[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2773":3,"related-2773":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":28,"primary_topic":34,"secondary_topics":40,"classification":46,"search_phrases":77,"slug":80,"view_count":22,"doi":81,"paper":82,"created_at":99},2773,"IoT-Enabled Sensor Applications in Smart Healthcare, Smart Agriculture, Environmental Monitoring and Smart Energy & Safety Monitoring","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22795428","The Internet of Things (IoT) is becoming a fundamental technology for the development of sensorized intelligent systems in several domains of society. This paper presents a structured literature review on architectures for sensing and intelligence in the scope of four research areas: Smart Healthcare, Smart Agriculture, Environmental Monitoring, and Smart Energy and Safety Monitoring. Recent journal articles, systematic reviews, and conference papers were analysed to report sensor modalities, communication architectures, and learning algorithms, namely machine learning (ML) and deep learning (DL), that have been proposed in literature for each of the above research areas. A consolidated inventory of algorithms that have been reported in the above surveyed works, as well as a comparison of sensing technologies, learning techniques, and their corresponding applications, are also presented. Finally, open challenges that have been found to be recurring, i.e., sensor calibration and drift, energy efficiency, security and privacy, heterogeneous data, and the gap between proof-of-concept prototypes and large-scale scalable and practical implementations, as well as future research directions, are outlined. The purpose of this work is to provide a useful, structured literature review that can serve as a background and reference for researchers interested in sensorized IoT applications using algorithmic approaches.","物联网（IoT）正在成为社会发展多个领域中传感智能系统开发的基础性技术。本文针对四个研究领域——智能医疗、智能农业、环境监测以及智能能源与安全监测——中的传感与智能架构进行了结构化文献综述。通过分析近期期刊论文、系统性综述和会议论文，报告了上述各研究领域中文献所提出的传感器模态、通信架构以及学习算法，即机器学习（ML）和深度学习（DL）。本文还提供了在上述综述工作中所报道算法的综合清单，以及传感技术、学习技术及其相应应用的对比。最后，概述了反复出现的开放挑战，即传感器校准与漂移、能效、安全与隐私、异构数据，以及概念验证原型与大规模可扩展实际实现之间的差距，并指出了未来研究方向。本工作的目的是提供一份有用的、结构化的文献综述，为对使用算法方法的传感物联网应用感兴趣的研究人员提供背景和参考。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-16T00:00:00Z","论文",25,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,9,1,"系统性文献综述，覆盖智慧农业等四大领域的传感与智能架构，方法梳理与开放挑战总结扎实，对农业物联网研究有参考价值，但非农业专属突破性成果。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22795429",[29,30,31,32,33],"智慧农业","农业人工智能","农业物联网","传感器","环境监测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[41],{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":47,"locked":14,"version":48,"method":49,"evidence":50},"classified","2026-10-22.2","rules",{"candidates":51,"agricultural_evidence":54},[52,61,68],{"code":35,"score":53,"supported":54,"title":55,"abstract":57,"tags":60},19,true,[56],"IoT",[58,59,56],"物联网","internet of things",[32,31],{"code":62,"score":63,"supported":54,"title":64,"abstract":66,"tags":67},"I1",12,[65],"smart agriculture",[65],[29],{"code":42,"score":69,"supported":54,"title":70,"abstract":71,"tags":76},10,[],[72,73,74,75],"深度学习","机器学习","machine learning","deep learning",[30],[78,79],"农业人工智能 农业物联网 智慧农业 环境监测","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业环境监测-2773","10.5281\u002Fzenodo.22795428",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7213433550",[85,87,89],{"name":86,"orcid":9},"Mrs.C.Nithya",{"name":88,"orcid":9},"Mr.M.K.Sampath",{"name":90,"orcid":9},"Mrs. P.Raga Keerthana",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"综述物联网传感与智能在医疗、农业、环境、能源安全四大领域的架构、算法与应用。","结构化文献综述，分析传感器模态、通信架构及机器学习\u002F深度学习算法。","梳理了各领域算法清单，指出传感器校准漂移、能效、安全隐私等共性挑战。","智慧农业 \u002F 农业物联网","农业物联网中传感器校准漂移与跨域异构数据融合的轻量化算法研究尚存空白。","openalex","2026-09-17T23:30:13.984507Z",{"total":101,"page":22,"page_size":101,"items":102},6,[103,168,233,291,361,445],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":69,"is_selected":14,"score":111,"score_detail":112,"sources":116,"tags":118,"primary_topic":121,"secondary_topics":122,"classification":124,"search_phrases":142,"slug":145,"view_count":91,"doi":146,"paper":147,"created_at":167},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":113,"substance":114,"depth":17,"authority":63,"freshness":21,"relevant":22,"comment":115},16,21,"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。",[117],{"name":109,"url":106},[29,30,31,119,120],"大语言模型","传感器标准化",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[123],{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":125},{"candidates":126,"agricultural_evidence":54},[127,131,137],{"code":35,"score":17,"supported":54,"title":128,"abstract":129,"tags":130},[56],[58,59,56],[31],{"code":42,"score":113,"supported":54,"title":132,"abstract":134,"tags":136},[133],"decision support",[135,133],"large language model",[30,119],{"code":62,"score":138,"supported":54,"title":139,"abstract":140,"tags":141},7,[],[29,65],[29],[143,144],"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":146,"openalex_id":148,"authors":149,"venue":109,"cited_by_count":91,"oa_url":106,"card":162,"direction":96,"ingested_from":98},"W7222920424",[150,152,154,156,158,160],{"name":151,"orcid":9},"Leqi Lai",{"name":153,"orcid":9},"Langtao Duan",{"name":155,"orcid":9},"Yuhao Chen",{"name":157,"orcid":9},"Yuhang Zheng",{"name":159,"orcid":9},"Jinyu Yang",{"name":161,"orcid":9},"Mingyao Wen",{"tldr":163,"method":164,"finding":165,"direction":96,"opportunity":166},"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":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":69,"is_selected":14,"score":176,"score_detail":177,"sources":179,"tags":181,"primary_topic":184,"secondary_topics":185,"classification":187,"search_phrases":214,"slug":217,"view_count":218,"doi":219,"paper":220,"created_at":232},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":63,"substance":17,"depth":113,"authority":20,"freshness":21,"relevant":22,"comment":178},"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。",[180],{"name":174,"url":171},[29,30,31,182,183],"遥感监测","作物估产",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[186],{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":188},{"candidates":189,"agricultural_evidence":54},[190,196,203,208],{"code":35,"score":17,"supported":54,"title":191,"abstract":192,"tags":195},[56],[58,193,59,56,194],"边缘计算","edge computing",[31],{"code":62,"score":197,"supported":54,"title":198,"abstract":199,"tags":202},15,[65],[200,201],"精准农业","precision agriculture",[29],{"code":42,"score":69,"supported":54,"title":204,"abstract":205,"tags":207},[],[72,73,74,75,206],"artificial intelligence",[30],{"code":209,"score":210,"supported":14,"title":211,"abstract":212,"tags":213},"I3",2,[],[],[183,182],[215,216],"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",3,"10.1007\u002Fs12083-026-02299-3",{"doi":219,"openalex_id":221,"authors":222,"venue":174,"cited_by_count":91,"oa_url":171,"card":227,"direction":96,"ingested_from":98},"W7221560555",[223,225],{"name":224,"orcid":9},"Keerthana T",{"name":226,"orcid":9},"Janarthanan R",{"tldr":228,"method":229,"finding":230,"direction":96,"opportunity":231},"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":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":246,"tags":250,"primary_topic":252,"secondary_topics":253,"classification":255,"search_phrases":270,"slug":273,"view_count":22,"doi":9,"paper":274,"created_at":290},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",47,{"impact":243,"substance":244,"depth":20,"authority":101,"freshness":101,"relevant":22,"comment":245},8,14,"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。",[247,248],{"name":239,"url":236},{"name":239,"url":249},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[29,30,31,200,251],"低功耗通信",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[254],{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":256},{"candidates":257,"agricultural_evidence":54},[258,262,266],{"code":35,"score":17,"supported":54,"title":259,"abstract":260,"tags":261},[56],[58,193,59,56,194],[31],{"code":42,"score":69,"supported":54,"title":263,"abstract":264,"tags":265},[],[73,74,206],[30],{"code":62,"score":243,"supported":54,"title":267,"abstract":268,"tags":269},[],[200,201],[29,200],[271,272],"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":275,"authors":276,"venue":239,"cited_by_count":91,"oa_url":236,"card":285,"direction":96,"ingested_from":98},"W7221414266",[277,279,281,283],{"name":278,"orcid":9},"Shital Chavan",{"name":280,"orcid":9},"Anam Bedrekar",{"name":282,"orcid":9},"Sanika Babar",{"name":284,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":286,"method":287,"finding":288,"direction":96,"opportunity":289},"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":292,"title":293,"url":294,"summary":295,"summary_zh":296,"content":9,"source_name":239,"source_url":294,"published_at":240,"category":12,"cover_url":9,"hotness":69,"is_selected":14,"score":297,"score_detail":298,"sources":300,"tags":302,"primary_topic":305,"secondary_topics":311,"classification":314,"search_phrases":340,"slug":343,"view_count":91,"doi":9,"paper":344,"created_at":360},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。",61,{"impact":63,"substance":113,"depth":197,"authority":63,"freshness":101,"relevant":22,"comment":299},"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[301],{"name":239,"url":294},[29,30,303,31,304],"精准施肥","智能农机",{"code":306,"slug":307,"name":308,"path":309,"parent_code":310},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[312,313],{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":315},{"candidates":316,"agricultural_evidence":54},[317,323,327,331,335],{"code":306,"score":197,"supported":54,"title":318,"abstract":320,"tags":322},[319],"fertilizer",[321,319],"肥料",[303],{"code":42,"score":69,"supported":54,"title":324,"abstract":325,"tags":326},[],[73,74,206],[30],{"code":35,"score":69,"supported":54,"title":328,"abstract":329,"tags":330},[],[58,59,56],[31],{"code":62,"score":138,"supported":54,"title":332,"abstract":333,"tags":334},[],[200,201],[29],{"code":336,"score":22,"supported":14,"title":337,"abstract":338,"tags":339},"F5",[],[],[304],[341,342],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":9,"openalex_id":345,"authors":346,"venue":239,"cited_by_count":91,"oa_url":294,"card":355,"direction":96,"ingested_from":98},"W7221394563",[347,349,351,353],{"name":348,"orcid":9},"Anjali Manoj Kumbhar",{"name":350,"orcid":9},"Shruti Prashant Kulkarni",{"name":352,"orcid":9},"Snehal Santosh Khune",{"name":354,"orcid":9},"B. B. Godbole",{"tldr":356,"method":357,"finding":358,"direction":96,"opportunity":359},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":10,"source_url":364,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":368,"score_detail":369,"sources":372,"tags":377,"primary_topic":380,"secondary_topics":381,"classification":387,"search_phrases":429,"slug":432,"view_count":22,"doi":433,"paper":434,"created_at":444},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。","2026-10-09T00:00:00Z",81,{"impact":370,"substance":18,"depth":17,"authority":20,"freshness":243,"relevant":22,"comment":371},22,"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。",[373,374],{"name":10,"url":364},{"name":375,"url":376},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[29,30,378,31,200,379],"农业机器人","自动驾驶农机",{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},[382,386],{"code":336,"slug":383,"name":384,"path":385,"parent_code":310},"machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":388},{"candidates":389,"agricultural_evidence":54},[390,396,403,407,413,418,424],{"code":42,"score":17,"supported":54,"title":391,"abstract":392,"tags":395},[206],[73,393,74,206,394],"计算机视觉","computer vision",[30],{"code":336,"score":19,"supported":54,"title":397,"abstract":399,"tags":402},[398],"agricultural machinery",[400,401,398],"农业机械","收割机",[],{"code":35,"score":69,"supported":54,"title":404,"abstract":405,"tags":406},[],[58,59,56],[31],{"code":209,"score":101,"supported":54,"title":408,"abstract":409,"tags":412},[],[410,411],"遥感","remote sensing",[],{"code":62,"score":414,"supported":54,"title":415,"abstract":416,"tags":417},5,[],[200],[29,200],{"code":419,"score":218,"supported":54,"title":420,"abstract":421,"tags":423},"F4",[],[422],"除草",[],{"code":425,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},"F6",[],[],[378],[430,431],"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":433,"openalex_id":435,"authors":436,"venue":10,"cited_by_count":91,"oa_url":364,"card":439,"direction":96,"ingested_from":98},"W7221138004",[437],{"name":438,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":440,"method":441,"finding":442,"direction":96,"opportunity":443},"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":446,"title":447,"url":448,"summary":449,"summary_zh":450,"content":9,"source_name":451,"source_url":448,"published_at":240,"category":12,"cover_url":9,"hotness":69,"is_selected":14,"score":452,"score_detail":453,"sources":455,"tags":457,"primary_topic":459,"secondary_topics":465,"classification":468,"search_phrases":503,"slug":506,"view_count":507,"doi":508,"paper":509,"created_at":529},4569,"SMART GREENHOUSE MEASUREMENT AND MONITORING SYSTEM BASED ON IOT AND COMPUTER VISION","https:\u002F\u002Fdoi.org\u002F10.46242\u002Fjstiuh.v83i05.6189","This paper presents a smart greenhouse measurement and monitoring system integrating IoT and computer vision for non-invasive plant growth observation and remote supervision. The proposed architecture combines environmental sensors, STM32-based data acquisition and control modules, a Raspberry Pi 4 with a USB camera, a lightweight deep learning-based stem segmentation module, Firebase Realtime Database, and a web application for monitoring and control. In the computer vision module, tomato plant images are processed directly on the Raspberry Pi to segment stem regions and estimate plant height from calibrated image measurements, enabling edge-based phenotypic monitoring without destructive sampling or manual measurement. Experimental results on a tomato cultivation prototype show that the system can collect temperature, air humidity, soil moisture, and light intensity data in real time while providing automated plant height estimation from RGB images. During the reported period, temperature ranged from 27.8°C to 38.8°C, air humidity from 40.2% to 77.0%, soil moisture from 52.0% to 82.4%, and plant height increased from 65.8 cm to 77.0 cm. The segmentation results demonstrate the feasibility of extracting stem-level structural information under practical greenhouse conditions, including complex background, leaf occlusion, and non-uniform illumination. The prototype confirms the potential of a low-cost smart greenhouse monitoring system and provides a practical basis for future expansion toward richer agronomic indicators, such as stem width, leaf area, canopy coverage, growth rate estimation, and automated environmental control.","本文提出了一种集成物联网与计算机视觉的智能温室测量与监测系统，用于非侵入式植物生长观测与远程监管。所提出的架构结合了环境传感器、基于STM32的数据采集与控制模块、配备USB摄像头的Raspberry Pi 4、基于轻量级深度学习的茎秆分割模块、Firebase实时数据库以及用于监测与控制的Web应用程序。在计算机视觉模块中，番茄植株图像直接在Raspberry Pi上处理，以分割茎秆区域并根据标定后的图像测量值估算株高，从而实现基于边缘的表型监测，无需破坏性采样或人工测量。在番茄栽培原型上的实验结果表明，该系统能够实时采集温度、空气湿度、土壤湿度和光照强度数据，同时基于RGB图像提供自动株高估算。在报告期内，温度范围为27.8°C至38.8°C，空气湿度为40.2%至77.0%，土壤湿度为52.0%至82.4%，株高从65.8 cm增长至77.0 cm。分割结果证明了在实际温室条件下（包括复杂背景、叶片遮挡和非均匀光照）提取茎秆级结构信息的可行性。该原型验证了低成本智能温室监测系统的潜力，并为未来扩展至更丰富的农艺指标（如茎宽、叶面积、冠层覆盖度、生长速率估算和自动化环境控制）提供了实践基础。","Journal of Science and Technology - IUH",66,{"impact":63,"substance":18,"depth":113,"authority":69,"freshness":243,"relevant":22,"comment":454},"低成本IoT与边缘视觉融合的温室监测原型，方法可行、数据翔实，但属细分技术验证，产业影响有限。",[456],{"name":451,"url":448},[29,30,393,31,458],"温室种植",{"code":460,"slug":461,"name":462,"path":463,"parent_code":464},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[466,467],{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":469},{"candidates":470,"agricultural_evidence":54},[471,475,481,485,492,499],{"code":42,"score":53,"supported":54,"title":472,"abstract":473,"tags":474},[394],[72,393,75,394],[30,393],{"code":460,"score":197,"supported":54,"title":476,"abstract":478,"tags":480},[477],"greenhouse",[479,477],"温室",[458],{"code":35,"score":197,"supported":54,"title":482,"abstract":483,"tags":484},[56],[58,56],[31],{"code":486,"score":101,"supported":54,"title":487,"abstract":488,"tags":491},"B1",[],[489,490],"番茄","tomato",[],{"code":493,"score":101,"supported":54,"title":494,"abstract":495,"tags":498},"H2",[],[496,497],"土壤","soil",[],{"code":62,"score":22,"supported":14,"title":500,"abstract":501,"tags":502},[],[],[29],[504,505],"Raspberry Pi 温室监测","番茄 株高 图像识别","RaspberryPi温室监测-4569",4,"10.46242\u002Fjstiuh.v83i05.6189",{"doi":508,"openalex_id":510,"authors":511,"venue":451,"cited_by_count":91,"oa_url":523,"card":524,"direction":96,"ingested_from":98},"W7221046739",[512,514,516,518,520],{"name":513,"orcid":9},"HOANG THI TU UYEN",{"name":515,"orcid":9},"NGUYEN HOANH",{"name":517,"orcid":9},"NGO THANH QUYEN",{"name":519,"orcid":9},"Lam Binh Minh",{"name":521,"orcid":522},"Nguyễn Trung Nhân","https:\u002F\u002Forcid.org\u002F0000-0001-9328-7693","https:\u002F\u002Fjst.iuh.edu.vn\u002Findex.php\u002Fjst-iuh\u002Farticle\u002Fdownload\u002F6189\u002F1131",{"tldr":525,"method":526,"finding":527,"direction":96,"opportunity":528},"集成物联网与计算机视觉的智能温室系统，实现非破坏性植物生长监测与远程管控。","环境传感器、STM32、树莓派4、USB摄像头、轻量茎分割模型、Firebase","原型可实时采集温湿度、土壤水分和光照，并从RGB图像估计株高，验证了边缘表型监测可行性。","可扩展至茎宽、叶面积、冠层覆盖度等更丰富农艺指标，并实现自动化环境控制闭环。","2026-10-10T23:30:11.420750Z"]