[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2925":3,"related-2925":94},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":93},2925,"From tradition to smart agriculture: how value and technology readiness shape farmers’ adoption decisions","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fjstpm-03-2025-0084","Purpose This study aims to investigate the slow adoption of smart farming technologies (SFTs) in developing regions, where farmers continue relying on traditional methods despite modernization policies. It examines how farmers’ value perceptions and technology readiness (TR) shape adoption intentions by integrating the value-based adoption model (VAM) with the theory of consumption values (TCV). Specifically, this study develops a value-centric framework in which TR moderates the link between overall perceived value (OPV) and adoption intention (INT). Design\u002Fmethodology\u002Fapproach Data were collected from 351 smallholder farmers through self-administered surveys and analyzed using partial least squares-structural equation modeling. Findings OPV emerged as the strongest predictor of adoption, driven by perceived efficiency, facilitating conditions and financial incentive policy. Among OPV dimensions, conditional value outweighed functional and monetary values, underscoring the importance of enabling conditions such as financial aid, training and infrastructure. Contrary to expectations, perceived cost increased OPV, while perceived complexity and uncertainty were insignificant, suggesting that familiarity or outsourcing may reduce traditional barriers. TR moderated the link between OPV and INT, where motivated farmers move toward adoption regardless, but reluctant farmers require stronger OPV to ease concerns. Originality\u002Fvalue To the best of the authors’ knowledge, this study is among the first to integrate VAM and TCV to explain the adoption of SFTs, establishing the value-centric perspective as a powerful lens for understanding technology acceptance in agriculture. It also demonstrates that TR differentially shapes how farmers rely on OPV to drive adoption.","目的 本研究旨在探究发展中地区智能农业技术（SFTs）采用缓慢的问题，尽管有现代化政策推动，农民仍继续依赖传统方法。研究通过将价值基础采用模型（VAM）与消费价值理论（TCV）相结合，考察农民的价值感知和技术准备度（TR）如何塑造采用意愿。具体而言，本研究构建了一个以价值为核心的框架，其中TR调节整体感知价值（OPV）与采用意愿（INT）之间的关系。设计\u002F方法\u002F路径 通过自填式问卷从351名小农户处收集数据，并采用偏最小二乘结构方程模型进行分析。研究发现 OPV是采用行为最强的预测因子，受感知效率、便利条件和财政激励政策驱动。在OPV各维度中，条件价值超过了功能价值和货币价值，凸显了财政援助、培训和基础设施等使能条件的重要性。与预期相反，感知成本增加了OPV，而感知复杂性和不确定性则不显著，表明熟悉度或外包可能降低了传统障碍。TR调节了OPV与INT之间的关系，有积极性的农民无论如何都会趋向采用，而犹豫不决的农民则需要更强的OPV来缓解顾虑。原创性\u002F价值 据作者所知，本研究是首批整合VAM和TCV来解释SFTs采用的研究之一，确立了以价值为核心的视角作为理解农业技术接受的有力透镜。研究还表明，TR以差异化方式塑造了农民依赖OPV来推动采用的过程。",null,"Journal of Science and Technology Policy Management","2026-09-18T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,8,1,"该研究整合VAM与TCV模型，基于351户小农户调查揭示价值感知与技术准备度对智慧农业技术采纳的影响，方法新颖、结论对发展区域推广政策有参考价值，但属学术论文、影响范围偏细分领域。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","小农户","农业技术采纳","农业政策激励","技术准备度",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48},[45,53,59],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},"I1",9,true,[50],"smart agriculture",[],[27],{"code":33,"score":54,"supported":48,"title":55,"abstract":56,"tags":58},7,[],[28,57],"smallholder",[28],{"code":60,"score":22,"supported":14,"title":61,"abstract":62,"tags":63},"G1",[],[],[29],[65,66],"智慧农业技术 小农户 采纳","价值感知 技术准备度 农户","智慧农业技术小农户采纳-2925",2,"10.1108\u002Fjstpm-03-2025-0084",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":84,"oa_url":9,"card":85,"direction":91,"ingested_from":92},"W7213541758",[73,76,79,82],{"name":74,"orcid":75},"Hazem Yusuf Osrof","https:\u002F\u002Forcid.org\u002F0000-0001-9622-5025",{"name":77,"orcid":78},"Cheng Ling Tan","https:\u002F\u002Forcid.org\u002F0000-0002-3759-6763",{"name":80,"orcid":81},"Sook Fern Yeo","https:\u002F\u002Forcid.org\u002F0000-0002-8060-5872",{"name":83,"orcid":9},"Kim Hua Tan",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"整合VAM与TCV模型，研究农户价值感知与技术准备度如何影响智慧农业技术采纳意愿。","对351户小农户问卷调查，采用PLS-SEM结构方程建模分析。","总体感知价值是采纳最强预测因子，条件价值作用最大，技术准备度起调节作用。","数字乡村与农业信息化","可探索不同区域与文化背景下价值维度差异，及技术准备度干预对采纳的长期效应。","智慧农业 \u002F 农业物联网","openalex","2026-09-19T23:30:10.915432Z",{"total":95,"page":22,"page_size":95,"items":96},6,[97,161,215,277,330,406],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":110,"tags":112,"primary_topic":116,"secondary_topics":117,"classification":124,"search_phrases":140,"slug":143,"view_count":84,"doi":144,"paper":145,"created_at":160},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":21,"substance":18,"depth":107,"authority":108,"freshness":13,"relevant":22,"comment":109},18,12,"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。",[111],{"name":103,"url":100},[27,113,28,114,115],"农业人工智能","植物病害识别","可解释性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[118],{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":125},{"candidates":126,"agricultural_evidence":48},[127,131,136],{"code":33,"score":108,"supported":48,"title":128,"abstract":129,"tags":130},[57],[57],[28],{"code":119,"score":13,"supported":48,"title":132,"abstract":134,"tags":135},[133],"computer vision",[],[113,114],{"code":46,"score":22,"supported":14,"title":137,"abstract":138,"tags":139},[],[],[27],[141,142],"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":144,"openalex_id":146,"authors":147,"venue":103,"cited_by_count":84,"oa_url":100,"card":154,"direction":158,"ingested_from":92},"W7223137323",[148,150,152],{"name":149,"orcid":9},"Akshay Ramgoolam",{"name":151,"orcid":9},"Priya Bundhoo Appadoo",{"name":153,"orcid":9},"Jean-Noël LEspérance",{"tldr":155,"method":156,"finding":157,"direction":158,"opportunity":159},"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。","2026-10-22T11:21:10.425290Z",{"id":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":9,"source_name":103,"source_url":164,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":167,"score_detail":168,"sources":171,"tags":173,"primary_topic":176,"secondary_topics":177,"classification":179,"search_phrases":195,"slug":198,"view_count":84,"doi":199,"paper":200,"created_at":214},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":21,"substance":169,"depth":107,"authority":108,"freshness":13,"relevant":22,"comment":170},22,"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。",[172],{"name":103,"url":164},[27,113,28,174,175],"作物病害识别","模型审计",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[178],{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},{"status":40,"locked":14,"version":41,"method":42,"evidence":180},{"candidates":181,"agricultural_evidence":48},[182,187,191],{"code":33,"score":183,"supported":48,"title":184,"abstract":185,"tags":186},15,[57],[28,57],[28],{"code":119,"score":13,"supported":48,"title":188,"abstract":189,"tags":190},[133],[],[174,113],{"code":46,"score":22,"supported":14,"title":192,"abstract":193,"tags":194},[],[],[27],[196,197],"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":199,"openalex_id":201,"authors":202,"venue":103,"cited_by_count":84,"oa_url":164,"card":209,"direction":158,"ingested_from":92},"W7223088030",[203,205,207],{"name":204,"orcid":9},"Abdi-Basid Idriss Farah",{"name":206,"orcid":9},"Amina Abdillahi Guedi",{"name":208,"orcid":9},"Moussa Omar Youssouf",{"tldr":210,"method":211,"finding":212,"direction":158,"opportunity":213},"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":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":9,"source_name":221,"source_url":218,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":224,"sources":226,"tags":228,"primary_topic":232,"secondary_topics":233,"classification":234,"search_phrases":260,"slug":263,"view_count":84,"doi":264,"paper":265,"created_at":276},4601,"Digital Agriculture and Sector Transformation in South Africa","https:\u002F\u002Fdoi.org\u002F10.36096\u002Fijbes.v8i4.1274","Digitalisation is increasingly promoted as a catalyst for agricultural productivity, climate resilience, and market integration. However, there is limited systematic analysis of how digital agricultural transformation unfolds within South Africa’s institutional and socio-economic context. This study examines the trajectory, drivers, and governance implications of digitalisation in South African agriculture. Anchored in an integrated Socio-Technical Systems and Digital Agriculture Ecosystem framework, the study applies a structured qualitative document analysis of 82 academic and policy documents published between 2013 and 2025. The findings reveal highly uneven digital adoption patterns that reflect a structural dualism between capital-intensive commercial agriculture and resource-constrained smallholder systems. While South Africa has developed a relatively advanced digital policy architecture, implementation fragmentation, infrastructure constraints, and limited capacity for extension impede inclusive digital outcomes. The study contributes by integrating socio-technical and ecosystem perspectives to analyse digital transitions in emerging economies and by providing a transparent analytical protocol for document-based research. Policy implications highlight the need for coordinated governance, investment in rural connectivity, and targeted support mechanisms to ensure equitable digital transformation.","数字化日益被视为提升农业生产力、增强气候韧性和促进市场整合的催化剂。然而，对于数字农业转型如何在南非的制度与社会经济语境中展开，尚缺乏系统性分析。本研究考察南非农业数字化的演进轨迹、驱动因素及治理意涵。研究以社会技术系统与数字农业生态系统的整合框架为依托，对2013年至2025年间发表的82份学术与政策文件进行了结构化定性文献分析。研究发现，数字技术采纳模式高度不均衡，反映出资本密集型商业农业与资源受限的小农体系之间的结构性二元分化。尽管南非已建立起相对先进的数字政策架构，但实施碎片化、基础设施制约以及推广能力有限等因素阻碍了包容性数字成果的实现。本研究的贡献在于整合社会技术与生态系统视角以分析新兴经济体的数字转型，并为基于文献的研究提供了透明的分析方案。政策启示强调，需通过协调治理、农村互联互通投资以及有针对性的支持机制，确保公平的数字转型。","International Journal of Business Ecosystem and Strategy (2687-2293)","2026-10-09T00:00:00Z",80,{"impact":107,"substance":169,"depth":107,"authority":20,"freshness":47,"relevant":22,"comment":225},"基于82份文献的系统性分析，揭示南非数字农业的结构性二元分化与治理碎片化，对新兴经济体数字乡村研究有参考价值。",[227],{"name":221,"url":218},[229,27,28,230,231],"数字农业","南非农业","农村数字治理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":235},{"candidates":236,"agricultural_evidence":48},[237,242,247,254],{"code":46,"score":17,"supported":48,"title":238,"abstract":240,"tags":241},[239],"digital agriculture",[229,239],[229,27],{"code":33,"score":243,"supported":48,"title":244,"abstract":245,"tags":246},4,[],[57],[28],{"code":248,"score":249,"supported":48,"title":250,"abstract":251,"tags":253},"H6",3,[],[252],"农业生态",[],{"code":255,"score":249,"supported":48,"title":256,"abstract":257,"tags":259},"H7",[],[258],"climate resilience",[],[261,262],"南非 数字农业 转型","南非 小农户 数字鸿沟","南非数字农业转型-4601","10.36096\u002Fijbes.v8i4.1274",{"doi":264,"openalex_id":266,"authors":267,"venue":221,"cited_by_count":84,"oa_url":218,"card":271,"direction":89,"ingested_from":92},"W7221113328",[268],{"name":269,"orcid":270},"Mokgadi Julia Ngoepe-Ntsoane","https:\u002F\u002Forcid.org\u002F0000-0001-7995-4824",{"tldr":272,"method":273,"finding":274,"direction":89,"opportunity":275},"系统分析南非农业数字化轨迹、驱动因素与治理影响，揭示数字鸿沟与政策碎片化。","社会技术系统与数字农业生态框架，对82份2013-2025年文献政策进行结构化质","数字采用高度不均，商业农业与小农二元分化；政策先进但实施碎片化、基建与推广能力不足阻碍包容性转型。","可延伸至新兴经济体小农数字包容性治理机制与农村连接性投资评估的实证研究。","2026-10-10T23:30:34.991942Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":283,"source_url":280,"published_at":284,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":285,"score_detail":286,"sources":288,"tags":290,"primary_topic":293,"secondary_topics":294,"classification":295,"search_phrases":310,"slug":313,"view_count":68,"doi":314,"paper":315,"created_at":329},4175,"Integrating Agricultural Technology Adoption and Financial Inclusion as a Strategy to Improve Micro-Scale Farm Productivity in Rural Areas of South Sumatra","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26sep241","Micro-scale farming faces various constraints in improving productivity, particularly in relation to farmers' ability to adopt technology and access financing that meets their business needs. Agricultural technology can improve the efficiency of input use, access to information, farm management, and decision-making. However, technology adoption among smallholder farmers is still influenced by digital literacy, infrastructure, economic conditions, and institutional factors. On the other hand, access to credit can improve farm productivity and technical efficiency by enabling farmers to obtain production inputs and make productive investments. This study aims to: (1) analyze the effect of agricultural technology adoption on micro-scale farm productivity; and (2) analyze the effect of financial inclusion access on micro-scale farm productivity. The study employs a quantitative explanatory approach using multiple linear regression analysis. A total of 300 farm observations are used as simulated data to demonstrate the proposed analytical framework; therefore, the numerical results do not represent findings from an actual field survey. The independent variables consist of an agricultural technology adoption index and a financial inclusion access index, while farm productivity serves as the dependent variable. Farmers' age, education level, and land area are included as control variables. The results indicate that agricultural technology adoption and financial inclusion access have positive and statistically significant effects on farm productivity. The model explains a substantial proportion of the variation in productivity and provides an initial basis for further empirical investigation into the role of technology and financing in micro-scale farming. These findings are illustrative in nature and should be validated using primary data collected from farmers.","微观农业在提高生产力方面面临各种制约，尤其是农民采用技术以及获取满足其经营需求的融资能力方面的制约。农业技术可以提高投入品使用效率、信息获取、农场管理和决策效率。然而，小农户的技术采用仍受到数字素养、基础设施、经济条件和制度因素的影响。另一方面，获得信贷可以通过使农民能够获取生产投入并进行生产性投资来提高农场生产力和技术效率。本研究旨在：(1) 分析农业技术采用对微观农场生产力的影响；(2) 分析普惠金融获取对微观农场生产力的影响。本研究采用定量解释性方法，运用多元线性回归分析。共使用300个农场观测值作为模拟数据来展示所提出的分析框架；因此，数值结果不代表实际实地调查的发现。自变量包括农业技术采用指数和普惠金融获取指数，农场生产力为因变量。农民年龄、教育水平和土地面积作为控制变量纳入分析。结果表明，农业技术采用和普惠金融获取对农场生产力具有正向且统计显著的影响。该模型解释了生产力变异的相当大比例，为进一步实证研究技术和融资在微观农业中的作用提供了初步基础。这些发现具有示例性质，应使用从农民处收集的一手数据加以验证。","International Journal of Innovative Science and Research Technology (IJISRT)","2026-10-03T00:00:00Z",41,{"impact":21,"substance":13,"depth":21,"authority":95,"freshness":47,"relevant":22,"comment":287},"主题相关但数据为模拟生成、期刊权威性一般，仅具方法参考价值，不建议进入每日精选。",[289],{"name":283,"url":280},[291,28,292,29],"农业信息化","普惠金融",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":296},{"candidates":297,"agricultural_evidence":48},[298,302,306],{"code":33,"score":54,"supported":48,"title":299,"abstract":300,"tags":301},[],[28,57],[28],{"code":60,"score":22,"supported":14,"title":303,"abstract":304,"tags":305},[],[],[29],{"code":46,"score":22,"supported":14,"title":307,"abstract":308,"tags":309},[],[],[291],[311,312],"南苏门答腊 小农户 生产力","农业技术采纳 普惠金融","南苏门答腊小农户生产力-4175","10.38124\u002Fijisrt\u002F26sep241",{"doi":314,"openalex_id":316,"authors":317,"venue":283,"cited_by_count":84,"oa_url":280,"card":324,"direction":89,"ingested_from":92},"W7218524136",[318,321],{"name":319,"orcid":320},"Lisa Hermawati","https:\u002F\u002Forcid.org\u002F0009-0001-5824-7052",{"name":322,"orcid":323},"Ema Pusvita","https:\u002F\u002Forcid.org\u002F0009-0002-2813-5567",{"tldr":325,"method":326,"finding":327,"direction":89,"opportunity":328},"用模拟数据回归分析农业技术采纳与金融普惠对微规模农场生产力的影响。","多元线性回归，300条模拟数据，构建技术采纳与金融普惠指数。","技术采纳和金融普惠均显著正向提升微规模农场生产力。","需用真实农户调查数据验证，并探究数字素养与信贷可得性的交互机制。","2026-10-04T23:30:30.503599Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":9,"source_name":336,"source_url":333,"published_at":337,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":338,"score_detail":339,"sources":341,"tags":343,"primary_topic":346,"secondary_topics":347,"classification":353,"search_phrases":380,"slug":383,"view_count":384,"doi":385,"paper":386,"created_at":405},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":107,"substance":169,"depth":107,"authority":20,"freshness":21,"relevant":22,"comment":340},"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。",[342],{"name":336,"url":333},[27,113,344,28,345],"农业遥感","作物分类",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[348],{"code":349,"slug":350,"name":351,"path":352,"parent_code":123},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":354},{"candidates":355,"agricultural_evidence":48},[356,360,366,372,376],{"code":33,"score":108,"supported":48,"title":357,"abstract":358,"tags":359},[57],[57],[28],{"code":349,"score":21,"supported":48,"title":361,"abstract":362,"tags":365},[],[363,364],"遥感","remote sensing",[345,344],{"code":367,"score":249,"supported":48,"title":368,"abstract":369,"tags":371},"A4",[],[370],"花生",[],{"code":46,"score":22,"supported":14,"title":373,"abstract":374,"tags":375},[],[],[27],{"code":119,"score":22,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[113],[381,382],"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",5,"10.1088\u002F2976-601x\u002Faea0b8",{"doi":385,"openalex_id":387,"authors":388,"venue":336,"cited_by_count":84,"oa_url":398,"card":399,"direction":91,"ingested_from":92},"W7214087594",[389,391,393,395],{"name":390,"orcid":9},"Madeline Lisaius",{"name":392,"orcid":9},"Srinivasan Keshav",{"name":394,"orcid":9},"Andrew Blake",{"name":396,"orcid":397},"Clement Atzberger","https:\u002F\u002Forcid.org\u002F0000-0003-2169-8009","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2976-601X\u002Faea0b8\u002Fpdf",{"tldr":400,"method":401,"finding":402,"direction":403,"opportunity":404},"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",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":9,"source_name":412,"source_url":409,"published_at":413,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":414,"sources":416,"tags":418,"primary_topic":421,"secondary_topics":422,"classification":424,"search_phrases":452,"slug":455,"view_count":95,"doi":456,"paper":457,"created_at":470},3512,"AI-Driven Agricultural Advisory and Diagnostic Systems for Smallholder Farming: Technical Architectures, Evidence and Deployment Priorities for North-East India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Farja\u002F2026\u002Fv19i4919","Artificial intelligence (AI) is being introduced into agricultural advisory services through machine learning, computer vision, conversational large language models, retrieval-augmented generation and multimodal interfaces. For smallholder farming, the central question is not whether these technologies can produce technically plausible outputs, but whether they can provide locally correct, actionable and safe recommendations under heterogeneous agronomic, linguistic and connectivity conditions. This critical narrative review integrates evidence on digital extension, AI-enabled agricultural advice, image-based diagnosis and responsible digital agriculture, with particular reference to North-East India. Literature published from 1 January 2010 to 17 July 2026 was considered, with emphasis on peer-reviewed field evaluations, technical validation studies, reviews and regionally relevant research. Evidence from digital extension provides the strongest causal baseline: mobile and personalised advisory services frequently improve information recall, agronomic knowledge and adoption of recommended practices, yet effects on yield, profit and welfare are inconsistent. Recent generative-AI studies show that large language models can produce useful agricultural responses, but site-specific rates, timing and local practice remain recurrent failure points. Retrieval grounding and expert feedback improve local relevance, although multi-season farm-level effectiveness evidence remains scarce. Image-based plant-disease systems achieve high accuracy in curated datasets, but performance can deteriorate sharply under field domain shift, class novelty and variable image quality. North-East Indian studies of mobile advisory systems in Meghalaya, Nagaland and Tripura demonstrate a valuable institutional foundation based on interactive voice response, local expert networks and user-centred service design; they do not, however, establish the effectiveness of autonomous AI. The most defensible deployment model is therefore an offline-tolerant, multilingual, multimodal and human-supervised architecture that grounds recommendations in curated regional knowledge, represents uncertainty, preserves provenance and escalates high-risk or out-of-distribution cases. Future research should prioritise prospective district- and season-spanning evaluations that connect model quality to farmer decisions, agronomic outcomes, equity, safety and cost-effectiveness.","人工智能（AI）正通过机器学习、计算机视觉、对话式大语言模型、检索增强生成和多模态界面被引入农业咨询服务。对于小农户而言，核心问题不在于这些技术能否产生技术上看似合理的输出，而在于它们能否在异质的农艺、语言和网络连接条件下提供本地正确、可操作且安全的建议。本批判性叙事综述整合了数字推广、AI赋能的农业建议、基于图像的诊断和负责任数字农业方面的证据，并特别关注印度东北部。本文考察了2010年1月1日至2026年7月17日期间发表的文献，重点关注同行评议的田间评估、技术验证研究、综述及区域相关研究。来自数字推广的证据提供了最强的因果基线：移动化和个性化咨询服务经常改善信息记忆、农艺知识和对推荐措施的采纳，但对产量、利润和福利的影响并不一致。近期生成式AI研究表明，大语言模型能够产生有用的农业回答，但针对具体地点的用量、时机和本地实践仍是反复出现的失败点。检索 grounding 和专家反馈可提高本地相关性，但多季农场层面的有效性证据仍然稀缺。基于图像的植物病害系统在精选数据集上达到高准确率，但在田间域偏移、类别新颖性和图像质量多变的情况下，性能可能急剧下降。印度东北部在梅加拉亚邦、那加兰邦和特里普拉邦开展的移动咨询系统研究展示了基于交互式语音应答、本地专家网络和以用户为中心的服务设计的宝贵制度基础；然而，这些研究并未确立自主AI的有效性。因此，最可辩护的部署模式是一种容忍离线、多语言、多模态且有人工监督的架构，该架构将建议建立在精选的区域知识之上，表征不确定性，保留来源信息，并对高风险或分布外案例进行升级处理。未来研究应优先开展前瞻性的跨区县和跨季节评估，将模型质量与农户决策、农艺结果、公平性、安全性和成本效益联系起来。","Asian Research Journal of Agriculture","2026-09-24T00:00:00Z",{"impact":107,"substance":169,"depth":107,"authority":20,"freshness":47,"relevant":22,"comment":415},"系统综述AI农业咨询与诊断系统在印度东北小农场景的技术架构与落地证据，指出人机协同、离线多语言与检索增强是可行路径，对智慧农业落地有参考价值。",[417],{"name":412,"url":409},[419,27,113,420,28],"数字乡村","农业技术推广",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[423],{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},{"status":40,"locked":14,"version":41,"method":42,"evidence":425},{"candidates":426,"agricultural_evidence":48},[427,431,439,443,447],{"code":33,"score":183,"supported":48,"title":428,"abstract":429,"tags":430},[57],[28,57],[28],{"code":119,"score":13,"supported":48,"title":432,"abstract":433,"tags":438},[],[434,435,436,437,133],"机器学习","计算机视觉","machine learning","artificial intelligence",[113],{"code":46,"score":54,"supported":48,"title":440,"abstract":441,"tags":442},[],[229,239],[27],{"code":60,"score":22,"supported":14,"title":444,"abstract":445,"tags":446},[],[],[420],{"code":448,"score":22,"supported":14,"title":449,"abstract":450,"tags":451},"I6",[],[],[419],[453,454],"印度东北部 农业AI 小农户","农业智能诊断 多语言 离线","印度东北部农业AI小农户-3512","10.9734\u002Farja\u002F2026\u002Fv19i4919",{"doi":456,"openalex_id":458,"authors":459,"venue":412,"cited_by_count":84,"oa_url":409,"card":465,"direction":89,"ingested_from":92},"W7214205238",[460,462],{"name":461,"orcid":9},"Pravangkar Boruah",{"name":463,"orcid":464},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":466,"method":467,"finding":468,"direction":158,"opportunity":469},"综述AI农业咨询与诊断系统，聚焦印度东北小农，提出人监督多模态部署架构。","批判性叙述综述，整合2010-2026年数字推广、生成式AI与图像诊断证据。","AI输出技术可行但本地化、安全与田间效果证据不足，需人监督与检索增强。","可开展跨区跨季前瞻评估，连接模型质量与农户决策、产量、公平及成本效益。","2026-09-25T23:30:39.745514Z"]