[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5085":3,"related-5085":109},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":89,"slug":92,"view_count":84,"doi":93,"paper":94,"created_at":108},5085,"AgriSense: IoT-Driven Precision Agriculture for National Food Sustainability and Development","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23335305","Precision agriculture integrates digital technologies to improve agricultural resource management, productivity, and sustainability, while the Internet of Things (IoT) enables real-time monitoring of soil and environmental conditions through connected sensors. In Nigeria, inefficient water and fertilizer use, declining soil health, climate variability, and limited access to timely agricultural information constrain sustainable food production. This study presents AgriSense, an IoT-driven precision agriculture platform designed to support data-informed farm management under Nigerian agricultural conditions. The study adopts a mixed-method research framework incorporating descriptive, experimental, and analytical approaches, with proposed data sources including IoT sensor measurements, farmer surveys and interviews, field observations, and relevant weather information. The AgriSense platform integrates soil-moisture, nitrogen-phosphorus-potassium (N-P-K), soil-pH, temperature, and humidity monitoring with farm and field management, sensor-status tracking, automated alerts, agronomic recommendations, and reporting functions. The demonstrated interface represents agricultural fields and crops across Asaba Demonstration Farm, Jos Highland Farm, and Sokoto Smart Farm. Its threshold-based alerts identify conditions associated with low soil moisture, low nitrogen, acidic soil, high temperature, and sensor connectivity faults, linking selected conditions to recommended management actions. The available demonstration establishes the platform’s integrated monitoring and decision-support functionality. However, because the interface identifies its environment as a live simulation, the displayed information does not independently establish improvements in crop yield, input-use efficiency, soil health, or farmer income. The study provides a technological foundation for subsequent field validation of IoT-enabled precision agriculture and its potential contribution to sustainable food security in Nigeria.","精准农业（precision agriculture）融合数字技术，以改善农业资源管理、生产力和可持续性，而物联网（Internet of Things, IoT）则通过互联传感器实现对土壤和环境条件的实时监测。在尼日利亚，水资源和肥料利用效率低下、土壤健康状况下降、气候多变以及难以及时获取农业信息等因素制约了可持续粮食生产。本研究提出AgriSense——一个由物联网驱动的精准农业平台，旨在支持尼日利亚农业条件下的数据驱动型农场管理。本研究采用混合方法研究框架，融合描述性、实验性和分析性方法，拟采用的数据来源包括物联网传感器测量数据、农民问卷调查与访谈、田间观察以及相关天气信息。AgriSense平台将土壤湿度、氮磷钾（N-P-K）、土壤pH值、温度和湿度监测与农场及田块管理、传感器状态追踪、自动警报、农艺建议和报告功能集成在一起。所展示的界面呈现了阿萨巴示范农场（Asaba Demonstration Farm）、乔斯高原农场（Jos Highland Farm）和索科托智慧农场（Sokoto Smart Farm）的农田和作物。其基于阈值的警报可识别与土壤湿度低、氮含量低、土壤酸性、高温和传感器连接故障相关的情况，并将选定情况与建议的管理措施相关联。现有演示验证了该平台的集成监测与决策支持功能。然而，由于该界面将其环境标识为实时模拟，所显示的信息并不能独立证明作物产量、投入品使用效率、土壤健康或农民收入方面的改善。本研究为后续对物联网赋能精准农业及其对尼日利亚可持续粮食安全潜在贡献的田间验证提供了技术基础。",null,"Tech-Sphere Journal for Pure and Applied Sciences","https:\u002F\u002Fstem.techspherejournals.com\u002Findex.php\u002Ftsjpas\u002Farticle\u002Fview\u002F80","2026-10-13T00:00:00Z","论文",10,false,56,{"impact":18,"substance":19,"depth":20,"authority":21,"freshness":21,"relevant":22,"comment":23},8,16,14,9,1,"论文提出面向尼日利亚的IoT精准农业平台AgriSense，集成土壤水分、N-P-K、pH等监测与阈值预警，但界面为模拟环境，尚未验证增产增效，属技术原型阶段，参考价值有限。",[25],{"name":10,"url":11},[27,28,29,30,31],"智慧农业","农业物联网","粮食安全","精准农业","土壤墒情监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":40,"locked":15,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,54,60,67,75,82],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":53},18,true,[49],"IoT",[51,52,49],"物联网","internet of things",[28],{"code":55,"score":19,"supported":47,"title":56,"abstract":58,"tags":59},"I1",[57],"precision agriculture",[30,57],[27,30],{"code":61,"score":62,"supported":47,"title":63,"abstract":64,"tags":66},"A8",7,[],[29,65],"food security",[29],{"code":68,"score":69,"supported":47,"title":70,"abstract":71,"tags":74},"F3",6,[],[72,73],"肥料","fertilizer",[],{"code":76,"score":69,"supported":47,"title":77,"abstract":78,"tags":81},"H2",[],[79,80],"土壤","soil",[],{"code":83,"score":84,"supported":47,"title":85,"abstract":86,"tags":88},"H3",3,[],[87],"水资源",[],[90,91],"AgriSense 物联网 精准农业","尼日利亚 智慧农业 平台","AgriSense物联网精准农业-5085","10.5281\u002Fzenodo.23335305",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":100,"oa_url":11,"card":101,"direction":105,"ingested_from":107},"W7221676154",[97],{"name":98,"orcid":99},"Innocent Uzougbo Onwuegbuzie","https:\u002F\u002Forcid.org\u002F0000-0002-1582-1739",0,{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"提出AgriSense物联网精准农业平台，用于尼日利亚农田监测与决策支持。","混合方法框架，集成土壤水分、N-P-K、pH、温湿度传感器与阈值告警。","平台实现监测与决策支持功能，但界面为模拟，尚未验证产量与效益提升。","智慧农业 \u002F 农业物联网","可开展真实田间验证，量化IoT精准农业对产量、投入效率和土壤健康的影响。","openalex","2026-10-16T23:30:14.088827Z",{"total":69,"page":22,"page_size":69,"items":110},[111,186,265,332,419,468],{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":118,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":119,"score_detail":120,"sources":125,"tags":127,"primary_topic":130,"secondary_topics":135,"classification":143,"search_phrases":167,"slug":170,"view_count":22,"doi":171,"paper":172,"created_at":185},5308,"Geopolitics and food security: A comprehensive review of emerging risks and responses","https:\u002F\u002Fdoi.org\u002F10.51200\u002Fjsffs.v2i2.7666","This review paper examines the relationship between geopolitics and food security through a Systematic Literature Review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. A total of 15 peer-reviewed journal articles published between 2015 and 2026 were reviewed to identify emerging geopolitical risks and responses affecting global food systems. The study synthesizes literature on armed conflicts, trade restrictions, climate change, supply chain disruptions, energy market instability, and technological transformation in agriculture. The findings identified four major themes. First, geopolitical conflicts and protectionist trade policies significantly disrupt global food supply chains and increase food price volatility. Second, climate-related shock such as droughts, floods, and extreme weather events intensifies food insecurity, particularly in import-dependent and developing economies. Third, weaknesses in global logistics and energy dependence expose food systems to external shocks and market instability. Fourth, technological innovations, including smart farming, precision agriculture, and digital monitoring systems, play an increasingly important role in strengthening agricultural productivity, improving resource efficiency, and enhancing resilience against geopolitical and environmental uncertainties. The policy implications of this study include strengthening regional food reserves, diversifying import sources, investing in climate-resilient agriculture, promoting the adoption of smart farming technologies, and enhancing institutional cooperation between governments and international organizations. The relevance of smart farming is increasingly evident, as precision agriculture, Internet of Things (IoT)-based monitoring systems, and data-driven farming technologies play a critical role in improving productivity, reducing resource inefficiency, and enhancing food system resilience under geopolitical stress. The study contributes to a broader understanding of how geopolitical dynamics shape food security and provides recommendations for building sustainable, resilient, and technology-driven food systems in an increasingly uncertain global environment.","本文通过遵循系统评价与荟萃分析首选报告条目（Preferred Reporting Items for Systematic Reviews and Meta-Analyses）框架的系统文献综述，考察地缘政治与粮食安全之间的关系。共 review 了2015年至2026年间发表的15篇同行评审期刊论文，以识别影响全球粮食系统的新兴地缘政治风险与应对措施。本研究综合了关于武装冲突、贸易限制、气候变化、供应链中断、能源市场不稳定以及农业技术转型的文献。研究结果识别出四大主题。第一，地缘政治冲突和保护主义贸易政策显著扰乱全球粮食供应链并加剧粮食价格波动。第二，干旱、洪水和极端天气事件等气候相关冲击加剧粮食不安全，尤其是在依赖进口的经济体和发展中经济体中。第三，全球物流的薄弱环节和能源依赖使粮食系统暴露于外部冲击和市场不稳定之中。第四，包括智慧农业、精准农业和数字监测系统在内的技术创新，在增强农业生产力、提高资源效率以及增强应对地缘政治和环境不确定性的韧性方面发挥着日益重要的作用。本研究的政策启示包括加强区域粮食储备、多元化进口来源、投资气候韧性农业、促进智慧农业技术的采用，以及加强政府与国际组织之间的制度合作。智慧农业的相关性日益明显，因为精准农业、基于物联网（IoT）的监测系统和数据驱动农业技术在提高生产力、减少资源低效以及增强地缘政治压力下粮食系统韧性方面发挥着关键作用。本研究有助于更广泛地理解地缘政治动态如何塑造粮食安全，并为在日益不确定的全球环境中构建可持续、有韧性且技术驱动的粮食系统提供建议。","Journal of Smart Farming and Food Security","2026-10-19T00:00:00Z",77,{"impact":46,"substance":121,"depth":122,"authority":123,"freshness":21,"relevant":22,"comment":124},20,17,13,"系统综述梳理地缘政治与粮食安全风险，并强调智慧农业的韧性价值，具政策与产业参考意义，但属综述性论文，非突破性成果。",[126],{"name":117,"url":114},[27,128,29,30,129],"农业供应链","地缘政治",{"code":61,"slug":131,"name":132,"path":133,"parent_code":134},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[136,142],{"code":137,"slug":138,"name":139,"path":140,"parent_code":141},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":15,"version":41,"method":42,"evidence":144},{"candidates":145,"agricultural_evidence":47},[146,151,156,163],{"code":61,"score":147,"supported":47,"title":148,"abstract":149,"tags":150},15,[65],[29,65],[29],{"code":55,"score":152,"supported":47,"title":153,"abstract":154,"tags":155},11,[],[27,30,57],[27,30],{"code":137,"score":21,"supported":47,"title":157,"abstract":158,"tags":162},[],[159,160,161],"气候变化","干旱","climate change",[],{"code":33,"score":21,"supported":47,"title":164,"abstract":165,"tags":166},[],[51,52,49],[],[168,169],"地缘政治 粮食安全","智慧农业 粮食系统韧性","地缘政治粮食安全-5308","10.51200\u002Fjsffs.v2i2.7666",{"doi":171,"openalex_id":173,"authors":174,"venue":117,"cited_by_count":100,"oa_url":114,"card":180,"direction":105,"ingested_from":107},"W7222902031",[175,177],{"name":176,"orcid":9},"Parameswaran Mahendran",{"name":178,"orcid":179},"Wong Sing Yun","https:\u002F\u002Forcid.org\u002F0000-0001-5776-8852",{"tldr":181,"method":182,"finding":183,"direction":105,"opportunity":184},"系统综述地缘政治风险对粮食安全的影响及应对，强调智慧农业的韧性作用。","按PRISMA框架系统综述2015-2026年15篇同行评议文献。","冲突、贸易限制、气候与能源冲击威胁粮食安全，智慧农业可提升韧性与效率。","可量化研究物联网与精准农业在地缘政治冲击下对粮食系统韧性的实证贡献。","2026-10-19T23:30:07.850994Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":194,"score_detail":195,"sources":199,"tags":201,"primary_topic":204,"secondary_topics":205,"classification":206,"search_phrases":235,"slug":238,"view_count":239,"doi":240,"paper":241,"created_at":264},4816,"Next Generation NPK Slow Release Hydrogel Fertilizers: A Critical Review of Material Design, Performance, and Field Translation","https:\u002F\u002Fdoi.org\u002F10.26434\u002Fchemrxiv.15010237\u002Fv1","Climate variability, water scarcity, declining soil quality, and inefficient fertilizer use are increasing the need for nutrient-delivery systems that support climate-resilient agriculture. Slow-release fertilizer hydrogels (SRFHs) are promising because they can retain water and control nutrient transport. This critical review examines SRFHs within conventional and enhanced-efficiency fertilizer strategies. It covers matrix, coated reservoir, and multilayer systems, along with their preparation methods and functional properties. The available evidence shows that polymer composition, crosslink density, pore structure, particle size, fertilizer loading, and environmental conditions affect swelling, mechanical behavior, and nutrient release. Important environmental factors include soil confinement, pH, ionic strength, and temperature. Kinetic models and response surface methodology can help explain release mechanisms and formulation trade-offs. Moisture-, pH-, and temperature-responsive systems may also support more adaptive nutrient delivery. However, high swelling or prolonged release in deionized water does not demonstrate improved nutrient uptake, crop yield, water-use efficiency, reduced nutrient loss, or environmental safety under field conditions. Translation is limited by inconsistent protocols, inadequate dose-matched controls, few multi-season trials, and limited evidence on degradation, residual materials, batch reproducibility, scale-up, and farm-level value. To address these gaps, this review proposes a target product profile that links crop, soil, and climate conditions with nutrient-specific release, mechanical service life, environmental fate, manufacturability, and economic performance. Model-guided responsive formulations could eventually be combined with sensor- and IoT-enabled monitoring for precision agriculture. However, this remains a prospective rather than field-validated approach. SRFHs should therefore be judged by reproducible and context-specific agronomic and environmental performance rather than maximum laboratory performance alone.","气候变异性、水资源短缺、土壤质量下降以及肥料利用效率低下，日益需要能够支撑气候韧性农业的养分递送系统。缓释肥料水凝胶（slow-release fertilizer hydrogels, SRFHs）因其能够保水并调控养分传输而具有广阔前景。本综述在常规肥料与增效肥料策略框架下审视了SRFHs，涵盖基质型、包膜储库型及多层系统，以及其制备方法和功能特性。现有证据表明，聚合物组成、交联密度、孔结构、粒径、肥料负载量及环境条件均影响溶胀性能、力学行为和养分释放。重要的环境因素包括土壤约束、pH、离子强度和温度。动力学模型与响应面法有助于解释释放机制及配方权衡。湿度、pH和温度响应型系统也可能支持更具适应性的养分递送。然而，在去离子水中具有高溶胀率或延长释放时间，并不能证明其在田间条件下能改善养分吸收、作物产量、水分利用效率、减少养分损失或环境安全性。成果转化受限于方案不一致、剂量匹配对照不足、多季试验缺乏，以及在降解、残留材料、批次重现性、规模化放大和农场层面价值方面证据有限。为弥补这些不足，本综述提出了一个目标产品概况，将作物、土壤和气候条件与养分特异性释放、力学使用寿命、环境归趋、可制造性和经济性能相关联。模型引导的响应型配方最终可能与传感器及物联网监测相结合，用于精准农业。然而，这仍是一种前瞻性方法，尚未经过田间验证。因此，SRFHs应以其可重现且情境特异性的农艺与环境性能来评判，而非仅以实验室最大性能为标准。","ChemRxiv","2026-10-11T00:00:00Z",78,{"impact":46,"substance":196,"depth":46,"authority":197,"freshness":18,"relevant":22,"comment":198},22,12,"系统评述缓释水凝胶肥料从材料设计到田间转化的瓶颈，提出目标产品画像，对智慧农业养分管理有参考价值。",[200],{"name":192,"url":189},[27,28,30,202,203],"水凝胶","缓释肥料",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":15,"version":41,"method":42,"evidence":207},{"candidates":208,"agricultural_evidence":47},[209,213,217,221,225,231],{"code":55,"score":18,"supported":47,"title":210,"abstract":211,"tags":212},[],[30,57],[27,30],{"code":33,"score":62,"supported":47,"title":214,"abstract":215,"tags":216},[],[51,49],[28],{"code":68,"score":69,"supported":47,"title":218,"abstract":219,"tags":220},[],[72,73],[],{"code":76,"score":69,"supported":47,"title":222,"abstract":223,"tags":224},[],[79,80],[],{"code":226,"score":84,"supported":47,"title":227,"abstract":228,"tags":230},"G1",[],[229],"成果转化",[],{"code":83,"score":84,"supported":47,"title":232,"abstract":233,"tags":234},[],[87],[],[236,237],"缓释水凝胶肥料 养分释放","NPK 水凝胶 田间验证","缓释水凝胶肥料养分释放-4816",2,"10.26434\u002Fchemrxiv.15010237\u002Fv1",{"doi":240,"openalex_id":242,"authors":243,"venue":192,"cited_by_count":100,"oa_url":257,"card":258,"direction":105,"ingested_from":107},"W7221577755",[244,247,249,251,254],{"name":245,"orcid":246},"Sayem Kawsar","https:\u002F\u002Forcid.org\u002F0009-0008-1281-5764",{"name":248,"orcid":9},"Ahasan Chowdhury Mridul",{"name":250,"orcid":9},"Khondoker Kabbyo Shariar",{"name":252,"orcid":253},"Nafisa B. Islam","https:\u002F\u002Forcid.org\u002F0000-0002-1062-5034",{"name":255,"orcid":256},"Ahsan Chowdhury Mridul","https:\u002F\u002Forcid.org\u002F0009-0007-2962-6196","https:\u002F\u002Fchemrxiv.org\u002Fdoi\u002Fpdf\u002F10.26434\u002Fchemrxiv.15010237\u002Fv1",{"tldr":259,"method":260,"finding":261,"direction":262,"opportunity":263},"综述缓释水凝胶肥料的设计、性能与田间转化瓶颈，提出目标产品画像。","文献综述，分析聚合物组成、交联、释放动力学与响应曲面法。","实验室高溶胀或缓释不等于田间增产、节水或环境安全，转化证据不足。","农业绿色发展与碳","可研究田间剂量匹配、多季试验及降解残留，结合传感器实现模型引导的响应型精准施肥。","2026-10-13T23:30:12.181973Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":9,"source_name":271,"source_url":268,"published_at":272,"category":13,"cover_url":9,"hotness":273,"is_selected":15,"score":274,"score_detail":275,"sources":277,"tags":281,"primary_topic":284,"secondary_topics":285,"classification":291,"search_phrases":311,"slug":314,"view_count":22,"doi":9,"paper":315,"created_at":331},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":18,"substance":20,"depth":123,"authority":69,"freshness":69,"relevant":22,"comment":276},"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。",[278,279],{"name":271,"url":268},{"name":271,"url":280},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[27,282,28,30,283],"农业人工智能","低功耗通信",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[286],{"code":287,"slug":288,"name":289,"path":290,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":15,"version":41,"method":42,"evidence":292},{"candidates":293,"agricultural_evidence":47},[294,300,307],{"code":33,"score":46,"supported":47,"title":295,"abstract":296,"tags":299},[49],[51,297,52,49,298],"边缘计算","edge computing",[28],{"code":287,"score":14,"supported":47,"title":301,"abstract":302,"tags":306},[],[303,304,305],"机器学习","machine learning","artificial intelligence",[282],{"code":55,"score":18,"supported":47,"title":308,"abstract":309,"tags":310},[],[30,57],[27,30],[312,313],"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":316,"authors":317,"venue":271,"cited_by_count":100,"oa_url":268,"card":326,"direction":105,"ingested_from":107},"W7221414266",[318,320,322,324],{"name":319,"orcid":9},"Shital Chavan",{"name":321,"orcid":9},"Anam Bedrekar",{"name":323,"orcid":9},"Sanika Babar",{"name":325,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":327,"method":328,"finding":329,"direction":105,"opportunity":330},"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",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":9,"source_name":338,"source_url":335,"published_at":339,"category":13,"cover_url":9,"hotness":273,"is_selected":15,"score":340,"score_detail":341,"sources":343,"tags":348,"primary_topic":351,"secondary_topics":352,"classification":360,"search_phrases":403,"slug":406,"view_count":22,"doi":407,"paper":408,"created_at":418},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-09T00:00:00Z",81,{"impact":196,"substance":121,"depth":46,"authority":123,"freshness":18,"relevant":22,"comment":342},"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[344,345],{"name":338,"url":335},{"name":346,"url":347},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,282,349,28,30,350],"农业机器人","自动驾驶农机",{"code":287,"slug":288,"name":289,"path":290,"parent_code":37},[353,359],{"code":354,"slug":355,"name":356,"path":357,"parent_code":358},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":15,"version":41,"method":42,"evidence":361},{"candidates":362,"agricultural_evidence":47},[363,369,376,380,387,392,398],{"code":287,"score":46,"supported":47,"title":364,"abstract":365,"tags":368},[305],[303,366,304,305,367],"计算机视觉","computer vision",[282],{"code":354,"score":122,"supported":47,"title":370,"abstract":372,"tags":375},[371],"agricultural machinery",[373,374,371],"农业机械","收割机",[],{"code":33,"score":14,"supported":47,"title":377,"abstract":378,"tags":379},[],[51,52,49],[28],{"code":381,"score":69,"supported":47,"title":382,"abstract":383,"tags":386},"I3",[],[384,385],"遥感","remote sensing",[],{"code":55,"score":388,"supported":47,"title":389,"abstract":390,"tags":391},5,[],[30],[27,30],{"code":393,"score":84,"supported":47,"title":394,"abstract":395,"tags":397},"F4",[],[396],"除草",[],{"code":399,"score":22,"supported":15,"title":400,"abstract":401,"tags":402},"F6",[],[],[349],[404,405],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":407,"openalex_id":409,"authors":410,"venue":338,"cited_by_count":100,"oa_url":335,"card":413,"direction":105,"ingested_from":107},"W7221138004",[411],{"name":412,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":414,"method":415,"finding":416,"direction":105,"opportunity":417},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":420,"title":421,"url":422,"summary":423,"summary_zh":9,"content":9,"source_name":424,"source_url":422,"published_at":425,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":426,"score_detail":427,"sources":429,"tags":431,"primary_topic":433,"secondary_topics":434,"classification":435,"search_phrases":450,"slug":453,"view_count":22,"doi":454,"paper":455,"created_at":467},4339,"GreenAI: An Explainable AI and IoT-Enabled Offline Framework for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11233315\u002Fv1","GreenAI: An Explainable AI and IoT-Enabled Offline Framework for Precision Agriculture。Research Square","Research Square","2026-10-06T00:00:00Z",63,{"impact":197,"substance":46,"depth":19,"authority":18,"freshness":21,"relevant":22,"comment":428},"提出可解释AI与物联网结合的离线精准农业框架，方法有新意但属预印本，权威性与产业影响有限。",[430],{"name":424,"url":422},[27,282,28,432,30],"可解释AI",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":15,"version":41,"method":42,"evidence":436},{"candidates":437,"agricultural_evidence":47},[438,442,446],{"code":55,"score":123,"supported":47,"title":439,"abstract":440,"tags":441},[57],[57],[27,30],{"code":33,"score":197,"supported":47,"title":443,"abstract":444,"tags":445},[49],[49],[28],{"code":287,"score":239,"supported":15,"title":447,"abstract":448,"tags":449},[],[],[282,432],[451,452],"GreenAI 精准农业 离线框架","可解释AI 农业物联网","GreenAI精准农业离线框架-4339","10.21203\u002Frs.3.rs-11233315\u002Fv1",{"doi":454,"openalex_id":456,"authors":457,"venue":424,"cited_by_count":100,"oa_url":466,"card":9,"direction":105,"ingested_from":107},"W7220656746",[458,461,464],{"name":459,"orcid":460},"Qasim Ali Hashmi","https:\u002F\u002Forcid.org\u002F0009-0009-2894-3688",{"name":462,"orcid":463},"Zain ul Sajjad","https:\u002F\u002Forcid.org\u002F0009-0003-8354-8134",{"name":465,"orcid":9},"Saqib Ali³","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11233315\u002Flatest.pdf","2026-10-07T23:30:08.629402Z",{"id":469,"title":470,"url":471,"summary":472,"summary_zh":473,"content":9,"source_name":474,"source_url":471,"published_at":475,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":194,"score_detail":476,"sources":478,"tags":480,"primary_topic":482,"secondary_topics":483,"classification":484,"search_phrases":509,"slug":512,"view_count":239,"doi":9,"paper":513,"created_at":525},4072,"Безпілотні літальні апарати та Інтернет речей у сільському господарстві: систематичний огляд технологічних напрямів і перспектив розвитку","https:\u002F\u002Fais.khpi.edu.ua\u002Farticle\u002Fview\u002F372716","Relevance. Modern agriculture is undergoing a profound cyber-physical transformation driven by the need to shift from extensive to precision, high-accuracy, and environmentally balanced production methods. The technological foundation of this transition is the integration of unmanned aerial vehicles (UAVs) and wireless Internet of Things (IoT) sensor networks, which together form the concept of Agriculture 5.0. Despite the rapid growth of publications on this topic, the accumulated body of research remains fragmented across directions (computer vision, network technologies, robotics, adoption economics), which complicates the formation of a holistic picture of the field and of priority directions for further research. The subject of this study are methods and technologies of applying UAVs and IoT networks to precision-agriculture tasks. The purpose of the article to systematize and classify current scientific research on the use of UAVs and IoT in agriculture, and to determine the leading technological directions, their quantitative proportions, and promising development vectors. The following results were obtained. A total of 198 scientific publications from 2024-2026 were processed and classified into seven thematic classes: crop health and biological threat monitoring, hardware engineering, edge computing and spraying, navigation, swarm intelligence and data collection, phenotyping, biomass and yield forecasting, soil science, water balance and precision irrigation, economics, ecology and socio-cultural perception, and specialized agrotechnical operations. For each class, consolidated tables of applied methods and results, as well as diagrams revealing the internal structure of the direction, were constructed. The dominance of navigation\u002Fswarm-control tasks and biological-threat monitoring was identified, along with key technical barriers (limited battery energy density, beyond-visual-line-of-sight flight regulations, and fragmentation of data-exchange standards). Conclusion. The obtained results provide a holistic assessment of the state and structure of the research field on UAV and IoT applications in agriculture and can serve as a basis for shaping further research programs and educational courses in precision agriculture. Future research directions concern improving the energy autonomy of platforms, standardizing data-exchange protocols, and developing edge artificial-intelligence models resilient to complex field conditions.","相关性。现代农业正在经历深刻的网络物理转型，其驱动力来自于从粗放型向精准、高精度和环境均衡型生产方式转变的需求。这一转型的技术基础是无人机（UAV）与无线物联网（IoT）传感器网络的集成，二者共同构成了农业5.0的概念。尽管该主题的出版物迅速增长，但积累的研究成果分散在多个方向（计算机视觉、网络技术、机器人技术、采用经济学），这使得形成该领域的整体图景及未来优先研究方向变得困难。本研究的研究对象是将无人机和物联网网络应用于精准农业任务的方法与技术。本文旨在系统化和分类当前关于无人机和物联网在农业中应用的科学研 究，并确定主导技术方向、其定量比例及有前景的发展路径。获得了以下结果。共处理了2024—2026年的198篇科学出版物，并将其分为七个主题类别：作物健康与生物威胁监测、硬件工程、边缘计算与喷洒、导航、群体智能与数据采集、表型分析、生物量与产量预测、土壤科学、水分平衡与精准灌溉、经济学、生态学与社会文化感知，以及专业农艺操作。针对每个类别，构建了应用方法与结果的汇总表，以及揭示该方向内部结构的图表。识别出导航\u002F群体控制任务和生物威胁监测的主导地位，以及关键技术障碍（电池能量密度有限、超视距飞行法规、数据交换标准的碎片化）。结论。所获得的结果对无人机和物联网在农业中应用的研究领域现状与结构提供了整体评估，可作为制定精准农业后续研究计划和教学课程的基础。未来研究方向涉及提高平台的能量自主性、标准化数据交换协议，以及开发对复杂田间条件具有鲁棒性的边缘人工智能模型。","The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","2026-10-02T00:00:00Z",{"impact":46,"substance":196,"depth":46,"authority":197,"freshness":18,"relevant":22,"comment":477},"系统梳理2024-2026年198篇无人机与物联网农业应用文献，分类清晰、结论可靠，对智慧农业研究方向有实质参考价值。",[479],{"name":474,"url":471},[27,282,28,481,30],"农业无人机",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":15,"version":41,"method":42,"evidence":485},{"candidates":486,"agricultural_evidence":47},[487,491,495,499,503],{"code":33,"score":14,"supported":47,"title":488,"abstract":489,"tags":490},[],[51,297,52,49,298],[28],{"code":55,"score":18,"supported":47,"title":492,"abstract":493,"tags":494},[],[30,57],[27,30],{"code":287,"score":62,"supported":47,"title":496,"abstract":497,"tags":498},[],[366,367],[282],{"code":76,"score":69,"supported":47,"title":500,"abstract":501,"tags":502},[],[79,80],[],{"code":83,"score":69,"supported":47,"title":504,"abstract":505,"tags":508},[],[506,507],"灌溉","irrigation",[],[510,511],"农业人工智能 农业无人机 农业物联网 智慧农业","农业人工智能 农业无人机","农业人工智能农业无人机农业物联网智慧农业-4072",{"doi":9,"openalex_id":514,"authors":515,"venue":474,"cited_by_count":100,"oa_url":471,"card":520,"direction":105,"ingested_from":107},"W7218223913",[516,518],{"name":517,"orcid":9},"Володимир Панченко",{"name":519,"orcid":9},"Георгій Кучук",{"tldr":521,"method":522,"finding":523,"direction":105,"opportunity":524},"系统综述2024-2026年198篇无人机与物联网农业应用文献，划分七类技术方向并指出发展瓶颈。","文献计量与主题分类，分析198篇论文的方法、结果与内部结构。","导航\u002F集群控制与生物威胁监测占主导，瓶颈为电池能量密度、超视距法规与数据标准碎片化。","可聚焦边缘AI模型在复杂田间条件下的鲁棒性、平台能量自治与跨厂商数据交换协议标准化研究。","2026-10-03T23:30:16.357874Z"]