[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4601":3,"related-4601":91},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":90},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。",null,"International Journal of Business Ecosystem and Strategy (2687-2293)","2026-10-09T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","智慧农业","小农户","南非农业","农村数字治理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,52,58,65],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},"I1",16,true,[49],"digital agriculture",[26,49],[26,27],{"code":32,"score":53,"supported":47,"title":54,"abstract":55,"tags":57},4,[],[56],"smallholder",[28],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":64},"H6",3,[],[63],"农业生态",[],{"code":66,"score":60,"supported":47,"title":67,"abstract":68,"tags":70},"H7",[],[69],"climate resilience",[],[72,73],"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",0,"10.36096\u002Fijbes.v8i4.1274",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":75,"oa_url":6,"card":83,"direction":87,"ingested_from":89},"W7221113328",[80],{"name":81,"orcid":82},"Mokgadi Julia Ngoepe-Ntsoane","https:\u002F\u002Forcid.org\u002F0000-0001-7995-4824",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"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。","数字乡村与农业信息化","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。","openalex","2026-10-10T23:30:34.991942Z",{"total":92,"page":21,"page_size":92,"items":93},6,[94,159,212,289,356,437],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":108,"tags":110,"primary_topic":114,"secondary_topics":115,"classification":122,"search_phrases":138,"slug":141,"view_count":75,"doi":142,"paper":143,"created_at":158},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":104,"substance":105,"depth":17,"authority":106,"freshness":13,"relevant":21,"comment":107},8,21,12,"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。",[109],{"name":100,"url":97},[27,111,28,112,113],"农业人工智能","植物病害识别","可解释性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[116],{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":123},{"candidates":124,"agricultural_evidence":47},[125,129,134],{"code":32,"score":106,"supported":47,"title":126,"abstract":127,"tags":128},[56],[56],[28],{"code":117,"score":13,"supported":47,"title":130,"abstract":132,"tags":133},[131],"computer vision",[],[111,112],{"code":45,"score":21,"supported":14,"title":135,"abstract":136,"tags":137},[],[],[27],[139,140],"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":142,"openalex_id":144,"authors":145,"venue":100,"cited_by_count":75,"oa_url":97,"card":152,"direction":156,"ingested_from":89},"W7223137323",[146,148,150],{"name":147,"orcid":9},"Akshay Ramgoolam",{"name":149,"orcid":9},"Priya Bundhoo Appadoo",{"name":151,"orcid":9},"Jean-Noël LEspérance",{"tldr":153,"method":154,"finding":155,"direction":156,"opportunity":157},"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":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":100,"source_url":162,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":165,"score_detail":166,"sources":168,"tags":170,"primary_topic":173,"secondary_topics":174,"classification":176,"search_phrases":192,"slug":195,"view_count":75,"doi":196,"paper":197,"created_at":211},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":104,"substance":18,"depth":17,"authority":106,"freshness":13,"relevant":21,"comment":167},"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。",[169],{"name":100,"url":162},[27,111,28,171,172],"作物病害识别","模型审计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[175],{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},{"status":39,"locked":14,"version":40,"method":41,"evidence":177},{"candidates":178,"agricultural_evidence":47},[179,184,188],{"code":32,"score":180,"supported":47,"title":181,"abstract":182,"tags":183},15,[56],[28,56],[28],{"code":117,"score":13,"supported":47,"title":185,"abstract":186,"tags":187},[131],[],[171,111],{"code":45,"score":21,"supported":14,"title":189,"abstract":190,"tags":191},[],[],[27],[193,194],"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":196,"openalex_id":198,"authors":199,"venue":100,"cited_by_count":75,"oa_url":162,"card":206,"direction":156,"ingested_from":89},"W7223088030",[200,202,204],{"name":201,"orcid":9},"Abdi-Basid Idriss Farah",{"name":203,"orcid":9},"Amina Abdillahi Guedi",{"name":205,"orcid":9},"Moussa Omar Youssouf",{"tldr":207,"method":208,"finding":209,"direction":156,"opportunity":210},"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":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":218,"source_url":215,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":223,"tags":225,"primary_topic":228,"secondary_topics":229,"classification":235,"search_phrases":262,"slug":265,"view_count":266,"doi":267,"paper":268,"created_at":288},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":17,"substance":18,"depth":17,"authority":19,"freshness":104,"relevant":21,"comment":222},"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。",[224],{"name":218,"url":215},[27,111,226,28,227],"农业遥感","作物分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[230],{"code":231,"slug":232,"name":233,"path":234,"parent_code":121},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":236},{"candidates":237,"agricultural_evidence":47},[238,242,248,254,258],{"code":32,"score":106,"supported":47,"title":239,"abstract":240,"tags":241},[56],[56],[28],{"code":231,"score":104,"supported":47,"title":243,"abstract":244,"tags":247},[],[245,246],"遥感","remote sensing",[227,226],{"code":249,"score":60,"supported":47,"title":250,"abstract":251,"tags":253},"A4",[],[252],"花生",[],{"code":45,"score":21,"supported":14,"title":255,"abstract":256,"tags":257},[],[],[27],{"code":117,"score":21,"supported":14,"title":259,"abstract":260,"tags":261},[],[],[111],[263,264],"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":267,"openalex_id":269,"authors":270,"venue":218,"cited_by_count":75,"oa_url":280,"card":281,"direction":287,"ingested_from":89},"W7214087594",[271,273,275,277],{"name":272,"orcid":9},"Madeline Lisaius",{"name":274,"orcid":9},"Srinivasan Keshav",{"name":276,"orcid":9},"Andrew Blake",{"name":278,"orcid":279},"Clement Atzberger","https:\u002F\u002Forcid.org\u002F0000-0003-2169-8009","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2976-601X\u002Faea0b8\u002Fpdf",{"tldr":282,"method":283,"finding":284,"direction":285,"opportunity":286},"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。","智慧农业 \u002F 农业物联网","2026-09-30T23:30:11.490990Z",{"id":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":9,"source_name":295,"source_url":292,"published_at":296,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":297,"sources":299,"tags":301,"primary_topic":304,"secondary_topics":305,"classification":307,"search_phrases":337,"slug":340,"view_count":92,"doi":341,"paper":342,"created_at":355},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":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":298},"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。",[300],{"name":295,"url":292},[302,27,111,303,28],"数字乡村","农业技术推广",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[306],{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},{"status":39,"locked":14,"version":40,"method":41,"evidence":308},{"candidates":309,"agricultural_evidence":47},[310,314,322,327,332],{"code":32,"score":180,"supported":47,"title":311,"abstract":312,"tags":313},[56],[28,56],[28],{"code":117,"score":13,"supported":47,"title":315,"abstract":316,"tags":321},[],[317,318,319,320,131],"机器学习","计算机视觉","machine learning","artificial intelligence",[111],{"code":45,"score":323,"supported":47,"title":324,"abstract":325,"tags":326},7,[],[26,49],[27],{"code":328,"score":21,"supported":14,"title":329,"abstract":330,"tags":331},"G1",[],[],[303],{"code":333,"score":21,"supported":14,"title":334,"abstract":335,"tags":336},"I6",[],[],[302],[338,339],"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":341,"openalex_id":343,"authors":344,"venue":295,"cited_by_count":75,"oa_url":292,"card":350,"direction":87,"ingested_from":89},"W7214205238",[345,347],{"name":346,"orcid":9},"Pravangkar Boruah",{"name":348,"orcid":349},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":351,"method":352,"finding":353,"direction":156,"opportunity":354},"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",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":362,"source_url":359,"published_at":296,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":363,"sources":365,"tags":367,"primary_topic":370,"secondary_topics":371,"classification":373,"search_phrases":407,"slug":410,"view_count":266,"doi":411,"paper":412,"created_at":436},3469,"ARTIFICIAL INTELLIGENCE FOR AGRICULTURE: A SYSTEMATIC REVIEW OF FARMERS' PERCEPTIONS, ACCEPTANCE, ADOPTION AND BARRIERS","https:\u002F\u002Fdoi.org\u002F10.64013\u002Fbbasrjlifess.v2026i1.70","Artificial intelligence (AI) is rapidly reshaping agriculture, enabling the application of data for efficient decision-making, precision farming, crop monitoring, pest management, disease detection, smart irrigation, yield prediction, and various aspects of farm automation. Yet, to successfully gain a foothold in farms, factors like farmers' perceptions, their acceptance, desire, and readiness to adopt, as well as the capability to surmount socioeconomic, technological, and institutional challenges, must also make a difference. This article systematically collates the available information on farmers' perspectives on, acceptance of, readiness to adopt, and the obstacles related to AI in the agricultural context. The PRISMA 2020 guideline has been followed to select studies that are related, evaluated for inclusion, and finally, per the criteria, combined into a systematic synthesis. The review mainly discusses the aspects that influence the use of AI, like perceived usefulness, ease of use, trust, digital literacy, affordability, farm size, socioeconomic characteristics, availability of digital infrastructure, and access to agricultural technical advisory services. Main challenges identified involve the high cost of implementing AI, poor connectivity, weak rural infrastructure, low levels of technological knowledge, unavailability of support services, concerns about data privacy, distrust of algorithmic bias, language barriers, and inequalities impacting smallholder farmers. Besides barriers, the review indicates the potential of leveraging AI through extension services, agricultural mobile apps, precision farming, climate-smart agriculture, early-warning systems, and tailored farm advisories. By drawing out technical, behavioral, socioeconomic, and institutional perspectives, the review pinpoints important research questions and presents a farmholder-oriented setup to explain the process of taking up AI in farming. The outcomes can be used by scientific experts, policymakers, extension personnel, and software developers to create low-cost, reliable, and accessible AI systems aimed at promoting sustainable agricultural development.","人工智能（AI）正迅速重塑农业，使数据得以应用于高效决策、精准农业、作物监测、病虫害管理、病害检测、智能灌溉、产量预测以及农场自动化的各个方面。然而，要在农场中成功立足，农民的认知、接受度、采用意愿和准备程度，以及克服社会经济、技术和制度挑战的能力，同样至关重要。本文系统梳理了现有关于农民对农业领域人工智能的看法、接受度、采用准备程度及相关障碍的信息。研究遵循PRISMA 2020指南，筛选相关研究，评估其纳入资格，并最终依据标准进行系统性综合。综述主要探讨了影响人工智能使用的因素，包括感知有用性、易用性、信任、数字素养、可负担性、农场规模、社会经济特征、数字基础设施的可用性以及农业技术咨询服务的获取。识别出的主要挑战包括人工智能实施成本高昂、网络连接不佳、农村基础设施薄弱、技术水平低下、支持服务缺乏、数据隐私担忧、对算法偏见的疑虑、语言障碍以及影响小农户的不平等问题。除障碍外，综述还指出了通过推广服务、农业移动应用、精准农业、气候智慧型农业、预警系统和定制化农场咨询来利用人工智能的潜力。通过梳理技术、行为、社会经济和制度层面的视角，本综述指出了重要的研究问题，并提出了一个以农场主为导向的框架，以解释在农业中采用人工智能的过程。研究结果可供科学专家、政策制定者、推广人员和软件开发者用于创建低成本、可靠且可及的人工智能系统，以促进可持续农业发展。","Journal of Life and Social Sciences",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":364},"基于PRISMA的系统综述，系统梳理农户对AI的认知、接受度与采纳障碍，对智慧农业推广与政策设计有实质参考价值。",[366],{"name":362,"url":359},[27,111,368,369,28],"数字素养","农户采纳",{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},[372],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":374},{"candidates":375,"agricultural_evidence":47},[376,380,384,390,397,403],{"code":117,"score":106,"supported":47,"title":377,"abstract":378,"tags":379},[320],[320],[111],{"code":32,"score":104,"supported":47,"title":381,"abstract":382,"tags":383},[],[28,56],[369,28],{"code":45,"score":323,"supported":47,"title":385,"abstract":386,"tags":389},[],[387,388],"精准农业","smart agriculture",[27],{"code":391,"score":92,"supported":47,"title":392,"abstract":393,"tags":396},"H3",[],[394,395],"灌溉","irrigation",[],{"code":398,"score":60,"supported":47,"title":399,"abstract":400,"tags":402},"H8",[],[401],"可持续农业",[],{"code":333,"score":21,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[368],[408,409],"农民 AI 采纳 障碍","农业人工智能 系统综述","农民AI采纳障碍-3469","10.64013\u002Fbbasrjlifess.v2026i1.70",{"doi":411,"openalex_id":413,"authors":414,"venue":362,"cited_by_count":75,"oa_url":359,"card":431,"direction":287,"ingested_from":89},"W7214189410",[415,417,419,421,423,425,427,429],{"name":416,"orcid":9},"MM JAMEEL",{"name":418,"orcid":9},"M SAEED",{"name":420,"orcid":9},"SA SHER",{"name":422,"orcid":9},"Z ALI",{"name":424,"orcid":9},"Q HAYYAT",{"name":426,"orcid":9},"S KIRBAG",{"name":428,"orcid":9},"S KHAN",{"name":430,"orcid":9},"HN AHMAD",{"tldr":432,"method":433,"finding":434,"direction":87,"opportunity":435},"系统综述农民对农业AI的感知、接受度、采纳意愿及障碍。","遵循PRISMA 2020指南，系统筛选并综合相关文献。","成本、基础设施、数字素养与信任是主要障碍，小农户受影响最大。","可研究低成本、本地化AI采纳模型及小农户数字包容机制。","2026-09-25T23:30:09.670568Z",{"id":438,"title":439,"url":440,"summary":441,"summary_zh":9,"content":9,"source_name":442,"source_url":9,"published_at":443,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":444,"sources":448,"tags":450,"primary_topic":453,"secondary_topics":454,"classification":455,"search_phrases":487,"slug":490,"view_count":20,"doi":491,"paper":492,"created_at":500},3327,"Digitalization of smallholder agriculture: Adoption and impacts——特刊导论（Sun et al. 2026）","https:\u002F\u002Fwww.sciopen.com\u002Flocal\u002Farticle_pdf\u002F10.1016\u002Fj.jia.2026.04.021.pdf","《Journal of Integrative Agriculture》2026年专刊导论，IAMO孙占力博士、华中农大熊航教授、北大易红梅教授等担任客座编辑。本专刊收录Liu et al. (2026)研究——使用面板数据和多种估计方法调查数字技术使用如何影响中国易地搬迁农户收入稳定性，发现数字技术显著改善收入水平和稳定性；Hu et al. (2026)使用来自5省1,833个农场的倾向得分匹配数据，发现网络信息使用实际上增加了小农户的化肥支出，反思了农业数字化的复杂性；Bai et al. (2026)研究数字素养通过改善农民对气候灾害风险的感知促进气候适应性生产行为；Amolegbe et al. (2026)研究尼日利亚数字知识与电商估值。","《Journal of Integrative Agriculture》2026专刊","2026-09-17T00:00:00Z",{"impact":17,"substance":18,"depth":445,"authority":446,"freshness":92,"relevant":21,"comment":447},19,14,"国际期刊专刊导论，汇集多国小农户数字化实证研究，方法扎实、结论具反思性，对农业信息化研究与实践有参考价值。",[449],{"name":442,"url":440},[26,451,368,28,452],"农村电商","数字鸿沟",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":456},{"candidates":457,"agricultural_evidence":47},[458,462,468,473,478,483],{"code":32,"score":106,"supported":47,"title":459,"abstract":460,"tags":461},[56],[28],[28],{"code":463,"score":60,"supported":47,"title":464,"abstract":465,"tags":467},"G6",[],[466],"农户收入",[],{"code":66,"score":60,"supported":47,"title":469,"abstract":470,"tags":472},[],[471],"气候适应",[],{"code":333,"score":474,"supported":14,"title":475,"abstract":476,"tags":477},2,[],[],[368,452],{"code":479,"score":21,"supported":14,"title":480,"abstract":481,"tags":482},"E5",[],[],[451],{"code":45,"score":21,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[26],[488,489],"Journal of Integrative Agriculture 数字农业 专刊","易地搬迁农户 数字技术 收入稳定性","JournalofIntegrativeAgriculture数字农业专刊-3327","10.1016\u002Fj.jia.2026.04.021.pdf",{"doi":491,"openalex_id":9,"authors":493,"venue":9,"cited_by_count":75,"oa_url":9,"card":494,"direction":87,"ingested_from":499},[],{"tldr":495,"method":496,"finding":497,"direction":87,"opportunity":498},"该专刊导论综述小农户农业数字化的采纳与影响，涵盖中国及尼日利亚的多项实证研究。","基于面板数据、倾向得分匹配等方法的专刊导论，整合多国小农户实证研究。","数字技术可提升收入稳定性，但也可能增加化肥支出，数字化影响具有复杂性。","可深入探究数字技术对不同农户群体的异质性影响及化肥增投的机制与调控路径。","agent","2026-09-24T00:04:03.016677Z"]