[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3482":3,"related-3482":54},{"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":28,"search_phrases":34,"slug":37,"view_count":38,"doi":39,"paper":40,"created_at":53},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-24T00:00:00Z","论文",25,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,9,1,"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。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932899",[29,30,31,32,33],"数字农业","智慧农业","农业人工智能","小农户","技术采纳",[35,36],"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",0,"10.5281\u002Fzenodo.22932900",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":38,"oa_url":6,"card":45,"direction":51,"ingested_from":52},"W7214187008",[43],{"name":44,"orcid":9},"Chilufya Banda",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"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。","数字乡村与农业信息化","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。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:18.463733Z",{"total":55,"page":22,"page_size":55,"items":56},6,[57,94,138,167,204,255],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":11,"category":12,"cover_url":9,"hotness":64,"is_selected":14,"score":65,"score_detail":66,"sources":69,"tags":71,"search_phrases":74,"slug":77,"view_count":38,"doi":78,"paper":79,"created_at":93},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",10,80,{"impact":17,"substance":67,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":68},22,"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。",[70],{"name":63,"url":60},[72,30,31,73,32],"数字乡村","农业技术推广",[75,76],"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":78,"openalex_id":80,"authors":81,"venue":63,"cited_by_count":38,"oa_url":60,"card":87,"direction":49,"ingested_from":52},"W7214205238",[82,84],{"name":83,"orcid":9},"Pravangkar Boruah",{"name":85,"orcid":86},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"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":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":11,"category":12,"cover_url":9,"hotness":64,"is_selected":14,"score":65,"score_detail":101,"sources":103,"tags":105,"search_phrases":108,"slug":111,"view_count":38,"doi":112,"paper":113,"created_at":137},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":67,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":102},"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。",[104],{"name":100,"url":97},[30,31,106,107,32],"数字素养","农户采纳",[109,110],"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":112,"openalex_id":114,"authors":115,"venue":100,"cited_by_count":38,"oa_url":97,"card":132,"direction":51,"ingested_from":52},"W7214189410",[116,118,120,122,124,126,128,130],{"name":117,"orcid":9},"MM JAMEEL",{"name":119,"orcid":9},"M SAEED",{"name":121,"orcid":9},"SA SHER",{"name":123,"orcid":9},"Z ALI",{"name":125,"orcid":9},"Q HAYYAT",{"name":127,"orcid":9},"S KIRBAG",{"name":129,"orcid":9},"S KHAN",{"name":131,"orcid":9},"HN AHMAD",{"tldr":133,"method":134,"finding":135,"direction":49,"opportunity":136},"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":139,"title":140,"url":141,"summary":142,"summary_zh":9,"content":9,"source_name":143,"source_url":9,"published_at":144,"category":12,"cover_url":9,"hotness":64,"is_selected":14,"score":65,"score_detail":145,"sources":149,"tags":151,"search_phrases":154,"slug":157,"view_count":38,"doi":9,"paper":158,"created_at":166},3439,"《Agentic Artificial Intelligence in Agriculture: A Systematic Mapping Review of Reported Architectures, Applications, Challenges, and Future Directions》","https:\u002F\u002Fwww.mdpi.com\u002F2227-7080\u002F14\u002F9\u002F591","作者按PRISMA 2020方案从4111项记录筛选至181项研究，对2020—2026年农业代理式人工智能文献做系统映射综述：领域跨度30多年但LLM子集非常年轻（2024年才出现、46\u002F47项发表于2025—2026年）；82%研究报告合作能力，计划和推理分别仅31%、记忆6%、反思4%；只有33项研究报告现场或实际部署，68项仍停留在概念性阶段，仅11项报告了一个季度以上的评估。","《Technologies》2026, 14(9), 591 \u002F MDPI","2026-09-22T00:00:00Z",{"impact":17,"substance":146,"depth":17,"authority":20,"freshness":147,"relevant":22,"comment":148},23,8,"基于PRISMA的农业代理式AI系统映射综述，量化揭示LLM应用年轻化与落地不足，信息增量与专业深度突出，值得进入每日精选。",[150],{"name":143,"url":141},[29,30,31,152,153],"农业大模型","智能体",[155,156],"农业代理式人工智能 系统映射综述","农业人工智能 农业大模型 数字农业 智慧农业","农业代理式人工智能系统映射综述-3439",{"doi":9,"openalex_id":9,"authors":159,"venue":9,"cited_by_count":38,"oa_url":9,"card":160,"direction":91,"ingested_from":165},[],{"tldr":161,"method":162,"finding":163,"direction":91,"opportunity":164},"系统映射181项研究，梳理农业代理式AI的架构、应用、挑战与未来方向。","按PRISMA 2020筛选4111项记录至181项，做系统映射综述。","LLM代理2024年才出现，多具合作能力但规划、记忆、反思薄弱，实际部署少。","农业LLM代理的长期田间部署、记忆与反思机制及跨季度评估仍是明显空白。","agent","2026-09-25T00:09:33.995675Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":173,"source_url":170,"published_at":144,"category":12,"cover_url":9,"hotness":64,"is_selected":14,"score":174,"score_detail":175,"sources":178,"tags":180,"search_phrases":183,"slug":186,"view_count":38,"doi":187,"paper":188,"created_at":203},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。","Preprints.org",67,{"impact":176,"substance":18,"depth":19,"authority":55,"freshness":147,"relevant":22,"comment":177},16,"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[179],{"name":173,"url":170},[29,30,31,181,182],"巴西农业","农村数字化",[184,185],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367","10.20944\u002Fpreprints202609.1880.v1",{"doi":187,"openalex_id":189,"authors":190,"venue":173,"cited_by_count":38,"oa_url":170,"card":198,"direction":49,"ingested_from":52},"W7214109425",[191,194,196],{"name":192,"orcid":193},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":195,"orcid":9},"Eduardo Dias",{"name":197,"orcid":9},"Lidia Scoton",{"tldr":199,"method":200,"finding":201,"direction":49,"opportunity":202},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","2026-09-24T23:30:27.046035Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":11,"category":12,"cover_url":9,"hotness":64,"is_selected":14,"score":211,"score_detail":212,"sources":214,"tags":216,"search_phrases":219,"slug":222,"view_count":38,"doi":223,"paper":224,"created_at":254},3347,"Integrating Material Flow Cost Accounting and IoT-Based Monitoring for Eco-Efficient Goat Farm Management","https:\u002F\u002Fdoi.org\u002F10.35145\u002F6e5wnv18","Goat farming plays an important role in supporting rural livelihoods, food production, and agricultural sustainability. However, conventional goat farm management often separates environmental monitoring, financial accounting, and livestock management, limiting the ability to identify resource inefficiencies and associated environmental impacts. This study aims to develop and implement GEMBALA (Green Eco-smart Management-Based Automation for Livestock and Accounting), an integrated digital platform that combines Internet of Things (IoT)-based environmental monitoring, Material Flow Cost Accounting (MFCA), emission analysis, artificial intelligence-based livestock management, and analytical reporting. The research employed a research and development approach in collaboration with CV Cahaya Firdaus (Fathur Farm). An IoT sensor prototype was developed, installed, and tested in a real goat farming environment to monitor temperature, humidity, Heat Index (THI), ammonia gas, and dust density. The platform also incorporates MFCA, emission, AI Estrus, AI Health, and analytical reporting modules. The results demonstrate progress toward integrating environmental, economic, and livestock management information within a unified digital platform. However, further validation is required to improve sensor data transmission, synchronization, emission calculations, MFCA data consistency, and AI performance evaluation. The study provides a foundation for eco-economic decision support, sustainable livestock management, and future commercialization of digital livestock technologies.","山羊养殖在支撑农村生计、粮食生产和农业可持续性方面发挥着重要作用。然而，传统的山羊养殖场管理往往将环境监测、财务核算和畜牧管理相互分离，限制了识别资源低效利用及相关环境影响的能力。本研究旨在开发并实施GEMBALA（基于绿色生态智能管理的畜牧与会计自动化平台），这是一个集成了基于物联网（IoT）的环境监测、物料流成本会计（MFCA）、排放分析、基于人工智能的畜牧管理以及分析报告的综合数字平台。研究采用研发方法，与CV Cahaya Firdaus（Fathur Farm）合作开展。研究开发了物联网传感器原型，并在真实山羊养殖环境中进行安装和测试，用于监测温度、湿度、热指数（THI）、氨气和粉尘密度。该平台还整合了MFCA、排放、AI发情检测、AI健康和分析报告模块。结果表明，在将环境、经济和畜牧管理信息整合到统一数字平台方面取得了进展。然而，仍需进一步验证，以改进传感器数据传输、同步、排放计算、MFCA数据一致性以及AI性能评估。本研究为生态经济决策支持、可持续畜牧管理以及数字畜牧技术的未来商业化提供了基础。","Journal of Applied Business and Technology",62,{"impact":147,"substance":17,"depth":176,"authority":64,"freshness":64,"relevant":22,"comment":213},"论文提出融合物联网监测与物料流成本核算的山羊养殖数字平台，方法有创新但尚处原型验证阶段，产业影响有限。",[215],{"name":210,"url":207},[29,30,31,217,218],"农业物联网","畜牧养殖",[220,221],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347","10.35145\u002F6e5wnv18",{"doi":223,"openalex_id":225,"authors":226,"venue":210,"cited_by_count":38,"oa_url":207,"card":249,"direction":51,"ingested_from":52},"W7214075234",[227,229,231,233,235,237,240,243,245,247],{"name":228,"orcid":9},"Nicholas Renaldo",{"name":230,"orcid":9},"Sulaiman Musa",{"name":232,"orcid":9},"Jaswar Koto",{"name":234,"orcid":9},"Kristy Veronica",{"name":236,"orcid":9},"Umar Faruq",{"name":238,"orcid":239},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":241,"orcid":242},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":244,"orcid":9},"Wilda Susanti",{"name":246,"orcid":9},"Achmad Tavip Junaedi",{"name":248,"orcid":9},"Nabila Wahid",{"tldr":250,"method":251,"finding":252,"direction":51,"opportunity":253},"开发集成物联网监测与物料流成本核算的山羊养殖数字平台GEMBALA。","研发方法，在真实羊场部署物联网传感器并集成MFCA、排放分析与AI模块。","平台初步实现环境、经济与养殖信息整合，但传感器传输与数据一致性仍需验证。","可延伸研究物联网数据与MFCA实时耦合的算法优化及AI模块的长期性能验证。","2026-09-24T23:30:09.863218Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":9,"content":260,"source_name":261,"source_url":9,"published_at":144,"category":262,"cover_url":9,"hotness":64,"is_selected":14,"score":263,"score_detail":264,"sources":268,"tags":270,"search_phrases":273,"slug":276,"view_count":38,"doi":9,"paper":9,"created_at":277},3215,"秋分逢丰收节 机器人成主角！浙江田野正被AI\"接管\"——浙江农科院数字农业研究所研发AI眼镜+害虫识别小程序","https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714","9-22 新蓝网专题报道：在湖州德清县农博家庭农场，种植大户王菊仙戴上一副AI眼镜，对着诱杀害虫的黄板轻轻一扫，\"镜片上、手机端，种类、数量、位置等数据瞬间显现\"。这套由浙江省农科院数字农业研究所研发的设备，正将虫害防控从\"事后补救\"推向\"提前预警\"。在湖州吴兴丰盛湾水产种业，\"云眸\"沼虾养殖AI系统能在3-5秒内捕捉沼虾触须末端细微影像，自动生成比对图谱，一旦发现活动异常即刻标记预警，自动投料机器人与水下传感器联动精准计算投喂量，饲料利用率提升12%-15%、巡塘人力节省六成、养殖效益整体提高10%以上。在杭州余杭区径山镇，无人驾驶拖拉机搭载北斗导航系统自主作业。在杭州临平区田立方未来农场，450亩无人智慧农场示范区配套200余个田间传感器和4个物联网微基站，可根据土壤饱和度和实时水位自动确定浇灌量，一亩地一季油菜花可节水约1000吨。浙江省农业农村厅数据显示，截至目前浙江已累计建成数字农业工厂729家、未来农场63家。","![Image 2](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FloginBg-OXMHhVd9.png)\n\n验证码登录\n\n获取验证码\n\n 一键登录 \n\n- [x]  \n\n登录代表同意 《用户协议》及 《隐私政策》\n\n扫码登录\n\n![Image 3](https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714)\n\n鼠标悬浮刷新二维码\n\n打开Z视介扫码登录\n\n![Image 4](blob:http:\u002F\u002Flocalhost\u002F08f361b6a75bd12fbead3ead9d0ae42d)\n\n[![Image 5](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[首页](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[新闻](https:\u002F\u002Fwww.cztv.com\u002Fheadlines)\n\n[文化](https:\u002F\u002Fwww.cztv.com\u002Fculture)\n\n 电视 \n\n 广播 \n\n[专区](https:\u002F\u002Fwww.cztv.com\u002Fzone)\n\n![Image 6](blob:http:\u002F\u002Flocalhost\u002F0062bfa43494c79fb356b384bfc51bc8)\n\n![Image 7: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faibtn_icon-Dqb11ejT.png)\n\n![Image 8](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode1-C1Z3XA1v.png)\n\n![Image 9: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_lxw-BTIOt7tT.png)更多精彩 中国蓝新闻\n\n![Image 10](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode2-CU2IzmPJ.png)\n\n![Image 11: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_zsj-C7ZTF9AF.png)更多精彩 下载Z视介\n\n[![Image 12](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_more_btn-DzzjA4z1.png)](https:\u002F\u002Fzmtv.cztv.com\u002Fcmsh5-share\u002Fprod\u002FcommonDownload\u002Findex.html)\n\n登录\n\n![Image 13: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FcreateCenter_btn-CP1dJyCT.png)\n\n![Image 14: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faiblue-BYHqI38h.png)\n\nNaN-NaN-NaN NaN:NaN\n\n编辑：\n\n作者：\n\n[](javascript:; 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