[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5586":3,"related-5586":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":58,"slug":61,"view_count":62,"doi":63,"paper":64,"created_at":79},5586,"Differential-Privacy Evaluation for Computer Agriculture Cooperative Yield Aggregation Under Smallholder Re-Identification Risk: An Artificial Intelligence Membership-Inference Audit","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544249","Cooperative yield aggregation is the standard route by which smallholder harvests become the training data of agricultural artificial intelligence, yet the privacy guarantees attached to those aggregates are rarely audited against the re-identification risk that smallholder membership actually carries. the present analysis treats the differential-privacy budget as a design parameter of a membership-inference audit and characterises, analytically and by replication, how the before-after-control-impact estimator behaves when the privacy mechanism is layered onto cooperative-level aggregation. The analysis is a design study: no observational data are used or claimed. Under a declared variance structure with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.640, power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effects of 3, 5 and 8 percentage points. The design requires 54 clusters to detect a 5-percentage-point effect at 80% power, a requirement that exceeds every cluster count examined and that the differential-privacy mechanism can only worsen by inflating residual variance. The contribution is a membership-inference audit protocol that reports the privacy budget, the intraclass correlation and the detectable effect jointly, so that a cooperative's disclosure claim is bounded by the same arithmetic that bounds its inferential claim.","合作产量聚合是小农户收成转化为农业人工智能训练数据的标准路径，然而这些聚合数据所附带的隐私保障，很少针对小农户成员身份实际带来的再识别风险进行审计。本分析将差分隐私预算视为成员推断审计的设计参数，并通过解析推导与重复实验，刻画当隐私机制叠加于合作社层级聚合之上时，前后对照影响估计量（before-after-control-impact estimator）的表现。本分析属于设计研究：不使用也不主张任何观测数据。在声明的方差结构下——组间标准差=6.0、残差标准差=4.5、组内相关系数=0.640——针对聚类数为8、16、24和32以及效应量为3、5和8个百分点的设计单元，每个单元重复2000次以估计检验功效。该设计需要54个聚类才能在80%功效下检测到5个百分点的效应，这一要求超过了所考察的所有聚类数，而差分隐私机制只会通过膨胀残差方差使情况进一步恶化。本研究的贡献在于一套成员推断审计协议，该协议联合报告隐私预算、组内相关系数和可检测效应，从而使合作社的披露声明与其推断声明受到同一套算术的约束。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},8,21,18,12,1,"设计研究提出将隐私预算、组内相关与可检测效应联合报告的成员推断审计协议，方法新颖但无实测数据，属细分方法学进展。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","数据治理","小农户","差分隐私","产量聚合",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":57},[38,46,52],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"G2",15,true,[43],"smallholder",[28,43],[28],{"code":47,"score":40,"supported":41,"title":48,"abstract":50,"tags":51},"I2",[49],"artificial intelligence",[26,49],[26],{"code":53,"score":21,"supported":14,"title":54,"abstract":55,"tags":56},"I6",[],[],[27],"多个产业证据相当，需要确认主归属",[59,60],"差分隐私 小农户 产量聚合","成员推断 隐私审计 农业AI","差分隐私小农户产量聚合-5586",0,"10.5281\u002Fzenodo.23544249",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":62,"oa_url":6,"card":71,"direction":77,"ingested_from":78},"W7223099347",[67,69],{"name":68,"orcid":9},"Momodou Lamin Bah",{"name":70,"orcid":9},"Haddy Trawally",{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"用成员推断审计评估小农户合作社产量聚合的差分隐私预算与可检测效应。","设计研究，基于BACI估计量与2000次重复模拟，分析聚类数和隐私预算的影响。","检测5个百分点效应需54个聚类，超出所有考察规模，差分隐私还会增大残差方差。","数字乡村与农业信息化","可研究小样本合作社下隐私预算与统计功效的权衡，开发联合报告隐私与推断边界的审计工具。","农业人工智能与决策模型","openalex","2026-10-22T11:21:01.436271Z",{"total":81,"page":21,"page_size":81,"items":82},6,[83,168,224,276,346,415],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":89,"source_url":86,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":91,"score_detail":92,"sources":97,"tags":99,"primary_topic":103,"secondary_topics":108,"classification":109,"search_phrases":141,"slug":144,"view_count":145,"doi":146,"paper":147,"created_at":167},2292,"A governance-first framework integrates artificial intelligence and blockchain to advance sustainable and inclusive horticultural value chains","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1894057","Horticultural value chains face persistent agronomic challenges including production inefficiencies, postharvest losses, poor resource management, and limited supply-chain transparency, particularly in smallholder-dominated systems across the Global South. These constraints undermine agricultural productivity, market access, and the long-term sustainability of horticultural farming. Despite growing research interest in digital agriculture, artificial intelligence (AI) and blockchain technology are increasingly explored together, including in emerging integrated AI-blockchain-IoT architectures for agricultural traceability. However, these existing integrations remain predominantly technology-centric, prioritizing traceability, automation, and data verification, without an explicit governance, equity, or data-sovereignty architecture designed specifically for smallholder-dominated horticultural production and distribution systems. This study proposes a comprehensive conceptual framework that integrates AI’s predictive and decision-support capabilities with blockchain’s data integrity, traceability, and automation features to enhance sustainability, inclusivity, and governance in horticultural value chains. Drawing on an integrative review of peer-reviewed sources identified through Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar, and grounded in the Technology-Organisation-Environment (TOE) framework, Sociotechnical Systems Theory, and Sustainable Digital Transformation Theory, the study addresses the agronomic, governance, and equity dimensions of digital transformation in horticulture. The framework is structured around five interlinked operational layers, data collection, AI-driven analytics and decision-making, blockchain validation and traceability, smart contracts and decision support, and user interface and stakeholder feedback, all operating within a governance architecture and ethical design layer that functions as a structural precondition for the framework rather than a downstream feature. This governing layer embeds algorithmic transparency, data sovereignty through community-governed data cooperatives, and inclusive digital access for smallholder farmers as non-negotiable design principles. The framework explicitly aligns with Sustainable Development Goals 2, 9, 10, 12, 13, and 16, and includes a three-phase empirical validation pathway comprising expert Delphi elicitation, simulation modeling, and pilot implementation in horticultural cooperatives. The integration of AI and blockchain within an institutionally governed, equity-oriented architecture represents a paradigm shift from technology-first to governance-first digital transformation in horticulture, providing a theoretically robust and practically actionable foundation for future research, systems design, and policy development, with direct application to resource-constrained smallholder systems in sub-Saharan Africa and comparable Global South contexts.","园艺价值链面临持续存在的农艺挑战，包括生产效率低下、采后损失、资源管理不善以及供应链透明度有限，在全球南方以小农为主的体系中尤为突出。这些制约因素削弱了农业生产力、市场准入以及园艺种植的长期可持续性。尽管数字农业的研究兴趣日益增长，人工智能（AI）与区块链技术正越来越多地被共同探索，包括用于农业溯源的新兴AI-区块链-物联网集成架构。然而，现有这些集成仍以技术为中心，优先考虑溯源、自动化和数据验证，缺乏专门针对以小农为主的园艺生产和分销体系而设计的明确的治理、公平或数据主权架构。本研究提出一个综合性概念框架，将AI的预测与决策支持能力与区块链的数据完整性、溯源性和自动化特征相结合，以增强园艺价值链的可持续性、包容性和治理水平。基于对通过Scopus、Web of Science、IEEE Xplore、ScienceDirect和Google Scholar识别的同行评审文献的整合性综述，并以技术-组织-环境（TOE）框架、社会技术系统理论和可持续数字化转型理论为基础，本研究探讨了园艺数字转型中的农艺、治理和公平维度。该框架围绕五个相互关联的操作层构建：数据采集、AI驱动的分析与决策、区块链验证与溯源、智能合约与决策支持，以及用户界面与利益相关者反馈，所有这些都在治理架构与伦理设计层内运作，该层作为框架的结构性前提而非下游功能发挥作用。这一治理层将算法透明度、通过社区治理的数据合作社实现的数据主权，以及小农的包容性数字接入作为不可协商的设计原则。该框架明确与可持续发展目标2、9、10、12、13和16相一致，并包含一个三阶段实证验证路径，包括专家德尔菲征询、仿真建模以及在","Frontiers in Sustainable Food Systems","2026-09-11T00:00:00Z",78,{"impact":19,"substance":93,"depth":19,"authority":94,"freshness":95,"relevant":21,"comment":96},22,13,7,"该研究提出治理优先的AI-区块链融合框架，聚焦小农园艺价值链的可持续与包容性，方法新颖、理论扎实，对数字农业政策设计有参考价值，但属概念框架尚未实证，时效性一般。",[98],{"name":89,"url":86},[100,26,27,28,101,102],"智慧农业","农业区块链","园艺价值链",{"code":47,"slug":104,"name":105,"path":106,"parent_code":107},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":110,"locked":14,"version":34,"method":35,"evidence":111},"classified",{"candidates":112,"agricultural_evidence":41},[113,118,125,132,137],{"code":47,"score":40,"supported":41,"title":114,"abstract":115,"tags":117},[49],[49,116],"decision support",[26],{"code":119,"score":95,"supported":41,"title":120,"abstract":121,"tags":124},"I1",[],[122,123],"数字农业","digital agriculture",[100],{"code":126,"score":81,"supported":41,"title":127,"abstract":128,"tags":131},"I5",[],[129,130],"物联网","IoT",[],{"code":39,"score":133,"supported":41,"title":134,"abstract":135,"tags":136},4,[],[43],[28],{"code":53,"score":21,"supported":14,"title":138,"abstract":139,"tags":140},[],[],[27],[142,143],"农业人工智能 农业区块链 园艺价值链 数据治理","农业人工智能 农业区块链","农业人工智能农业区块链园艺价值链数据治理-2292",3,"10.3389\u002Ffsufs.2026.1894057",{"doi":146,"openalex_id":148,"authors":149,"venue":89,"cited_by_count":62,"oa_url":86,"card":161,"direction":166,"ingested_from":78},"W7212272522",[150,152,155,158],{"name":151,"orcid":9},"Olaoluwa Olarewaju",{"name":153,"orcid":154},"Tobi Fadiji","https:\u002F\u002Forcid.org\u002F0000-0001-5525-4495",{"name":156,"orcid":157},"Olaniyi Amos Fawole","https:\u002F\u002Forcid.org\u002F0000-0001-5591-4633",{"name":159,"orcid":160},"Lembe Samukelo Magwaza","https:\u002F\u002Forcid.org\u002F0000-0001-9809-2254",{"tldr":162,"method":163,"finding":164,"direction":75,"opportunity":165},"提出治理优先框架，整合AI与区块链提升园艺价值链可持续性与包容性。","整合性文献综述，基于TOE、社会技术系统与可持续数字化转型理论。","治理架构应作为结构性前提，嵌入算法透明、数据主权与包容性数字接入。","可实证检验社区数据合作社与治理层对小型农户采纳和公平收益的影响。","智慧农业 \u002F 农业物联网","2026-09-13T23:30:07.102172Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":10,"source_url":171,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":174,"sources":176,"tags":178,"primary_topic":181,"secondary_topics":186,"classification":188,"search_phrases":204,"slug":207,"view_count":62,"doi":208,"paper":209,"created_at":223},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。",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":175},"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。",[177],{"name":10,"url":171},[100,26,28,179,180],"植物病害识别","可解释性",{"code":39,"slug":182,"name":183,"path":184,"parent_code":185},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[187],{"code":47,"slug":104,"name":105,"path":106,"parent_code":107},{"status":110,"locked":14,"version":34,"method":35,"evidence":189},{"candidates":190,"agricultural_evidence":41},[191,195,200],{"code":39,"score":20,"supported":41,"title":192,"abstract":193,"tags":194},[43],[43],[28],{"code":47,"score":13,"supported":41,"title":196,"abstract":198,"tags":199},[197],"computer vision",[],[26,179],{"code":119,"score":21,"supported":14,"title":201,"abstract":202,"tags":203},[],[],[100],[205,206],"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":208,"openalex_id":210,"authors":211,"venue":10,"cited_by_count":62,"oa_url":171,"card":218,"direction":77,"ingested_from":78},"W7223137323",[212,214,216],{"name":213,"orcid":9},"Akshay Ramgoolam",{"name":215,"orcid":9},"Priya Bundhoo Appadoo",{"name":217,"orcid":9},"Jean-Noël LEspérance",{"tldr":219,"method":220,"finding":221,"direction":77,"opportunity":222},"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":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":9,"source_name":10,"source_url":227,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":233,"tags":235,"primary_topic":238,"secondary_topics":239,"classification":241,"search_phrases":256,"slug":259,"view_count":62,"doi":260,"paper":261,"created_at":275},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":17,"substance":93,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":232},"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。",[234],{"name":10,"url":227},[100,26,28,236,237],"作物病害识别","模型审计",{"code":39,"slug":182,"name":183,"path":184,"parent_code":185},[240],{"code":47,"slug":104,"name":105,"path":106,"parent_code":107},{"status":110,"locked":14,"version":34,"method":35,"evidence":242},{"candidates":243,"agricultural_evidence":41},[244,248,252],{"code":39,"score":40,"supported":41,"title":245,"abstract":246,"tags":247},[43],[28,43],[28],{"code":47,"score":13,"supported":41,"title":249,"abstract":250,"tags":251},[197],[],[236,26],{"code":119,"score":21,"supported":14,"title":253,"abstract":254,"tags":255},[],[],[100],[257,258],"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":260,"openalex_id":262,"authors":263,"venue":10,"cited_by_count":62,"oa_url":227,"card":270,"direction":77,"ingested_from":78},"W7223088030",[264,266,268],{"name":265,"orcid":9},"Abdi-Basid Idriss Farah",{"name":267,"orcid":9},"Amina Abdillahi Guedi",{"name":269,"orcid":9},"Moussa Omar Youssouf",{"tldr":271,"method":272,"finding":273,"direction":77,"opportunity":274},"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":277,"title":278,"url":279,"summary":280,"summary_zh":9,"content":9,"source_name":281,"source_url":9,"published_at":282,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":283,"score_detail":284,"sources":289,"tags":291,"primary_topic":9,"secondary_topics":294,"classification":295,"search_phrases":333,"slug":336,"view_count":62,"doi":9,"paper":337,"created_at":345},4784,"Smart Farming and Modern Agricultural Technologies","https:\u002F\u002Fscientiafoundation.in\u002Fpopular-science--article\u002Fsmart-farming-and-modern-agricultural","Reddy等学者在Scientia Review 2026年第2卷第10期发表综述,系统梳理智慧农业从传统实践到数字农业的演进路径。文章解析精准农业、物联网、遥感与无人机、人工智能、机器人、智能灌溉、保护地栽培、畜牧监测、数字市场与溯源等关键技术的工作原理与效益,讨论成本、数字素养、融资、培训等阻碍因素,特别关注小农户和边缘农户的采纳瓶颈。","Scientia Review 2026-10-07","2026-10-07T00:00:00Z",73,{"impact":19,"substance":285,"depth":286,"authority":94,"freshness":287,"relevant":21,"comment":288},20,17,5,"系统梳理智慧农业关键技术与小农户采纳瓶颈的综述，信息增量足但属学术综述、时效一般，可作主题聚合素材。",[290],{"name":281,"url":279},[100,26,292,293,28],"农业物联网","精准农业",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":296},{"candidates":297,"agricultural_evidence":41,"reason":57},[298,303,307,311,317,323,329],{"code":119,"score":299,"supported":41,"title":300,"abstract":301,"tags":302},11,[],[100,293,122],[100,293],{"code":39,"score":133,"supported":41,"title":304,"abstract":305,"tags":306},[],[28],[28],{"code":126,"score":133,"supported":41,"title":308,"abstract":309,"tags":310},[],[129],[292],{"code":312,"score":145,"supported":41,"title":313,"abstract":314,"tags":316},"C",[],[315],"畜牧",[],{"code":318,"score":145,"supported":41,"title":319,"abstract":320,"tags":322},"H3",[],[321],"灌溉",[],{"code":324,"score":145,"supported":41,"title":325,"abstract":326,"tags":328},"I3",[],[327],"遥感",[],{"code":47,"score":21,"supported":14,"title":330,"abstract":331,"tags":332},[],[],[26],[334,335],"Scientia Review 智慧农业","Reddy 智慧农业 数字农业","ScientiaReview智慧农业-4784",{"doi":9,"openalex_id":9,"authors":338,"venue":9,"cited_by_count":62,"oa_url":9,"card":339,"direction":75,"ingested_from":344},[],{"tldr":340,"method":341,"finding":342,"direction":75,"opportunity":343},"综述智慧农业从传统到数字化的演进，梳理关键技术、效益与采纳障碍。","文献综述，覆盖精准农业、物联网、遥感、AI、机器人等关键技术。","技术效益显著，但成本、数字素养与融资障碍制约小农户采纳。","可针对小农户低成本、低门槛的智慧农业适配方案与采纳机制开展实证研究。","agent","2026-10-13T00:08:38.433355Z",{"id":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":9,"source_name":352,"source_url":349,"published_at":353,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":354,"score_detail":355,"sources":357,"tags":359,"primary_topic":361,"secondary_topics":365,"classification":366,"search_phrases":388,"slug":391,"view_count":62,"doi":392,"paper":393,"created_at":414},4663,"Sensor Data Transmission in Smart Agriculture: Challenges, Potentials, and Limitations in the Fourth Industrial Revolution","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsignals7050098","Smart agriculture is a key application domain of the Fourth Industrial Revolution (4IR), where cyber–physical systems, the Internet of Things (IoT), and data-driven analytics converge to improve productivity, sustainability, and resource efficiency. In this context, large volumes of sensor data collected in the field form the basis for automated monitoring, decision support, and adaptive control across agricultural processes. However, the value of such data depends not only on sensing technologies and connectivity, but also on how data are generated, transferred, processed, and interpreted throughout their lifecycle. This paper is organized in two parts. The first part reports a narrative review of the literature, based on a documented search protocol, and provides an overview of sensor data and sensor data transmission networks in smart agriculture. It examines the main challenges, potentials, and limitations associated with IoT-based field deployments in 4IR environments, and consolidates them into ten open problems. These problems are ordered from the device layer to the socio-economic layer and span physical robustness, device security, rural connectivity, aerial sensing, interoperability, labelled-data scarcity, explainability, federated access control, data ownership, and cost. The second part responds to these open problems with the conceptualization of a layered architecture and a set of design proposals, covering hierarchical edge security, offline-first synchronization, semantic interoperability, explainable decision support, and farmer-centric data governance, that are grounded in established techniques and standards but have not been implemented or empirically validated in this work; they are intended as a design blueprint and a starting point for future prototyping and evaluation rather than as measured results. The paper is accordingly positioned as a conceptual, perspective-oriented contribution: it states the procedure by which the open problems were derived from the reviewed works, it states what the proposed integration is claimed to add over existing smart-agriculture architectures, and it sets out an evaluation plan that fixes the metrics and the baselines against which each proposed component would have to be tested. Throughout both parts, particular attention is given to the role of data integrity and provenance as foundational elements for trustworthy agricultural data, enabling reliable analysis, auditability, and long-term reuse. By synthesizing current approaches, open issues, and conceptual solution patterns, this paper aims to support the design of resilient, transparent, and future-ready smart agriculture systems aligned with the principles of the 4IR.","智慧农业是第四次工业革命（4IR）的关键应用领域，信息物理系统、物联网（IoT）和数据驱动分析在此汇聚，以提升生产力、可持续性和资源效率。在此背景下，田间采集的大量传感器数据构成了农业过程自动化监测、决策支持和自适应控制的基础。然而，此类数据的价值不仅取决于传感技术和连接能力，还取决于数据在其生命周期中如何生成、传输、处理和解释。本文分为两部分。第一部分基于有据可查的检索方案，对文献进行叙述性综述，概述智慧农业中的传感器数据及传感器数据传输网络。它考察了4IR环境下基于物联网的田间部署所面临的主要挑战、潜力和局限，并将其归纳为十个开放性问题。这些问题从设备层到社会经济层依次排列，涵盖物理鲁棒性、设备安全、农村连接、空中传感、互操作性、标注数据稀缺、可解释性、联邦访问控制、数据所有权和成本。第二部分针对这些开放性问题，提出了分层架构的概念化和一组设计建议，涵盖分层边缘安全、离线优先同步、语义互操作性、可解释决策支持以及以农户为中心的数据治理。这些建议以已有技术和标准为基础，但尚未在本文中实现或经过实证验证；它们旨在作为设计蓝图和未来原型开发与评估的起点，而非实测结果。因此，本文定位为概念性、视角导向的贡献：它说明了开放性问题如何从所综述的工作中推导出来的过程，阐明了所提出的集成方案相对于现有智慧农业架构声称增加的贡献，并提出了一个评估计划，确定了每个所提组件必须接受测试的指标和基线。在两部分中，特别关注数据完整性和来源作为可信农业数据基础要素的作用，从而实现可靠分析、可审计性和长期复用。","Signals","2026-10-10T00:00:00Z",71,{"impact":40,"substance":19,"depth":286,"authority":94,"freshness":17,"relevant":21,"comment":356},"系统梳理智慧农业传感器数据传输的十大开放问题并提出分层架构蓝图，属概念性综述，有参考价值但未经实证验证。",[358],{"name":352,"url":349},[100,26,292,27,360],"传感器网络",{"code":126,"slug":362,"name":363,"path":364,"parent_code":107},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":110,"locked":14,"version":34,"method":35,"evidence":367},{"candidates":368,"agricultural_evidence":41},[369,374,379,383],{"code":119,"score":40,"supported":41,"title":370,"abstract":372,"tags":373},[371],"smart agriculture",[100,371],[100],{"code":126,"score":13,"supported":41,"title":375,"abstract":376,"tags":378},[],[129,377,130],"internet of things",[292],{"code":47,"score":133,"supported":41,"title":380,"abstract":381,"tags":382},[],[116],[26],{"code":53,"score":133,"supported":41,"title":384,"abstract":385,"tags":387},[],[386],"agricultural data",[27],[389,390],"智慧农业 传感器数据传输","农业物联网 数据完整性","智慧农业传感器数据传输-4663","10.3390\u002Fsignals7050098",{"doi":392,"openalex_id":394,"authors":395,"venue":352,"cited_by_count":62,"oa_url":349,"card":409,"direction":166,"ingested_from":78},"W7221365687",[396,398,401,404,406],{"name":397,"orcid":9},"Alvertos Ioannis Mourikis",{"name":399,"orcid":400},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":402,"orcid":403},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":405,"orcid":9},"Romanos Kalamatianos",{"name":407,"orcid":408},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":410,"method":411,"finding":412,"direction":166,"opportunity":413},"综述智慧农业传感器数据传输挑战，归纳十大开放问题并提出分层架构设计蓝图。","文献综述与概念架构设计，基于搜索协议梳理IoT部署问题。","从设备到社会经济层归纳十大开放问题，提出边缘安全、语义互操作等设计提案。","所提分层架构尚未实现与实证，可开展原型验证并量化数据完整性、溯源与离线同步效果。","2026-10-11T23:30:08.094185Z",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":422,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":423,"score_detail":424,"sources":426,"tags":428,"primary_topic":431,"secondary_topics":432,"classification":437,"search_phrases":463,"slug":466,"view_count":287,"doi":467,"paper":468,"created_at":487},3829,"Towards accessible smallholder crop classification in the groundnut basin of Senegal","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2976-601x\u002Faea0b8","Abstract Crop type mapping is a critical lever for decision making in smallholder food systems, yet high cost and expertise requirements of developing remote sensing-driven maps as well as need for recurrent data are persistent barriers to incorporating geospatial tools into decision making. To address this gap, we establish a three-part framework to evaluate crop type mapping: (1) performance, (2) temporal transferability, and (3) accessibility. We evaluate geospatial foundation model embeddings (TESSERA and AlphaEarth) against traditional baselines in Senegal’s groundnut basin. We find that the embedding-based approaches, in particular TESSERA, best fulfill these criteria. The TESSERA-based method shows a 78% reduction in both compute compared to a timeseries approach, a reduction in need for custom feature generation, and 28% higher relative accuracy (an absolute improvement of approximately 0.10 weighted F 1) in one crop type classification temporal transfer test. By reducing the reliance on training data, computational resource use, and expertise for data processing, this approach shows potential to contribute to improved smallholder crop type classification.","摘要 作物类型制图是小农粮食系统决策的关键手段，但开发遥感驱动地图的高成本与专业知识要求，以及 recurring 数据的需求，仍是将地理空间工具纳入决策的持续障碍。为弥补这一差距，我们建立了一个三部分框架来评估作物类型制图：（1）性能，（2）时间可迁移性，（3）可及性。我们在塞内加尔花生盆地评估了地理空间基础模型嵌入（TESSERA 和 AlphaEarth）与传统基线方法。我们发现，基于嵌入的方法，尤其是 TESSERA，最能满足这些标准。基于 TESSERA 的方法显示，与时间序列方法相比，计算量减少 78%，对自定义特征生成的需求降低，并且在一项作物类型分类时间迁移测试中相对精度提高 28%（加权 F1 绝对提升约 0.10）。通过减少对训练数据、计算资源使用和数据处理专业知识的依赖，该方法显示出改善小农作物类型分类的潜力。","Environmental Research Food Systems","2026-09-28T00:00:00Z",79,{"impact":19,"substance":93,"depth":19,"authority":94,"freshness":17,"relevant":21,"comment":425},"该研究提出兼顾性能、时序迁移性与可及性的作物分类评估框架，用地理空间基础模型显著降低算力与训练数据依赖，对小农户农业遥感应用具有实质参考价值。",[427],{"name":421,"url":418},[100,26,429,28,430],"农业遥感","作物分类",{"code":39,"slug":182,"name":183,"path":184,"parent_code":185},[433],{"code":324,"slug":434,"name":435,"path":436,"parent_code":107},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":110,"locked":14,"version":34,"method":35,"evidence":438},{"candidates":439,"agricultural_evidence":41},[440,444,449,455,459],{"code":39,"score":20,"supported":41,"title":441,"abstract":442,"tags":443},[43],[43],[28],{"code":324,"score":17,"supported":41,"title":445,"abstract":446,"tags":448},[],[327,447],"remote sensing",[430,429],{"code":450,"score":145,"supported":41,"title":451,"abstract":452,"tags":454},"A4",[],[453],"花生",[],{"code":119,"score":21,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[100],{"code":47,"score":21,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[26],[464,465],"塞内加尔 花生盆地 作物分类","TESSERA 遥感 作物制图","塞内加尔花生盆地作物分类-3829","10.1088\u002F2976-601x\u002Faea0b8",{"doi":467,"openalex_id":469,"authors":470,"venue":421,"cited_by_count":62,"oa_url":480,"card":481,"direction":166,"ingested_from":78},"W7214087594",[471,473,475,477],{"name":472,"orcid":9},"Madeline Lisaius",{"name":474,"orcid":9},"Srinivasan Keshav",{"name":476,"orcid":9},"Andrew Blake",{"name":478,"orcid":479},"Clement Atzberger","https:\u002F\u002Forcid.org\u002F0000-0003-2169-8009","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2976-601X\u002Faea0b8\u002Fpdf",{"tldr":482,"method":483,"finding":484,"direction":485,"opportunity":486},"评估地理空间基础模型嵌入在塞内加尔花生盆地小农作物分类中的性能、时序迁移性和可及性。","对比TESSERA和AlphaEarth嵌入与传统基线，用加权F1和计算量评估。","TESSERA嵌入精度相对提高28%，计算量减少78%，且无需自定义特征生成。","农业遥感与作物表型","可探索基础模型嵌入在更多小农区域和作物类型上的泛化能力及低成本部署方案。","2026-09-30T23:30:11.490990Z"]