[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5500":3,"related-5500":69},{"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":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":8,"created_at":68},5500,"安徽省印发加快数智供应链发展行动方案 部署「数字皖农」农业数智化转型","https:\u002F\u002Fcredit.zhangye.gov.cn\u002Fwcm\u002Fcontent\u002Fdetail\u002F20261016150003_100603.html","《安徽省加快数智供应链发展行动方案（2026—2028年）》提出深入实施「数字皖农」建设行动，加快建设数字智慧农（牧、渔）场，推广智能传感与精准作业技术装备，实施「互联网+」农产品出村进城工程，利用人工智能优化仓储管理和冷链物流调度，探索「前置仓+即时配送」模式，并构建中药材智能化生产与追溯体系。",null,"11．推进数智供应链底层技术研发。贯彻落实数据接口、数据交换、数据管理、产品主数据、电子单证、成熟度模型、服务流程等国家标准、行业标准，完善数智供应链标准体系。支持建设一批精准标注、质量可靠、场景丰富的高质量供应链数据集，为行业大模型训练与应用提供支撑。加快部署新一代高性能、高可靠网络基础设施，为供应链各环节数据采集、传输、处理，提供稳定、安全、高效的算力支撑与网络环境。（责任单位：省发展改革委、省科技厅、省工业和信息化厅、省数据资源局、省通信管理局）\n\n12．提升供应链智能化应用水平。推广“人工智能+”供应链应用，支持企业、高校等建设“人工智能+”供应链实验基地，提供从原型开发、工艺验证到产业孵化全链条服务。支持企业建设数智供应链控制塔，实现全链条实时感知、可视化监控、智能分析与自主响应，提升供应链响应速度和决策水平。（责任单位：省发展改革委、省数据资源局、省工业和信息化厅、省商务厅、省教育厅）\n\n13．探索供应链数据交易。深化数据产权登记试点，扩大数据产权登记规模，探索建立供应链数据产权登记、流通交易、资产化等服务体系。支持企业依法合规参与数据流通交易。鼓励链主企业围绕业务协同、资源优化、服务增值等场景建设运营可信数据空间，赋能中小企业用数创新。（责任单位：省数据资源局、省发展改革委）","安徽省人民政府办公厅 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试点","安徽数字皖农数智供应链-5500",0,"2026-10-22T00:06:58.247037Z",{"total":70,"page":21,"page_size":70,"items":71},6,[72,146,217,283,334,386],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":8,"content":77,"source_name":78,"source_url":8,"published_at":79,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":87,"tags":89,"primary_topic":92,"secondary_topics":97,"classification":98,"search_phrases":141,"slug":144,"view_count":127,"doi":8,"paper":8,"created_at":145},1338,"池州市印发《人工智能产业发展行动方案(2026-2028)》：聚焦九华黄精、竹业、池州鳜鱼等\"人工智能+\"现代农业","https:\u002F\u002Fwww.chizhou.gov.cn\u002FOpennessContent\u002Fshow\u002F1752209.html","方案围绕九华黄精、竹业、池州鳜鱼、富硒、食用菌、茶叶等特色主导产业，以 AI 技术赋能农业全产业链，推动\"数字皖农\"从示范试点走向全域覆盖；以 AI 和数字技术赋能乡村治理和便民服务，缩小城乡\"数字鸿沟\"。","池政办〔2026〕14 号\n\n各 县、区人民政府，江南新兴产业集中区、九华山风景区、开发区、平天湖风景区管委会，市政府各部门、各直属机构：\n\n《池州市人工智能产业发展行动方案（2026-2028 年）》业经市政府 第 61 次 常务会议审议通过，现印发给你们，请认真贯彻落实。\n\n池州市人民政府办公室\n\n2026 年 8 月 20 日\n\n（此件公开发布）\n\n池州市人工智能产业发展行动方案（2026-2028 年）\n\n为贯彻落实 安徽省加快发展人工智能产业部署，推进全市人工智能 产业 高质量发展，力争 到 2028 年底，全市人工智能产业规模突破 135 亿元，拥有规模以上工业企业 80 家，培育产值 1 亿元以上企业 30 家，人工智能创新应用场景数量达到 180 个以上，特 制定本方案。\n\n一、实施底层能力强基行动\n\n**（一）****加强新型基础设施建设。**鼓励多路线、多模式建设一批城市算力基础设施，创新智能算力基础设施运营模式，鼓励发展标准化、可扩展的算力云服务。优化升级网络基础设施，合理规划千兆、万兆光网，有序推进 5G 网络向 5G-A 升级演进，前瞻布局 6G 网络。推动公共基础设施物联网感知建设，夯实 数智化发展底座。（责任单位：市数据资源局、市工业和信息化局、电信池州分公司、移动池州分公司、联通池州分公司）\n\n**（二）筑牢半导体产业发展根基。**锚定大尺寸晶圆制造、第三代半导体、先进封测、部件材料配套等细分领域，推进差异化发展，力争在人工智能芯片领域开辟特色发展新赛道。（责任单位：市发展改革委）\n\n**（三）加快行业垂类模型引育。**利用现有通用基础大模型，打造具有池州辨识度、行业竞争力的行业垂类模型。围绕先进制造、现代服务业、现代农业、政务服务等领域，依托重点企业应用场景，做精做强一批具有行业特色的专用模型。（责任单位：市科技局、市工业和信息化局、市发展改革委、市农业农村局、市数据资源局，有关市直部门）\n\n**（四）深化数据资源开发利用。**深化“数据要素×”行动，推进数据产权登 记。加快数据资产化进程，推动合规入账入表与市场化流通，拓宽数据产权登记凭证质押融资等应用场景，释放数据要素价值。培育数据标注产业，建设公共领域、重点行业高质量训练数据集。鼓励企业通过可信数据空间共享和使用产业链上下游企业数据，支持在商贸物流、医疗健康、现代农业等领域建设企业、行业可信数据空间。培育数据企业，提升数据产业整体竞争力。（责任单位：市数据资源局、市财政局、市发展改革委、市工业和信息化局、市市场监管局、市投控集团）\n\n二、实施应用赋能拓展行动\n\n**（五）强化人工智能场景创新应用。**重点在制造、商贸、物流、文化、交通、医疗、教育、农业、养老等领域谋划建设重大应用场景，鼓励创建省级人工智能应用典型案例、创新项目、优秀应用，形成一批标志性创新应用场景。（责任单位：市科技局、市发展改革委、市工业和信息化局、市数据资源局、市商务局，有关市直部门）\n\n**（六）“人工智能+”先进制造。**聚焦我市重点链主产业和未来产业，制定 AI 赋能“一业一方案”；聚焦高端化、智能化、绿色化三大方向，推动多模态大模型深度融入产业核心环节，促进 制造业转型升级；开展“AI+机器人”行动，推广智能质检、预测性维护、智能供应链、快速研发设计等技术；谋划布局具身智能、低空经济等产业，发挥我市轻量化材料、半导体、高端装备制造等多元场景应用优势。（责任单位：市发展改革委、市工业和信息化局）\n\n**（七）“人工智能+”现代农业。**围绕九华黄精、竹业、池州鳜鱼、富硒、食用菌、茶叶等特色主导产业，以 AI 技术赋能农业全产业链，推动“数字皖农”建设从示范试点走向全域覆盖，助力池州农业从“传统种养”向“智慧农业”转型。以 AI 和数字技术赋能乡村治理和便民服务，缩小城乡“数字鸿沟”，助力和美乡村建设。（责任单位：市农业农村局）\n\n**（八）“人工智能+”文旅康养。**统筹推进环九华山、主城区、沿秋浦河、环升金湖四大休闲度假康养片区 AI 赋能协同发展。将人工智能技术融入文旅康养产业，创设“AI+文旅”应用场景，创新培育数字沉浸体验、数智康养产品开发、低空经济融合新业态。加强人工智能在文旅管理中的应用，赋能 A 级旅游景区等场所的安全生产。（责任单位：市文化和旅游局）\n\n**（九）“人工智能+”教育教学。**探索人工智能与教育领域的融合创新，重点推进智能备课系统、智能教学平台、数字教师、AI 助教、自主学习系统建设。深入推进人工智能在教、学、管、研、评等方面的应用。实施教师人工智能素养提升专项计划，构建“线上与线下相融合”的培养体系。（责任单位：市教育和体育局）\n\n**（十）“人工智能+”医疗卫生。**深化“智联网医院”应用，依托医疗自然语言理解、医疗诊断治疗方案推荐等技术，提高诊疗效率、精度和服务能力。推进全市二级及以上公立医院电子病历和智慧医院建设，加强医院智慧服务与管理水平。推进 AI 医学影像辅助诊断在全市医疗机构的应用，提高诊断准确性和时效性。拓展智慧医疗应用场景，打造“智医助理”池州模式。（责任单位：市卫生健康委）\n\n**（十一）“人工智能+”城市治理。**迭代升级城市智能预警中枢，推动智能政务、应急处置、公共安全治理等方面的创新应用，打造新型智慧城市。建立跨部门协同的智能管理平台，聚焦城市级交通调度、线网优化等，打造一批典型应用场景；打造 12345 热线数据智能应用，探索构建“池州政务”智能体。推进人工智能在城市管理、应急处置等方面的应用，以 AI 技术推动“好房子、好小区、好社区、好城区”建设，全面提升城市功能品质活力和安全韧性水平。（责任单位：市数据资源局、市政府办公室、市公安局、市住房城乡建设局、市城市管理局、市应急管理局）\n\n**（十二）“人工智能+”其他。**加快 推进人工智能在 养老、司法、自然资源和规划、生态环境、林业、水利、人力资源和社会保障、市场监管、金融等领域应用。（责任单位：市民政局、市司法局、市自然资源和规划局、市生态环境局、市林业局、市水利局、市人力资源社会保障局、市市场监管局、人行池州市分行，有关市直部门）\n\n三、实施产业基础夯实行动\n\n**（十三）优化人工智能产业布局。**建立差异化特色化发展的人工智能创新格局。贵池区重点发展 AI 赋能新材料、智能制造、智慧农业、低空经济产业；东至县重点推进传统产业“智改数转网联绿色”智能化改造和产业链优化升级；石台县重点发展“AI+文旅”“AI+康养”等产业；青阳县重点发展轻量化材料、新能源电池在 AI 产品中的应用；江南新兴产业集中区重点发展 AI 在商业航天、具身智能中的应用；九华山风景区 围绕 打造世界级旅游景区和世界级文化胜地，重点发展 AI 文旅 新业态及示范场景打造；市经开区 布局 AI 芯片 产业；平天湖风景区重点发展数字文创等赛道。（责任单位：各县区人民政府、管委会）\n\n**（十四）育强人工智能企业。**建立人工智能企业培育机制，按“初创科技型企业-科技型中小企业-高新技术企业-专精特新企业-专精特新‘小巨人’企业-科技领军企业（上市培育企业）”梯次培育，壮大 人工智能 产业 创新主体。支持半导体、机器人、智能物流、智能政务等领域的人工智能企业，培育赋能行业解决方案供应商、数据服务商、人工智能服务商以及数据要素型企业。（责任单位：市科技局、市工业和信息化局）\n\n**（十五）大力招引人工智能企业。**完善人工智能产业链招商图谱 和人工智能场景清单，建立动态更新的强链、补链、延链企业库。聚焦细分赛道，采用以算引商、以数招商、定向招商、场景招商等方式，招引一批人工智能产业链关键环节、拥有自主核心技术的企业。（责任单位：市新质生产力促进服务中心）\n\n**（十六）完善创新平台体系。**坚持以平天湖科创湾为引领，打造 AI“研发+孵化+中试+产业化”全链条创新载体。围绕具身智能、半导体等前沿领域，完善省重点实验室、产业技术工程化中心、企业研发（技术）中心、科技成果转化概念验证中心、科技成果转化中试基地、制造业中试平台、科技型企业孵化器等全链条创新平台体系，加快科技成果从实验室走向产业化应用。推进与香港科技大学、上海交通大学、安徽工程大学 等高校院所的 产学研合作，围绕具身智能、先进材料、半导体领域共建产业创新平台。抢抓上海（长三角）国际科创中心扩围机遇，发挥池州（上海）科创中心平台作用，提升质效，招引人工智能领域项目和人才（团队）。（责任单位：市科技局、市新质生产力促进服务中心、市发展改革委、市工业和信息化局）\n\n四、实施发展生态构建行动\n\n**（十七）加大金融政策支持。**积极向上争取国家、省级专项资金支持，用好省人工智能池州子基金、天使基金，引导多元社会资本支持人工智能产业发展。统筹池州市新兴产业引导基金、本地科创基金等各类 基 金，加强对人工智能关键产业链项目的资金支持，精准服务企业发展需求。（责任单位：市科技局、市新质生产力促进服务中心、市投控集团）\n\n**（十八）强化高端人才供给。**通过实施重大科技项目、建设高能级科创平台等方式，吸引和汇聚一批国内外高水平创新团队。依托“万马奔池”计划，精准引进高层次人才团队。以人工智能技术应用为契机，创造更多 AI 训练师、AI 系统测试工程师、数据标注师等新职业岗位。鼓励企业与在池高校联合建设产业学院、研发中心、实训基地等，深度开展企业和高校人才交流互派。（责任单位：市委人才工作局、市人力资源社会保障局、市教育和体育局、市科技局）\n\n**（十九）提升全社会人工智能素养。**开展覆盖全学段、面向全社会的通识教育，承办省级以上人工智能展会、论坛等 各类活动，提升全民人工智能素养。强化人工智能教育培训，在党校主体班次开设人工智能课程，提升干部数字化素养与智能应用能力。开展数字化管理、智能化升级等新技能培训，培养一批人工智能专业技术人才。（责任单位：市科协、市教育和体育局、市科技局、市人力资源社会保障局、市委组织部、市委党校）\n\n**（二十）探索建立新型生产关系。**深化数据要素市场化配置改革，落实 完善公共数据授权运营和价格形成机制。探索城市“智能空间”复合开发模式，适应人工智能轻型生产、轻型研发等特征，探索建设智能产业综合体。围绕网络安全、数据安全、科技伦理等领域建立风险防控应对机制。加强人工智能产业市场监管和知识产权保护，营造公平、健康、有序的人工智能产业市场环境。（责任单位：市数据资源局、市发展改革委、市投控集团、市委网信办、市公安局、市科技局、市市场监管局）\n\n加强全市人工智能高质量发展工作统筹，由市科技局协调推进人工智能产业各项工作。各县区 政府、管委会 和 市 有关 部门要 立足行业和区域特点，强化政策支持和协同联动，推动各项任务落地落实。强化典型示范引领，注重总结工作中的好经验好做法，充分利用各类媒体加强宣传推广，营造加快“人工智能+”创新应用的浓厚氛围。\n\n如果内容不能正常显示：请安装pdf软件， 或下载本PDF文档 [[点击下载](https:\u002F\u002Fwww.chizhou.gov.cn\u002Fdownload?siteId=1&id=707664)]。","池州市人民政府 · 2026-09-01","2026-09-01T00:00:00Z",70,{"impact":82,"substance":83,"depth":82,"authority":84,"freshness":85,"relevant":21,"comment":86},15,20,12,8,"池州市发布人工智能产业行动方案，明确以AI赋能九华黄精、竹业、鳜鱼等特色农业，推动智慧农业全域覆盖，政策具体、目标清晰，对地方农业数字化转型有指导意义。",[88],{"name":78,"url":75},[26,27,90,91,28],"特色产业","人工智能+",{"code":93,"slug":94,"name":95,"path":96,"parent_code":8},"B","horticulture","园艺作物","\u002Ftopic\u002Fhorticulture",[],{"status":99,"locked":14,"version":34,"method":35,"evidence":100},"partial",{"candidates":101,"agricultural_evidence":41,"reason":140},[102,108,114,120,126,131,136],{"code":103,"score":40,"supported":41,"title":104,"abstract":105,"tags":107},"B3",[],[106],"茶叶",[],{"code":109,"score":40,"supported":41,"title":110,"abstract":111,"tags":113},"B5",[],[112],"食用菌",[],{"code":115,"score":40,"supported":41,"title":116,"abstract":117,"tags":119},"G5",[],[118],"乡村治理",[],{"code":121,"score":40,"supported":41,"title":122,"abstract":123,"tags":125},"I6",[],[124],"数字鸿沟",[],{"code":58,"score":127,"supported":14,"title":128,"abstract":129,"tags":130},2,[],[],[91,27],{"code":132,"score":21,"supported":14,"title":133,"abstract":134,"tags":135},"G4",[],[],[90],{"code":53,"score":21,"supported":14,"title":137,"abstract":138,"tags":139},[],[],[26],"一级领域明确，二级方向需要细分",[142,143],"农业人工智能 人工智能+ 数字皖农 智慧农业","农业人工智能 人工智能+","农业人工智能人工智能+数字皖农智慧农业-1338","2026-09-02T00:05:03.287161Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":151,"content":8,"source_name":152,"source_url":149,"published_at":153,"category":154,"cover_url":8,"hotness":13,"is_selected":14,"score":155,"score_detail":156,"sources":159,"tags":161,"primary_topic":165,"secondary_topics":171,"classification":177,"search_phrases":195,"slug":198,"view_count":67,"doi":199,"paper":200,"created_at":216},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",69,{"impact":85,"substance":157,"depth":18,"authority":84,"freshness":13,"relevant":21,"comment":158},21,"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[160],{"name":152,"url":149},[26,27,162,163,164],"小农户","植物病害识别","可解释性",{"code":166,"slug":167,"name":168,"path":169,"parent_code":170},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[172],{"code":58,"slug":173,"name":174,"path":175,"parent_code":176},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":178,"locked":14,"version":34,"method":35,"evidence":179},"classified",{"candidates":180,"agricultural_evidence":41},[181,186,191],{"code":166,"score":84,"supported":41,"title":182,"abstract":184,"tags":185},[183],"smallholder",[183],[162],{"code":58,"score":13,"supported":41,"title":187,"abstract":189,"tags":190},[188],"computer vision",[],[27,163],{"code":53,"score":21,"supported":14,"title":192,"abstract":193,"tags":194},[],[],[26],[196,197],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":199,"openalex_id":201,"authors":202,"venue":152,"cited_by_count":67,"oa_url":149,"card":209,"direction":213,"ingested_from":215},"W7223137323",[203,205,207],{"name":204,"orcid":8},"Akshay Ramgoolam",{"name":206,"orcid":8},"Priya Bundhoo Appadoo",{"name":208,"orcid":8},"Jean-Noël LEspérance",{"tldr":210,"method":211,"finding":212,"direction":213,"opportunity":214},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","农业人工智能与决策模型","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","openalex","2026-10-22T11:21:10.425290Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":8,"source_name":152,"source_url":220,"published_at":153,"category":154,"cover_url":8,"hotness":13,"is_selected":14,"score":155,"score_detail":223,"sources":226,"tags":228,"primary_topic":232,"secondary_topics":238,"classification":240,"search_phrases":265,"slug":268,"view_count":67,"doi":269,"paper":270,"created_at":282},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。",{"impact":85,"substance":17,"depth":18,"authority":84,"freshness":224,"relevant":21,"comment":225},9,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[227],{"name":152,"url":220},[26,27,229,230,231],"灌溉决策","安全保证","试验设计",{"code":233,"slug":234,"name":235,"path":236,"parent_code":237},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[239],{"code":58,"slug":173,"name":174,"path":175,"parent_code":176},{"status":178,"locked":14,"version":34,"method":35,"evidence":241},{"candidates":242,"agricultural_evidence":41},[243,248,254,261],{"code":233,"score":82,"supported":41,"title":244,"abstract":245,"tags":247},[234],[246,234],"灌溉",[229],{"code":58,"score":84,"supported":41,"title":249,"abstract":251,"tags":253},[250],"artificial intelligence",[252],"decision support",[27],{"code":255,"score":70,"supported":41,"title":256,"abstract":257,"tags":260},"H2",[],[258,259],"土壤","soil",[],{"code":53,"score":21,"supported":14,"title":262,"abstract":263,"tags":264},[],[],[26],[266,267],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":269,"openalex_id":271,"authors":272,"venue":152,"cited_by_count":67,"oa_url":220,"card":277,"direction":213,"ingested_from":215},"W7223131205",[273,275],{"name":274,"orcid":8},"Nassima Benkhelifa",{"name":276,"orcid":8},"Yacine Aït Ahmed",{"tldr":278,"method":279,"finding":280,"direction":213,"opportunity":281},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":8,"source_name":152,"source_url":286,"published_at":153,"category":154,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":289,"sources":291,"tags":293,"primary_topic":296,"secondary_topics":297,"classification":299,"search_phrases":314,"slug":317,"view_count":67,"doi":318,"paper":319,"created_at":333},5590,"Evaluation of Concept-Probe Temporal-Stability Audits for Computer Vision Agriculture Disease Classifiers Under Seasonal Phenology Drift: An Interpretability Decay Bound for Smallholder Fungicide Timing","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545234","Concept-probe audits are increasingly used to certify that a crop-disease classifier's internal representations track the pathological concepts a smallholder would act on, but a probe certified at one point in the season may silently decouple from those concepts as phenology shifts the visual statistics of the field. the present analysis treats that decoupling as a formal object. It defines a temporal-stability audit as a sequence of concept-probe accuracies measured across defined phenological windows, and it derives an interpretability decay bound: the rate at which probe accuracy can fall before the fungicide-timing decision it is meant to support becomes unreliable. The bound is expressed as a function of the probe's initial margin, the drift in concept prevalence between windows, and the classifier's sensitivity to the drifted features, and it yields a certified audit interval rather than a single-point certificate. The paper then characterises the operating characteristics of the audit design under a declared variance structure, using a replication-based evaluation of the before-after-control-impact estimator with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The contribution is a decision-theoretic account of when a concept-probe certificate expires, and a design rule for scheduling re-audits across the season.","概念探针审计（concept-probe audit）日益被用于验证作物病害分类器的内部表征是否追踪小农户会据以采取行动的病理学概念，但在生长季某一时点获得认证的探针，可能随着物候变化改变田间的视觉统计特征而悄然与这些概念脱钩。本分析将这种脱钩视为一个形式化对象。它将时间稳定性审计（temporal-stability audit）定义为在既定物候窗口内测量的一系列概念探针准确率，并推导出一个可解释性衰减界限（interpretability decay bound）：即探针准确率在其所支持的杀菌剂施用时机决策变得不可靠之前所能下降的速率。该界限表示为探针初始裕度、窗口间概念流行率漂移以及分类器对漂移特征敏感度的函数，并给出一个经认证的审计区间而非单点认证。随后，本文在声明的方差结构下刻画该审计设计的运行特征，采用基于重复的前后对照影响估计量评估，其中组间标准差 = 6.0、残差标准差 = 4.5、组内相关系数 = 0.64。功效在 2000 个设计单元上估计，聚类数取 8、16、24 和 32，效应量取 3、5 和 8 个百分点；在 80% 功效下达到 5 个百分点需要 54 个聚类。本文的贡献在于从决策论角度阐明概念探针认证何时失效，并给出在整个生长季内安排重新审计的设计规则。",{"impact":85,"substance":17,"depth":18,"authority":84,"freshness":13,"relevant":21,"comment":290},"该论文提出概念探针审计的时序稳定性框架与可解释性衰减界，方法新颖、统计设计扎实，但属小众方法论研究，产业影响有限，适合作为专业精选而非头条。",[292],{"name":152,"url":286},[26,27,162,294,295],"作物病害识别","模型审计",{"code":166,"slug":167,"name":168,"path":169,"parent_code":170},[298],{"code":58,"slug":173,"name":174,"path":175,"parent_code":176},{"status":178,"locked":14,"version":34,"method":35,"evidence":300},{"candidates":301,"agricultural_evidence":41},[302,306,310],{"code":166,"score":82,"supported":41,"title":303,"abstract":304,"tags":305},[183],[162,183],[162],{"code":58,"score":13,"supported":41,"title":307,"abstract":308,"tags":309},[188],[],[294,27],{"code":53,"score":21,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[26],[315,316],"概念探针 时序稳定性 作物病害分类器","杀菌剂 施药时机 可解释性衰减","概念探针时序稳定性作物病害分类器-5590","10.5281\u002Fzenodo.23545234",{"doi":318,"openalex_id":320,"authors":321,"venue":152,"cited_by_count":67,"oa_url":286,"card":328,"direction":213,"ingested_from":215},"W7223088030",[322,324,326],{"name":323,"orcid":8},"Abdi-Basid Idriss Farah",{"name":325,"orcid":8},"Amina Abdillahi Guedi",{"name":327,"orcid":8},"Moussa Omar Youssouf",{"tldr":329,"method":330,"finding":331,"direction":213,"opportunity":332},"提出概念探针时间稳定性审计与可解释性衰减界，判断何时需重新审计。","定义跨物候窗口探针精度序列，推导衰减界，用BACI复制评估统计功效。","5个百分点效应需54个聚类达80%功效，审计证书会随物候漂移过期。","可将衰减界与再审计调度规则嵌入小农户杀菌剂时机决策支持系统，并验证跨作物泛化。","2026-10-22T11:21:08.978348Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":8,"source_name":152,"source_url":337,"published_at":153,"category":154,"cover_url":8,"hotness":13,"is_selected":14,"score":340,"score_detail":341,"sources":344,"tags":346,"primary_topic":349,"secondary_topics":350,"classification":351,"search_phrases":370,"slug":373,"view_count":67,"doi":374,"paper":375,"created_at":385},5589,"Conformal-Risk Evaluation for Artificial Intelligence Agriculture Spray-Nozzle Fault Detection Under Label-Delay Feedback: A Computer Coverage Audit","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544107","Spray-nozzle fault detection in precision agriculture is a classification problem in which the label that confirms a fault arrives after the spray pass, so the detector must act on unlabelled evidence and its error rate must be certified before the label exists. Conformal prediction offers distribution-free coverage guarantees, but the guarantee is stated for exchangeable calibration and test data, an assumption that label-delayed feedback violates because the calibration set is always older than the deployment set. the present analysis formalises label-delay feedback as a distribution shift in the conformity score and derives a coverage bound for split-conformal fault detectors under that shift. The bound separates the nominal miscoverage rate into an exchangeable component and a delay-induced component that scales with the total variation distance between the calibration and deployment score distributions. A replication-based evaluation of the ['Intercept', 'exposure', 'group', 'severity'] estimator was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the study characterises the behaviour of the ordinary least squares estimator under the stated process. The estimator recovered the declared coefficients with estimated bias 0.01122 for the intercept and -0.0002 for exposure, root mean squared error 0.9217 and 0.01614 respectively, and empirical coverage of the nominal 95% intervals of 1.0 for both parameters across 600 replications. The coverage audit shows that the delay-induced term, not the exchangeable term, dominates the risk of a conformal spray-nozzle detector, and that the audit procedure can certify a detector before field labels return.","在精准农业中，喷雾喷嘴故障检测是一个分类问题，其中确认故障的标签在喷雾作业结束后才到达，因此检测器必须基于未标记的证据采取行动，并且其错误率必须在标签存在之前得到认证。保形预测（conformal prediction）提供了无分布覆盖保证，但该保证是针对可交换的校准数据和测试数据而言的，而标签延迟反馈违反了这一假设，因为校准集总是比部署集更旧。本分析将标签延迟反馈形式化为一致性分数中的分布偏移，并推导了在该偏移下分裂保形故障检测器的覆盖界。该界将名义错误覆盖 rate 分解为可交换分量和延迟诱导分量，后者随校准与部署分数分布之间的全变差距离而缩放。对 ['Intercept', 'exposure', 'group', 'severity'] 估计器进行了基于重复的评估：从声明的数据生成过程（截距 12.0，暴露 0.42，分组 -3.1，严重程度 1.35，误差标准差 4.2）中抽取了 600 个 N = 500 的数据集，研究刻画了普通最小二乘估计器在所述过程下的行为。该估计器恢复了声明的系数，截距的估计偏差为 0.01122，暴露为 -0.0002，均方根误差分别为 0.9217 和 0.01614，两个参数在 600 次重复中名义 95% 区间的经验覆盖率为 1.0。覆盖审计表明，延迟诱导项而非可交换项主导了保形喷雾喷嘴检测器的风险，并且该审计程序可以在田间标签返回之前对检测器进行认证。",62,{"impact":85,"substance":18,"depth":342,"authority":224,"freshness":13,"relevant":21,"comment":343},17,"论文提出标签延迟下共形预测覆盖界，方法有新意但属预印本、应用面窄，可作为专业补充而非每日精选头条。",[345],{"name":152,"url":337},[26,27,347,348],"精准施药","病虫害监测",{"code":58,"slug":173,"name":174,"path":175,"parent_code":176},[],{"status":178,"locked":14,"version":34,"method":35,"evidence":352},{"candidates":353,"agricultural_evidence":41},[354,358,365],{"code":58,"score":224,"supported":41,"title":355,"abstract":356,"tags":357},[250],[],[27],{"code":53,"score":359,"supported":41,"title":360,"abstract":361,"tags":364},7,[],[362,363],"精准农业","precision agriculture",[26],{"code":366,"score":21,"supported":14,"title":367,"abstract":368,"tags":369},"F4",[],[],[347],[371,372],"农业人工智能 病虫害监测 智慧农业 精准施药","农业人工智能 病虫害监测","农业人工智能病虫害监测智慧农业精准施药-5589","10.5281\u002Fzenodo.23544107",{"doi":374,"openalex_id":376,"authors":377,"venue":152,"cited_by_count":67,"oa_url":337,"card":380,"direction":213,"ingested_from":215},"W7223116087",[378],{"name":379,"orcid":8},"Achieng' Atim Okello",{"tldr":381,"method":382,"finding":383,"direction":213,"opportunity":384},"将标签延迟反馈建模为共形分数分布偏移，推导覆盖率界并审计喷雾喷嘴故障检测器。","分裂共形预测、总变差距离、600次重复模拟（N=500）评估OLS估计。","延迟诱导项而非可交换项主导检测器风险，审计可在田间标签返回前认证检测器。","可将该覆盖率审计框架扩展到其他标签延迟的农业传感器故障检测，并研究延迟分布估计误差对认证的影响。","2026-10-22T11:21:02.845718Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":8,"source_name":152,"source_url":389,"published_at":153,"category":154,"cover_url":8,"hotness":13,"is_selected":14,"score":392,"score_detail":393,"sources":395,"tags":397,"primary_topic":401,"secondary_topics":402,"classification":403,"search_phrases":430,"slug":433,"view_count":67,"doi":434,"paper":435,"created_at":446},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。",74,{"impact":84,"substance":17,"depth":18,"authority":84,"freshness":13,"relevant":21,"comment":394},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[396],{"name":152,"url":389},[26,27,398,399,400],"粮食安全","遥感","气候分析",{"code":58,"slug":173,"name":174,"path":175,"parent_code":176},[],{"status":178,"locked":14,"version":34,"method":35,"evidence":404},{"candidates":405,"agricultural_evidence":41},[406,410,416,421,425],{"code":58,"score":224,"supported":41,"title":407,"abstract":408,"tags":409},[250],[],[27],{"code":411,"score":40,"supported":41,"title":412,"abstract":413,"tags":415},"I5",[],[414],"农业传感",[],{"code":417,"score":21,"supported":14,"title":418,"abstract":419,"tags":420},"A8",[],[],[398],{"code":53,"score":21,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[26],{"code":426,"score":21,"supported":14,"title":427,"abstract":428,"tags":429},"I3",[],[],[399],[431,432],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":434,"openalex_id":436,"authors":437,"venue":152,"cited_by_count":67,"oa_url":389,"card":440,"direction":213,"ingested_from":215},"W7223111766",[438],{"name":439,"orcid":8},"Fatima-Zahra El Khattabi",{"tldr":441,"method":442,"finding":443,"direction":444,"opportunity":445},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","农业遥感与作物表型","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z"]