code execution tools, and to revise reasoning on the basis of execution results [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B19-agriculture-16-01808)]. These examples should not be treated as equivalent. Rather, they indicate a spectrum from tool-augmented advisory systems to verifiable task-execution systems.\n\nBased on this boundary, agricultural intelligent agents generally involve four core characteristics. First, they are oriented toward explicit agricultural task objectives, such as advisory support, disease diagnosis, irrigation scheduling, phenotyping workflow construction, farm recordkeeping, or mission planning. Second, they use large language models, multimodal foundation models, or related foundation-model components as important cognitive, reasoning, or interaction cores. Third, they are able to access or coordinate external resources, including knowledge bases, domain-specific models, sensor streams, weather APIs, code execution environments, crop models, digital twins, farm databases, or equipment interfaces. Fourth, they possess some form of multi-step process organization, such as task decomposition, workflow orchestration, intermediate result interpretation, feedback-based refinement, or multi-agent collaboration.\n\nIt should be emphasized that these characteristics are not merely a checklist of technical components. The same component may indicate different levels of agenticity depending on how it is embedded in the system. For example, RAG can support ordinary knowledge retrieval, but it becomes more agent-relevant when retrieval is selectively triggered, compared with other information sources, and used to revise downstream reasoning. Likewise, external APIs or domain models do not necessarily make a system agentic unless the system can select, invoke, interpret, and adjust their use according to task requirements. Feedback and verification mechanisms represent a stronger level of agentic capability because they allow the system to reassess intermediate outputs rather than simply generate a final response.\n\nAccordingly, the agricultural intelligent agents reviewed in the following sections mainly refer to systems that satisfy three minimum conditions: they are oriented toward specific agricultural tasks; they use foundation models as important reasoning, interaction, or orchestration components; and they connect knowledge, data, tools, workflows, feedback, or collaboration mechanisms in a way that supports task progression rather than isolated response generation. This definition provides the basis for distinguishing agricultural intelligent agents from standalone agricultural foundation models, static RAG-based question-answering systems, conventional agricultural AI models, and traditional MAS\u002FABM approaches. It should be noted, however, that the boundary criteria in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#table_body_display_agriculture-16-01808-t002) define the inclusion scope of this review rather than the maturity level of individual systems. The evidence reported in the original studies and the deployment readiness of representative systems are therefore further examined after the application synthesis in [Section 4.5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#sec4dot5-agriculture-16-01808).\n\n**Table 2.** Research boundary and analytical criteria for agricultural intelligent agents in this review.\n\n## 3. Basic Architectures and Key Capabilities of Agents\n\nAfter clarifying the conceptual boundaries of agents and agricultural intelligent agents in [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#sec2-agriculture-16-01808), it is necessary to further discuss the internal structures and key mechanisms through which modern agents execute tasks. Unlike traditional single-turn generative models, modern large language model-driven agents are increasingly designed as composite systems consisting of multiple components, including goal setting, state and context perception, memory management, task planning, tool use, action execution, and feedback-based refinement. Existing studies generally suggest that memory, planning, and tool use constitute the core capability dimensions of LLM-based agents [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B3-agriculture-16-01808),[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B4-agriculture-16-01808),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B20-agriculture-16-01808)]. On this basis, this section reviews the key methodological foundations of modern agents from three perspectives: basic architectures, task planning and tool-based behavioral loops, and multi-agent collaboration and reliability evaluation.\n\n### 3.1. Basic Architectures of Agents\n\nBased on existing representative systems, the architectural design of modern LLM-based agents has gradually shifted from a single mode in which large language models directly respond to inputs toward composite systems supported by task objectives, contextual states, memory mechanisms, decision-making processes, and action interfaces. Generative Agents [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B21-agriculture-16-01808)] enable agents to retain past experiences, derive higher-level reflections, and generate subsequent behaviors through a “memory stream–reflection–planning” mechanism. MemGPT [[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1808#B22-agriculture-16-01808)] introduces an operating system-like hierarchical memory management approach, improving the ability of agents to maintain persistent states in long-range conversations and complex document-based tasks through main context, external storage, and control-flow scheduling. These studies indicate that the basic architecture of modern agents is not merely an external encapsulation of large language models, but an effort to construct stable internal states and behavioral organization mechanisms for continuous task execution.\n\nThe basic architecture of LLM-based agents can generally be summarized into five interrelated functional units. The first is the goal and task representation module, which receives user intentions, environmental objectives, or system constraints and transforms them into task descriptions for subsequent processing. The second unit concerns perception and context construction, drawing together user inputs, external observations, historical information, and tool-returned results as the current-state basis for reasoning. The third unit is memory management, which retains short-term task states, longer-term experiences,","Agriculture 2026, 16(17), 1808","2026-08-23T00:00:00Z",79,{"impact":212,"substance":29,"depth":212,"authority":184,"freshness":161,"relevant":32,"comment":554},"系统综述LLM驱动的农业智能体，架构与应用全面，对智慧农业决策支持有重要参考价值。",[556],{"name":550,"url":547},[37,558,39,559,560,561,562],"综述","智能体","江苏大学","多模态基础模型","多智能体协作",{"code":564,"slug":565,"name":566,"path":567,"parent_code":201},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":570},{"candidates":571,"agricultural_evidence":25},[572,577,583],{"code":71,"score":31,"supported":25,"title":573,"abstract":574,"tags":576},[37],[37,575],"smart agriculture",[37],{"code":564,"score":406,"supported":25,"title":578,"abstract":579,"tags":582},[],[580,581],"农业机械","机械化",[],{"code":76,"score":139,"supported":52,"title":584,"abstract":585,"tags":586},[],[],[39,559],[588,589],"多模态基础模型 多智能体协作 大语言模型 智慧农业","多模态基础模型 多智能体协作","多模态基础模型多智能体协作大语言模型智慧农业-969","2026-08-26T00:09:32.297224Z",{"id":593,"title":594,"url":595,"summary":596,"summary_zh":19,"content":597,"source_name":598,"source_url":19,"published_at":599,"category":136,"cover_url":19,"hotness":24,"is_selected":52,"score":600,"score_detail":601,"sources":603,"tags":605,"primary_topic":613,"secondary_topics":619,"classification":620,"search_phrases":628,"slug":631,"view_count":65,"doi":19,"paper":19,"created_at":632},970,"数字化与可持续农业：基于PRISMA的系统文献综述 涵盖62项2015-2026年实证研究","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807","原文标题: Digitalisation and Sustainable Agriculture: A PRISMA-Based Systematic Literature Review。作者: Zhanna Smagulova、Urpash Shalbolova、Aigul Mukhanova、Gulshat Duzelbayeva(哈萨克斯坦Korkyt Ata Kyzylorda大学、L.N. Gumilyov欧亚国立大学、纳尔霍兹大学)。本研究基于PRISMA 2020指南对2015-2026年间发表的实证证据进行批判性综合分析，考察数字化对可持续农业发展的影响。在Scopus和Web of Science数据库中识别并分析62项实证研究，采用演绎与归纳相结合的主题分析方法，围绕「三重底线」(经济、环境、社会)框架展开。研究发现2022年后学术关注度显著上升，研究高度集中在亚洲(特别是中国，占42%)、欧洲(22.58%)和非洲(17.7%)；现有研究主要凸显数字化在生产力提升、效率改善、收入增长与资源优化配置、排放减少方面的积极影响，但也记录了劳动力替代、不平等加剧、数字工资不透明等不均衡、情境依赖的社会维度负面结果；整合性分析极其匮乏——仅不到5%的研究同时考察经济、环境和社会三个维度。本综述识别出数字化对可持续农业复杂、不均衡且情境依赖影响的四个未来研究优先方向。","## 1. Introduction\n\nAgriculture occupies a structurally irreplaceable position in the global economy. It contributes approximately 24% of global gross domestic product, employs around 1.3 billion people (roughly 16% of the world’s workforce), and underpins food security across approximately 1.2 to 1.5 billion hectares of cultivated land [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B1-sustainability-18-07807)]. Yet the sector faces mounting pressure on multiple fronts: a growing global population, rising per capita food demand, accelerating climate change, and persistent rural poverty [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B2-sustainability-18-07807)]. These intersecting challenges have renewed the urgency of a question posed by the International Assessment of Agriculture, Science and Technology for Development (IAASTD): how can hunger and poverty be alleviated, rural livelihoods enhanced, and equitable, environmentally, socially, and economically sustainable development advanced through the generation and application of agricultural knowledge and technology [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B3-sustainability-18-07807)]?\n\nOver the past decade, one of the most prominent responses to this challenge has been the rapid digitalisation of the agricultural sector. Digital technologies, including information and communication technologies (ICTs), precision agriculture tools, artificial intelligence, remote sensing, and digital platforms, are increasingly regarded as instruments of agricultural transformation. Existing research identifies a convergence of drivers: economic pressures that reward efficiency gains, environmental imperatives that demand more precise use of resources, and social objectives that require the inclusion of smallholder farmers and rural communities in the benefits of technological change [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B4-sustainability-18-07807)]. Studies from diverse national contexts reflect these dynamics: they document productivity gains in the Chinese agricultural sector [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B5-sustainability-18-07807),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B6-sustainability-18-07807)], report increased adoption of modern practices among Saudi Arabian farmers following access to digital information [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B7-sustainability-18-07807)], and link digital agriculture to reductions in agricultural carbon emissions [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B8-sustainability-18-07807)]. Finally, the literature highlights that the transformative potential of digital tools depends critically on inclusive, co-created knowledge transfer among farmers, consultants, and other stakeholders [[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B9-sustainability-18-07807)]. Despite this growing body of work, the literature remains fragmented. Most studies focus on a single technology, country, or dimension of sustainability, typically the economic dimension. Integrative analyses that assess the impact of digitalisation across all three dimensions of sustainable development remain scarce, and potential trade-offs between dimensions are insufficiently explored [[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B10-sustainability-18-07807)]. The present study addresses this gap by analysing the impact of digitalisation on the sustainable development of agriculture through a systematic synthesis of empirical evidence and by identifying directions for future research.\n\nTo achieve this aim, three objectives were set: to conduct a PRISMA-compliant systematic review of empirical literature on digitalisation and sustainable agriculture; to systematically identify and compare the positive and negative effects of digitalisation on the economic, environmental, and social dimensions of agricultural sustainability; and to identify research gaps and priority directions for further enquiry. The study is guided by two research questions: (1) Which geographical regions have been the focus of research on digitalisation and agricultural sustainability, and how has the volume of related publications changed from 2015 to 2026? (2) Do all studies report positive sustainability outcomes associated with digitalisation, or have adverse effects also been documented?\n\n### Theoretical Framework\n\nThe analytical structure of this review is grounded in the concept of sustainable development, as defined by the World Commission on Environment and Development (the Brundtland Commission), which characterised sustainability as development that “meets the needs of the present without compromising the ability of future generations to meet their own needs” [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B11-sustainability-18-07807)]. This foundational definition has since been operationalised through the triple-bottom-line framework, which recognises three interdependent dimensions of sustainability: economic, environmental, and social [[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B12-sustainability-18-07807)]. The framework is now embedded in the United Nations’ 2030 Agenda and its 17 Sustainable Development Goals (SDGs) [[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B13-sustainability-18-07807)]. Several of these, including SDG 2 (Zero Hunger), SDG 8 (Decent Work and Economic Growth), SDG 13 (Climate Action), and SDG 15 (Life on Land), are directly relevant to agriculture.\n\nIn the agricultural context, these three dimensions can be defined as follows. Economic sustainability is the capacity of agricultural systems to generate productive output, maintain profitability, and support rural livelihoods over the long term, as reflected in indicators such as total factor productivity, crop yields, income, and market participation. Environmental sustainability is the capacity to preserve natural resources and ecological functions, particularly through reduced carbon emissions, efficient use of land and water, and improvements in biodiversity and soil health. Social sustainability concerns the conditions under which agricultural labour is performed, including the distribution of income gains, equitable access to technology, the quality of working conditions, and the protection of rural communities from displacement or marginalisation.\n\nThis three-dimensional framework directly informs the review’s organisational structure. The synthesis of empirical findings in [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#sec3-sustainability-18-07807) is organised around these three dimensions, enabling a systematic examination of whether digitalisation contributes to, detracts from, or produces uneven outcomes across economic, environmental, and social sustainability. Critically, the framework also highlights potential trade-offs between dimensions, for example, when gains in economic productivity come at the cost of labour displacement, or when environmental improvements require capital investments that disadvantage smallholders. Recognising these trade-offs is a central objective of this review and a distinctive contribution to the existing literature.\n\n## 2. Materials and Methods\n\n### 2.1. Overview and Design\n\nThis study employs a systematic literature review in line with the PRISMA 2020 guidelines [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B14-sustainability-18-07807)], and the checklist could be found in the [Supplementary Material](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#app1-sustainability-18-07807). The PRISMA framework was selected for its transparent, reproducible protocol for identifying, screening, and synthesising empirical evidence, thereby minimising selection bias and enhancing the reliability of conclusions. The review adopts a combined deductive–inductive analytical approach: deductive methods were used to map the geographical and temporal distribution of research (RQ1), while inductive thematic analysis was applied to synthesise substantive findings across sustainability dimensions (RQ2). Data analysis drew on both quantitative methods, aggregating numerical indicators such as yield changes and productivity growth, and qualitative thematic coding, in which the 62 included studies were systematically categorised according to the economic, environmental, and social dimensions of the theoretical framework described in the “Theoretical Framework” section. A formal meta-analysis was not conducted because preliminary analysis revealed substantial heterogeneity across the included studies in outcome measures, research designs, analytical methods, and reported effect estimates. Consequently, calculating comparable pooled effect sizes and applying statistical methods for quantitative synthesis, including a funnel plot and Egger’s test to assess publication bias, were not methodologically justified.\n\n### 2.2. Data Sources and Search Strategy\n\nA systematic search was conducted in two stages: the first stage was conducted on 29 January 2025 using a single electronic database; Scopus and Web of Science (WoS) was incorporated only in the second stage, the 2026 update (see [Section 2.4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#sec2dot4-sustainability-18-07807)).\n\nScopus was selected as the primary database for its extensive multidisciplinary coverage and strong performance in systematic search retrieval. WoS was included as a complementary source to broaden disciplinary coverage and reduce the risk of database-specific indexing gaps, consistent with best practice in systematic reviewing [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B14-sustainability-18-07807)].\n\nThe search string actually executed in Scopus (Article title, Abstract, Keywords field) was the free-text phrase “digitalisation of agriculture”, which Scopus parsed as digitalisation AND of AND agriculture. The term “precision agriculture” was not used as a separate search term, as the review aimed to examine the broader phenomenon of agricultural digitalisation rather than a single technological approach. Precision agriculture was therefore treated as one form of agricultural digitalisation within the review’s broader conceptual scope. It is important to note that studies focused on precision agriculture and related technologies were identified through the search strategy and are represented among the 62 studies included in the final sample.\n\nThe following filters were then applied sequentially, with the resulting record counts: publication years 2015–2025 (n = 1353); subject areas Computer Science, Environmental Science, Agricultural and Biological Sciences, Social Sciences, Business, Management and Accounting, Economics, Econometrics and Finance, Decision Sciences, and Multidisciplinary (n = 1233); document language limited to English (n = 1186); source\u002Fdocument type limited to Journal and Conference Proceeding (n = 927); the Environmental Science subject category was then excluded to avoid duplication with other categories (n = 717).\n\n### 2.3. Eligibility Criteria\n\nInclusion and exclusion criteria were developed collaboratively by all four authors (Zh.S., U.S., A.M., G.D.) before screening and are presented in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#table_body_display_sustainability-18-07807-t001). The criteria were designed to include only empirical studies that directly examined the impact of digitalisation on agricultural sustainability outcomes, while excluding review articles, theoretical or conceptual papers, and studies focused on non-agricultural sectors.\n\n**Table 1.** Inclusion and exclusion criteria applied in the systematic literature review.\n\n### 2.4. Study Selection\n\nAfter database searches and deduplication, 739 records were retained for title and abstract screening. Screening was conducted independently by three authors (Zh.S., A.M., G.D.) using the eligibility criteria outlined in [Section 2.3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#sec2dot3-sustainability-18-07807). Discrepancies between screeners were recorded: Zh.S. identified 42 eligible articles, A.M. 45, and G.D. 42. All discrepancies were resolved through discussion and consensus arbitration by the fourth author (U.S.). A total of 40 studies were included in the final synthesis. The full selection process is documented in the PRISMA 2020 flow diagram ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#fig_body_display_sustainability-18-07807-f001)).\n\n**Figure 1.** PRISMA flowchart illustrating the study selection process for the first search round (Round 1, Scopus, January 2025). See [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#table_body_display_sustainability-18-07807-t002) for the second search round (Round 2, 2026 update). Source: (Page et al., 2021) [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B14-sustainability-18-07807)].\n\n**Table 2.** PRISMA-style flow for the second search round (Round 2, 2026 update), combining Scopus and Web of Science.\n\n**Figure 1.** PRISMA flowchart illustrating the study selection process for the first search round (Round 1, Scopus, January 2025). See [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#table_body_display_sustainability-18-07807-t002) for the second search round (Round 2, 2026 update). Source: (Page et al., 2021) [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B14-sustainability-18-07807)].\n\n[![Image 1: Sustainability 18 07807 g001](https:\u002F\u002Fwww.mdpi.com\u002Fsustainability\u002Fsustainability-18-07807\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fsustainability-18-07807-g001.png)](https:\u002F\u002Fwww.mdpi.com\u002Fsustainability\u002Fsustainability-18-07807\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fsustainability-18-07807-g001.png)\n\n#### Search Update (Round 2)\n\nOn 5 February 2026, the Scopus search was re-run, retrieving 278 additional records published in 2026. On 17 February 2026, Web of Science was searched for the first time, covering 2015–2026 and retrieving 259 records. The authors judged that a single-database search was insufficient for a comprehensive review and therefore incorporated WoS at this stage. The combined total of 537 records (278 Scopus + 259 WoS) was screened for duplicates; 25 duplicate records were identified and removed, leaving 512 distinct records (278 Scopus + 234 WoS) for screening against the eligibility criteria in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#table_body_display_sustainability-18-07807-t001). Applying these criteria to the 512 records identified 22 additional eligible studies, which were added to the 40 studies retained from the first search round, yielding a final total of 62 studies included in the review.\n\n### 2.5. Data Extraction and Quality Assessment\n\nData from the 62 included studies were extracted by three authors (Zh.S., A.M., G.D.) using a standardised extraction template. For each study, the following information was recorded: author(s) and year; country\u002Fregion of study; research design and data collection method; digital technology or intervention examined; sustainability dimension(s) addressed; key quantitative or qualitative findings; and direction of effect (positive, negative, or mixed). The full extraction is presented in the [Appendix A](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#app2-sustainability-18-07807).\n\nNo formal quality appraisal tool (such as the Mixed Methods Appraisal Tool, MMAT [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B15-sustainability-18-07807)], or the Joanna Briggs Institute checklists) [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B16-sustainability-18-07807)] was used in this review, given the heterogeneity of study designs and methodological approaches across the 62 included studies. This is acknowledged as a limitation of the review (see [Section 4.5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#sec4dot5-sustainability-18-07807)).\n\n### 2.6. Synthesis Approach\n\nGiven the heterogeneity of study designs, geographic contexts, and outcome measures across the included studies, a narrative synthesis was adopted rather than a meta-analysis. Synthesis proceeded in two stages. First, a descriptive analysis mapped the distribution of studies by geographical region, publication year, and sustainability dimension addressed (addressing RQ1). Second, a deductive–inductive thematic analysis synthesised the substantive findings within each sustainability dimension (addressing RQ2). Initial codes were derived deductively from the three-dimensional theoretical framework (economic, environmental, and social sustainability. Sub-themes were then developed inductively from patterns emerging within each dimension. Coding was conducted by three authors (Zh.S., A.M., G.D.) and reviewed for consistency by the fourth (U.S.).\n\n## 3. Results\n\nThis section is organised in accordance with the study’s four guiding research questions. The first question provides contextual information on the digitalisation of agriculture, whereas the subsequent three questions focus on a detailed analysis of how digitalisation affects the sustainable development of the agricultural sector.\n\n### 3.1. RQ1. Which Geographical Regions Have Been the Focus of Research on the Impact of Digital Technologies on Agricultural Sustainability, and How Has the Volume of Related Publications Evolved in Recent Years?\n\nAll studies included in this review were conducted in countries across three continents, Europe, Asia and Africa. However, the geographic distribution of studies was uneven across these regions (see [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#fig_body_display_sustainability-18-07807-f002)). Among the 37 studies conducted in Asia (59.7% of the studies), the overwhelming majority, 26 studies or 42% of the total sample, were conducted in China, which is not surprising. China is one of the world’s largest producers of agricultural products and has been striving not only to expand production volumes but also to improve product quality. The Chinese government has strengthened agricultural land protection and implemented various incentives to promote agricultural production [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B17-sustainability-18-07807)]. In addition, the country continues to liberalise trade in agricultural products [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B18-sustainability-18-07807)]. As a result of these combined factors, China has effectively integrated digital technologies into both the production and trade sectors of its agriculture. Following China in the ranking are India (4.84%) and Kazakhstan (3.2%), both of which are major producers of staple crops such as rice and wheat. Other contributing countries include Indonesia (1.6%), Uzbekistan (1.6%), Vietnam (3.2%), Azerbaijan (1.6%) and members of the South Asian Association for Regional Cooperation (SAARC), including Bangladesh, Nepal, Pakistan, Bhutan, the Maldives, and Afghanistan (1.6%).\n\n**Figure 2.** Geographical distribution of studies. Source: Author’s elaboration using Bing Maps.\n\nEurope accounts for 22.58% of the studies (14 articles), making it the second most represented continent. Germany stands out as the most researched country in Europe (6.45%). As one of the leading agricultural producers in the European Union [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B19-sustainability-18-07807)], German researchers, citing previous studies, argue that nearly 50% of agricultural professional activities in the country could be replaced by digital technologies [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B20-sustainability-18-07807)]. These trends have contributed to Germany’s heightened interest in the digitalisation of its agricultural sector.\n\nAfrica ranks third in terms of the number of studies, accounting for 17.7% of the total. In Africa, the greatest interest in the impact of digitalisation on agriculture was shown by researchers in Ghana (6.45%) and Sub-Saharan African countries (4.84%). Additional studies were carried out in Tanzania (1.6%), Nigeria (1.6%), Somali (1.6%) and other African nations (1.6%). The focus on digital agriculture in Africa is lar","Sustainability 2026, 18(15), 7807","2026-08-02T00:00:00Z",78,{"impact":94,"substance":29,"depth":94,"authority":114,"freshness":171,"relevant":32,"comment":602},"系统综述涵盖62项研究，全面分析数字化对农业可持续性的多维影响，具有较高学术价值和信息增量，但时效性稍差。",[604],{"name":598,"url":595},[606,607,608,609,610,611,612],"数字化","Sustainability","可持续农业","哈萨克斯坦","PRISMA","三重底线","文献综述",{"code":614,"slug":615,"name":616,"path":617,"parent_code":618},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":621},{"candidates":622,"agricultural_evidence":25},[623],{"code":614,"score":31,"supported":25,"title":624,"abstract":625,"tags":627},[608],[608,626],"sustainable agriculture",[608],[629,630],"可持续农业 哈萨克斯坦 三重底线 文献综述","可持续农业 哈萨克斯坦","可持续农业哈萨克斯坦三重底线文献综述-970","2026-08-26T00:09:32.321765Z",{"id":634,"title":635,"url":636,"summary":637,"summary_zh":19,"content":638,"source_name":340,"source_url":19,"published_at":639,"category":23,"cover_url":19,"hotness":24,"is_selected":52,"score":600,"score_detail":640,"sources":642,"tags":644,"primary_topic":652,"secondary_topics":653,"classification":655,"search_phrases":694,"slug":697,"view_count":281,"doi":19,"paper":19,"created_at":698},951,"农药兽药整治在行动丨山东安丘：让农产品「放心出海」","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F08\u002F25\u002F991026773.html","作为「全国农产品出口第一县」，山东潍坊安丘蔬菜出口量连续17年领跑山东，大姜、大葱、洋葱三大品类出口量均占据全国八成以上。安丘早在2007年成立全国唯一的副县级农产品质量安全监管中心，下设14个镇级管理服务站；108人专职农安员队伍年抽样超过10万批次，国有检测实验室年定量检测能力达1.1万批次。当地供销社联合科研院所，在全国率先推广土壤熏蒸修复社会化服务技术规范，使生长期化学农药使用量减少30%至50%。覆盖全域的农产品质量安全综合管理服务平台为13万个生产经营主体赋予专属「数字身份证—质量安全码」，并与生态原产地码组成「双码」体系。安丘建起全国首个农产品国际标准数据库，动态跟踪67个国家和地区的法规变化，农产品正以日均180个标准柜的规模发往世界各地。","在山东潍坊安丘市长生源生态农场，负责人王宝杰的手机上装着一个特殊的APP，动动手指就能远程操控温室内的水肥一体化系统。“过去种姜全凭经验，化肥撒下去，土壤板结、姜瘟频发。现在用上了‘脱毒姜种+系统化植保’技术，化肥用量减少了一大半，土壤有机质上来了，亩产翻了一番，大姜成功打进了欧盟市场。”谈起种植之变，王宝杰满是感慨。\n\n作为“全国农产品出口第一县”，安丘蔬菜出口量连续17年领跑山东，大姜、大葱、洋葱三大品类出口量均占据全国八成以上。这份成绩的背后，是当地对农产品质量安全的极致追求和对农业投入品的铁腕整治。\n\n早在2007年，安丘就在全国率先成立了唯一的副县级农产品质量安全监管中心，下设14个镇级管理服务站，从顶层设计上把监管、检测、执法、追溯拧成一股绳。与此同时，一支108人的专职农安员队伍常年活跃在田间地头，他们负责全市农产品的普查、取样、检测，年抽样超过10万批次；国有检测实验室则具备CMA、CATL、CNAS等权威资质，年定量检测能力达1.1万批次。这支“专业力量+技术支撑”的队伍，让安丘实现了对每一块地的动态监控、对每一批农产品的精准把关。\n\n生姜，作为安丘农产品出口中的“当家花旦”，农药残留一直是影响出口的一项重要指标。针对生姜种植中“连作重茬”诱发的土传病害顽疾，安丘没有简单依赖化学农药，而是从土壤源头开启一场“革命”。\n\n一方面，当地供销社联合科研院所，在全国率先推广土壤熏蒸修复社会化服务技术规范，通过物理和生物手段杀灭病原菌，使生长期化学农药使用量减少30%至50%。另一方面，安丘多部门联动，对辖区农资门店开展拉网式排查，严厉打击违规销售使用溴甲烷等违禁土壤熏蒸剂行为，从源头斩断高毒农药流通链条。\n\n此外，生态种植理念也被贯彻到极致。合力生态农场采用沼液替代传统化肥，借助智能水肥一体化系统精准调控，构建起“以种防病、以肥养地”的良性循环。在安丘，像这样的家庭农场已超过1900家，全市40万亩生态基地告别了对化肥农药的过度依赖，土壤有机质含量显著提升，生产的农产品农残合格率常年稳定在99%以上。\n\n质量过硬，让安丘农产品的“国际通行证”更有成色。依托覆盖全域的农产品质量安全综合管理服务平台，安丘为全市13万个生产经营主体赋予专属的“数字身份证—质量安全码”，并与生态原产地码组成“双码”体系。消费者扫码即知产品从种植到检测的全过程，这种全透明追溯极大增强了国际市场信任。\n\n面对全球各国频繁调整的农残标准，安丘建起了全国首个农产品国际标准数据库，动态跟踪67个国家和地区的法规变化，提前半年向出口企业精准预警，帮助企业化壁垒为坦途。\n\n从源头“土壤革命”的深层治理，到出口端“数字制导”的精准护航，安丘用近乎苛刻的质量安全闭环，将“安丘标准”转化为了实实在在的全球竞争力。\n\n如今，安丘的农产品正以日均180个标准柜的规模发往世界各地，用最严苛的农药整治行动，守护住了“舌尖上的安全”，也擦亮了“中国农产品出口第一县”这块金字招牌。","2026-08-25T09:20:28Z",{"impact":29,"substance":94,"depth":343,"authority":24,"freshness":185,"relevant":32,"comment":641},"报道安丘农产品质量安全与农药整治成效，涉及智慧农业与出口，数据详实，具行业参考价值。",[643],{"name":340,"url":636},[645,646,647,648,649,650,651],"山东","农产品出口","安丘","质量安全码","农药整治","土壤熏蒸","国际标准数据库",{"code":468,"slug":469,"name":470,"path":471,"parent_code":472},[654],{"code":258,"slug":259,"name":260,"path":261,"parent_code":201},{"status":51,"locked":52,"version":53,"method":54,"evidence":656},{"candidates":657,"agricultural_evidence":25},[658,662,667,673,679,685,689],{"code":258,"score":58,"supported":25,"title":659,"abstract":660,"tags":661},[317],[317],[],{"code":468,"score":663,"supported":25,"title":664,"abstract":665,"tags":666},8,[485],[],[],{"code":668,"score":65,"supported":25,"title":669,"abstract":670,"tags":672},"B1",[],[671],"蔬菜",[],{"code":674,"score":65,"supported":25,"title":675,"abstract":676,"tags":678},"E4",[],[677],"农产品质量",[],{"code":680,"score":65,"supported":25,"title":681,"abstract":682,"tags":684},"G2",[],[683],"社会化服务",[],{"code":223,"score":65,"supported":25,"title":686,"abstract":687,"tags":688},[],[226],[],{"code":690,"score":32,"supported":52,"title":691,"abstract":692,"tags":693},"E5",[],[],[646],[695,696],"国际标准数据库 农产品出口 质量安全码 农药整治","国际标准数据库 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