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推动电商平台下沉赋能","http:\u002F\u002Fwww.jinchuan.gov.cn\u002Fzfxxgk\u002Fxzbmxxgk\u002Fzfbmxxgk\u002Fqswj\u002Ffdzdnrgk\u002Fgzdt\u002Fart\u002F2026\u002Fart_4eba5a9c8bc94d8b96e17aeb0abc4d7e.html","商务部等7部门印发品质电商\"五优\"行动,深入实施数商兴农和农村电商高质量发展,推动电商平台完善县域服务设施、增加绿色有机名特优新和地理标志农产品供给、对接农业品牌精品培育计划,建设中国消费名品方阵;并推动人工智能与电商深度融合、创新智能体等电商服务新模式。","（四）提升商家品质运营能力。深入开展电子商务惠民惠企活动，做优全国电子商务公共服务网，面向中小商家举办定制化培训。利用电子商务国家专业技术人员继续教育基地、“云上大讲堂”等载体开发公益性品质电商课程。引导地方建立健全电商产业园区质量管理服务体系，引入专业可信的质量检测等服务，结合园区内中小制造企业、跨境电商、直播电商等商家需求，针对性开展质量管理、标准应用、诚信合规、品牌建设等主题培训。\n\n（五）创新发展电商消费品牌。加快电商领域消费品牌建设，构建品牌库、孵化库等分级服务体系，为品牌成长提供系统化支撑。深入实施“数商兴农”和农村电商高质量发展、“互联网+”农产品出村进城工程，推动电商平台完善县域服务设施，提升本地化服务能力，增加绿色、有机、名特优新和地理标志农产品供给，发展农产品区域公用品牌，对接农业品牌精品培育计划，让更多源头好物直连消费者。继续深化数字化助力消费品工业“三品”行动，建设中国消费名品方阵，支持符合条件的消费品生产制造企业申报专精特新企业。\n\n（六）赋能品质商家市场开拓。开展“电商+产业带”培育行动，鼓励各地规划布局电商选品中心，重点支持具有核心技术、原创设计的中小卖家对接电商渠道，提升品牌知名度。发挥电商平台作用，依托订单直采、外贸优品专区等渠道，支持外贸企业拓展国内市场，扩大外贸优品消费。支持电商相关产品研发和服务创新，推动人工智能与电商深度融合，创新智能体等电商服务新模式，提升用户体验。开展“产业电商惠企对接”，推动一批产业带企业与电商平台达成合作，促进产业升级，形成经济新增长点。","金川区人民政府(2026-10-15)",85,{"impact":105,"substance":30,"depth":157,"authority":203,"freshness":106,"relevant":34,"comment":263},"七部门联合部署品质电商“五优”行动，含数商兴农、县域电商服务设施、农产品区域公用品牌等实质条款，全国性政策层级高，值得进入每日精选。",[265],{"name":260,"url":257},[208,212,267,268,269],"农产品品牌","电商平台","品质电商",{"code":214,"slug":215,"name":216,"path":217,"parent_code":218},[],{"status":52,"locked":53,"version":54,"method":55,"evidence":273},{"candidates":274,"agricultural_evidence":26},[275],{"code":214,"score":33,"supported":26,"title":276,"abstract":277,"tags":279},[],[208,278],"地理标志",[267,208,212],[281,282],"商务部 品质电商 五优行动","数商兴农 农产品出村进城","商务部品质电商五优行动-5251","2026-10-19T00:07:31.152933Z",{"id":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":20,"source_name":291,"source_url":288,"published_at":292,"category":293,"cover_url":20,"hotness":25,"is_selected":53,"score":294,"score_detail":295,"sources":297,"tags":299,"primary_topic":306,"secondary_topics":312,"classification":313,"search_phrases":369,"slug":372,"view_count":80,"doi":373,"paper":374,"created_at":388},5235,"Role of Smart Fertilization in Modern Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijpss\u002F2026\u002Fv38i106378","Global food demand is increasing rapidly owing to population growth, placing considerable pressure on agricultural systems to enhance productivity under constraints on natural resources. Although conventional fertiliser use has contributed significantly to improved yields, its excessive and imbalanced application has resulted in soil degradation, nutrient leaching, groundwater contamination, and increased emissions of nitrous oxide (N₂O), thereby reducing nutrient use efficiency (NUE). In this context, smart fertilisation has emerged as a science-driven nutrient management strategy that emphasises the application of the right nutrient source at the right rate, time and place (4R principle), supported by advanced tools such as soil and plant sensors, remote sensing, geographic information systems, and decision-support platforms. Key components include controlled-release fertilisers, nano-fertilisers, bio-formulations, fertigation systems, variable rate technology (VRT), and unmanned aerial vehicle (UAV)-based crop monitoring. Evidence from multi-location field studies across diverse agro-climatic regions of India and other countries indicates that smart fertilisation can enhance nutrient use efficiency by 50–80%, reduce fertiliser input by 19–30%, and improve crop yields by 22–30% in major crops such as rice, wheat, maize and pulses. With the global fertiliser market exceeding USD 200 billion, the adoption of precision nutrient management technologies is gaining momentum. Overall, smart fertilisation offers a sustainable intensification pathway by improving productivity while simultaneously minimising environmental externalities. However, challenges related to technology accessibility, cost, and farmer adoption remain critical constraints that require policy and institutional support.","由于人口增长，全球粮食需求迅速上升，给农业系统带来了巨大压力，要求在自然资源约束下提高生产力。尽管常规施肥对提高产量作出了显著贡献，但过量和不平衡施肥已导致土壤退化、养分淋失、地下水污染以及氧化亚氮（N₂O）排放增加，从而降低了养分利用效率（NUE）。在此背景下，智能施肥作为一种以科学为驱动的养分管理策略应运而生，强调在正确的时间、正确的地点以正确的用量施用正确的养分来源（4R原则），并借助土壤与植物传感器、遥感、地理信息系统和决策支持平台等先进工具加以支撑。其关键组成部分包括控释肥料、纳米肥料、生物制剂、水肥一体化系统、变量施肥技术（VRT）以及基于无人机（UAV）的作物监测。来自印度及其他国家不同农业气候区多点田间研究的数据表明，智能施肥可使养分利用效率提高50%–80%，减少肥料投入19%–30%，并使水稻、小麦、玉米和豆类等主要作物增产22%–30%。随着全球肥料市场规模超过2000亿美元，精准养分管理技术的采用正在加速推进。总体而言，智能施肥通过提高生产力同时最大限度减少环境负外部性，提供了一条可持续集约化路径。然而，技术可及性、成本和农民采用等方面的挑战仍是关键制约因素，需要政策和制度支持。","International Journal of Plant & Soil Science","2026-10-17T00:00:00Z","论文",83,{"impact":30,"substance":30,"depth":157,"authority":203,"freshness":158,"relevant":34,"comment":296},"系统综述智能施肥技术体系与多国田间数据，量化增产节肥效果，对智慧农业与绿色投入品方向有参考价值，但属综述性论文且非国内案例，时效与权威度中等。",[298],{"name":291,"url":288},[300,301,302,303,304,305],"智慧农业","无人机","精准施肥","变量施肥","遥感监测","养分利用效率",{"code":307,"slug":308,"name":309,"path":310,"parent_code":311},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":314},{"candidates":315,"agricultural_evidence":26},[316,327,334,341,346,353,357,364],{"code":307,"score":317,"supported":26,"title":318,"abstract":320,"tags":326},20,[319],"fertilization",[321,322,323,324,325],"施肥","肥料","水肥一体化","养分管理","fertigation",[305,303,302],{"code":328,"score":59,"supported":26,"title":329,"abstract":330,"tags":333},"I3",[],[331,332],"遥感","remote sensing",[304],{"code":335,"score":106,"supported":26,"title":336,"abstract":337,"tags":340},"A1",[],[338,339],"水稻","rice",[],{"code":66,"score":106,"supported":26,"title":342,"abstract":343,"tags":345},[],[70,344],"wheat",[],{"code":347,"score":106,"supported":26,"title":348,"abstract":349,"tags":352},"A3",[],[350,351],"玉米","maize",[],{"code":169,"score":106,"supported":26,"title":354,"abstract":355,"tags":356},[],[181,170],[],{"code":358,"score":106,"supported":26,"title":359,"abstract":360,"tags":363},"I4",[],[361,362],"地理信息","geographic information",[],{"code":365,"score":34,"supported":53,"title":366,"abstract":367,"tags":368},"I1",[],[],[300],[370,371],"智能施肥 4R原则","变量施肥技术 无人机","智能施肥4R原则-5235","10.9734\u002Fijpss\u002F2026\u002Fv38i106378",{"doi":373,"openalex_id":375,"authors":376,"venue":291,"cited_by_count":145,"oa_url":288,"card":381,"direction":385,"ingested_from":387},"W7222838957",[377,379],{"name":378,"orcid":20},"S. T. Panusha",{"name":380,"orcid":20},"T. M. Soumya",{"tldr":382,"method":383,"finding":384,"direction":385,"opportunity":386},"综述智能施肥（4R原则+传感器\u002F遥感\u002FVRT）提升养分利用效率与产量的机制与证据。","整合多地点田间试验证据，梳理传感器、遥感、GIS、控释\u002F纳米肥与无人机监测等技术","智能施肥可提高养分利用效率50–80%、减施化肥19–30%、增产22–30%，但受成本与农户采纳制","智慧农业 \u002F 农业物联网","可研究低成本传感器+无人机变量施肥在小农户场景的适配与采纳机制，及减氮减排的量化评估。","openalex","2026-10-18T23:30:07.850055Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":20,"source_name":395,"source_url":392,"published_at":292,"category":293,"cover_url":20,"hotness":25,"is_selected":53,"score":294,"score_detail":396,"sources":398,"tags":400,"primary_topic":404,"secondary_topics":409,"classification":410,"search_phrases":455,"slug":458,"view_count":145,"doi":459,"paper":460,"created_at":472},5232,"Current applications of artificial intelligence for advancing agriculture, sustainability and climate mitigation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02540-1","Artificial intelligence (AI) is reshaping agriculture by enabling data-driven decision-making across crop production systems and supporting the transition toward more sustainable, resilient, and climate-smart farming. This review critically synthesizes advances in AI applications spanning precision irrigation, nutrient and soil management, yield prediction, pest and disease monitoring, computer vision, autonomous agricultural robotics, and large language model (LLM)-based decision support systems. Beyond summarizing technological developments, the review evaluates their practical deployment, emphasizing that successful agricultural AI depends not only on predictive accuracy but also on model generalizability, explainability, economic feasibility, interoperability, and integration with existing agronomic knowledge. Emerging technologies including Vision Transformers, foundation models, Digital Twins, multimodal AI, Retrieval-Augmented Generation, and federated and continual learning—are discussed as important directions for overcoming limitations related to data scarcity, environmental variability, and scalable field implementation. The review highlights that responsible AI adoption requires transparent governance frameworks addressing data ownership, privacy, algorithmic fairness, cybersecurity, and equitable access, particularly for smallholder farming systems. Although numerous AI models report outstanding performance, widespread agricultural adoption remains constrained by cross-region validation, fragmented datasets, infrastructure requirements, and insufficient real-world deployment. Future progress will depend on multidisciplinary collaboration integrating advances in artificial intelligence, sensing technologies, robotics, crop science, and agricultural engineering to develop robust, trustworthy, and farmer-centered decision-support systems. By critically evaluating technological innovations and implementation challenges, this review provides a framework for understanding the current state of AI in agriculture while identifying research priorities that will shape the next generation of sustainable and climate-resilient agricultural systems.","人工智能（AI）正在重塑农业，通过在作物生产系统中实现数据驱动的决策，并支持向更可持续、更具韧性和气候智能型农业转型。本综述批判性地综合了AI应用的进展，涵盖精准灌溉、养分与土壤管理、产量预测、病虫害监测、计算机视觉、自主农业机器人以及基于大语言模型（LLM）的决策支持系统。除总结技术发展外，本综述还评估了其实际部署情况，强调农业AI的成功不仅取决于预测准确性，还取决于模型泛化能力、可解释性、经济可行性、互操作性以及与现有农学知识的整合。文中讨论了包括视觉Transformer、基础模型、数字孪生、多模态AI、检索增强生成以及联邦学习和持续学习在内的新兴技术，将其视为克服数据稀缺、环境变异性和田间可扩展实施等局限的重要方向。本综述强调，负责任的AI采用需要透明的治理框架，以解决数据所有权、隐私、算法公平性、网络安全和公平获取等问题，尤其是对小农耕作系统而言。尽管众多AI模型报告了卓越的性能，但农业领域的广泛采用仍受限于跨区域验证、碎片化数据集、基础设施要求以及现实世界部署不足。未来的进展将取决于多学科协作，整合人工智能、传感技术、机器人技术、作物科学和农业工程领域的进展，以开发稳健、可信且以农民为中心的决策支持系统。通过批判性评估技术创新和实施挑战，本综述提供了一个理解AI在农业中现状的框架，同时确定了将塑造下一代可持续和气候韧性农业系统的研究优先事项。","Discover Artificial 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