[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-919":3,"related-919":64},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":59,"slug":62,"view_count":21,"doi":8,"paper":8,"created_at":63},919,"内蒙古科技重大专项瞄准寒地农业场景需求 农业生产多智能体时空大模型项目启动","https:\u002F\u002Fnm.people.com.cn\u002Fn2\u002F2026\u002F0824\u002Fc192247-41675862.html","内蒙古自治区科技重大专项农业生产多智能体时空大模型关键技术研发与示范应用项目启动会在呼和浩特召开，由呼伦贝尔农垦集团牵头联合内蒙古大学、内蒙古工业大学、内蒙古农业大学、哈尔滨工业大学、中科院计算所、华南农业大学、西华大学共同实施，中国工程院院士罗锡文领衔担任首席科学家，路战远院士担任咨询委员会主任。项目立足呼伦贝尔百万亩寒地农业场景，研发农业专用大模型和智能农机一体化平台，为5万亩示范区搭建标准化数据集与一体化管控平台。",null,"人民网内蒙古频道 2026-08-24","2026-08-24T00:00:00Z","报道",10,false,80,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},24,20,15,12,9,1,"省级重大科技专项，聚焦寒地农业智能体大模型，院士领衔，信息详实，权威性高，时效性强。",[24],{"name":9,"url":6},[26,27,28,29,30,31,32],"内蒙古","多智能体","寒地农业","罗锡文","时空大模型","路战远","呼伦贝尔农垦",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":41,"locked":13,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":47},[46,52],{"code":34,"score":19,"supported":47,"title":48,"abstract":50,"tags":51},true,[49],"大模型",[49],[27],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"F5",3,[],[57],"农机",[],[60,61],"呼伦贝尔农垦 时空大模型 多智能体 寒地农业","呼伦贝尔农垦 时空大模型","呼伦贝尔农垦时空大模型多智能体寒地农业-919","2026-08-25T00:03:26.562463Z",{"total":65,"page":21,"page_size":65,"items":66},6,[67,170,228,273,335,396],{"id":68,"title":69,"url":70,"summary":71,"summary_zh":72,"content":8,"source_name":73,"source_url":70,"published_at":74,"category":75,"cover_url":8,"hotness":12,"is_selected":13,"score":76,"score_detail":77,"sources":83,"tags":85,"primary_topic":91,"secondary_topics":92,"classification":93,"search_phrases":144,"slug":147,"view_count":148,"doi":149,"paper":150,"created_at":169},2503,"Agentic AI for Livestock Housing Management: Applications, Benchmarking, and Readiness Assessment","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112361","Agentic artificial intelligence is emerging as an extension of Precision Livestock Farming by linking perception, reasoning, planning, and bounded action within human-supervised livestock-housing workflows. This review synthesizes 90 publications on agentic AI, multi-agent systems, retrieval-augmented generation, large language models, foundation models, robotics, digital twins, simulation, computer vision, cyber-physical control, and related enabling technologies for livestock-housing management. We propose a Perception–Reasoning–Action–Safety (PRAS) loop and an Agentic Livestock Housing Readiness Scale to classify systems from passive monitoring and advisory decision support to supervised, safety-constrained closed-loop operation. A staged benchmarking perspective is also used to integrate algorithmic performance, biological relevance, safety, auditability, economic feasibility, and human–AI interaction. Current evidence is strongest for perception, advisory reasoning, natural-language data access, welfare-risk interpretation, and simulation-supported decision support, whereas robust barn-wide autonomous control remains largely unvalidated. Technology categories were coded non-exclusively; therefore, publication frequencies indicate representation within the selected corpus rather than effectiveness, evidence strength, or deployment readiness. Across species, dairy cattle provide the most developed evidence base, poultry studies mainly address environmental comfort and nutrition support, swine systems emphasize simulation-based precision feeding, and small-ruminant evidence remains concentrated in advisory tools and contextual embodied monitoring. Overall, agentic AI in livestock housing is currently more mature as an orchestration, explanation, and decision-support layer than as an autonomous control technology. By distinguishing direct housing applications, semi-agentic prototypes, and enabling technologies, this review clarifies the gap between current evidence and deployable autonomy. Progress towards higher readiness will require cross-farm validation, biological plausibility, source-grounding audits, safety assurance, interoperability, economic assessment, transparent benchmarking, and explicit human oversight.","智能体人工智能（Agentic AI）正在成为精准畜牧养殖（Precision Livestock Farming）的延伸，通过在人工监督的畜舍工作流程中将感知、推理、规划与有限行动相连接。本综述综合了90篇文献，涵盖智能体人工智能、多智能体系统、检索增强生成、大语言模型、基础模型、机器人技术、数字孪生、仿真、计算机视觉、信息物理控制及相关使能技术在畜舍管理中的应用。我们提出了感知—推理—行动—安全（Perception–Reasoning–Action–Safety, PRAS）循环和智能体畜舍就绪度量表（Agentic Livestock Housing Readiness Scale），将系统从被动监测与建议性决策支持分类至受监督、安全约束的闭环运行。同时采用分阶段基准测试视角，整合算法性能、生物学相关性、安全性、可审计性、经济可行性及人机交互。当前证据最充分的领域为感知、建议性推理、自然语言数据访问、福利风险解读及仿真支持的决策支持，而稳健的畜舍级自主控制仍基本未经验证。技术类别采用非排他性编码，因此文献频次仅表示在所选语料中的代表性，而非有效性、证据强度或部署就绪度。跨物种来看，奶牛提供了最成熟的证据基础，家禽研究主要涉及环境舒适度与营养支持，猪系统侧重于基于仿真的精准饲喂，而小反刍动物的证据仍集中于建议性工具与情境化具身监测。总体而言，畜舍中的智能体人工智能目前作为编排、解释与决策支持层比作为自主控制技术更为成熟。通过区分直接畜舍应用、半智能体原型与使能技术，本综述厘清了当前证据与可部署自主性之间的差距。迈向更高就绪度需要跨农场验证、生物学合理性、来源溯源审计、安全保障、互操作性、经济评估、透明基准测试及明确的人工监督。","Computers and Electronics in Agriculture","2026-09-14T00:00:00Z","论文",86,{"impact":78,"substance":79,"depth":80,"authority":81,"freshness":20,"relevant":21,"comment":82},22,23,18,14,"该综述系统梳理了智能体AI在畜禽舍管理中的应用，提出PRAS框架与准备度评估量表，方法新颖、结论审慎，对智慧畜牧研究与落地具有较高参考价值。",[84],{"name":73,"url":70},[86,87,27,88,89,90],"智慧农业","农业人工智能","数字孪生","智能畜牧","精准养殖",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":13,"version":42,"method":43,"evidence":94},{"candidates":95,"agricultural_evidence":47},[96,103,112,119,126,132,138],{"code":97,"score":81,"supported":47,"title":98,"abstract":100,"tags":102},"C",[99],"livestock",[101,99],"畜牧",[],{"code":34,"score":104,"supported":47,"title":105,"abstract":106,"tags":111},11,[],[107,108,109,110],"计算机视觉","artificial intelligence","computer vision","decision 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Pezzuolo",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0168169926009592\u002Fpdf",{"tldr":162,"method":163,"finding":164,"direction":165,"opportunity":166},"综述90篇文献，提出PRAS框架与就绪度量表，评估智能体AI在畜舍管理中的应用与成熟度。","文献综述，提出感知-推理-行动-安全循环与就绪度量表，分阶段基准评估。","智能体AI目前更适合作为决策支持与解释层，而非自主控制，牛的证据最充分。","农业人工智能与决策模型","可研究跨农场验证、安全约束闭环控制与可审计基准，填补自主畜舍管理空白。","智慧农业 \u002F 农业物联网","openalex","2026-09-15T23:30:01.732357Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":8,"content":175,"source_name":176,"source_url":8,"published_at":177,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":178,"score_detail":179,"sources":181,"tags":183,"primary_topic":189,"secondary_topics":190,"classification":191,"search_phrases":223,"slug":226,"view_count":21,"doi":8,"paper":8,"created_at":227},983,"AI下田地 开启“算天吃饭”新模式 内蒙古以《“人工智能+”行动实施方案(2026—2028年)》勾勒现代农牧业蓝图","https:\u002F\u002Finews.nmgnews.com.cn\u002Fsystem\u002F2026\u002F08\u002F26\u002F030391371.shtml","圣牧高科生态草业基地引进6台『八爪』智能除草机器人，可精准判别12种田间常见农作物和杂草，效率是人工除草的40倍，全程物理除草不施化学药剂。呼和浩特市玉泉区姜家营村落地自治区首个MAP农业种植应用大模型，将智能监测、数字管理和田间农事深度融合。呼伦贝尔农垦特泥河农牧场公司示范基地『云耕一号』智能灌溉机器人下田，小麦、油菜等作物开启无人化、精准化、绿色化灌溉新模式。《内蒙古自治区『人工智能+』行动实施方案(2026—2028年)》为全区现代农牧业发展勾勒出清晰蓝图：加快农牧业智慧化进程，推动传统育种技术与人工智能深度融合；推广应用智能项圈、无人机巡牧、精准饲喂系统，实现牲畜个体识别、行为监测、疫病早筛、繁育管控的全流程智能化。","智能除草机器人列队驶过田垄，灵活的机械臂精准拔除田间杂草……这般高效智能的农耕场景，并非科幻影片，而是巴彦淖尔市磴口县田间地头的真实日常。圣牧高科生态草业基地引进的6台“八爪”智能除草机器人，可精准判别12种田间常见农作物和杂草，效率是人工除草的40倍，全程物理除草不施化学药剂，从源头上提升牛羊养殖原料质量。\n\n如今，AI干农活，不再是展会上的样机、PPT里的概念，而是真真切切扎进了内蒙古大地的泥土里，从单点应用走向全产业渗透。\n\n过去农耕看天吃饭，现在越来越多的农田已然开启“算天吃饭”的新模式。呼和浩特市玉泉区姜家营村落地自治区首个MAP农业种植应用大模型，将智能监测、数字管理和田间农事深度融合，助力农业生产提质增效、绿色低碳发展。AI改变的不只是种植方式，更是决策逻辑。过去蹲田头看长势、捏土块，估摸估摸“该浇水了”“该施肥了”，现在算法根据土壤墒情、气象数据、苗情长势自动生成处方图，人只需要点一下确认，就能实现科学田管。在呼伦贝尔农垦特泥河农牧场公司示范基地，“云耕一号”智能灌溉机器人下田，小麦、油菜等作物开启农作物无人化、精准化、绿色化灌溉新模式。有了这样的智能装备，人不再是驾驶着机器跑的劳动者，而是站在屏幕前的决策者。AI种地的本质，是把经验变成数据、用算力替代体力。\n\n如果说田间地头的变化是看得见的，那么更深的变化，发生在整条农牧产业链上。AI不只是在田间牧场干活，它还渗透到从种子到餐桌的每一个环节。《内蒙古自治区“人工智能+”行动实施方案（2026—2028年）》，为全区现代农牧业发展勾勒出清晰蓝图：加快农牧业智慧化进程，推动传统育种技术与人工智能深度融合；推广应用人工智能实现主要粮油作物的智能灌溉、精准施肥播种，加大病虫害人工智能识别与预警；推广应用智能项圈、无人机巡牧、精准饲喂系统，实现牲畜个体识别、行为监测、疫病早筛、繁育管控的全流程智能化。从育种到种养、从加工到监管，AI正在嵌入农牧业生产的每一个关键环节。\n\n产业链上的变革，不只发生在生产端。最近，内蒙古数据交易中心线上平台便进行了这样一笔数据交易，农牧业数据从“副产品”变成了可交易的生产要素。内蒙古益牧智联科技有限公司提供相关数据支撑，内蒙古工业大学提供算法，并研发出智慧牧业智能分析模型，包括牛羊生长及养殖成本预测、肉产品分级与溯源等功能。该模型的落地，让养殖场和冷库迎来了“数字化副场长”“数字化车间主任”，让每一头牛羊都有了可追溯的数字档案。过去种地和养殖靠化肥、农药、饲料、机械，现在还要靠数据、算法、模型。数据越用越值钱，模型越训越聪明，这就是农牧业领域实实在在的新质生产力。\n\n放眼未来，AI赋能农牧业前景广阔、大有可为。这场从田间到餐桌、从经验到数据、从体力到算力的变革，正在重塑内蒙古农牧业的生产方式、产业形态和价值链条。更值得期待的是，随着数据成为新农资、手机成为新农具，越来越多懂技术、会经营的年轻人回到田间牧场，借力AI技术赋能乡村发展，农牧业就有了源源不断的新活力，内蒙古也将在智慧农牧业的新赛道上跑出更远的未来。**（袁宝年）**","内蒙古新闻网 2026-08-26 · 2026-08-26","2026-08-26T00:00:00Z",72,{"impact":17,"substance":80,"depth":18,"authority":12,"freshness":20,"relevant":21,"comment":180},"省级政策结合具体案例，展现AI在农牧业的应用与数据交易，内容详实，但深度稍欠。",[182],{"name":176,"url":173},[26,184,185,186,187,188],"人工智能+","智慧农牧业","八爪智能除草机器人","MAP大模型","云耕一号",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":13,"version":42,"method":43,"evidence":192},{"candidates":193,"agricultural_evidence":47},[194,199,205,211,217],{"code":34,"score":195,"supported":47,"title":196,"abstract":197,"tags":198},4,[],[49],[184],{"code":200,"score":54,"supported":47,"title":201,"abstract":202,"tags":204},"A2",[],[203],"小麦",[],{"code":206,"score":54,"supported":47,"title":207,"abstract":208,"tags":210},"A4",[],[209],"油菜",[],{"code":212,"score":54,"supported":47,"title":213,"abstract":214,"tags":216},"F4",[],[215],"除草",[],{"code":218,"score":54,"supported":47,"title":219,"abstract":220,"tags":222},"H3",[],[221],"灌溉",[],[224,225],"八爪智能除草机器人 智慧农牧业 人工智能+ 云耕一号","八爪智能除草机器人 智慧农牧业","八爪智能除草机器人智慧农牧业人工智能+云耕一号-983","2026-08-27T00:05:17.451386Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":8,"content":233,"source_name":234,"source_url":8,"published_at":235,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":236,"score_detail":237,"sources":239,"tags":241,"primary_topic":244,"secondary_topics":245,"classification":246,"search_phrases":267,"slug":270,"view_count":271,"doi":8,"paper":8,"created_at":272},431,"'五谷丰登·定风波'农业区域气象大模型发布——东北农大与华为联合,服务寒地黑土智慧农业","https:\u002F\u002Fwww.cnr.cn\u002Fhlj\u002Fjykj\u002F20260811\u002Ft20260811_527758211.shtml","8月9日,华为中国行2026·黑龙江省'人工智能+'赋能行业高质量发展峰会在哈尔滨举办,东北农业大学与华为签署框架合作协议并联合发布'五谷丰登·定风波'农业区域气象大模型。模型采用'AI+作物机理'耦合创新技术路线,华为提供盘古气象大模型与云羲微观气象引擎作为算力与AI底座,学校构建寒地作物种植大模型与千金方农田智能处方系统,实现公里级、小时级高精度气象预报,输出35项精细化农业气象指标。模型以'一品一策'为核心理念,涵盖播种决策、灌溉优化、灾害防御、产量预估等应用场景。峰会同步揭牌成立国家人工智能应用中试基地(农业领域种植方向)与人工智能+农业产业联盟。","ⱨ滷ڣũҵѧУɽԡȷǡ粨ӡԷ֪Ϊ⣬ϵͳƽ˶ũҵѧϻΪͬзġȷǡ粨ũҵģ͡澫׼ͳũҵձڿռֱʴ֡ʱֱʵ͡ȱϡȱƷֲ컯ƿģͲáAI+ϴ¼·ߡΪṩ̹ģ΢ΪAIѧУֲģǧũܴϵͳʵֹＶСʱ߾Ԥ35ϸũҵָꡣģԡһƷһߡΪԲͬƷṩ컯ũ¾֧֣Dz־ߡŻֺԤӦóɽʾģͽֽ׶ڶƹ㣬ƶũҵģʽӡԷ֪Խʽ","央广网 2026-08-11","2026-08-11T00:00:00Z",73,{"impact":78,"substance":80,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":238},"高校与华为联合发布农业气象大模型，服务寒地黑土智慧农业，属省级以上产业级进展，信息具体，央媒报道，时效性好。",[240],{"name":234,"url":231},[86,28,242,243],"气象大模型","盘古大模型",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":13,"version":42,"method":43,"evidence":247},{"candidates":248,"agricultural_evidence":47},[249,253,257,261],{"code":34,"score":104,"supported":47,"title":250,"abstract":251,"tags":252},[49],[49],[],{"code":127,"score":20,"supported":47,"title":254,"abstract":255,"tags":256},[86],[],[86],{"code":218,"score":54,"supported":47,"title":258,"abstract":259,"tags":260},[],[221],[],{"code":262,"score":54,"supported":47,"title":263,"abstract":264,"tags":266},"H7",[],[265],"农业气象",[],[268,269],"气象大模型 盘古大模型 寒地农业 智慧农业","气象大模型 盘古大模型","气象大模型盘古大模型寒地农业智慧农业-431",8,"2026-08-12T23:57:20.512105Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":8,"source_name":279,"source_url":276,"published_at":280,"category":75,"cover_url":8,"hotness":12,"is_selected":13,"score":281,"score_detail":282,"sources":285,"tags":287,"primary_topic":291,"secondary_topics":297,"classification":299,"search_phrases":315,"slug":318,"view_count":159,"doi":319,"paper":320,"created_at":334},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",69,{"impact":271,"substance":283,"depth":80,"authority":19,"freshness":12,"relevant":21,"comment":284},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。",[286],{"name":279,"url":276},[86,87,288,289,290],"小农户","植物病害识别","可解释性",{"code":292,"slug":293,"name":294,"path":295,"parent_code":296},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[298],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":13,"version":42,"method":43,"evidence":300},{"candidates":301,"agricultural_evidence":47},[302,307,311],{"code":292,"score":19,"supported":47,"title":303,"abstract":305,"tags":306},[304],"smallholder",[304],[288],{"code":34,"score":12,"supported":47,"title":308,"abstract":309,"tags":310},[109],[],[87,289],{"code":127,"score":21,"supported":13,"title":312,"abstract":313,"tags":314},[],[],[86],[316,317],"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":319,"openalex_id":321,"authors":322,"venue":279,"cited_by_count":159,"oa_url":276,"card":329,"direction":165,"ingested_from":168},"W7223137323",[323,325,327],{"name":324,"orcid":8},"Akshay Ramgoolam",{"name":326,"orcid":8},"Priya Bundhoo Appadoo",{"name":328,"orcid":8},"Jean-Noël LEspérance",{"tldr":330,"method":331,"finding":332,"direction":165,"opportunity":333},"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。","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。","2026-10-22T11:21:10.425290Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":8,"source_name":279,"source_url":338,"published_at":280,"category":75,"cover_url":8,"hotness":12,"is_selected":13,"score":281,"score_detail":341,"sources":343,"tags":345,"primary_topic":349,"secondary_topics":354,"classification":356,"search_phrases":378,"slug":381,"view_count":159,"doi":382,"paper":383,"created_at":395},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":271,"substance":78,"depth":80,"authority":19,"freshness":20,"relevant":21,"comment":342},"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。",[344],{"name":279,"url":338},[86,87,346,347,348],"灌溉决策","安全保证","试验设计",{"code":218,"slug":350,"name":351,"path":352,"parent_code":353},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[355],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":13,"version":42,"method":43,"evidence":357},{"candidates":358,"agricultural_evidence":47},[359,363,367,374],{"code":218,"score":18,"supported":47,"title":360,"abstract":361,"tags":362},[350],[221,350],[346],{"code":34,"score":19,"supported":47,"title":364,"abstract":365,"tags":366},[108],[110],[87],{"code":368,"score":65,"supported":47,"title":369,"abstract":370,"tags":373},"H2",[],[371,372],"土壤","soil",[],{"code":127,"score":21,"supported":13,"title":375,"abstract":376,"tags":377},[],[],[86],[379,380],"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":382,"openalex_id":384,"authors":385,"venue":279,"cited_by_count":159,"oa_url":338,"card":390,"direction":165,"ingested_from":168},"W7223131205",[386,388],{"name":387,"orcid":8},"Nassima Benkhelifa",{"name":389,"orcid":8},"Yacine Aït Ahmed",{"tldr":391,"method":392,"finding":393,"direction":165,"opportunity":394},"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":397,"title":398,"url":399,"summary":400,"summary_zh":401,"content":8,"source_name":279,"source_url":399,"published_at":280,"category":75,"cover_url":8,"hotness":12,"is_selected":13,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":410,"secondary_topics":411,"classification":413,"search_phrases":428,"slug":431,"view_count":159,"doi":432,"paper":433,"created_at":447},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。",70,{"impact":271,"substance":78,"depth":80,"authority":19,"freshness":12,"relevant":21,"comment":404},"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。",[406],{"name":279,"url":399},[86,87,288,408,409],"作物病害识别","模型审计",{"code":292,"slug":293,"name":294,"path":295,"parent_code":296},[412],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":13,"version":42,"method":43,"evidence":414},{"candidates":415,"agricultural_evidence":47},[416,420,424],{"code":292,"score":18,"supported":47,"title":417,"abstract":418,"tags":419},[304],[288,304],[288],{"code":34,"score":12,"supported":47,"title":421,"abstract":422,"tags":423},[109],[],[408,87],{"code":127,"score":21,"supported":13,"title":425,"abstract":426,"tags":427},[],[],[86],[429,430],"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":432,"openalex_id":434,"authors":435,"venue":279,"cited_by_count":159,"oa_url":399,"card":442,"direction":165,"ingested_from":168},"W7223088030",[436,438,440],{"name":437,"orcid":8},"Abdi-Basid Idriss Farah",{"name":439,"orcid":8},"Amina Abdillahi Guedi",{"name":441,"orcid":8},"Moussa Omar Youssouf",{"tldr":443,"method":444,"finding":445,"direction":165,"opportunity":446},"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"]