[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-69":3,"related-69":90},{"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":33,"secondary_topics":39,"classification":40,"search_phrases":84,"slug":87,"view_count":88,"doi":8,"paper":8,"created_at":89},69,"工信部+农业农村部部署2026年度农业领域机器人典型应用场景遴选：3领域11关键环节","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F559242.html","2026-07-01 工业和信息化部办公厅、农业农村部办公厅联合发文（工信厅联通装函〔2026〕269号），部署2026年度农业领域机器人典型应用场景遴选工作。聚焦种植、养殖、农产品初加工3个重点领域，覆盖育苗繁育、耕整、播栽、田间管理、收获、饲养、繁育、畜禽产品采集、废弃物资源化利用与无害化处理、农产品初制加工、储运等关键环节。种植类重点征集农情监测、精细耕整、育种育苗巡检、授粉嫁接、精密播种移栽、施药除草、果蔬采摘与收获机器人；养殖类重点征集养殖巡检、精准饲喂、行为监测、疫苗注射、清污消杀、挤奶、水产捕捞机器人；初加工类重点征集智能化屠宰、无人吊运、检测分级、搬运码垛、仓储管理机器人。申报采取机器人研制单位牵头+农业社会化服务组织\u002F家庭农场\u002F农业企业联合体形式。",null,"面向种植、养殖、农产品初加工3个重点领域，聚焦育苗繁育、耕整、播栽、田间管理、收获，饲养、繁育、畜禽产品采集、废弃物资源化利用与无害化处理，农产品初制加工、储运等关键环节，征集遴选一批技术先进、成熟度高、成效明显、应用前景广阔、可复制推广的机器人典型应用场景，并加强推广应用。征集遴选方向见《农业机器人典型应用场景参考》（附件1）。\n\n（一）种植。针对作物多样、种植条件复杂、精准作业要求高、全天候管控难、劳动强度大等特点，征集农情监测、精细耕整、育种育苗巡检、授粉嫁接、精密播种移栽、施药除草、果蔬采摘与收获等机器人典型应用场景。\n\n（二）养殖。针对畜牧水产养殖环境中动物行为复杂、生产作业环境恶劣、疫病防控难度大等特点，征集养殖巡检、精准饲喂、行为监测、疫苗注射、清污消杀、挤奶、水产捕捞等机器人典型应用场景。\n\n（三）农产品初加工。针对农产品品类多样、物料特性差异大、分级分选要求高等特点，征集智能化屠宰、无人吊运、检测分级、搬运与码垛、仓储管理等机器人典型应用场景。","工信部 · 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[预印本]","2026-10-06T00:00:00Z","论文",60,{"impact":152,"substance":19,"depth":88,"authority":48,"freshness":101,"relevant":22,"comment":153},12,"低成本多机器人田间早期胁迫检测方案有数据支撑，但属未经同行评议的预印本且未实地验证，可作为技术动态参考。",[155],{"name":147,"url":145},[157,27,158,159,160],"智慧农业","多模态感知","作物胁迫","精准农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":42,"version":43,"method":44,"evidence":164},{"candidates":165,"agricultural_evidence":14},[166,171,176,180],{"code":34,"score":56,"supported":14,"title":167,"abstract":168,"tags":170},[],[169],"小农户",[],{"code":172,"score":21,"supported":42,"title":173,"abstract":174,"tags":175},"I1",[],[],[157,160],{"code":80,"score":22,"supported":42,"title":177,"abstract":178,"tags":179},[],[],[27],{"code":181,"score":22,"supported":42,"title":182,"abstract":183,"tags":184},"I2",[],[],[158],[186,187],"多机器人 田间 胁迫检测","叶片感知 作物健康 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农业农村部公布对十四届全国人大四次会议第1626号建议（关于支持家庭农场与农民合作社参与财政支农项目申报）的答复。截至2024年底，全国共有家庭农场395.2万个、农民合作社203.5万家、各类农业社会化服务主体111.1万个，服务小农户近9300万户、年服务面积超22.9亿亩次。2026年新型农业经营主体提质增效带动小农户增收行动方案明确将\"一码通\"赋码、信息化财务记账软件\"随手记\"纳入申报前置条件，扶持政策与带动农户增收挂钩，粮油规模种植主体单产提升专项首次开辟\"集中连片种植+率先集成应用单产提升技术和模式\"的政策通道。","中央财政粮油生产保障、农业产业发展、农业经营主体能力提升、农业生态资源保护等资金均将符合条件的家庭农场、农民合作社等新型农业经营主体纳入支持范围。其中，2026年安排资金重点支持家庭农场、农民合作社等新型农业经营主体，因地制宜推广应用单产提升品种、技术、装备、模式，提高生产经营规范化发展水平；安排资金支持符合条件的农民合作社、家庭农场等各类服务主体围绕粮食和重要农产品生产、聚焦小农户和农业生产薄弱环节开展社会化服务。据农经统计，截至2024年底，全国共有家庭农场395.2万个，农民合作社203.5万家，各类农业社会化服务主体数量达111.1万个，服务小农户近9300万户，年服务面积超过22.9亿亩次。","农业农村部 · 2026-07-28","2026-07-28T00:00:00Z",92,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":210,"relevant":22,"comment":211},9,"全国性政策，含具体资金安排与最新统计数据，信息增量大，权威性高，时效性好。",[213],{"name":206,"url":203},[12,28,51,215,216,217,218,219],"合作社","一码通","随手记","数字化","单产提升",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":42,"version":43,"method":44,"evidence":223},{"candidates":224,"agricultural_evidence":14},[225,231,236],{"code":34,"score":226,"supported":14,"title":227,"abstract":228,"tags":230},19,[51],[229,51,52,169],"农民合作社",[215,51],{"code":232,"score":117,"supported":14,"title":233,"abstract":234,"tags":235},"A8",[],[219],[219],{"code":237,"score":56,"supported":14,"title":238,"abstract":239,"tags":241},"G3",[],[240],"财政支农",[],[243,244],"农业农村部 单产提升 家庭农场 一码通","农业农村部 单产提升","农业农村部单产提升家庭农场一码通-62","2026-07-29T23:54:12.361730Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":8,"source_name":253,"source_url":250,"published_at":254,"category":149,"cover_url":8,"hotness":13,"is_selected":42,"score":4,"score_detail":255,"sources":257,"tags":259,"primary_topic":263,"secondary_topics":264,"classification":270,"search_phrases":287,"slug":290,"view_count":189,"doi":291,"paper":292,"created_at":308},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",{"impact":101,"substance":140,"depth":19,"authority":152,"freshness":13,"relevant":22,"comment":256},"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。",[258],{"name":253,"url":250},[157,260,169,261,262],"农业人工智能","植物病害识别","可解释性",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[265],{"code":181,"slug":266,"name":267,"path":268,"parent_code":269},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":41,"locked":42,"version":43,"method":44,"evidence":271},{"candidates":272,"agricultural_evidence":14},[273,278,283],{"code":34,"score":152,"supported":14,"title":274,"abstract":276,"tags":277},[275],"smallholder",[275],[169],{"code":181,"score":13,"supported":14,"title":279,"abstract":281,"tags":282},[280],"computer vision",[],[260,261],{"code":172,"score":22,"supported":42,"title":284,"abstract":285,"tags":286},[],[],[157],[288,289],"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":291,"openalex_id":293,"authors":294,"venue":253,"cited_by_count":189,"oa_url":250,"card":301,"direction":305,"ingested_from":307},"W7223137323",[295,297,299],{"name":296,"orcid":8},"Akshay Ramgoolam",{"name":298,"orcid":8},"Priya Bundhoo Appadoo",{"name":300,"orcid":8},"Jean-Noël LEspérance",{"tldr":302,"method":303,"finding":304,"direction":305,"opportunity":306},"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":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":8,"source_name":253,"source_url":312,"published_at":254,"category":149,"cover_url":8,"hotness":13,"is_selected":42,"score":315,"score_detail":316,"sources":318,"tags":320,"primary_topic":323,"secondary_topics":324,"classification":326,"search_phrases":341,"slug":344,"view_count":189,"doi":345,"paper":346,"created_at":360},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":101,"substance":18,"depth":19,"authority":152,"freshness":13,"relevant":22,"comment":317},"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。",[319],{"name":253,"url":312},[157,260,169,321,322],"作物病害识别","模型审计",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[325],{"code":181,"slug":266,"name":267,"path":268,"parent_code":269},{"status":41,"locked":42,"version":43,"method":44,"evidence":327},{"candidates":328,"agricultural_evidence":14},[329,333,337],{"code":34,"score":20,"supported":14,"title":330,"abstract":331,"tags":332},[275],[169,275],[169],{"code":181,"score":13,"supported":14,"title":334,"abstract":335,"tags":336},[280],[],[321,260],{"code":172,"score":22,"supported":42,"title":338,"abstract":339,"tags":340},[],[],[157],[342,343],"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":345,"openalex_id":347,"authors":348,"venue":253,"cited_by_count":189,"oa_url":312,"card":355,"direction":305,"ingested_from":307},"W7223088030",[349,351,353],{"name":350,"orcid":8},"Abdi-Basid Idriss Farah",{"name":352,"orcid":8},"Amina Abdillahi Guedi",{"name":354,"orcid":8},"Moussa Omar Youssouf",{"tldr":356,"method":357,"finding":358,"direction":305,"opportunity":359},"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":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":8,"source_name":367,"source_url":364,"published_at":254,"category":149,"cover_url":8,"hotness":13,"is_selected":42,"score":368,"score_detail":369,"sources":374,"tags":376,"primary_topic":381,"secondary_topics":382,"classification":383,"search_phrases":420,"slug":423,"view_count":189,"doi":424,"paper":425,"created_at":454},5548,"Agroecological transition in olive based farming systems among smallholder farms in northwestern Tunisia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1903591","Agricultural intensification has increased production and economic growth but has also generated significant environmental and social challenges that threaten long-term sustainability, highlighting the need for more sustainable farming models. Agroecology offers a promising pathway to enhance resilience and support inclusive rural development. In this context, Tunisia’s olive sector provides a relevant case for examining agroecological transitions in semi-arid smallholder systems. This study evaluates the degree of agroecological transition among smallholder olive farms in the Elles region of northwestern Tunisia using the FAO Tool for Agroecology Performance Evaluation (TAPE). Based on a sample of 60 farms, the analysis applies the Characterization of Agroecological Transition (CAET) index, grounded in the 10 Elements of Agroecology, to compare four production systems: (i) olive monoculture, (ii) diversified cropping, (iii) olive–livestock integration, and (iv) integrated olive–crop–livestock systems. Economic, social, environmental and institutional performance were explored using the TAPE Core Criteria of Performance (Step 2) and examined in relation to CAET scores. Results reveal a clear agroecological gradient, with diversified and integrated systems achieving markedly higher CAET scores (up to 52%) compared to monoculture systems (31%). Spearman correlations show strong associations between CAET and key dimensions, including synergy (ρ = 0.79), human and social values (ρ = 0.78), and diversity (ρ = 0.71). Several TAPE economic performance indicators, including monthly income, which ranged from approximately 1,349 TND in monoculture systems to 4,892 TND in diversified olive–crop–livestock systems, were positively associated with higher CAET scores, suggesting that agroecological transition may not necessarily imply a trade-off between productivity and sustainability. However, limited collective action, weak market integration, and insufficient extension services constrain broader adoption. These findings highlight the potential of diversification as a key lever for agroecological transition and underscore the importance of targeted policy support.","农业集约化提高了产量并促进了经济增长，但也产生了威胁长期可持续性的重大环境与社会挑战，凸显了对更可持续耕作模式的需求。农业生态学为增强韧性和支持包容性农村发展提供了一条有前景的路径。在此背景下，突尼斯橄榄产业为研究半干旱小农系统中的农业生态转型提供了相关案例。本研究利用联合国粮农组织农业生态性能评价工具（TAPE），评估突尼斯西北部埃勒斯地区小农橄榄农场的农业生态转型程度。基于60个农场的样本，分析采用以农业生态学十大要素为基础的农业生态转型特征（CAET）指数，比较四种生产系统：（i）橄榄单作，（ii）多样化种植，（iii）橄榄—畜牧结合，以及（iv）橄榄—作物—畜牧综合系统。研究利用TAPE核心性能标准（第二步）探讨经济、社会、环境和制度绩效，并将其与CAET得分进行关联分析。结果揭示了明显的农业生态梯度，多样化与综合系统的CAET得分（最高达52%）显著高于单作系统（31%）。斯皮尔曼相关性分析显示，CAET与关键维度之间存在强关联，包括协同性（ρ = 0.79）、人文与社会价值（ρ = 0.78）和多样性（ρ = 0.71）。若干TAPE经济绩效指标——包括月收入，其从单作系统的约1，349突尼斯第纳尔到橄榄—作物—畜牧多样化系统的4，892突尼斯第纳尔不等——与更高的CAET得分呈正相关，表明农业生态转型未必意味着生产力与可持续性之间的权衡。然而，集体行动有限、市场整合薄弱和推广服务不足制约了更广泛的采纳。这些发现凸显了多样化作为农业生态转型关键杠杆的潜力，并强调了有针对性的政策支持的重要性。","Frontiers in Sustainable Food Systems",71,{"impact":152,"substance":370,"depth":371,"authority":372,"freshness":210,"relevant":22,"comment":373},20,17,13,"基于FAO TAPE方法对突尼斯小农户橄榄系统农生态转型的实证研究，数据扎实、结论有新意，但属区域性案例，公共影响有限。",[375],{"name":367,"url":364},[169,377,378,379,380],"农业转型","生态农业","突尼斯","油橄榄",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":42,"version":43,"method":44,"evidence":384},{"candidates":385,"agricultural_evidence":14},[386,390,396,402,409,415],{"code":34,"score":152,"supported":14,"title":387,"abstract":388,"tags":389},[275],[275],[169],{"code":55,"score":48,"supported":14,"title":391,"abstract":392,"tags":395},[],[393,394],"畜牧","livestock",[],{"code":38,"score":48,"supported":14,"title":397,"abstract":398,"tags":401},[],[399,400],"农村发展","rural development",[],{"code":403,"score":48,"supported":14,"title":404,"abstract":405,"tags":408},"H6",[],[406,407],"农业生态","agroecology",[],{"code":410,"score":56,"supported":14,"title":411,"abstract":412,"tags":414},"H7",[],[413],"干旱",[],{"code":416,"score":22,"supported":42,"title":417,"abstract":418,"tags":419},"H8",[],[],[378],[421,422],"突尼斯 油橄榄 小农户","TAPE 农生态 评估","突尼斯油橄榄小农户-5548","10.3389\u002Ffsufs.2026.1903591",{"doi":424,"openalex_id":426,"authors":427,"venue":367,"cited_by_count":189,"oa_url":364,"card":448,"direction":452,"ingested_from":307},"W7223108121",[428,430,432,435,438,440,443,446],{"name":429,"orcid":8},"Rihab Mejri",{"name":431,"orcid":8},"Meriem Oueslati Zlaoui",{"name":433,"orcid":434},"Inès Gharbi","https:\u002F\u002Forcid.org\u002F0000-0003-2558-2134",{"name":436,"orcid":437},"Asma Souissi","https:\u002F\u002Forcid.org\u002F0000-0002-8680-5875",{"name":439,"orcid":8},"Ghada Ferchichi",{"name":441,"orcid":442},"Rania Soula","https:\u002F\u002Forcid.org\u002F0000-0002-6086-9176",{"name":444,"orcid":445},"Boubaker Dhehibi","https:\u002F\u002Forcid.org\u002F0000-0003-3854-6669",{"name":447,"orcid":8},"Mohamed Zied Dhraief",{"tldr":449,"method":450,"finding":451,"direction":452,"opportunity":453},"用FAO TAPE工具评估突尼斯西北小农橄榄农场生态农业转型程度。","FAO TAPE框架与CAET指数，调查60个农场比较四类种植系统。","多样化与综合系统CAET得分达52%，单作仅31%，且收入更高。","农业绿色发展与碳","可将TAPE与CAET指标数字化，构建小农生态转型监测与决策支持系统。","2026-10-22T11:20:15.716064Z"]