[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2641":3,"related-2641":90},{"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":70,"slug":73,"view_count":54,"doi":74,"paper":75,"created_at":89},2641,"Smart Agriculture and Sustainable Development in Agro-Ecosystems: Innovative Computational Modeling for Digital Twins","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232015.2026.22.80","By boosting yields, improving efficiency, and reducing costs (while managing resources), digital technologies have driven innovation in agro-ecosystems in recent years. As a means to overcome ever-limited resources, Smart Agriculture – sometimes also referred to as Agriculture 4.0 – has increasingly leveraged digital solutions to achieve efficiency and sustainability. Digital twins in rural systems perform as computational (in silico) replicas of food production lines, which can drive innovation by enabling real-time optimization as well as predictive decision-making. Considering that both non-human and human actors are directly involved, this paper discusses (from a strategic alignment viewpoint) a prospective symbiotic ethos that farmers and ranchers may pursue when it comes to integrated, participatory and efficient transfer and usage of digital technology in rural activities. Bearing particularly in mind smallholders, challenges and opportunities for Smart Agriculture include: (i) implementation of hybrid in-house simulators of agro-ecosystems by suitably combining mechanistic modeling with data-driven simulation; (ii) use of dimensionless mathematical modeling to expedite scale-up, optimization, and translation of innovative digital technologies; and (iii) validating as well as transferring novel digital solutions in consideration of strategic issues identified by rural end-users.","近年来，数字技术通过提高产量、提升效率、降低成本（同时管理资源），推动了农业生态系统的创新。作为克服日益有限的资源的一种手段，智慧农业（有时也被称为农业4.0）越来越多地利用数字解决方案来实现效率和可持续性。农村系统中的数字孪生作为食品生产线的计算（计算机模拟）副本，通过实现实时优化和预测性决策来推动创新。考虑到非人类和人类参与者都直接参与其中，本文（从战略协同的视角）探讨了农民和牧场主在将数字技术整合、参与式和高效地转移及应用于农村活动时可能追求的一种前瞻性共生理念。特别考虑到小农户，智慧农业面临的挑战和机遇包括：（i）通过将机理建模与数据驱动模拟适当结合，实施农业生态系统的混合内部模拟器；（ii）使用无量纲数学建模来加速创新数字技术的规模化、优化和转化；（iii）在考虑农村终端用户所识别的战略问题的基础上，验证和转移新型数字解决方案。",null,"WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT","2026-09-15T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,13,8,1,"核心期刊论文，提出农业生态数字孪生的混合建模与无量纲化方法，对小农户数字化转型有参考价值，但偏理论、时效略滞后。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","数字孪生","小农户","农业建模",{"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},[45,53,58,65],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},"I1",19,true,[50],"smart agriculture",[27,29,50],[29,27],{"code":33,"score":54,"supported":48,"title":55,"abstract":56,"tags":57},4,[],[30],[30],{"code":59,"score":60,"supported":48,"title":61,"abstract":62,"tags":64},"H6",3,[],[63],"农业生态",[],{"code":66,"score":22,"supported":14,"title":67,"abstract":68,"tags":69},"I2",[],[],[28],[71,72],"农业人工智能 农业建模 数字孪生 智慧农业","农业人工智能 农业建模","农业人工智能农业建模数字孪生智慧农业-2641","10.37394\u002F232015.2026.22.80",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":80,"oa_url":81,"card":82,"direction":86,"ingested_from":88},"W7213267978",[78],{"name":79,"orcid":9},"Jose Rabi",0,"https:\u002F\u002Fwseas.com\u002Fjournals\u002Fead\u002F2026\u002Fb625115-032(2026).pdf",{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"探讨数字孪生与计算建模在智慧农业可持续发展中的应用，聚焦小农户的挑战与机遇。","混合机理与数据驱动模拟、无量纲数学建模、数字孪生计算副本。","提出农民与牧场主共生的数字技术转移理念，强调小农户的参与式整合。","智慧农业 \u002F 农业物联网","可研究小农户场景下混合模拟器的轻量化与低成本部署，以及数字孪生技术的参与式验证方法。","openalex","2026-09-16T23:30:10.078180Z",{"total":91,"page":22,"page_size":91,"items":92},6,[93,157,211,287,359,439],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":107,"tags":109,"primary_topic":112,"secondary_topics":113,"classification":119,"search_phrases":136,"slug":139,"view_count":80,"doi":140,"paper":141,"created_at":156},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":103,"depth":104,"authority":105,"freshness":13,"relevant":22,"comment":106},21,18,12,"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。",[108],{"name":99,"url":96},[27,28,30,110,111],"植物病害识别","可解释性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[114],{"code":66,"slug":115,"name":116,"path":117,"parent_code":118},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":120},{"candidates":121,"agricultural_evidence":48},[122,127,132],{"code":33,"score":105,"supported":48,"title":123,"abstract":125,"tags":126},[124],"smallholder",[124],[30],{"code":66,"score":13,"supported":48,"title":128,"abstract":130,"tags":131},[129],"computer vision",[],[28,110],{"code":46,"score":22,"supported":14,"title":133,"abstract":134,"tags":135},[],[],[27],[137,138],"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":140,"openalex_id":142,"authors":143,"venue":99,"cited_by_count":80,"oa_url":96,"card":150,"direction":154,"ingested_from":88},"W7223137323",[144,146,148],{"name":145,"orcid":9},"Akshay Ramgoolam",{"name":147,"orcid":9},"Priya Bundhoo Appadoo",{"name":149,"orcid":9},"Jean-Noël LEspérance",{"tldr":151,"method":152,"finding":153,"direction":154,"opportunity":155},"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":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":99,"source_url":160,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":163,"score_detail":164,"sources":167,"tags":169,"primary_topic":172,"secondary_topics":173,"classification":175,"search_phrases":191,"slug":194,"view_count":80,"doi":195,"paper":196,"created_at":210},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":165,"depth":104,"authority":105,"freshness":13,"relevant":22,"comment":166},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。",[168],{"name":99,"url":160},[27,28,30,170,171],"作物病害识别","模型审计",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[174],{"code":66,"slug":115,"name":116,"path":117,"parent_code":118},{"status":40,"locked":14,"version":41,"method":42,"evidence":176},{"candidates":177,"agricultural_evidence":48},[178,183,187],{"code":33,"score":179,"supported":48,"title":180,"abstract":181,"tags":182},15,[124],[30,124],[30],{"code":66,"score":13,"supported":48,"title":184,"abstract":185,"tags":186},[129],[],[170,28],{"code":46,"score":22,"supported":14,"title":188,"abstract":189,"tags":190},[],[],[27],[192,193],"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":195,"openalex_id":197,"authors":198,"venue":99,"cited_by_count":80,"oa_url":160,"card":205,"direction":154,"ingested_from":88},"W7223088030",[199,201,203],{"name":200,"orcid":9},"Abdi-Basid Idriss Farah",{"name":202,"orcid":9},"Amina Abdillahi Guedi",{"name":204,"orcid":9},"Moussa Omar Youssouf",{"tldr":206,"method":207,"finding":208,"direction":154,"opportunity":209},"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":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":9,"source_name":217,"source_url":214,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":219,"score_detail":220,"sources":222,"tags":224,"primary_topic":227,"secondary_topics":228,"classification":234,"search_phrases":261,"slug":264,"view_count":265,"doi":266,"paper":267,"created_at":286},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":104,"substance":165,"depth":104,"authority":20,"freshness":21,"relevant":22,"comment":221},"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。",[223],{"name":217,"url":214},[27,28,225,30,226],"农业遥感","作物分类",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[229],{"code":230,"slug":231,"name":232,"path":233,"parent_code":118},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":235},{"candidates":236,"agricultural_evidence":48},[237,241,247,253,257],{"code":33,"score":105,"supported":48,"title":238,"abstract":239,"tags":240},[124],[124],[30],{"code":230,"score":21,"supported":48,"title":242,"abstract":243,"tags":246},[],[244,245],"遥感","remote sensing",[226,225],{"code":248,"score":60,"supported":48,"title":249,"abstract":250,"tags":252},"A4",[],[251],"花生",[],{"code":46,"score":22,"supported":14,"title":254,"abstract":255,"tags":256},[],[],[27],{"code":66,"score":22,"supported":14,"title":258,"abstract":259,"tags":260},[],[],[28],[262,263],"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":266,"openalex_id":268,"authors":269,"venue":217,"cited_by_count":80,"oa_url":279,"card":280,"direction":86,"ingested_from":88},"W7214087594",[270,272,274,276],{"name":271,"orcid":9},"Madeline Lisaius",{"name":273,"orcid":9},"Srinivasan Keshav",{"name":275,"orcid":9},"Andrew Blake",{"name":277,"orcid":278},"Clement Atzberger","https:\u002F\u002Forcid.org\u002F0000-0003-2169-8009","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2976-601X\u002Faea0b8\u002Fpdf",{"tldr":281,"method":282,"finding":283,"direction":284,"opportunity":285},"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":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":299,"tags":301,"primary_topic":304,"secondary_topics":305,"classification":307,"search_phrases":339,"slug":342,"view_count":91,"doi":343,"paper":344,"created_at":358},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",80,{"impact":104,"substance":165,"depth":104,"authority":20,"freshness":297,"relevant":22,"comment":298},9,"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":293,"url":290},[302,27,28,303,30],"数字乡村","农业技术推广",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[306],{"code":66,"slug":115,"name":116,"path":117,"parent_code":118},{"status":40,"locked":14,"version":41,"method":42,"evidence":308},{"candidates":309,"agricultural_evidence":48},[310,314,322,329,334],{"code":33,"score":179,"supported":48,"title":311,"abstract":312,"tags":313},[124],[30,124],[30],{"code":66,"score":13,"supported":48,"title":315,"abstract":316,"tags":321},[],[317,318,319,320,129],"机器学习","计算机视觉","machine learning","artificial intelligence",[28],{"code":46,"score":323,"supported":48,"title":324,"abstract":325,"tags":328},7,[],[326,327],"数字农业","digital agriculture",[27],{"code":330,"score":22,"supported":14,"title":331,"abstract":332,"tags":333},"G1",[],[],[303],{"code":335,"score":22,"supported":14,"title":336,"abstract":337,"tags":338},"I6",[],[],[302],[340,341],"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":343,"openalex_id":345,"authors":346,"venue":293,"cited_by_count":80,"oa_url":290,"card":352,"direction":357,"ingested_from":88},"W7214205238",[347,349],{"name":348,"orcid":9},"Pravangkar Boruah",{"name":350,"orcid":351},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":353,"method":354,"finding":355,"direction":154,"opportunity":356},"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。","数字乡村与农业信息化","2026-09-25T23:30:39.745514Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":366,"sources":368,"tags":370,"primary_topic":373,"secondary_topics":374,"classification":376,"search_phrases":409,"slug":412,"view_count":265,"doi":413,"paper":414,"created_at":438},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":104,"substance":165,"depth":104,"authority":20,"freshness":297,"relevant":22,"comment":367},"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。",[369],{"name":365,"url":362},[27,28,371,372,30],"数字素养","农户采纳",{"code":66,"slug":115,"name":116,"path":117,"parent_code":118},[375],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":377},{"candidates":378,"agricultural_evidence":48},[379,383,387,392,399,405],{"code":66,"score":105,"supported":48,"title":380,"abstract":381,"tags":382},[320],[320],[28],{"code":33,"score":21,"supported":48,"title":384,"abstract":385,"tags":386},[],[30,124],[372,30],{"code":46,"score":323,"supported":48,"title":388,"abstract":389,"tags":391},[],[390,50],"精准农业",[27],{"code":393,"score":91,"supported":48,"title":394,"abstract":395,"tags":398},"H3",[],[396,397],"灌溉","irrigation",[],{"code":400,"score":60,"supported":48,"title":401,"abstract":402,"tags":404},"H8",[],[403],"可持续农业",[],{"code":335,"score":22,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[371],[410,411],"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":413,"openalex_id":415,"authors":416,"venue":365,"cited_by_count":80,"oa_url":362,"card":433,"direction":86,"ingested_from":88},"W7214189410",[417,419,421,423,425,427,429,431],{"name":418,"orcid":9},"MM JAMEEL",{"name":420,"orcid":9},"M SAEED",{"name":422,"orcid":9},"SA SHER",{"name":424,"orcid":9},"Z ALI",{"name":426,"orcid":9},"Q HAYYAT",{"name":428,"orcid":9},"S KIRBAG",{"name":430,"orcid":9},"S KHAN",{"name":432,"orcid":9},"HN AHMAD",{"tldr":434,"method":435,"finding":436,"direction":357,"opportunity":437},"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":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":9,"source_name":445,"source_url":442,"published_at":446,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":219,"score_detail":447,"sources":449,"tags":451,"primary_topic":454,"secondary_topics":460,"classification":467,"search_phrases":507,"slug":510,"view_count":511,"doi":512,"paper":513,"created_at":527},3010,"A Multi-Task Stacked Ensemble and IoT-Enabled Decision Support System for Precision Fertigation in Smallholder Agriculture","https:\u002F\u002Fdoi.org\u002F10.5120\u002Fijca9a6e56b81235","Nigerian agriculture's fixed-schedule fertigation causes low efficiency and nutrient leaching.A stacked-ensemble model is developed for precision fertigation that jointly predicts fertigation need, rate (kg\u002Fha) and timing (Early\u002FOptimal\u002FLate).To train and evaluate the ensemble, a unified dataset was integrated, comprising 12,840 records and 42 variables from a Nigerian soil-weather-yield dataset, a locally sourced Nigerian IoT sensor series and historical weather\u002FNDVI feeds.An LSTM soil-dynamics model, an XGBoost rate regressor and Random Forest need\u002Ftiming classifiers are fused through an XGBoost meta-learner trained on out-of-fold predictions.On held-out partitions, the ensemble reduced rate MAE from 0.55 to 0.49 kg\u002Fha (-10.9%) and RMSE from 0.68 to 0.61 kg\u002Fha (-10.3%;R² 0.88→0.92),raised need F1 from 0.83 to 0.86 (accuracy 0.87→0.89;AUC 0.89→0.93)and timing macro-F1 from 0.84 to 0.86, with well-calibrated probabilities (Brier 0.082).The trained ensemble was deployed through a RESTful API and responsive dashboard; under concurrent load, the system recorded 0% request errors with 1.88s median API latency, demonstrating practical deployability for Nigerian smallholder agriculture.","尼日利亚农业的固定日程水肥一体化导致效率低下和养分淋失。本研究开发了一种堆叠集成模型用于精准水肥管理，可联合预测灌溉施肥需求、施用量（kg\u002Fha）和时机（早\u002F最佳\u002F晚）。为训练和评估该集成模型，整合了一个统一数据集，包含来自尼日利亚土壤-天气-产量数据集、本地尼日利亚物联网传感器序列及历史天气\u002FNDVI数据的12,840条记录和42个变量。通过基于折外预测训练的XGBoost元学习器，将LSTM土壤动力学模型、XGBoost施用量回归器和随机森林需求\u002F时机分类器进行融合。在留出集上，该集成模型将施用量MAE从0.55降至0.49 kg\u002Fha（-10.9%），RMSE从0.68降至0.61 kg\u002Fha（-10.3%；R² 0.88→0.92），需求F1从0.83提升至0.86（准确率0.87→0.89；AUC 0.89→0.93），时机宏平均F1从0.84提升至0.86，且概率校准良好（Brier 0.082）。训练后的集成模型通过RESTful API和响应式仪表板部署；在并发负载下，系统录得0%请求错误，API延迟中位数为1.88秒，展示了在尼日利亚小农农业中的实际可部署性。","International Journal of Computer Applications","2026-09-18T00:00:00Z",{"impact":104,"substance":165,"depth":104,"authority":20,"freshness":21,"relevant":22,"comment":448},"面向小农户的精准水肥一体化多任务集成模型与物联网决策支持系统，数据规模与方法验证扎实，对智慧农业落地有参考价值。",[450],{"name":445,"url":442},[27,28,452,30,453],"农业物联网","精准灌溉",{"code":455,"slug":456,"name":457,"path":458,"parent_code":459},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[461,466],{"code":462,"slug":463,"name":464,"path":465,"parent_code":118},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":468},{"candidates":469,"agricultural_evidence":48},[470,477,483,487,492,499,503],{"code":455,"score":19,"supported":48,"title":471,"abstract":473,"tags":476},[472],"fertigation",[474,475,472],"施肥","水肥一体化",[],{"code":462,"score":179,"supported":48,"title":478,"abstract":480,"tags":482},[479],"IoT",[481,479],"物联网",[452],{"code":33,"score":105,"supported":48,"title":484,"abstract":485,"tags":486},[124],[124],[30],{"code":66,"score":297,"supported":48,"title":488,"abstract":490,"tags":491},[489],"decision support",[],[28],{"code":493,"score":91,"supported":48,"title":494,"abstract":495,"tags":498},"H2",[],[496,497],"土壤","soil",[],{"code":393,"score":54,"supported":48,"title":500,"abstract":501,"tags":502},[],[396],[453],{"code":46,"score":22,"supported":14,"title":504,"abstract":505,"tags":506},[],[],[27],[508,509],"尼日利亚 精准灌溉 物联网","堆叠集成 施肥决策 小农户","尼日利亚精准灌溉物联网-3010",2,"10.5120\u002Fijca9a6e56b81235",{"doi":512,"openalex_id":514,"authors":515,"venue":445,"cited_by_count":80,"oa_url":442,"card":522,"direction":86,"ingested_from":88},"W7213663876",[516,518,520],{"name":517,"orcid":9},"Awojide S.",{"name":519,"orcid":9},"Ikpotokin F.O.",{"name":521,"orcid":9},"Sadiq F.I.",{"tldr":523,"method":524,"finding":525,"direction":86,"opportunity":526},"构建多任务堆叠集成模型与物联网决策支持系统，实现小农户精准水肥一体化。","LSTM、XGBoost、随机森林堆叠集成，融合尼日利亚土壤气象、IoT与NDV","集成模型将施肥量MAE降低10.9%，需求与时机分类F1提升，系统部署零错误。","可探索多任务集成模型在非洲小农户不同作物与气候区的迁移能力及低成本IoT部署。","2026-09-20T23:30:09.003454Z"]