[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-83":3,"related-83":78},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":73,"slug":76,"view_count":47,"doi":8,"paper":8,"created_at":77},83,"浙江'十五五'农业农村现代化规划:2030年农业生产信息化率达60%","https:\u002F\u002Fwww.zj.gov.cn\u002Fcol\u002Fcol1229019365\u002Fart\u002F2026\u002Fart_66c5fe09cbe542148638bdb159f4ff6e.html","浙江省人民政府办公厅印发《浙江省'十五五'农业农村现代化规划》。规划明确,到2030年,农业生产信息化率达到60%,建成数字农业工厂1200家、未来农场120家。重点实施'人工智能+农业'行动:夯实农业数字设施、人工智能+农业育种、人工智能+种植养殖,支持建设省级农业人工智能(AI)算力分中心。",null,"1.聚力打造滨海都市农业大产业。高水平建设种业强市，构建科研院校、院士工作站、农业科技试验基地、种业阵型企业一体联动机制，谋划打造中部“甬农硅谷”和南北滨海农业科创大走廊，力争在生物育种、智能农机、智慧生产、数智加工等方面突破，形成一批标志性应用场景。健全杂交水稻和瓜菜两大龙头品牌、岱衢族黄鱼等细分领域品牌组成的种业品牌矩阵，重点推进镇海智慧农科谷和鄞州区稻菜、宁海县与象山县水产等种业聚集区建设，2030年现代种业总产值力争突破70亿元。做精做优绿色食品、有机农产品和全国名特优新农产品，完善农业地方标准，打响“宁波农好”质量安全追溯品牌。做强现代化大产业，大力发展设施农业、智慧农业；加力发展陆基循环水养殖和深远海养殖，推进国家级沿海渔港经济区、南极磷虾加工产业园等重点项目建设；全链发展“土特产”，培育10亿元以上全产业链20条以上，其中百亿级2条，总产值冲刺千亿元。坚持品牌增效，做强“阿拉宁波”统领的区域公共品牌，打造宁波农博会品牌IP，推动区域公用品牌从地方特产升级为行业标杆、文化符号和市场优选；积极拓展创汇蔬菜、水产品、水果罐头等海外市场，推动外向型农业高质量发展。\n\n2.聚力绘就甬派幸福乡村新画卷。完善环都市乡村圈、沿四明山带、沿杭州湾带、沿象山港带“一圈三带”空间布局，深化片区组团和专业运营双轮驱动，跨镇域、跨县域统筹推进城乡融合、片区联动，分类打造乡村组团特色片区集群，构建“宜居有温度、宜业有活力、宜游有韵味”的全域幸福和美乡村新格局，让艺术赋能乡野、人文滋养民心。推行运营前置，引育专业运营团队和优质经营主体深度参与，一体化推进乡村建设、运营提质与文化传承，全面激活乡村内生动力。布局建设一批生态美丽田园，到2030年实现全市涉农乡镇全覆盖。\n\n3.聚力呈现乡村现代生活好图景。深化“共富工坊”增收质效综合改革，打造农村“家门口”零工市场，拓展开发农文旅、物业、环境管护等灵活就业岗位和公益性岗位，建设技能培训共同体，打造“30分钟职业技能培训圈”。发展订单农业，深化“龙头企业+合作社+基地+农户”等联农带农机制，带动更多农户分享产业链增效和两次分配收益。探索农村承包土地经营权流转价格形成机制，激活闲置资源与创业活力，发展庭院经济、民宿经济。深化“国企联村助共富”模式，打造一批“新型帮共体”，推进低收入农户集成帮扶增收。\n\n4.聚力构建全域一体融合共同体。构建区域协调发展机制，加快推进新型城镇化，构建产业升级、人口集聚、城镇发展良性互动机制；大力支持山区海岛乡镇高质量发展，进一步释放绿色发展潜力，增强协同发展能力。推动城乡资源要素均衡配置，深化农村投融资体制改革，完善农村一二三产业融合发展用地政策，有序推进农村集体经营性建设用地入市。深入实施现代“新农人”培育工程、浙农英才培育计划，强化“甬江人才”现代农业领域项目支持，大力培育农村集体经济经理人，畅通青年入乡渠道，引导各类人才返乡入乡创新创业，到2030年现代“新农人”总数突破10万人。健全城乡公共服务一体化机制，创新公共服务供给制度，科学高效配置基本公共服务资源，有序推进标准、服务和政策城乡一体化。","浙江省人民政府 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人工智能","十五五规划人工智能数字农业未来农场-83","2026-07-31T00:00:53.394948Z",{"total":79,"page":22,"page_size":79,"items":80},6,[81,120,179,266,329,400],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":8,"content":86,"source_name":87,"source_url":8,"published_at":88,"category":89,"cover_url":8,"hotness":13,"is_selected":48,"score":90,"score_detail":91,"sources":95,"tags":97,"primary_topic":100,"secondary_topics":101,"classification":102,"search_phrases":115,"slug":118,"view_count":93,"doi":8,"paper":8,"created_at":119},84,"人民日报：'神农'从云端走向地头——每周经济评论","https:\u002F\u002Fpaper.people.com.cn\u002Frmrb\u002Fpc\u002Fcontent\u002F202607\u002F31\u002Fcontent_30172233.html","人民日报7月31日刊发评论《'神农'从云端走向地头》。文章指出,中国农业大学王耀君团队发布神农大模型4.0与神农·农业世界模型1.0两项重大成果。2025年农业人工智能应用进程不断加快,Token(词元)年度消耗数量已超过2.4亿个。场景牵引下,数据和代码转化为现实生产力,深刻重构传统农业生产方式。","习近平总书记指出，人工智能是世界经济增长的新引擎和新旧动能转换的加速器，正从“数字世界”走向“物理世界”。\n\n人工智能，加速迭代创新。前不久，中国农业大学王耀君团队发布两项农业人工智能重大成果——神农大模型4.0与神农·农业世界模型1.0。\n\n这两项成果的分量不一般。前者在数据覆盖、病虫害识别、基因组分析及服务能力等方面全面升级，推动农业人工智能向“看得更准、懂得更多”迈进；后者相当于为农田建立了一块会生长、会响应的数字孪生试验田，回答“做了会怎样、该怎么做”。短短数年时间，农业人工智能的感知、认知、推演和决策能力不断触碰新高度。\n\n作为延续数千年的古老行业，农业为技术赋能提供了一扇独特的观察视窗。2025年，农业人工智能应用进程不断加快，Token（词元）年度消耗数量已超过2.4亿个。场景牵引下，数据和代码转化为现实生产力，深刻重构传统农业生产方式。\n\n技术的生命力在于解决实际问题。在海南，国内首个种业大模型“丰登”，“读基因、编序列、算组合”，大幅缩短育种周期；在江苏扬州的荷塘里，人工智能系统自动调控灌溉水量，精准测算用肥量，让特色种养的规模化、标准化成为可能；在江西赣州，智能分选设备快速剔除瑕疵果，有效降低脐橙产后损耗，助好果卖上好价……可见，顺应农业现代化趋势、击中各环节痛点，人工智能不仅有助于破解“谁来种地”的难题，对于统筹发展科技农业、绿色农业、质量农业、品牌农业，有巨大施展空间。\n\n目前，我国智能经济核心产业规模已经超过万亿元人民币，多地纷纷布局算力、大数据等相关赛道。尽管我国人工智能技术发展日新月异，但就农业领域而言，从点状突破到系统集成，仍然有很长一段路要走。\n\n这是由我国特有的农情决定的。小农经营下，生产数据高度碎片化，而各地水土、气候千差万别，种养对象复杂多样，又带来海量的垂直应用场景。此外，机械化水平的区域不均衡，数字素养的人群差异，也影响技术落地效果。推动“人工智能+农业”从“盆景”走向风景，势必要立足各地的资源和条件，避免一哄而上搞同质化建设，并通过持续夯实现代农业的数字底座，形成各具特色的发展格局。\n\n技术发展始终要以人为本，这也是中国式现代化应有的温度。在广袤田野上，一个个具体的小农户就是最广阔的服务对象。目前，一些地方发布的农技服务智能体，轻量化、低成本，能精准理解农户日常用语，大幅降低了使用门槛。面向未来，人工智能如何兼顾普惠公平，让不同层次主体共享数字红利？如何通过技术服务手段的创新，真正弥合小农户与大生产之间的鸿沟？这些课题仍有待进一步思考。\n\n今年中央一号文件以及《加快农业农村现代化“十五五”规划》，均对人工智能赋能农业作出部署，从传统要素驱动转向科技创新驱动，未来农业的发展路径越来越清晰。在完整产业体系、超大规模市场、丰富应用场景等优势加持下，将人工智能融入我国农业现代化的独特肌理，田野阡陌将释放出更多可能。","人民日报 2026-07-31 18版","2026-07-31T00:00:00Z","报道",93,{"impact":92,"substance":18,"depth":19,"authority":93,"freshness":13,"relevant":22,"comment":94},28,15,"央媒深度报道农业AI重大成果，结合政策与案例，信息增量大，时效性强。",[96],{"name":87,"url":84},[27,30,98,99],"神农大模型","中国农业大学",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":103},{"candidates":104,"agricultural_evidence":48},[105,111],{"code":33,"score":106,"supported":48,"title":107,"abstract":108,"tags":110},8,[],[61,109],"大模型",[30,98],{"code":54,"score":22,"supported":14,"title":112,"abstract":113,"tags":114},[],[],[27],[116,117],"中国农业大学 神农大模型 人工智能 数字农业","中国农业大学 神农大模型","中国农业大学神农大模型人工智能数字农业-84","2026-07-31T00:00:53.423153Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":8,"content":8,"source_name":125,"source_url":8,"published_at":126,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":133,"tags":135,"primary_topic":139,"secondary_topics":140,"classification":141,"search_phrases":163,"slug":166,"view_count":167,"doi":168,"paper":169,"created_at":178},5529,"数字农业技术采纳的驱动因素与障碍：基于复杂网络与演化博弈的分析","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1894648\u002Ffull","文章从演化博弈视角构建数字农业技术采纳的驱动与阻碍机制框架，系统梳理影响农户采纳数字农业技术的关键因素与障碍路径。研究强调通用大模型难以直接满足专业知识、结果依据与溯源能力要求，为政策设计提供了技术采纳侧的理论依据。","Frontiers in Sustainable Food Systems 2026-10-19","2026-10-19T00:00:00Z","论文",67,{"impact":20,"substance":19,"depth":130,"authority":131,"freshness":106,"relevant":22,"comment":132},16,13,"从演化博弈视角分析农户采纳数字农业技术的驱动与障碍，方法有新意、结论有政策参考价值，但属理论性论文，影响范围限于细分领域。",[134],{"name":125,"url":123},[27,136,137,138],"智慧农业","技术采纳","农户行为",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":142},{"candidates":143,"agricultural_evidence":48},[144,148,153,158],{"code":54,"score":131,"supported":48,"title":145,"abstract":146,"tags":147},[27],[27],[27,136],{"code":33,"score":149,"supported":48,"title":150,"abstract":151,"tags":152},3,[],[109],[],{"code":154,"score":22,"supported":14,"title":155,"abstract":156,"tags":157},"G1",[],[],[137],{"code":159,"score":22,"supported":14,"title":160,"abstract":161,"tags":162},"G2",[],[],[138],[164,165],"数字农业 技术采纳 演化博弈","农户行为 技术采纳 数字农业 智慧农业","数字农业技术采纳演化博弈-5529",0,"10.3389\u002Ffsufs.2026.1894648\u002Ffull",{"doi":168,"openalex_id":8,"authors":170,"venue":8,"cited_by_count":167,"oa_url":8,"card":171,"direction":175,"ingested_from":177},[],{"tldr":172,"method":173,"finding":174,"direction":175,"opportunity":176},"构建演化博弈框架分析农户采纳数字农业技术的驱动因素与障碍路径。","演化博弈建模与复杂网络分析，梳理采纳影响因素。","通用大模型难以满足专业知识、结果依据与溯源要求，需政策引导。","数字乡村与农业信息化","可结合实证数据检验博弈模型，并探索面向溯源与专业知识的专用农业模型设计。","agent","2026-10-22T00:07:09.498742Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":8,"source_name":185,"source_url":182,"published_at":186,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":4,"score_detail":187,"sources":191,"tags":193,"primary_topic":197,"secondary_topics":198,"classification":205,"search_phrases":243,"slug":246,"view_count":149,"doi":247,"paper":248,"created_at":265},4693,"Scalable, cloud-native decision support system for Variable Rate Application (VRA) based on multi-year NDRE time series in arable precision agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23282433","This paper presents an automated, cloud-native geospatial Decision Support System (DSS) that generates variable rate prescription maps directly from Sentinel-2 satellite observations. Designed as an end-to-end arable field VRA architecture, the system operates completely autonomously with minimal input, requiring only the field boundary polygon, the crop phenology class (winter or spring crop), and the multi-year observation window. The computation engine streams Cloud-Optimized GeoTIFF (COG) rasters via asynchronous cloud connectivity through the Microsoft Planetary Computer (MPC) SpatioTemporal Asset Catalog (STAC) Application Programming Interface (API), enforcing data quality through multi-stage Scene Classification Layer (SCL) filtering. Instead of the rapidly saturating Normalized Difference Vegetation Index (NDVI), the model relies on the Normalized Difference Red Edge (NDRE) index, which accurately captures canopy chlorophyll and green biomass dynamics. For each native 10×10 m grid cell, the system extracts the 95th percentile (P95) peak value within a crop-specific reproductive phenological window, eliminating single-day snapshot noise while validating winter or spring growth trajectories via automated phenological ratio calculations. The decision and prescription engine is built upon two core agronomic safety pillars: An exponential empirical Bayesian stability shrinkage that dampens unstable, high-variance zones toward the safe field average, preventing the over-application of inputs on drought-prone areas. A trigonometric Horn slope attenuation derived from digital elevation models that systematically reduces application rates on erosion-prone steep slopes. Cell-level rates are distributed within the target tolerance range using a rank-allocation algorithm coupled with a Hamilton quota (largest remainder method) correction, guaranteeing strict integer-based machine compatibility and zero-sum conservation of the total field input budget. The technology was validated on a 139.03 ha commercial production field across four reference seasons (2022, 2023, 2024, and 2026) for Variable Rate Seeding (VRS) in winter wheat (Triticum aestivum L., 230 kg\u002Fha baseline seed rate) in a rolling hilly landscape in Hungary, benchmarked against conventional uniform application and conservative multi-year VRS planning. The primary quantitative findings are summarized below: · Failure of conventional uniform application: Across the 14,250 cells, the flat 230 kg\u002Fha rate was optimal on only 1.1% of the field area. · Input reallocation: The model reallocated 1,633.7 kg of seed (and 1,167.1 kg compared to conservative VRS) from unproductive areas into high-capacity zones (Figure 1.). Across 98.9% of the field, conventional uniform management generated systematic under- or over-seeding, inducing yield losses or wasted operational expenditure. · Drought mitigation: In temporally unstable zones subject to extreme weather risk (covering 33.6% of the field, 46.69 ha), Bayesian stability shrinkage reduced seed allocation by 495 kg compared to uniform seeding. Establishing lower canopy densities in these vulnerable zones mitigates intra-crop water competition and prevents early-season forced ripening (Lollato et al., 2017). · Erosion control: On steep slopes exceeding 12% inclination (accounting for 9.75% of the field, 13.56 ha), the Horn topographic attenuation engine reduced seed input by 553 kg relative to the fixed 230 kg\u002Fha baseline. This intervention minimizes surface runoff damage and agrochemical leaching. · Economic return: Spatially optimizing plant density based on pedological and topographic constraints delivers substantial yield gains. Reallocating seed across the field—without any expenditure on supplemental fertilizer—produces a projected yield increase of +0.20 to +0.40 t\u002Fha (+28 to +56 tons of grain across the evaluated field) (Kindred et al., 2016). At a baseline wheat price of €192\u002Ft (70,000 HUF\u002Ft), this generates an estimated net value of €5,376 to €10,752 (1.96 to 3.92M HUF) on this single 139 ha field.","本文提出了一种自动化、云原生的地理空间决策支持系统（DSS），可直接从Sentinel-2卫星观测数据生成变量施肥处方图。该系统设计为端到端的耕地变量施肥（VRA）架构，完全自主运行且输入需求极低，仅需田块边界多边形、作物物候类别（冬作或春作）以及多年观测窗口。计算引擎通过Microsoft Planetary Computer（MPC）时空资产目录（STAC）应用程序接口（API）以异步云连接方式流式传输云优化GeoTIFF（COG）栅格数据，并通过多阶段场景分类层（SCL）滤波确保数据质量。该模型不采用易快速饱和的归一化差异植被指数（NDVI），而是依赖归一化差异红边指数（NDRE），后者能够准确捕捉冠层叶绿素和绿色生物量动态。对于每个原生10×10 m网格单元，系统提取作物特定生殖物候窗口内的第95百分位数（P95）峰值，消除单日快照噪声，同时通过自动化物候比率计算验证冬作或春作生长轨迹。决策与处方引擎建立在两个核心农艺安全支柱之上：指数经验贝叶斯稳定性收缩，将不稳定的高方差区域向安全的田块平均值收缩，防止在易旱区域过量施用投入品；基于数字高程模型推导的三角函数Horn坡度衰减，系统性地降低易侵蚀陡坡上的施用量。单元级施用量通过秩分配算法结合Hamilton配额（最大余数法）校正，在目标容差范围内分配，确保严格的基于整数的机器兼容性和田块总投入预算的零和守恒。该技术已在匈牙利起伏丘陵地貌的139.03 ha商业化生产田块上，针对冬小麦（Triticum aestivum L.，基准播种量230 kg\u002Fha）的变量播种（VRS），在四个参考季节（2022、2023、2024和2026年）进行了验证，并以常规均一施用和保守的多年VRS规划为基准进行比较。主要定量结果总结如下：· 常规均一施用的失败：在14,250个单元中，统一的230 kg\u002Fha施","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",{"impact":19,"substance":188,"depth":189,"authority":131,"freshness":13,"relevant":22,"comment":190},23,19,"基于多年度NDRE时序的云原生变量施肥决策系统，方法新颖、验证数据扎实，对精准农业信息化有较高参考价值。",[192],{"name":185,"url":182},[27,136,194,195,196],"变量施肥","精准农业","遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[199],{"code":200,"slug":201,"name":202,"path":203,"parent_code":204},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",{"status":40,"locked":14,"version":41,"method":42,"evidence":206},{"candidates":207,"agricultural_evidence":48},[208,213,218,224,232,238],{"code":54,"score":47,"supported":48,"title":209,"abstract":211,"tags":212},[210],"precision agriculture",[],[27,136,195],{"code":33,"score":47,"supported":48,"title":214,"abstract":216,"tags":217},[215],"decision support",[215],[],{"code":200,"score":219,"supported":48,"title":220,"abstract":221,"tags":223},9,[],[202,222,201],"winter wheat",[],{"code":225,"score":226,"supported":48,"title":227,"abstract":228,"tags":231},"F3",7,[],[229,230],"施肥","fertilizer",[194],{"code":233,"score":149,"supported":48,"title":234,"abstract":235,"tags":237},"H7",[],[236],"drought",[],{"code":239,"score":22,"supported":14,"title":240,"abstract":241,"tags":242},"I3",[],[],[196],[244,245],"Sentinel-2 NDRE 变量施肥","云原生 决策支持系统 精准农业","Sentinel-2NDRE变量施肥-4693","10.5281\u002Fzenodo.23282433",{"doi":247,"openalex_id":249,"authors":250,"venue":185,"cited_by_count":167,"oa_url":182,"card":258,"direction":175,"ingested_from":264},"W7221365319",[251,253,255],{"name":252,"orcid":8},"Zoltán Szenek",{"name":254,"orcid":8},"Donát Szám",{"name":256,"orcid":257},"Gábor Milics","https:\u002F\u002Forcid.org\u002F0000-0002-4499-8322",{"tldr":259,"method":260,"finding":261,"direction":262,"opportunity":263},"构建云原生决策支持系统，用多年NDRE时序自动生成精准农业变量施肥\u002F播种处方图。","Sentinel-2 NDRE、COG\u002FSTAC云流式处理、贝叶斯收缩与坡度衰减","在匈牙利139公顷冬小麦田验证，系统优于传统均一施用与保守多年VRS规划。","农业遥感与作物表型","可探索将NDRE时序DSS扩展至多作物、多区域及实时物联网数据融合的通用处方框架。","openalex","2026-10-11T23:30:36.790037Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":8,"source_name":185,"source_url":269,"published_at":186,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":276,"tags":278,"primary_topic":281,"secondary_topics":282,"classification":283,"search_phrases":311,"slug":314,"view_count":22,"doi":315,"paper":316,"created_at":328},4657,"Agriculture and Farmer Digital Assistant: An AI-Based Digital Decision Support Platform for Smart Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23278187","Abstract Farmers frequently make decisions under uncertain weather, incomplete agricultural information, changing soil conditions, and limited access to timely expert guidance. The Agriculture and Farmer Digital Assistant (AFDA) is a web-based digital decision-support platform designed to bring agricultural data, artificial intelligence, recommendation services, weather information, and role-based administration into a single system. The platform accepts agricultural inputs from farmers and processes them to provide crop recommendations and basic farming guidance. The design also considers soil analysis, pest and disease support, market information, notifications, multilingual assistance, and future sensor integration. The implemented project version focuses on user registration and login, agricultural data input, crop recommendation, basic weather guidance, and a recommendation dashboard, while several advanced capabilities remain future extensions. A layered architecture separates the user interface, application logic, AI processing, database, and notification\u002Frecommendation functions. The project further defines functional and non-functional requirements, data-flow and entity relationships, user workflows, testing procedures, and maintenance considerations. The reported evaluation is functional rather than numerical: the major input, authentication, recommendation, navigation, and output-display workflows were checked during development, while no measured accuracy or response-time benchmark is claimed. AFDA therefore provides a foundation for a connected and extensible agricultural assistance system intended to support better-informed and more sustainable farming decisions. Keywords Agriculture and Farmer Digital Assistant, AFDA, artificial intelligence, machine learning, crop recommendation, smart farming, agricultural decision support, weather guidance, role-based access control.","农户常常在天气不确定、农业信息不完整、土壤条件不断变化以及难以及时获得专家指导的情况下作出决策。农业与农户数字助手（Agriculture and Farmer Digital Assistant，AFDA）是一个基于网络的数字决策支持平台，旨在将农业数据、人工智能、推荐服务、天气信息和基于角色的管理整合到一个系统中。该平台接收农户输入的农业数据，并对其进行处理，以提供作物推荐和基本农耕指导。其设计还考虑了土壤分析、病虫害支持、市场信息、通知、多语言辅助以及未来的传感器集成。已实现的项目版本侧重于用户注册与登录、农业数据输入、作物推荐、基本天气指导以及推荐仪表板，而若干高级功能仍留作未来扩展。分层架构将用户界面、应用逻辑、人工智能处理、数据库以及通知\u002F推荐功能相互分离。该项目进一步定义了功能性与非功能性需求、数据流与实体关系、用户工作流程、测试程序以及维护方面的考虑。所报告的评估是功能性的而非数值性的：在开发过程中检查了主要输入、身份验证、推荐、导航和输出显示等工作流程，但未声称有任何经过测量的准确性或响应时间基准。因此，AFDA为一个互联且可扩展的农业辅助系统提供了基础，旨在支持更明智且更可持续的农耕决策。关键词：农业与农户数字助手，AFDA，人工智能，机器学习，作物推荐，智慧农业，农业决策支持，天气指导，基于角色的访问控制。",54,{"impact":106,"substance":274,"depth":20,"authority":13,"freshness":13,"relevant":22,"comment":275},14,"面向农户的AI决策支持平台论文，架构与需求设计较完整，但仅做功能验证、无精度或性能数据，属早期原型，参考价值有限。",[277],{"name":185,"url":269},[27,136,61,279,280],"作物推荐","农业决策支持",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":284},{"candidates":285,"agricultural_evidence":48},[286,293,300,305],{"code":33,"score":19,"supported":48,"title":287,"abstract":288,"tags":292},[215],[289,290,291,215],"机器学习","machine learning","artificial intelligence",[61],{"code":294,"score":79,"supported":48,"title":295,"abstract":296,"tags":299},"H2",[],[297,298],"土壤","soil",[],{"code":54,"score":301,"supported":48,"title":302,"abstract":303,"tags":304},5,[],[136],[27,136],{"code":306,"score":149,"supported":48,"title":307,"abstract":308,"tags":310},"I6",[],[309],"agricultural data",[],[312,313],"AFDA 农业数字助手","农业人工智能 农业决策支持 作物推荐 数字农业","AFDA农业数字助手-4657","10.5281\u002Fzenodo.23278187",{"doi":315,"openalex_id":317,"authors":318,"venue":185,"cited_by_count":167,"oa_url":269,"card":321,"direction":327,"ingested_from":264},"W7221331787",[319],{"name":320,"orcid":8},"B. Siddhu, G. Naveen, S. Guru Charan, T. Ajay, C. Naresh",{"tldr":322,"method":323,"finding":324,"direction":325,"opportunity":326},"开发了基于AI的农业数字决策支持平台AFDA，为农民提供作物推荐和农事指导。","采用分层架构的Web平台，集成AI处理、数据库和推荐服务，支持多语言。","平台实现了用户认证、数据输入、作物推荐和天气指导等核心功能，但未提供精度或响应时间基准。","农业人工智能与决策模型","可补充传感器实时数据集成与模型精度评估，提升决策支持的可靠性和实时性。","智慧农业 \u002F 农业物联网","2026-10-11T23:30:07.538409Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":8,"source_name":335,"source_url":332,"published_at":336,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":337,"score_detail":338,"sources":340,"tags":342,"primary_topic":344,"secondary_topics":345,"classification":346,"search_phrases":381,"slug":384,"view_count":21,"doi":385,"paper":386,"created_at":399},4105,"Artificial Intelligence in Agriculture: Enhancing Yield and Sustainable Farming Practices among Farmers in India","https:\u002F\u002Fdoi.org\u002F10.31235\u002Fosf.io\u002Fg7ypt_v1","Artificial intelligence (AI) is increasingly being applied across sectors, including agriculture. In India, agriculture remains central to employment, rural livelihoods, and national food security. Rising population pressure, climate change, declining land availability, pest and disease risks, labour shortages, and resource constraints have intensified the need for more productive and sustainable farming systems. This paper uses secondary data to examine how AI can enhance agricultural yield and support sustainable farming practices among farmers in India. It reviews applications such as precision agriculture, drones, crop disease detection, digital advisory services, market information systems, and resource optimisation tools. The paper also analyses the major barriers to AI adoption, including weak rural connectivity, high initial investment, limited digital literacy, and the need for localised solutions. The study concludes that AI has strong potential to improve productivity and sustainability in Indian agriculture, provided that adoption is supported through public investment, affordable access models, local-language advisory systems, farmer training, and public- private partnerships.","人工智能（AI）正日益应用于包括农业在内的各个领域。在印度，农业仍然是就业、农村生计和国家粮食安全的核心。人口压力不断上升、气候变化、可用土地减少、病虫害风险、劳动力短缺以及资源约束，加剧了对更高生产力和更可持续农业体系的需求。本文利用二手数据，考察人工智能如何提升印度农民的农业产量并支持可持续耕作方式。文章综述了精准农业、无人机、作物病害检测、数字咨询服务、市场信息系统和资源优化工具等应用。本文还分析了人工智能应用的主要障碍，包括农村连通性薄弱、初始投资高、数字素养有限以及对本地化解决方案的需求。研究认为，人工智能在提升印度农业生产力和可持续性方面具有巨大潜力，前提是通过公共投资、可负担的获取模式、本地语言咨询系统、农民培训以及公私合作来支持其应用。","OpenAlex","2026-10-02T00:00:00Z",69,{"impact":19,"substance":130,"depth":274,"authority":20,"freshness":219,"relevant":22,"comment":339},"基于二手数据综述印度AI农业应用与推广障碍，结论有参考价值但缺乏一手数据与实证方法创新。",[341],{"name":335,"url":332},[27,136,61,195,343],"印度农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":347},{"candidates":348,"agricultural_evidence":48},[349,353,357,364,370,376],{"code":33,"score":20,"supported":48,"title":350,"abstract":351,"tags":352},[291],[291],[61],{"code":54,"score":219,"supported":48,"title":354,"abstract":355,"tags":356},[],[195,210],[27,136,195],{"code":358,"score":79,"supported":48,"title":359,"abstract":360,"tags":363},"A8",[],[361,362],"粮食安全","food security",[],{"code":233,"score":79,"supported":48,"title":365,"abstract":366,"tags":369},[],[367,368],"气候变化","climate change",[],{"code":371,"score":149,"supported":48,"title":372,"abstract":373,"tags":375},"H8",[],[374],"可持续农业",[],{"code":377,"score":22,"supported":14,"title":378,"abstract":379,"tags":380},"J3",[],[],[343],[382,383],"印度 农业人工智能 可持续农业","印度 精准农业 无人机","印度农业人工智能可持续农业-4105","10.31235\u002Fosf.io\u002Fg7ypt_v1",{"doi":385,"openalex_id":387,"authors":388,"venue":8,"cited_by_count":167,"oa_url":332,"card":394,"direction":175,"ingested_from":264},"W7215740376",[389,392],{"name":390,"orcid":391},"Seema Wankhade","https:\u002F\u002Forcid.org\u002F0009-0005-6663-3729",{"name":393,"orcid":8},"Subhash Patil",{"tldr":395,"method":396,"finding":397,"direction":327,"opportunity":398},"基于二手数据综述AI在印度农业中提升产量与可持续性的应用及障碍。","使用二手数据，综述精准农业、无人机、病害检测等AI应用。","AI能提升印度农业生产力与可持续性，但受网络、成本、数字素养等制约。","可研究小农户低成本AI采纳路径、本地语言咨询系统及公私合作模式的效果评估。","2026-10-03T23:30:42.370830Z",{"id":401,"title":402,"url":403,"summary":404,"summary_zh":8,"content":405,"source_name":406,"source_url":8,"published_at":407,"category":89,"cover_url":8,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":416,"secondary_topics":417,"classification":418,"search_phrases":433,"slug":436,"view_count":106,"doi":8,"paper":8,"created_at":437},3938,"浙江省农科院农业人工智能创新中心算力服务平台建设（二期）项目中标","https:\u002F\u002Fsci-equip.net\u002Findex.php\u002Findex\u002Fbidder\u002Fdetail?id=246564","浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目中标结果公告显示，浙江移动数智科技有限公司以投标总价398.6万元中标，项目评审总得分85.1。代理机构为浙江省政府采购中心，合同信息详见公告附件。","## **关于浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目中标(成交)结果公告[浙江省政府采购中心]**\n\n浏览次数：39 发布日期：2026-09-28 12:33:32 来源：科器在线\n\n一、项目编号：330000263560010000421-ZZCG2026E-GK-147\n\n二、项目名称：浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目\n\n三、中标（成交）信息\n\n1.中标结果：\n\n**序号****中标（成交）金额(元)****中标供应商名称****中标供应商地址****评审总得分**\n1 投标总价：3986000（元）浙江移动数智科技有限公司 杭州市体育场路406号 85.1\n\n四、主要标的信息\n\n货物类主要标的信息：\n\n序号 标项名称 标的名称 品牌 规格型号 数量 单价(元)\n1 浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目 详见附件 详见附件 详见附件 详见附件 3986000\n\n五、评审专家（单一来源采购人员）名单\n\n龚建新，孙才俊（第1标项采购人代表），高夫迅，岳丹，智庆芳\n\n六、代理服务收费标准及金额：\n\n1.代理服务收费标准：无\n\n2.代理服务收费金额（元）：0\n\n七、公告期限\n\n自本公告发布之日起1个工作日。\n\n八、其他补充事宜\n\n1.各参加政府采购活动的供应商认为该中标\u002F成交结果和采购过程等使自己的权益受到损害的，可以自本公告期限届满之日（本公告发布之日后第2个工作日）起7个工作日内，以书面形式向采购人或受其委托的采购代理机构提出质疑。质疑供应商对采购人、采购代理机构的答复不满意或者采购人、采购代理机构未在规定的时间内作出答复的，可以在答复期满后十五个工作日内向同级政府采购监督管理部门投诉。质疑函范本、投诉书范本请到浙江政府采购网下载专区下载。\n\n2.其他事项：无\n\n九、对本次公告内容提出询问、质疑、投诉，请按以下方式联系\n\n1.采购人信息\n\n名 称：浙江省农业科学院（本级）\n\n地 址：杭州市石桥路198号\n\n传 真：\u002F\n\n项目联系人（询问）：孙才俊\n\n项目联系方式（询问）：13121117700\n\n质疑联系人：叶自然\n\n质疑联系方式：0571-88105537\n\n2.采购代理机构信息\n\n名 称：浙江省政府采购中心\n\n地 址：浙江省杭州市西湖区宝石一路3号\n\n传 真：\u002F\n\n项目联系人（询问）：吴皓哲\n\n项目联系方式（询问）：0571-88907791\n\n质疑联系人：张哲\n\n质疑联系方式：0571-88907707\n\n3.同级政府采购监督管理部门\n\n名 称：浙江省财政厅政府采购监管处、浙江省政府采购行政裁决服务中心（杭州）\n\n地 址：杭州市上城区清泰街549号城建综合大楼11楼\n\n传 真：\u002F\n\n联 系 人：匡老师\n\n监督投诉电话：0571-87227986（投诉专用电话）\n\n附件信息：\n\n*   [报价文件.pdf](https:\u002F\u002Fzcy-gov-open-doc.oss-cn-north-2-gov-1.aliyuncs.com\u002F1024FPA\u002F339900\u002F10016441811\u002F20269\u002Ff2a75c21-da03-4d50-8a08-1b6adce07406.pdf)\n\n351.2K\n\n*   [浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目采购文件.docx](https:\u002F\u002Fzcy-gov-open-doc.oss-cn-north-2-gov-1.aliyuncs.com\u002F1024FPA\u002F339900\u002F10013504574\u002F20268\u002Fb9d6d0aa-8538-406b-a100-7d56fa5799d9.docx)\n\n339.0K\n\n*   [关于符合本国产品标准的声明函](https:\u002F\u002Fzcy-gov-open-doc.oss-cn-north-2-gov-1.aliyuncs.com\u002F1024FPA\u002F20269\u002F769d3bc9-b2ea-433d-bf6e-a9de9f825f43.pdf)\n\n439.1K","浙江省政府采购中心·2026年09月29日","2026-09-29T00:00:00Z",50,{"impact":20,"substance":274,"depth":79,"authority":47,"freshness":226,"relevant":22,"comment":410},"省级农科院算力平台二期采购中标公告，属农业AI基础设施建设的实质进展，但内容为程序性公告，信息增量有限。",[412],{"name":406,"url":403},[136,61,28,414,415],"政府采购","算力平台",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":419},{"candidates":420,"agricultural_evidence":48},[421,425,429],{"code":33,"score":20,"supported":48,"title":422,"abstract":423,"tags":424},[61],[61],[61],{"code":54,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[136],{"code":69,"score":22,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[414],[434,435],"浙江省农科院 算力服务平台","浙江移动数智 农业人工智能","浙江省农科院算力服务平台-3938","2026-10-02T11:53:11.533911Z"]