[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1268":3,"related-1268":107},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":76,"slug":79,"view_count":19,"doi":80,"paper":81,"created_at":106},1268,"IoT-based Electronic Nose and Environmental Sensor Fusion for Melon Root Rot Classification Using Adaptive Feature Optimization","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.0930.55","This study proposes an IoT-based sensor fusion framework for melon root rot detection and severity classification by integrating a six-sensor electronic nose array with soil pH, electrical conductivity, and humidity sensors into a unified nine-dimensional feature space.The principal contribution is a classifier-adaptive sequential forward selection instantiation, wherein each of eight classifiers spanning classical machine learning, neural network, and deep learning paradigms independently identifies its optimal feature subset from the fused space.Evaluated across six experimental scenarios with stratified ten-fold cross-validation, hold-out testing, and Wilcoxon signed-rank analysis -with bootstrap resampling and standardization applied exclusively within the training combination of each cross-validation fold after fold assignment, ensuring validation folds remain untouched at all stages -all eight models achieved perfect classification accuracy of 1.0000 on the locked hold-out test set using only three to eight features, with cross-validation accuracy under the proposed S6 scenario ranging from 0.9865 to 0.9973 across the eight models, while hold-out test accuracy ranged more broadly from 0.6957 to 1.0000 across all six evaluated scenarios depending on the feature configuration, and while delivering training time reductions up to 26.16% and model size reductions up to 28.55% for classical and neural network architectures under controlled laboratory conditions.Soil pH and humidity emerged as universal cross-architecture biomarkers, providing evidence-based guidance for minimal-sensor IoT node design in precision agriculture.",null,"International journal of intelligent engineering and systems","2026-08-29T00:00:00Z","论文",10,false,65,{"impact":16,"substance":17,"depth":18,"authority":16,"freshness":19,"relevant":20,"comment":21},12,20,18,3,1,"提出物联网传感器融合框架用于甜瓜根腐病检测，方法新颖且数据详实，对精准农业有参考价值，但时效性稍差。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","病害检测","传感器融合","甜瓜",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,58,65,71],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},11,true,[47],"IoT",[47],[],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":57},"I2",7,[],[55,56],"machine learning","deep learning",[26],{"code":59,"score":60,"supported":45,"title":61,"abstract":62,"tags":64},"I1",4,[],[63],"precision agriculture",[25],{"code":66,"score":19,"supported":45,"title":67,"abstract":68,"tags":70},"H2",[],[69],"soil",[],{"code":72,"score":20,"supported":13,"title":73,"abstract":74,"tags":75},"B1",[],[],[29],[77,78],"农业人工智能 传感器融合 智慧农业 病害检测","农业人工智能 传感器融合","农业人工智能传感器融合智慧农业病害检测-1268","10.22266\u002Fijies2026.0930.55",{"doi":80,"openalex_id":82,"authors":83,"venue":9,"cited_by_count":97,"oa_url":6,"card":98,"direction":104,"ingested_from":105},"W7204656622",[84,87,90,92,94],{"name":85,"orcid":86},"Helmy Widyantara","https:\u002F\u002Forcid.org\u002F0000-0003-3187-4037",{"name":88,"orcid":89},"Ahmad Wali Satria Bahari Johan","https:\u002F\u002Forcid.org\u002F0000-0003-4634-125X",{"name":91,"orcid":8},"Khodijah Amiroh",{"name":93,"orcid":8},"Michael Angello Qadosy Riyadi",{"name":95,"orcid":96},"Muhammad Adib Kamali","https:\u002F\u002Forcid.org\u002F0000-0002-1013-4358",0,{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"提出物联网传感器融合框架，结合电子鼻与土壤传感器，自适应特征选择实现甜瓜根腐病高精度分类。","融合六传感器电子鼻与土壤pH、电导率、湿度，采用分类器自适应序贯前向选择特征。","八种模型在锁定测试集上准确率均达1.0，土壤pH和湿度为通用生物标志物。","农业人工智能与决策模型","可探索田间复杂环境下最小传感器节点设计，及跨作物病害检测的通用性验证。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:12.947592Z",{"total":108,"page":20,"page_size":108,"items":109},6,[110,231,325,402,465,556],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":8,"source_name":116,"source_url":113,"published_at":117,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":118,"score_detail":119,"sources":123,"tags":125,"primary_topic":129,"secondary_topics":135,"classification":141,"search_phrases":201,"slug":204,"view_count":97,"doi":205,"paper":206,"created_at":230},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":18,"substance":120,"depth":18,"authority":16,"freshness":121,"relevant":20,"comment":122},22,9,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[124],{"name":116,"url":113},[25,26,126,127,128],"遥感","作物水分胁迫","精准灌溉",{"code":130,"slug":131,"name":132,"path":133,"parent_code":134},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[136,140],{"code":51,"slug":137,"name":138,"path":139,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":142},{"candidates":143,"agricultural_evidence":45},[144,150,156,162,168,174,181,188,195],{"code":130,"score":145,"supported":45,"title":146,"abstract":147,"tags":149},15,[131],[148,131],"灌溉",[128],{"code":51,"score":12,"supported":45,"title":151,"abstract":152,"tags":155},[],[153,55,154],"机器学习","artificial intelligence",[26],{"code":31,"score":121,"supported":45,"title":157,"abstract":158,"tags":161},[],[159,160,47],"物联网","internet of things",[],{"code":59,"score":52,"supported":45,"title":163,"abstract":164,"tags":167},[],[165,166],"数字孪生","digital twin",[25],{"code":169,"score":52,"supported":45,"title":170,"abstract":171,"tags":173},"I3",[],[126,172],"remote sensing",[126],{"code":175,"score":108,"supported":45,"title":176,"abstract":177,"tags":180},"A2",[],[178,179],"小麦","wheat",[],{"code":182,"score":108,"supported":45,"title":183,"abstract":184,"tags":187},"A3",[],[185,186],"玉米","maize",[],{"code":189,"score":108,"supported":45,"title":190,"abstract":191,"tags":194},"A5",[],[192,193],"马铃薯","potato",[],{"code":196,"score":19,"supported":45,"title":197,"abstract":198,"tags":200},"B2",[],[199],"葡萄",[],[202,203],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":205,"openalex_id":207,"authors":208,"venue":116,"cited_by_count":97,"oa_url":113,"card":224,"direction":104,"ingested_from":105},"W7223088921",[209,212,215,218,221],{"name":210,"orcid":211},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":213,"orcid":214},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":216,"orcid":217},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":219,"orcid":220},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":222,"orcid":223},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":225,"method":226,"finding":227,"direction":228,"opportunity":229},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":8,"source_name":237,"source_url":234,"published_at":117,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":238,"score_detail":239,"sources":242,"tags":244,"primary_topic":247,"secondary_topics":248,"classification":251,"search_phrases":301,"slug":304,"view_count":97,"doi":305,"paper":306,"created_at":324},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":18,"substance":120,"depth":18,"authority":240,"freshness":121,"relevant":20,"comment":241},13,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[243],{"name":237,"url":234},[25,26,159,245,246],"精准农业","气候韧性",{"code":51,"slug":137,"name":138,"path":139,"parent_code":35},[249,250],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":130,"slug":131,"name":132,"path":133,"parent_code":134},{"status":38,"locked":13,"version":39,"method":40,"evidence":252},{"candidates":253,"agricultural_evidence":45},[254,261,266,272,277,284,289,295],{"code":51,"score":18,"supported":45,"title":255,"abstract":256,"tags":260},[154],[257,153,258,55,56,259],"深度学习","计算机视觉","computer vision",[26],{"code":59,"score":262,"supported":45,"title":263,"abstract":264,"tags":265},16,[63],[245,63],[25,245],{"code":31,"score":12,"supported":45,"title":267,"abstract":268,"tags":271},[],[159,269,47,270],"边缘计算","edge computing",[159],{"code":130,"score":121,"supported":45,"title":273,"abstract":274,"tags":276},[],[148,275,131],"水资源",[],{"code":278,"score":52,"supported":45,"title":279,"abstract":280,"tags":283},"H7",[],[281,282],"气候变化","climate change",[246],{"code":66,"score":108,"supported":45,"title":285,"abstract":286,"tags":288},[],[287,69],"土壤",[],{"code":290,"score":19,"supported":45,"title":291,"abstract":292,"tags":294},"A7",[],[293],"间作",[],{"code":296,"score":19,"supported":45,"title":297,"abstract":298,"tags":300},"F3",[],[299],"养分管理",[],[302,303],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":305,"openalex_id":307,"authors":308,"venue":237,"cited_by_count":97,"oa_url":234,"card":319,"direction":104,"ingested_from":105},"W7223121456",[309,312,314,316],{"name":310,"orcid":311},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":313,"orcid":8},"Oluwadare OYEBODE",{"name":315,"orcid":8},"Iretiayo Adelaiye",{"name":317,"orcid":318},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":320,"method":321,"finding":322,"direction":102,"opportunity":323},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":8,"source_name":331,"source_url":328,"published_at":332,"category":11,"cover_url":8,"hotness":333,"is_selected":13,"score":334,"score_detail":335,"sources":339,"tags":343,"primary_topic":346,"secondary_topics":347,"classification":349,"search_phrases":379,"slug":382,"view_count":97,"doi":383,"paper":384,"created_at":401},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":120,"substance":336,"depth":337,"authority":240,"freshness":97,"relevant":20,"comment":338},21,17,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[340,341],{"name":331,"url":328},{"name":331,"url":342},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[25,26,344,345,128],"农业物联网","区块链溯源",{"code":130,"slug":131,"name":132,"path":133,"parent_code":134},[348],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":350},{"candidates":351,"agricultural_evidence":45},[352,356,360,364,370,375],{"code":130,"score":145,"supported":45,"title":353,"abstract":354,"tags":355},[131],[148,131],[128],{"code":31,"score":12,"supported":45,"title":357,"abstract":358,"tags":359},[],[159,160,47],[344],{"code":51,"score":60,"supported":45,"title":361,"abstract":362,"tags":363},[],[154],[26],{"code":365,"score":19,"supported":45,"title":366,"abstract":367,"tags":369},"G2",[],[368],"小农户",[],{"code":371,"score":20,"supported":13,"title":372,"abstract":373,"tags":374},"E4",[],[],[345],{"code":59,"score":20,"supported":13,"title":376,"abstract":377,"tags":378},[],[],[25],[380,381],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":383,"openalex_id":385,"authors":386,"venue":331,"cited_by_count":97,"oa_url":328,"card":395,"direction":104,"ingested_from":105},"W7223034914",[387,390,392],{"name":388,"orcid":389},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":391,"orcid":8},"Dr.T.Baskar",{"name":393,"orcid":394},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":396,"method":397,"finding":398,"direction":399,"opportunity":400},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":8,"source_name":408,"source_url":405,"published_at":409,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":418,"secondary_topics":419,"classification":421,"search_phrases":439,"slug":442,"view_count":97,"doi":443,"paper":444,"created_at":464},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":262,"substance":336,"depth":18,"authority":16,"freshness":121,"relevant":20,"comment":412},"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[414],{"name":408,"url":405},[25,26,344,416,417],"大语言模型","传感器标准化",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[420],{"code":51,"slug":137,"name":138,"path":139,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":422},{"candidates":423,"agricultural_evidence":45},[424,428,434],{"code":31,"score":18,"supported":45,"title":425,"abstract":426,"tags":427},[47],[159,160,47],[344],{"code":51,"score":262,"supported":45,"title":429,"abstract":431,"tags":433},[430],"decision support",[432,430],"large language model",[26,416],{"code":59,"score":52,"supported":45,"title":435,"abstract":436,"tags":438},[],[25,437],"smart agriculture",[25],[440,441],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":443,"openalex_id":445,"authors":446,"venue":408,"cited_by_count":97,"oa_url":405,"card":459,"direction":104,"ingested_from":105},"W7222920424",[447,449,451,453,455,457],{"name":448,"orcid":8},"Leqi Lai",{"name":450,"orcid":8},"Langtao Duan",{"name":452,"orcid":8},"Yuhao Chen",{"name":454,"orcid":8},"Yuhang Zheng",{"name":456,"orcid":8},"Jinyu Yang",{"name":458,"orcid":8},"Mingyao Wen",{"tldr":460,"method":461,"finding":462,"direction":104,"opportunity":463},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z",{"id":466,"title":467,"url":468,"summary":469,"summary_zh":470,"content":8,"source_name":471,"source_url":468,"published_at":472,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":473,"score_detail":474,"sources":477,"tags":479,"primary_topic":482,"secondary_topics":488,"classification":494,"search_phrases":533,"slug":536,"view_count":97,"doi":8,"paper":537,"created_at":555},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":16,"substance":336,"depth":337,"authority":121,"freshness":475,"relevant":20,"comment":476},8,"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[478],{"name":471,"url":468},[25,26,344,480,481],"温室环境","小气候预测",{"code":483,"slug":484,"name":485,"path":486,"parent_code":487},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[489,490],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":72,"slug":491,"name":492,"path":493,"parent_code":487},"vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":38,"locked":13,"version":39,"method":40,"evidence":495},{"candidates":496,"agricultural_evidence":45},[497,501,508,517,521,525,529],{"code":31,"score":145,"supported":45,"title":498,"abstract":499,"tags":500},[47],[159,47],[344],{"code":483,"score":502,"supported":45,"title":503,"abstract":505,"tags":507},14,[504],"greenhouse",[506,504],"温室",[],{"code":72,"score":121,"supported":45,"title":509,"abstract":510,"tags":516},[],[511,512,513,514,515],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":59,"score":52,"supported":45,"title":518,"abstract":519,"tags":520},[],[245,63],[25],{"code":51,"score":52,"supported":45,"title":522,"abstract":523,"tags":524},[],[257,56],[26],{"code":66,"score":108,"supported":45,"title":526,"abstract":527,"tags":528},[],[287,69],[],{"code":130,"score":108,"supported":45,"title":530,"abstract":531,"tags":532},[],[148,131],[],[534,535],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":8,"openalex_id":538,"authors":539,"venue":471,"cited_by_count":97,"oa_url":8,"card":550,"direction":104,"ingested_from":105},"W7222909837",[540,542,544,546,548],{"name":541,"orcid":8},"Siman Emmanuel*",{"name":543,"orcid":8},"Edward Onyebueke Agu",{"name":545,"orcid":8},"Musa Gani",{"name":547,"orcid":8},"Oladunjoye John Abiodun",{"name":549,"orcid":8},"Anagu Emmanuel John",{"tldr":551,"method":552,"finding":553,"direction":104,"opportunity":554},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z",{"id":557,"title":558,"url":559,"summary":560,"summary_zh":561,"content":8,"source_name":562,"source_url":559,"published_at":563,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":564,"score_detail":565,"sources":567,"tags":569,"primary_topic":572,"secondary_topics":573,"classification":575,"search_phrases":591,"slug":594,"view_count":97,"doi":595,"paper":596,"created_at":619},5236,"A cross-domain review of artificial intelligence techniques for iot security, computer networks, image processing and smart agriculture in connected environments","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02500-9","The rapid digitization of modern systems has increased the demand for intelligent, adaptive, and data-driven solutions across connected systems. However, these domains face persistent challenges such as heterogeneous datasets, limited labeled data, real-time processing constraints, privacy concerns, model interpretability, and poor generalization across environments. This review examines the application of major Artificial Intelligence (AI) techniques for solving classification, prediction, optimization, anomaly detection, routing, image analysis, and decision-support problems. The empirical patterns across the literature are summarized cautiously across studies. General lessons that emerged across the reviewed domains were also presented. Overall, the review provides researchers with a concise foundation for understanding current AI methods, their practical limitations, and future research directions in the aspect of connected environments. This article has tried to clarify various aspects and overall issues related to the use of AI techniques in computer networks, image processing and computer vision, smart agriculture and related intelligent systems. The authors believe that this is a comprehensive and integrated survey of these AI techniques in a number of application domains, with an emphasis on their practical suitability, their current empirical limitations, and future research directions. Thus, the article is informative to researchers in the field and may be a guide to future research on the development of robust, interpretable, scalable, and context-aware AI-based connected systems.","现代系统的快速数字化增加了对互联系统中智能、自适应和数据驱动解决方案的需求。然而，这些领域面临诸如异构数据集、标注数据有限、实时处理约束、隐私问题、模型可解释性以及跨环境泛化能力差等持续挑战。本综述考察了主要人工智能（Artificial Intelligence, AI）技术在解决分类、预测、优化、异常检测、路由、图像分析和决策支持问题中的应用。本文谨慎地总结了文献中的实证规律。文中还提出了在所综述领域中浮现的一般性经验教训。总体而言，本综述为研究人员提供了一个简明的基础，以理解当前AI方法、其实际局限性以及互联环境方面的未来研究方向。本文试图阐明与AI技术在计算机网络、图像处理与计算机视觉、智慧农业及相关智能系统中的应用有关的各个方面和总体问题。作者认为，这是对若干应用领域中这些AI技术的全面且综合的调查，重点在于其实际适用性、当前实证局限性和未来研究方向。因此，本文对该领域的研究人员具有参考价值，并可为开发稳健、可解释、可扩展且具有情境感知能力的基于AI的互联系统的未来研究提供指导。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",51,{"impact":475,"substance":16,"depth":12,"authority":16,"freshness":121,"relevant":20,"comment":566},"跨领域AI综述，涉及智慧农业但仅为其一分支，方法层面无突破性新结论，公共价值有限，不宜进入每日精选。",[568],{"name":562,"url":559},[25,26,258,570,571],"物联网安全","智能系统",{"code":51,"slug":137,"name":138,"path":139,"parent_code":35},[574],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":576},{"candidates":577,"agricultural_evidence":45},[578,583,587],{"code":51,"score":579,"supported":45,"title":580,"abstract":581,"tags":582},19,[154],[258,154,259],[26,258],{"code":59,"score":145,"supported":45,"title":584,"abstract":585,"tags":586},[437],[25,437],[25],{"code":31,"score":475,"supported":45,"title":588,"abstract":589,"tags":590},[47],[],[],[592,593],"AI 物联网安全 智慧农业","农业人工智能 物联网安全 计算机视觉 智慧农业","AI物联网安全智慧农业-5236","10.1007\u002Fs44163-026-02500-9",{"doi":595,"openalex_id":597,"authors":598,"venue":562,"cited_by_count":97,"oa_url":559,"card":614,"direction":104,"ingested_from":105},"W7222847048",[599,602,605,608,611],{"name":600,"orcid":601},"Bashir Olaniyi Sadiq","https:\u002F\u002Forcid.org\u002F0000-0001-7868-4076",{"name":603,"orcid":604},"Mohammed Dahiru Buhari","https:\u002F\u002Forcid.org\u002F0000-0002-1076-216X",{"name":606,"orcid":607},"IBRAHIM ADABARA","https:\u002F\u002Forcid.org\u002F0000-0001-8949-7540",{"name":609,"orcid":610},"Enerst Edozie","https:\u002F\u002Forcid.org\u002F0000-0002-7616-0962",{"name":612,"orcid":613},"Asaba Shabiluh","https:\u002F\u002Forcid.org\u002F0000-0001-6342-9099",{"tldr":615,"method":616,"finding":617,"direction":104,"opportunity":618},"综述AI在物联网安全、计算机网络、图像处理与智慧农业中的应用与局限。","跨领域文献综述，归纳分类、预测、优化、异常检测等方法。","各领域普遍受限于异构数据、标注少、实时性与可解释性不足。","可聚焦农业物联网中少标注、跨环境泛化的可解释实时AI模型研究。","2026-10-18T23:30:07.908595Z"]