[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3469":3,"related-3469":65},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":64},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。",null,"Journal of Life and Social Sciences","2026-09-24T00:00:00Z","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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","数字素养","农户采纳","小农户",[32,33],"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",0,"10.64013\u002Fbbasrjlifess.v2026i1.70",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":56,"direction":62,"ingested_from":63},"W7214189410",[40,42,44,46,48,50,52,54],{"name":41,"orcid":9},"MM JAMEEL",{"name":43,"orcid":9},"M SAEED",{"name":45,"orcid":9},"SA SHER",{"name":47,"orcid":9},"Z ALI",{"name":49,"orcid":9},"Q HAYYAT",{"name":51,"orcid":9},"S KIRBAG",{"name":53,"orcid":9},"S KHAN",{"name":55,"orcid":9},"HN AHMAD",{"tldr":57,"method":58,"finding":59,"direction":60,"opportunity":61},"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。","数字乡村与农业信息化","可研究低成本、本地化AI采纳模型及小农户数字包容机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:09.670568Z",{"total":66,"page":21,"page_size":66,"items":67},6,[68,102,138,175,207,243],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":73,"content":9,"source_name":74,"source_url":71,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":75,"sources":77,"tags":79,"search_phrases":82,"slug":85,"view_count":35,"doi":86,"paper":87,"created_at":101},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",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":76},"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。",[78],{"name":74,"url":71},[80,26,27,81,30],"数字乡村","农业技术推广",[83,84],"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":86,"openalex_id":88,"authors":89,"venue":74,"cited_by_count":35,"oa_url":71,"card":95,"direction":60,"ingested_from":63},"W7214205238",[90,92],{"name":91,"orcid":9},"Pravangkar Boruah",{"name":93,"orcid":94},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"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。","农业人工智能与决策模型","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":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":11,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":110,"score_detail":111,"sources":115,"tags":119,"search_phrases":122,"slug":125,"view_count":35,"doi":126,"paper":127,"created_at":137},3482,"Digital Transformation and the Reconfiguration of Farming Systems: Understanding How Tech-nological Adoption and Institutional Conditions Shape Sustainable and Inclusive Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932900","Digital agriculture is progressively reshaping farming through the integration of sensors, artificial intelligence, Internet of Things technologies, digital platforms, data analytics, robotics and automated decision-support systems. This paper examines this transformation by connecting three complementary dimensions: the socio-technical development of digital agriculture, the determinants and processes of technological adoption, and the economic, organizational and inclusive consequences of digitalization for farming systems. The literature indicates that agricultural digitalization cannot be reduced to the availability of increasingly sophisticated technologies. Adoption and sustained use depend on farmers’ perceptions of usefulness and compatibility, farm resources, human capital, infrastructure, institutional support, advisory systems and the capacity to integrate technologies into established production routines. Digital technologies may improve resource efficiency, information management, productivity and sustainability, while also modifying labor organization, farmer autonomy, data governance and relationships among actors within agricultural value chains. These effects remain uneven across farms and territories, particularly where smallholders face financial, infrastructural and capability constraints. The paper therefore interprets digital agriculture as a multidimensional transformation whose outcomes depend on the interaction between technological capabilities, farmer behavior and institutional conditions. Inclusive and sustainable digitalization requires attention not only to innovation diffusion but also to governance, skills, accessibility and the distribution of technological benefits.","数字农业正通过传感器、人工智能、物联网技术、数字平台、数据分析、机器人技术和自动化决策支持系统的整合，逐步重塑农业生产方式。本文通过连接三个互补维度来考察这一转型：数字农业的社会技术发展、技术采纳的决定因素与过程，以及数字化对农业系统产生的经济、组织和包容性后果。文献表明，农业数字化不能简化为日益复杂的技术供给。采纳和持续使用取决于农民对有用性和兼容性的认知、农场资源、人力资本、基础设施、制度支持、咨询系统以及将技术整合到既定生产惯例中的能力。数字技术可能提高资源效率、信息管理、生产力和可持续性，同时也会改变劳动组织、农民自主性、数据治理以及农业价值链中参与者之间的关系。这些影响在农场和区域之间仍不均衡，尤其是小农户面临资金、基础设施和能力约束的地方。因此，本文将数字农业解释为一种多维转型，其结果取决于技术能力、农民行为和制度条件之间的相互作用。包容和可持续的数字化不仅需要关注创新扩散，还需要关注治理、技能、可及性和技术收益的分配。","Zenodo (CERN European Organization for Nuclear Research)",25,77,{"impact":17,"substance":112,"depth":113,"authority":19,"freshness":20,"relevant":21,"comment":114},20,17,"系统梳理数字农业技术采纳与制度条件的研究综述，对智慧农业政策与推广有参考价值，但属文献综述类，非重大突破。",[116,117],{"name":108,"url":105},{"name":108,"url":118},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932899",[120,26,27,30,121],"数字农业","技术采纳",[123,124],"农业人工智能 技术采纳 数字农业 智慧农业","农业人工智能 技术采纳","农业人工智能技术采纳数字农业智慧农业-3482","10.5281\u002Fzenodo.22932900",{"doi":126,"openalex_id":128,"authors":129,"venue":108,"cited_by_count":35,"oa_url":105,"card":132,"direction":62,"ingested_from":63},"W7214187008",[130],{"name":131,"orcid":9},"Chilufya Banda",{"tldr":133,"method":134,"finding":135,"direction":60,"opportunity":136},"综述数字农业转型，分析技术采纳与制度条件如何共同塑造可持续包容性农业。","文献综述，整合社会技术、技术采纳与数字化经济组织后果三维度。","数字化成效取决于技术能力、农户行为与制度条件的交互，小农面临多重约束。","可实证检验制度支持与农户能力如何调节数字技术对包容性和可持续性的影响。","2026-09-25T23:30:18.463733Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":9,"source_name":144,"source_url":141,"published_at":145,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":147,"sources":150,"tags":152,"search_phrases":155,"slug":158,"view_count":35,"doi":159,"paper":160,"created_at":174},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",79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":148,"relevant":21,"comment":149},8,"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。",[151],{"name":144,"url":141},[26,27,153,30,154],"农业物联网","精准灌溉",[156,157],"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","10.5120\u002Fijca9a6e56b81235",{"doi":159,"openalex_id":161,"authors":162,"venue":144,"cited_by_count":35,"oa_url":141,"card":169,"direction":62,"ingested_from":63},"W7213663876",[163,165,167],{"name":164,"orcid":9},"Awojide S.",{"name":166,"orcid":9},"Ikpotokin F.O.",{"name":168,"orcid":9},"Sadiq F.I.",{"tldr":170,"method":171,"finding":172,"direction":62,"opportunity":173},"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",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":145,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":182,"sources":185,"tags":187,"search_phrases":190,"slug":193,"view_count":35,"doi":194,"paper":195,"created_at":206},2948,"Machine learning and remote sensing for smallholder precision agriculture in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04538-2","While machine learning (ML) and remote sensing (RS) are frequently heralded as the definitive solutions for agricultural resilience in Sub-Saharan Africa, a profound ‘implementation gap’ persists between laboratory-validated computational maturity and field-level utility for smallholder farmers. This systematic review, conducted under preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, critically analyzes why sophisticated models optimized for large-scale monocultures fail within the fragmented, intercropped landscapes of Ethiopia. By synthesizing empirical evidence across four domains—in-season crop yield forecasting, digital soil mapping, real-time biotic stress detection, and agro-meteorological modeling—the review uncover a fundamental scale mismatch between coarse-resolution satellite observations and sub-hectare micro-plots. The critique identifies localized data scarcity, hardware constraints, and the ‘last-mile’ connectivity divide as the primary friction points obstructing the transition from macro-level pixels to actionable, site-specific agricultural intelligence. Moving beyond simple summary, the study propose a strategic roadmap centered on decentralized edge computing, tinyML optimizations, and a restructuring of extension services to integrate digital intelligence into daily smallholder decision-making. These structural shifts are essential to bridge the digital divide and secure Ethiopia’s national food security against escalating climate variability. This review foregrounds the significance of digital agriculture within the context of the sustainable development goals (SDGs), specifically addressing SDG 2 (zero hunger) and SDG 13 (climate action) by enhancing crop productivity and building resilience in smallholder systems.","尽管机器学习（ML）与遥感（RS）常被标榜为撒哈拉以南非洲农业韧性的终极解决方案，但实验室验证的计算成熟度与小农户田间实用性之间仍存在深刻的“实施鸿沟”。本系统综述依据系统综述和荟萃分析首选报告条目（PRISMA）2020指南开展，批判性地分析了为何针对大规模单一种植优化的复杂模型在埃塞俄比亚碎片化、间作化的景观中失效。通过综合四个领域的实证证据——季内作物产量预测、数字土壤制图、实时生物胁迫检测和农业气象建模——本综述揭示了粗分辨率卫星观测与亚公顷微地块之间的根本性尺度错配。该批判性分析将局部数据稀缺、硬件约束和“最后一公里”连接鸿沟确定为阻碍从宏观像元向可操作、因地制宜的农业智能转化的主要摩擦点。本研究超越简单的总结，提出了一条以去中心化边缘计算、tinyML优化和推广服务体系重构为核心的战略路线图，旨在将数字智能融入小农户的日常决策。这些结构性转变对于弥合数字鸿沟、保障埃塞俄比亚在日益加剧的气候变率下的国家粮食安全至关重要。本综述凸显了数字农业在可持续发展目标（SDGs）背景下的重要意义，特别是通过提升作物生产力和增强小农系统韧性来回应SDG 2（零饥饿）和SDG 13（气候行动）。","Discover Sustainability",{"impact":17,"substance":183,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":184},21,"系统综述揭示机器学习与遥感在小农场景的落地鸿沟，并提出边缘计算与tinyML路线图，对数字农业与SDG研究有参考价值。",[186],{"name":181,"url":178},[26,27,30,188,189],"数字鸿沟","遥感",[191,192],"埃塞俄比亚 小农户 精准农业","机器学习 遥感 小农","埃塞俄比亚小农户精准农业-2948","10.1007\u002Fs43621-026-04538-2",{"doi":194,"openalex_id":196,"authors":197,"venue":181,"cited_by_count":35,"oa_url":178,"card":200,"direction":204,"ingested_from":63},"W7213562005",[198],{"name":199,"orcid":9},"Abrha Asefa",{"tldr":201,"method":202,"finding":203,"direction":204,"opportunity":205},"系统综述埃塞俄比亚小农精准农业中机器学习和遥感的应用鸿沟与出路。","PRISMA 2020 系统综述，综合四领域实证证据。","粗分辨率卫星与亚公顷微地块尺度不匹配，数据稀缺和连接鸿沟阻碍落地。","农业遥感与作物表型","面向碎片化间作小农的 tinyML 边缘计算与本地化数据采集，是填补落地鸿沟的关键方向。","2026-09-19T23:30:33.334404Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":214,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":219,"tags":221,"search_phrases":224,"slug":227,"view_count":35,"doi":228,"paper":229,"created_at":242},2746,"Fostering artificial intelligence in the livestock sector: Adoption factors and policy insights from the case of Spain","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104956","CONTEXT AND PURPOSE The integration of Artificial Intelligence (AI) and Precision Livestock Farming (PLF) offers significant opportunities to improve the productivity, animal welfare and sustainability of the livestock sector. However, the adoption of these technologies remains highly uneven. Moving beyond descriptive statistics, this study investigates the complex, interacting factors associated with AI adoption in the Spanish livestock sector. METHODOLOGY Utilising a survey-based sample of 666 Spanish livestock holdings, drawn from a national survey whose sampling was designed to be proportional to the 2020 Agricultural Census by subsector and region, a Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was employed to evaluate a comprehensive adoption framework across different farming systems and operational scales. AI adoption was modelled as a formative composite of five self-reported AI system categories, and the framework was tested through mediation, moderation, subgroup and out-of-sample predictive analyses with a 10,000-resample bootstrap. MAIN FINDINGS The empirical results indicate that AI adoption is most strongly associated with a holding's baseline technological infrastructure (β = 0.6464, p \u003C 0.001); advanced predictive tools appear to presuppose a pre-existing ecosystem of digital farm books, automated feeding systems and interoperable sensors. A ‘perception-capability gap’ is formally supported: perceived benefits are associated with adoption only indirectly, through the infrastructure baseline (indirect-only mediation), and their association strengthens as infrastructure increases (positive interaction). Recognition of AI's theoretical benefits or awareness of public subsidies is thus not independently associated with adoption in the absence of foundational digital capability. Furthermore, the study identifies a stark structural divide: adoption reaches 56.3% in intensive and semi-intensive holdings but only 24.4% in extensive ones (χ2 = 57.61, p \u003C 0.001), as traditional extensive systems face severe physical and connectivity barriers that leave them systematically disadvantaged. Results are robust across reflective, count, reduced-indicator and logistic specifications, and the model shows substantial out-of-sample predictive power (Q 2 predict ≈ 0.53). CONCLUSIONS To facilitate a more inclusive digital transformation, the findings suggest that policymakers should adopt a ‘readiness-conditional’ model, ensuring farms meet baseline digital prerequisites and have access to robust extension services before subsidising advanced AI tools.","背景与目的 人工智能（Artificial Intelligence, AI）与精准畜牧业（Precision Livestock Farming, PLF）的融合为提升畜牧部门的生产力、动物福利和可持续性提供了重要机遇。然而，这些技术的采用仍然极不均衡。本研究超越描述性统计，探讨了西班牙畜牧部门中与AI采用相关的复杂交互因素。方法 基于一项全国性调查中抽取的666个西班牙畜牧养殖场的样本（该调查的抽样设计按2020年农业普查的子部门和地区比例进行），采用偏最小二乘结构方程模型（Partial Least Squares Structural Equation Modelling, PLS-SEM）方法，评估了一个跨不同养殖系统和经营规模的综合采用框架。AI采用被建模为五个自我报告的AI系统类别的形成性复合变量，并通过中介、调节、子组和样本外预测分析（10,000次重抽样自助法）对框架进行了检验。主要发现 实证结果表明，AI采用与养殖场的基线技术基础设施关联最为密切（β = 0.6464，p \u003C 0.001）；先进的预测工具似乎以已有的数字牧场记录、自动化饲喂系统和可互操作传感器生态系统为前提。“感知—能力差距”得到正式支持：感知收益仅通过基础设施基线间接与采用相关（仅间接中介），且其关联随基础设施的增加而增强（正向交互）。因此，在缺乏基础数字能力的情况下，对AI理论效益的认可或对公共补贴的知晓与采用并无独立关联。此外，研究识别出一条鲜明的结构性分界线：集约化和半集约化养殖场的采用率达到56.3%，而粗放型养殖场仅为24.4%（χ2 = 57.61，p \u003C 0.001），因为传统粗放型系统面临严重的物理和连接障碍，使其处于系统性劣势。结果在反映性、计数、简化指标和逻辑斯谛设定下均稳健，且模型显示出显著的样本外预测能力（Q 2 predict ≈ 0.53）。结论 为促进更具包容性的数字化转型，研究结果表明政策制定者应采取“就绪条件”模型，确保养殖场","Agricultural Systems","2026-09-16T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":217,"freshness":20,"relevant":21,"comment":218},14,"基于666个西班牙牧场样本的实证研究，揭示AI采纳受数字基础设施制约的“感知—能力鸿沟”，并提出“就绪条件式”补贴政策思路，对智慧畜牧推广与数字乡村政策设计有参考价值。",[220],{"name":213,"url":210},[26,27,28,222,223],"农业补贴","精准畜牧",[225,226],"农业人工智能 农业补贴 数字素养 智慧农业","农业人工智能 农业补贴","农业人工智能农业补贴数字素养智慧农业-2746","10.1016\u002Fj.agsy.2026.104956",{"doi":228,"openalex_id":230,"authors":231,"venue":213,"cited_by_count":35,"oa_url":210,"card":237,"direction":60,"ingested_from":63},"W7213425399",[232,234],{"name":233,"orcid":9},"Carlos Parra-Lopez",{"name":235,"orcid":236},"Carmen Carmona‐Torres","https:\u002F\u002Forcid.org\u002F0000-0002-6982-1363",{"tldr":238,"method":239,"finding":240,"direction":60,"opportunity":241},"基于西班牙666家养殖场调查，用PLS-SEM分析AI采纳的影响因素与政策启示。","666份养殖场问卷，PLS-SEM建模，含中介、调节与分组分析。","AI采纳最依赖既有数字基础设施；感知收益仅间接起作用，集约场采纳率远高于粗放场。","可研究粗放养殖场数字基础设施与连接性短板，设计‘就绪度条件’式推广与补贴机制。","2026-09-17T23:30:04.844104Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":255,"tags":257,"search_phrases":260,"slug":263,"view_count":35,"doi":264,"paper":265,"created_at":276},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。","WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT","2026-09-15T00:00:00Z",74,{"impact":253,"substance":112,"depth":113,"authority":19,"freshness":148,"relevant":21,"comment":254},16,"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。",[256],{"name":249,"url":246},[26,27,258,30,259],"数字孪生","农业建模",[261,262],"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":264,"openalex_id":266,"authors":267,"venue":249,"cited_by_count":35,"oa_url":270,"card":271,"direction":62,"ingested_from":63},"W7213267978",[268],{"name":269,"orcid":9},"Jose Rabi","https:\u002F\u002Fwseas.com\u002Fjournals\u002Fead\u002F2026\u002Fb625115-032(2026).pdf",{"tldr":272,"method":273,"finding":274,"direction":62,"opportunity":275},"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。","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。","2026-09-16T23:30:10.078180Z"]