[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1417":3,"related-1417":108},{"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":30,"primary_topic":35,"secondary_topics":41,"classification":47,"search_phrases":79,"slug":82,"view_count":83,"doi":84,"paper":85,"created_at":107},1417,"A Survey on Data-Driven Demand Forecasting and Decision Support Systems for Agrochemical Supply Chain Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22237188","Demand forecasting plays a pivotal role in enhancing the efficiency, sustainability, and profitability of agrochemical supply chain management. Accurate demand prediction enables organizations to optimize inventory levels, minimize operational costs, reduce product shortages, and improve customer satisfaction. Traditional statistical forecasting methods have long been employed for demand estimation; however, the increasing complexity of agricultural markets, seasonal variations, climate uncertainties, and dynamic consumer demand have encouraged researchers to adopt advanced forecasting techniques. In recent years, Machine Learning (ML), Deep Learning (DL), and time-series forecasting models such as ARIMA and Facebook Prophet have demonstrated significant improvements in forecasting accuracy and decision-making capabilities. Simultaneously, Decision Support Systems (DSS) integrated with Enterprise Resource Planning (ERP) platforms have transformed supply chain operations by enabling real-time monitoring, inventory optimization, and data-driven production planning. This survey presents a comprehensive review of recent research on data-driven demand forecasting and decision support systems in agrochemical supply chain management. The study systematically analyzes statistical forecasting approaches, time-series models, machine learning algorithms, deep learning architectures, and intelligent decision support frameworks adopted by researchers over recent years. Furthermore, the survey compares the strengths, limitations, application domains, and performance characteristics of existing approaches while identifying current research gaps and practical challenges. Finally, future research directions involving hybrid forecasting models, explainable artificial intelligence, Internet of Things (IoT), cloud computing, and digital twin technologies are discussed to highlight emerging opportunities for intelligent and sustainable agrochemical supply chain management.","需求预测在提升农化供应链管理的效率、可持续性和盈利能力方面发挥着关键作用。准确的需求预测使组织能够优化库存水平、降低运营成本、减少产品短缺并提高客户满意度。传统的统计预测方法长期以来一直用于需求估计；然而，农业市场日益复杂、季节性变化、气候不确定性以及动态的消费者需求，促使研究人员采用先进的预测技术。近年来，机器学习（ML）、深度学习（DL）以及ARIMA和Facebook Prophet等时间序列预测模型在预测准确性和决策能力方面展现出显著提升。同时，与企业资源计划（ERP）平台集成的决策支持系统（DSS）通过实现实时监控、库存优化和数据驱动的生产规划，彻底改变了供应链运营。本综述对近年来农化供应链管理中数据驱动需求预测和决策支持系统的相关研究进行了全面回顾。研究系统分析了研究人员近年来采用的统计预测方法、时间序列模型、机器学习算法、深度学习架构和智能决策支持框架。此外，本综述比较了现有方法的优势、局限性、应用领域和性能特征，同时指出了当前的研究空白和实际挑战。最后，讨论了涉及混合预测模型、可解释人工智能、物联网（IoT）、云计算和数字孪生技术的未来研究方向，以突出智能化和可持续农化供应链管理的新兴机遇。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-01T00:00:00Z","论文",40,false,75,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,12,7,1,"系统综述农化供应链需求预测与决策支持系统，涵盖ML\u002FDL及未来方向，对智慧农业研究有参考价值。",[24,25,27],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22237187",{"name":28,"url":29},"International Journal of Scientific Research in Computer Science Engineering and Information Technology","https:\u002F\u002Fdoi.org\u002F10.32628\u002Fcseit26124237",[31,32,33,34],"智慧农业","农业人工智能","供应链管理","需求预测",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[42],{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":48,"locked":14,"version":49,"method":50,"evidence":51},"classified","2026-10-22.2","rules",{"candidates":52,"agricultural_evidence":54},[53,64,72],{"code":36,"score":17,"supported":54,"title":55,"abstract":57,"tags":63},true,[56],"decision support",[58,59,60,61,62,56],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[32],{"code":43,"score":65,"supported":54,"title":66,"abstract":67,"tags":71},9,[],[68,69,70],"物联网","internet of things","IoT",[],{"code":73,"score":20,"supported":54,"title":74,"abstract":75,"tags":78},"I1",[],[76,77],"数字孪生","digital twin",[31],[80,81],"农业人工智能 供应链管理 智慧农业 需求预测","农业人工智能 供应链管理","农业人工智能供应链管理智慧农业需求预测-1417",2,"10.5281\u002Fzenodo.22237188",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":98,"oa_url":6,"card":99,"direction":105,"ingested_from":106},"W7204919413",[88,90,92,94,96],{"name":89,"orcid":9},"Dr. Rais Abdul Hamid Khan",{"name":91,"orcid":9},"Vrushali Arote",{"name":93,"orcid":9},"Sanjana Shejwalkar",{"name":95,"orcid":9},"Palkhi Nirgude",{"name":97,"orcid":9},"Soham Pawar",0,{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"综述数据驱动的农化供应链需求预测与决策支持系统，分析统计、ML\u002FDL等方法并指出未来方向。","系统综述统计、时间序列、机器学习、深度学习及DSS\u002FERP集成方法，比较性能与局","先进预测模型提升准确性，但存在数据稀缺、可解释性差等挑战，混合模型与新兴技术是趋势。","农业人工智能与决策模型","可探索结合IoT与数字孪生的实时预测系统，或开发可解释的混合模型以增强农化供应链韧性。","智慧农业 \u002F 农业物联网","openalex","2026-09-02T23:30:15.459810Z",{"total":109,"page":21,"page_size":109,"items":110},6,[111,224,320,384,449,505],{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":118,"category":12,"cover_url":9,"hotness":119,"is_selected":14,"score":120,"score_detail":121,"sources":124,"tags":126,"primary_topic":130,"secondary_topics":136,"classification":139,"search_phrases":194,"slug":197,"view_count":98,"doi":198,"paper":199,"created_at":223},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",10,79,{"impact":17,"substance":122,"depth":17,"authority":19,"freshness":65,"relevant":21,"comment":123},22,"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。",[125],{"name":117,"url":114},[31,32,127,128,129],"遥感","作物水分胁迫","精准灌溉",{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[137,138],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":140},{"candidates":141,"agricultural_evidence":54},[142,148,152,156,160,166,173,180,187],{"code":131,"score":143,"supported":54,"title":144,"abstract":145,"tags":147},15,[132],[146,132],"灌溉",[129],{"code":36,"score":119,"supported":54,"title":149,"abstract":150,"tags":151},[],[59,60,62],[32],{"code":43,"score":65,"supported":54,"title":153,"abstract":154,"tags":155},[],[68,69,70],[],{"code":73,"score":20,"supported":54,"title":157,"abstract":158,"tags":159},[],[76,77],[31],{"code":161,"score":20,"supported":54,"title":162,"abstract":163,"tags":165},"I3",[],[127,164],"remote sensing",[127],{"code":167,"score":109,"supported":54,"title":168,"abstract":169,"tags":172},"A2",[],[170,171],"小麦","wheat",[],{"code":174,"score":109,"supported":54,"title":175,"abstract":176,"tags":179},"A3",[],[177,178],"玉米","maize",[],{"code":181,"score":109,"supported":54,"title":182,"abstract":183,"tags":186},"A5",[],[184,185],"马铃薯","potato",[],{"code":188,"score":189,"supported":54,"title":190,"abstract":191,"tags":193},"B2",3,[],[192],"葡萄",[],[195,196],"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":198,"openalex_id":200,"authors":201,"venue":117,"cited_by_count":98,"oa_url":114,"card":217,"direction":105,"ingested_from":106},"W7223088921",[202,205,208,211,214],{"name":203,"orcid":204},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":206,"orcid":207},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":209,"orcid":210},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":212,"orcid":213},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":215,"orcid":216},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":218,"method":219,"finding":220,"direction":221,"opportunity":222},"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":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":9,"source_name":230,"source_url":227,"published_at":118,"category":12,"cover_url":9,"hotness":119,"is_selected":14,"score":231,"score_detail":232,"sources":235,"tags":237,"primary_topic":240,"secondary_topics":241,"classification":244,"search_phrases":296,"slug":299,"view_count":98,"doi":300,"paper":301,"created_at":319},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":17,"substance":122,"depth":17,"authority":233,"freshness":65,"relevant":21,"comment":234},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。",[236],{"name":230,"url":227},[31,32,68,238,239],"精准农业","气候韧性",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[242,243],{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},{"status":48,"locked":14,"version":49,"method":50,"evidence":245},{"candidates":246,"agricultural_evidence":54},[247,253,259,265,270,277,284,290],{"code":36,"score":17,"supported":54,"title":248,"abstract":249,"tags":252},[62],[58,59,250,60,61,251],"计算机视觉","computer vision",[32],{"code":73,"score":254,"supported":54,"title":255,"abstract":257,"tags":258},16,[256],"precision agriculture",[238,256],[31,238],{"code":43,"score":119,"supported":54,"title":260,"abstract":261,"tags":264},[],[68,262,70,263],"边缘计算","edge computing",[68],{"code":131,"score":65,"supported":54,"title":266,"abstract":267,"tags":269},[],[146,268,132],"水资源",[],{"code":271,"score":20,"supported":54,"title":272,"abstract":273,"tags":276},"H7",[],[274,275],"气候变化","climate change",[239],{"code":278,"score":109,"supported":54,"title":279,"abstract":280,"tags":283},"H2",[],[281,282],"土壤","soil",[],{"code":285,"score":189,"supported":54,"title":286,"abstract":287,"tags":289},"A7",[],[288],"间作",[],{"code":291,"score":189,"supported":54,"title":292,"abstract":293,"tags":295},"F3",[],[294],"养分管理",[],[297,298],"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":300,"openalex_id":302,"authors":303,"venue":230,"cited_by_count":98,"oa_url":227,"card":314,"direction":105,"ingested_from":106},"W7223121456",[304,307,309,311],{"name":305,"orcid":306},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":308,"orcid":9},"Oluwadare OYEBODE",{"name":310,"orcid":9},"Iretiayo Adelaiye",{"name":312,"orcid":313},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":315,"method":316,"finding":317,"direction":103,"opportunity":318},"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":321,"title":322,"url":323,"summary":324,"summary_zh":325,"content":9,"source_name":326,"source_url":323,"published_at":327,"category":12,"cover_url":9,"hotness":119,"is_selected":14,"score":328,"score_detail":329,"sources":332,"tags":334,"primary_topic":338,"secondary_topics":339,"classification":341,"search_phrases":358,"slug":361,"view_count":98,"doi":362,"paper":363,"created_at":383},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":254,"substance":330,"depth":17,"authority":19,"freshness":65,"relevant":21,"comment":331},21,"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。",[333],{"name":326,"url":323},[31,32,335,336,337],"农业物联网","大语言模型","传感器标准化",{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},[340],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":342},{"candidates":343,"agricultural_evidence":54},[344,348,353],{"code":43,"score":17,"supported":54,"title":345,"abstract":346,"tags":347},[70],[68,69,70],[335],{"code":36,"score":254,"supported":54,"title":349,"abstract":350,"tags":352},[56],[351,56],"large language model",[32,336],{"code":73,"score":20,"supported":54,"title":354,"abstract":355,"tags":357},[],[31,356],"smart agriculture",[31],[359,360],"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":362,"openalex_id":364,"authors":365,"venue":326,"cited_by_count":98,"oa_url":323,"card":378,"direction":105,"ingested_from":106},"W7222920424",[366,368,370,372,374,376],{"name":367,"orcid":9},"Leqi Lai",{"name":369,"orcid":9},"Langtao Duan",{"name":371,"orcid":9},"Yuhao Chen",{"name":373,"orcid":9},"Yuhang Zheng",{"name":375,"orcid":9},"Jinyu Yang",{"name":377,"orcid":9},"Mingyao Wen",{"tldr":379,"method":380,"finding":381,"direction":105,"opportunity":382},"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":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":9,"source_name":390,"source_url":387,"published_at":391,"category":12,"cover_url":9,"hotness":119,"is_selected":14,"score":392,"score_detail":393,"sources":396,"tags":398,"primary_topic":401,"secondary_topics":402,"classification":404,"search_phrases":420,"slug":423,"view_count":98,"doi":424,"paper":425,"created_at":448},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":394,"substance":19,"depth":119,"authority":19,"freshness":65,"relevant":21,"comment":395},8,"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。",[397],{"name":390,"url":387},[31,32,250,399,400],"物联网安全","智能系统",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[403],{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":405},{"candidates":406,"agricultural_evidence":54},[407,412,416],{"code":36,"score":408,"supported":54,"title":409,"abstract":410,"tags":411},19,[62],[250,62,251],[32,250],{"code":73,"score":143,"supported":54,"title":413,"abstract":414,"tags":415},[356],[31,356],[31],{"code":43,"score":394,"supported":54,"title":417,"abstract":418,"tags":419},[70],[],[],[421,422],"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":424,"openalex_id":426,"authors":427,"venue":390,"cited_by_count":98,"oa_url":387,"card":443,"direction":105,"ingested_from":106},"W7222847048",[428,431,434,437,440],{"name":429,"orcid":430},"Bashir Olaniyi Sadiq","https:\u002F\u002Forcid.org\u002F0000-0001-7868-4076",{"name":432,"orcid":433},"Mohammed Dahiru Buhari","https:\u002F\u002Forcid.org\u002F0000-0002-1076-216X",{"name":435,"orcid":436},"IBRAHIM ADABARA","https:\u002F\u002Forcid.org\u002F0000-0001-8949-7540",{"name":438,"orcid":439},"Enerst Edozie","https:\u002F\u002Forcid.org\u002F0000-0002-7616-0962",{"name":441,"orcid":442},"Asaba Shabiluh","https:\u002F\u002Forcid.org\u002F0000-0001-6342-9099",{"tldr":444,"method":445,"finding":446,"direction":105,"opportunity":447},"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",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":455,"source_url":452,"published_at":456,"category":12,"cover_url":9,"hotness":119,"is_selected":14,"score":457,"score_detail":458,"sources":460,"tags":462,"primary_topic":465,"secondary_topics":466,"classification":468,"search_phrases":487,"slug":490,"view_count":189,"doi":491,"paper":492,"created_at":504},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":19,"substance":17,"depth":254,"authority":233,"freshness":65,"relevant":21,"comment":459},"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[461],{"name":455,"url":452},[31,32,335,463,464],"遥感监测","作物估产",{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},[467],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":469},{"candidates":470,"agricultural_evidence":54},[471,475,479,483],{"code":43,"score":17,"supported":54,"title":472,"abstract":473,"tags":474},[70],[68,262,69,70,263],[335],{"code":73,"score":143,"supported":54,"title":476,"abstract":477,"tags":478},[356],[238,256],[31],{"code":36,"score":119,"supported":54,"title":480,"abstract":481,"tags":482},[],[58,59,60,61,62],[32],{"code":161,"score":83,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[464,463],[488,489],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809","10.1007\u002Fs12083-026-02299-3",{"doi":491,"openalex_id":493,"authors":494,"venue":455,"cited_by_count":98,"oa_url":452,"card":499,"direction":105,"ingested_from":106},"W7221560555",[495,497],{"name":496,"orcid":9},"Keerthana T",{"name":498,"orcid":9},"Janarthanan R",{"tldr":500,"method":501,"finding":502,"direction":105,"opportunity":503},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z",{"id":506,"title":507,"url":508,"summary":509,"summary_zh":510,"content":9,"source_name":511,"source_url":508,"published_at":512,"category":12,"cover_url":9,"hotness":513,"is_selected":14,"score":514,"score_detail":515,"sources":518,"tags":522,"primary_topic":524,"secondary_topics":525,"classification":527,"search_phrases":542,"slug":545,"view_count":21,"doi":9,"paper":546,"created_at":562},4672,"AI-BASED ENERGY EFFICIENT COMMUNICATION IN IOT SYSTEM","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F309","The rapid growth of the Internet of Things (IoT) has led to a massive increase in the number of connected devices across various domains such as smart homes, healthcare, agriculture, and industrial automation. As these devices are often battery-powered and deployed in remote or large scale environments, energy efficiency has become a critical concern in IoT communication systems. Traditional IoT networks typically operate using fixed transmission power levels, which do not adapt to changing environmental or network conditions, resulting in unnecessary energy consumption and reduced battery life.To address these limitations, there is a growing need for intelligent and adaptive communication mechanisms that can dynamically optimize power usage without compromising reliability. In this context, Artificial Intelligence (AI)-based adaptive transmission power control has emerged as a promising solution. This review paper presents an analysis of AI-driven techniques that enable IoT devices to adjust their transmission power based on real-time conditions and network requirements.The study explores approaches such as machine learning-based prediction models, which utilize parameters like Received Signal Strength Indicator (RSSI), distance, interference levels, and packet loss rates to determine optimal transmission power. Additionally, the role of edge computing is examined, enabling faster decision-making with reduced latency. The integration of Low Power Wide Area Network (LPWAN) technologies is also discussed for supporting long-range and energy-efficient communication.Furthermore, the paper highlights how adaptive power control improves network performance by reducing energy wastage, minimizing interference, and extending device lifetime. These benefits are especially useful in applications like smart cities, precision agriculture, and industrial automation.Despite these advantages, challenges such as implementation complexity, resource constraints, and data privacy concerns remain. The paper also outlines future research directions, including the development of lightweight AI models and improved integration with emerging technologies.In conclusion, AIbased adaptive transmission power control significantly enhances energy efficiency and communication reliability, making it essential for the sustainable development of next- generation IoT systems. It also supports scalable and cost-effective deployment of IoT networks in diverse real- world environments.","物联网（IoT）的快速发展使得智能家居、医疗健康、农业和工业自动化等各个领域的联网设备数量大幅增加。由于这些设备通常由电池供电，且部署在偏远或大规模环境中，能效已成为物联网通信系统中的关键问题。传统物联网网络通常采用固定传输功率运行，无法适应不断变化的环境或网络条件，导致不必要的能耗和电池寿命缩短。为解决这些局限，人们日益需要能够在不影响可靠性的前提下动态优化功率使用的智能自适应通信机制。在此背景下，基于人工智能（AI）的自适应传输功率控制已成为一种有前景的解决方案。本综述论文分析了使物联网设备能够根据实时条件和网络需求调整传输功率的AI驱动技术。研究探讨了基于机器学习的预测模型等方法，这些方法利用接收信号强度指示（RSSI）、距离、干扰水平和丢包率等参数来确定最优传输功率。此外，还考察了边缘计算的作用，其能够以更低延迟实现更快决策。论文还讨论了低功耗广域网（LPWAN）技术的集成，以支持长距离和节能通信。进一步地，本文强调了自适应功率控制如何通过减少能量浪费、最小化干扰和延长设备寿命来改善网络性能。这些优势在智慧城市、精准农业和工业自动化等应用中尤为有用。尽管具有这些优势，实施复杂性、资源约束和数据隐私问题等挑战仍然存在。论文还概述了未来研究方向，包括开发轻量级AI模型以及与新兴技术的改进集成。总之，基于AI的自适应传输功率控制显著提升了能效和通信可靠性，使其成为下一代物联网系统可持续发展的关键。它还支持物联网网络在多样化现实环境中的可扩展且具有成本效益的部署。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",25,47,{"impact":394,"substance":516,"depth":233,"authority":109,"freshness":109,"relevant":21,"comment":517},14,"综述性论文，方法层面有参考价值，但非农业专属突破，且期刊权威性一般，仅作主题聚合素材。",[519,520],{"name":511,"url":508},{"name":511,"url":521},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[31,32,335,238,523],"低功耗通信",{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},[526],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":528},{"candidates":529,"agricultural_evidence":54},[530,534,538],{"code":43,"score":17,"supported":54,"title":531,"abstract":532,"tags":533},[70],[68,262,69,70,263],[335],{"code":36,"score":119,"supported":54,"title":535,"abstract":536,"tags":537},[],[59,60,62],[32],{"code":73,"score":394,"supported":54,"title":539,"abstract":540,"tags":541},[],[238,256],[31,238],[543,544],"AI 物联网 传输功率控制","LPWAN 精准农业","AI物联网传输功率控制-4672",{"doi":9,"openalex_id":547,"authors":548,"venue":511,"cited_by_count":98,"oa_url":508,"card":557,"direction":105,"ingested_from":106},"W7221414266",[549,551,553,555],{"name":550,"orcid":9},"Shital Chavan",{"name":552,"orcid":9},"Anam Bedrekar",{"name":554,"orcid":9},"Sanika Babar",{"name":556,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":558,"method":559,"finding":560,"direction":105,"opportunity":561},"综述AI自适应发射功率控制提升IoT通信能效，面向智慧农业等场景。","综述机器学习预测模型、边缘计算与LPWAN技术，基于RSSI等参数。","AI自适应功率控制可降低能耗、减少干扰并延长设备寿命，但存在实现复杂与隐私挑战。","可研究面向农田的轻量级AI功率控制模型，结合土壤墒情与作物需水优化能耗。","2026-10-11T23:30:16.397155Z"]