[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2298":3,"related-2298":83},{"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":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":82},2298,"A systematic review of data handling and management techniques in edge computing for IoT","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09533-w","The proliferation of Internet of Things (IoT) devices and latency-sensitive applications has increased the need for edge computing, which complements existing cloud systems by enabling data processing closer to the source. By processing data closer to the source, edge computing reduces latency, bandwidth consumption, and response time. Organisations face major challenges in data management because edge environments use decentralised systems and limited resources, which require them to manage multiple data management tasks, including storage resources, data caching, data aggregation and data integrity verification. The resolution of these problems is necessary to create Edge-IoT systems that can operate at a large scale while maintaining trustworthy and secure operations. This paper presents a comprehensive and systematic review of recent advancements that occurred between 2022 and 2025 in data handling optimisation techniques for edge computing in IoT applications. The study employs a structured review methodology that meets Scopus and Web of Science standards to evaluate research studies that are divided into four main categories, which include data storage optimisation, data caching strategies, data aggregation mechanisms and data integrity assurance techniques. A total of 52 primary technical studies were selected for detailed comparative analysis, while additional secondary literature sources were used for theoretical background and contextual discussion. The study conducts a comprehensive comparative analysis, which assesses performance through latency and energy consumption, system scalability, security overhead and system performance under varying workloads. The review evaluates application-specific use cases, which include smart cities, healthcare, industrial IoT, autonomous vehicles and smart agriculture, to demonstrate the real-world significance of present solutions. The review identifies gaps in adaptive optimisation, heterogeneous device support, privacy preservation, and cross-layer coordination as critical research problems. The document describes existing challenges and future research paths that will help create advanced data processing systems for edge-enabled Internet of Things networks.","物联网（IoT）设备的激增以及对延迟敏感的应用需求推动了边缘计算的发展，边缘计算通过在更接近数据源的位置进行数据处理，对现有云系统形成补充。通过在更接近数据源的位置处理数据，边缘计算降低了延迟、带宽消耗和响应时间。由于边缘环境采用去中心化系统且资源有限，组织在数据管理方面面临重大挑战，需要管理多项数据管理任务，包括存储资源、数据缓存、数据聚合和数据完整性验证。解决这些问题对于构建能够大规模运行且保持可信和安全操作的边缘物联网系统至关重要。本文对2022年至2025年间物联网应用中边缘计算数据处理优化技术的最新进展进行了全面系统的综述。本研究采用符合Scopus和Web of Science标准的结构化综述方法，将研究文献分为四大类进行评估，包括数据存储优化、数据缓存策略、数据聚合机制和数据完整性保障技术。共选取52篇主要技术研究进行详细比较分析，同时使用其他二手文献来源提供理论背景和情境讨论。本研究进行了全面的比较分析，从延迟和能耗、系统可扩展性、安全开销以及不同工作负载下的系统性能等方面评估性能。综述评估了特定应用场景，包括智慧城市、医疗健康、工业物联网、自动驾驶汽车和智慧农业，以展示当前解决方案的实际意义。综述指出了自适应优化、异构设备支持、隐私保护和跨层协调方面的不足，将其列为关键研究问题。本文描述了现有挑战和未来研究方向，有助于为边缘赋能的物联网网络构建先进的数据处理系统。",null,"Discover Applied Sciences","2026-09-11T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"系统综述边缘计算数据管理技术，含智慧农业等应用场景，对农业物联网部署有参考价值，但非农业专属研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","边缘计算","物联网","农业传感器","数据管理",{"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":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,54],{"code":33,"score":46,"supported":47,"title":48,"abstract":51,"tags":53},28,true,[49,50],"IoT","edge computing",[29,28,52,49,50],"internet of things",[30,29,28],{"code":55,"score":56,"supported":47,"title":57,"abstract":58,"tags":60},"I1",7,[],[27,59],"smart agriculture",[27],[62,63],"农业传感器 数据管理 智慧农业 边缘计算","农业传感器 数据管理","农业传感器数据管理智慧农业边缘计算-2298",2,"10.1007\u002Fs42452-026-09533-w",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":74,"oa_url":6,"card":75,"direction":79,"ingested_from":81},"W7212114265",[70,72],{"name":71,"orcid":9},"Sourabh Natu",{"name":73,"orcid":9},"Ankur Goyal",0,{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"系统综述2022-2025年IoT边缘计算数据处理与管理优化技术，涵盖存储、缓存、聚合与完整性四类。","遵循Scopus\u002FWoS标准的结构化综述，比较分析52篇主要技术研究。","识别出自适应优化、异构设备支持、隐私保护与跨层协调四大关键研究空白。","智慧农业 \u002F 农业物联网","可将边缘数据管理优化方法迁移至农业IoT场景，填补农业边缘计算中自适应与跨层协调的研究空白。","openalex","2026-09-13T23:30:09.503310Z",{"total":84,"page":22,"page_size":84,"items":85},6,[86,169,227,310,400,462],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":9,"source_name":92,"source_url":89,"published_at":93,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":99,"tags":101,"primary_topic":104,"secondary_topics":105,"classification":111,"search_phrases":148,"slug":151,"view_count":22,"doi":152,"paper":153,"created_at":168},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。","International Journal for Research in Applied Science and Engineering Technology","2026-10-07T00:00:00Z",55,{"impact":21,"substance":96,"depth":97,"authority":84,"freshness":21,"relevant":22,"comment":98},18,15,"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[100],{"name":92,"url":89},[27,102,28,29,103],"农业人工智能","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[106],{"code":107,"slug":108,"name":109,"path":110,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":112},{"candidates":113,"agricultural_evidence":47},[114,119,128,135,141],{"code":33,"score":115,"supported":47,"title":116,"abstract":117,"tags":118},11,[],[29,52,49],[29,28],{"code":107,"score":13,"supported":47,"title":120,"abstract":121,"tags":127},[],[122,123,124,125,126],"深度学习","计算机视觉","deep learning","computer vision","decision support",[102],{"code":129,"score":56,"supported":47,"title":130,"abstract":131,"tags":134},"H3",[],[132,133],"灌溉","irrigation",[103],{"code":55,"score":56,"supported":47,"title":136,"abstract":137,"tags":140},[],[138,139],"精准农业","precision agriculture",[27],{"code":142,"score":84,"supported":47,"title":143,"abstract":144,"tags":147},"H2",[],[145,146],"土壤","soil",[],[149,150],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":152,"openalex_id":154,"authors":155,"venue":92,"cited_by_count":74,"oa_url":89,"card":163,"direction":79,"ingested_from":81},"W7220855887",[156,159,161],{"name":157,"orcid":158},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":160,"orcid":9},"Shetty Chirag",{"name":162,"orcid":9},"Sunay Kanade",{"tldr":164,"method":165,"finding":166,"direction":79,"opportunity":167},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","2026-10-08T23:30:09.663393Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":172,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":182,"tags":184,"primary_topic":187,"secondary_topics":191,"classification":193,"search_phrases":210,"slug":213,"view_count":74,"doi":214,"paper":215,"created_at":226},4295,"Smart Soil Monitoring Systems: Challenges and Strategic Solutions for Enhanced Adoption","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no12.2025.pg245.258","Smart Soil Monitoring Systems (SSMS) represent a key innovation in precision agriculture, enabling continuous monitoring of soil parameters such as moisture, pH, nutrient content, temperature, and salinity. These systems integrate Internet of Things (IoT) technologies, wireless sensor networks, and data analytics to provide real-time insights that support data-driven decision-making, optimize resource use, and enhance crop productivity. Despite their potential, the adoption of SSMS in developing institutions, including Federal Polytechnic Bali (FPB), remains limited due to financial constraints, inadequate technical skills, low awareness, and infrastructural challenges such as unreliable internet connectivity and power supply. The high cost of devices, lack of training, and insufficient institutional policies further impede practical implementation in teaching, research, and extension activities. This study employed a descriptive survey design targeting academic and technical staff in FPB’s agricultural departments. Data collected via a structured questionnaire were analyzed using descriptive statistics, with mean scores indicating key challenges and potential strategies. Findings revealed that high device costs, poor network infrastructure, and low technical capacity were the most critical barriers. To address these, respondents emphasized capacity-building workshops, improved digital infrastructure, awareness campaigns, funding support, and policy frameworks as strategic interventions. The study concludes that enhancing SSMS adoption requires integrated solutions addressing economic, technical, and institutional barriers. Implementation of these strategies is expected to strengthen hands-on learning, improve research outputs, and promote sustainable, technology-driven agriculture at FPB. The study contributes to understanding practical adoption challenges of smart agricultural technologies in resource-constrained educational settings.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）是精准农业的一项关键创新，能够对土壤湿度、pH值、养分含量、温度和盐度等参数进行持续监测。这些系统集成了物联网（IoT）技术、无线传感器网络和数据分析，可提供实时洞察，支持数据驱动的决策、优化资源利用并提高作物生产力。尽管潜力巨大，但包括巴厘联邦理工学院（Federal Polytechnic Bali, FPB）在内的发展中机构对SSMS的采用仍然有限，原因在于资金约束、技术技能不足、认知度低以及互联网连接和电力供应不可靠等基础设施挑战。设备成本高昂、缺乏培训以及制度政策不完善，进一步阻碍了其在教学、研究和推广活动中的实际应用。本研究采用描述性调查设计，以FPB农业部门的学术和技术人员为研究对象。通过结构化问卷收集的数据采用描述性统计进行分析，以平均分表明主要挑战和潜在策略。研究发现，设备成本高、网络基础设施差和技术能力低是最关键的障碍。为应对这些问题，受访者强调能力建设研讨会、改善数字基础设施、宣传活动、资金支持和政策框架是战略性干预措施。研究得出结论，提升SSMS的采用需要综合解决方案，以应对经济、技术和制度层面的障碍。这些策略的实施有望加强实践学习、提高研究产出，并推动FPB可持续的、技术驱动的农业发展。本研究有助于理解资源受限教育环境中智能农业技术的实际采用挑战。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-10-05T00:00:00Z",52,{"impact":21,"substance":179,"depth":180,"authority":84,"freshness":21,"relevant":22,"comment":181},16,14,"聚焦资源受限院校智慧土壤监测落地障碍的实证调查，结论实用但样本局限于单一院校，影响力有限。",[183],{"name":175,"url":172},[185,27,29,186,30],"数字农业","土壤监测",{"code":142,"slug":146,"name":188,"path":189,"parent_code":190},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[192],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":194},{"candidates":195,"agricultural_evidence":47},[196,200,206],{"code":142,"score":180,"supported":47,"title":197,"abstract":198,"tags":199},[146],[145,146],[],{"code":33,"score":115,"supported":47,"title":201,"abstract":202,"tags":205},[],[29,203,52,49,204],"无线传感","wireless sensor",[30,29],{"code":55,"score":21,"supported":47,"title":207,"abstract":208,"tags":209},[],[138,139],[185,27],[211,212],"农业传感器 土壤监测 数字农业 智慧农业","农业传感器 土壤监测","农业传感器土壤监测数字农业智慧农业-4295","10.56201\u002Fwjimt.v9.no12.2025.pg245.258",{"doi":214,"openalex_id":216,"authors":217,"venue":175,"cited_by_count":74,"oa_url":220,"card":221,"direction":79,"ingested_from":81},"W7220426775",[218],{"name":219,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 12 2025\u002FSmart Soil Monitoring Systems 245-258.pdf",{"tldr":222,"method":223,"finding":224,"direction":79,"opportunity":225},"调查尼日利亚某院校智能土壤监测系统采纳障碍并提出策略。","描述性问卷调研农业院系教职工，用描述统计与均值分析。","设备成本高、网络差、技术能力低是主要障碍，需培训与政策支持。","可研究资源受限院校中低成本物联网土壤监测方案与采纳机制。","2026-10-06T23:30:07.776821Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":9,"content":9,"source_name":232,"source_url":230,"published_at":233,"category":12,"cover_url":9,"hotness":94,"is_selected":14,"score":234,"score_detail":235,"sources":239,"tags":249,"primary_topic":251,"secondary_topics":252,"classification":257,"search_phrases":288,"slug":291,"view_count":292,"doi":293,"paper":294,"created_at":309},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",74,{"impact":96,"substance":236,"depth":96,"authority":20,"freshness":237,"relevant":22,"comment":238},22,3,"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[240,241,244,246],{"name":232,"url":230},{"name":242,"url":243},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":242,"url":245},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":247,"url":248},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[27,102,28,29,250],"智能灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[253,256],{"code":129,"slug":133,"name":254,"path":255,"parent_code":190},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":107,"slug":108,"name":109,"path":110,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":258},{"candidates":259,"agricultural_evidence":47},[260,265,269,274,280,284],{"code":33,"score":261,"supported":47,"title":262,"abstract":263,"tags":264},27,[49,50],[52,49,50],[29,28],{"code":129,"score":17,"supported":47,"title":266,"abstract":267,"tags":268},[133],[133],[250],{"code":107,"score":17,"supported":47,"title":270,"abstract":272,"tags":273},[271],"machine learning",[271],[102],{"code":275,"score":237,"supported":47,"title":276,"abstract":277,"tags":279},"A8",[],[278],"food security",[],{"code":142,"score":237,"supported":47,"title":281,"abstract":282,"tags":283},[],[146],[],{"code":55,"score":22,"supported":14,"title":285,"abstract":286,"tags":287},[],[],[27],[289,290],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业智能灌溉边缘计算-1249",4,"10.3389\u002Ffsufs.2026.1827416",{"doi":293,"openalex_id":295,"authors":296,"venue":232,"cited_by_count":74,"oa_url":303,"card":304,"direction":79,"ingested_from":81},"W7204189894",[297,300],{"name":298,"orcid":299},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":301,"orcid":302},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":305,"method":306,"finding":307,"direction":79,"opportunity":308},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":9,"source_name":316,"source_url":313,"published_at":317,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":318,"score_detail":319,"sources":322,"tags":324,"primary_topic":326,"secondary_topics":327,"classification":330,"search_phrases":375,"slug":378,"view_count":74,"doi":379,"paper":380,"created_at":399},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)","2026-10-21T00:00:00Z",80,{"impact":96,"substance":236,"depth":96,"authority":20,"freshness":320,"relevant":22,"comment":321},9,"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。",[323],{"name":316,"url":313},[27,102,29,138,325],"气候韧性",{"code":107,"slug":108,"name":109,"path":110,"parent_code":37},[328,329],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":129,"slug":133,"name":254,"path":255,"parent_code":190},{"status":40,"locked":14,"version":41,"method":42,"evidence":331},{"candidates":332,"agricultural_evidence":47},[333,339,343,347,352,359,363,369],{"code":107,"score":96,"supported":47,"title":334,"abstract":336,"tags":338},[335],"artificial intelligence",[122,337,123,271,124,125],"机器学习",[102],{"code":55,"score":179,"supported":47,"title":340,"abstract":341,"tags":342},[139],[138,139],[27,138],{"code":33,"score":13,"supported":47,"title":344,"abstract":345,"tags":346},[],[29,28,49,50],[29],{"code":129,"score":320,"supported":47,"title":348,"abstract":349,"tags":351},[],[132,350,133],"水资源",[],{"code":353,"score":56,"supported":47,"title":354,"abstract":355,"tags":358},"H7",[],[356,357],"气候变化","climate change",[325],{"code":142,"score":84,"supported":47,"title":360,"abstract":361,"tags":362},[],[145,146],[],{"code":364,"score":237,"supported":47,"title":365,"abstract":366,"tags":368},"A7",[],[367],"间作",[],{"code":370,"score":237,"supported":47,"title":371,"abstract":372,"tags":374},"F3",[],[373],"养分管理",[],[376,377],"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":379,"openalex_id":381,"authors":382,"venue":316,"cited_by_count":74,"oa_url":313,"card":393,"direction":79,"ingested_from":81},"W7223121456",[383,386,388,390],{"name":384,"orcid":385},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":387,"orcid":9},"Oluwadare OYEBODE",{"name":389,"orcid":9},"Iretiayo Adelaiye",{"name":391,"orcid":392},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":394,"method":395,"finding":396,"direction":397,"opportunity":398},"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。","农业人工智能与决策模型","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":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":416,"secondary_topics":417,"classification":419,"search_phrases":438,"slug":441,"view_count":74,"doi":442,"paper":443,"created_at":461},5378,"IoT-Enabled Smart Irrigation Using ESP32 and LoRa for Precision Water Management in Connectivity-Limited Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204831","In this paper, a new method for irrigation systems in agriculture has been reported. This work aims to design and implement a smart irrigation system based on the ESP32 platform and Internet of Things (IoT) technologies for optimizing water usage management. With the rapid growth of Internet of Things (IoT) technologies, where devices are connected and can be controlled through the internet, it is now feasible to design smart irrigation systems based on real-time environmental data to deliver optimal water use with minimal human intervention. In Sahara areas, where communication networks and internet connections are lacking, alternative communication protocols are necessary for data transfer. LoRa technology based on the SX1278 transceiver provides long-range, low-power communication that can achieve multi-kilometer ranges (up to 10–15 km in rural areas) under ideal conditions: line-of-sight, high antenna height, low data rate, and open rural environments between distributed sensor nodes and a central gateway. However, this theoretical range cannot be assumed in practice. Field tests under standard farm deployment conditions (ground-level placement and vegetation interference) showed that the link was lost at approximately 120 m, which validates the system at the scale of a field plot. This project used Arduino (version 2.3.7) software to program the NodeMCU (ESP32) board to link the board with the Blynk cloud application. The project is used to monitor the temperature, humidity, and soil moisture and do the command for the irrigation system, with master and slave cards using LoRa technology as a communication tool to replace communication systems like WiFi or cellular networks in deserted or remote areas, which might not offer sufficient coverage or could be too expensive to build. To address this challenge, this paper presents the design and implementation of a smart irrigation system based on the ESP32 microcontroller and LoRa (SX1278) technology, with the long-term objective of helping in the modernization of traditional agriculture and environmental sustainability. Extending the coverage to larger fields and Sahara areas would require elevated antennas, optimized LoRa parameters (spreading factor and bandwidth), multi-hop relay nodes, or multiple gateways; these strategies are proposed as future work.","本文报道了一种农业灌溉系统的新方法。本工作旨在设计和实现一种基于ESP32平台和物联网（Internet of Things, IoT）技术的智能灌溉系统，以优化用水管理。随着物联网技术的快速发展，设备得以互联并可通过互联网进行控制，如今基于实时环境数据设计智能灌溉系统，以在最少人工干预下实现最优用水已成为可能。在撒哈拉地区，由于缺乏通信网络和互联网连接，数据传输需要采用替代通信协议。基于SX1278收发器的LoRa技术可提供远距离、低功耗通信，在理想条件下（视距传输、天线高度较高、低数据速率以及分布式传感器节点与中心网关之间开阔的农村环境），其通信距离可达数公里（农村地区可达10–15 km）。然而，这一理论距离在实际中并不能假定成立。在标准农田部署条件下（地面放置和植被干扰）进行的现场测试表明，链路在约120 m处即发生中断，这验证了系统在田块尺度上的可行性。本项目使用Arduino（版本2.3.7）软件对NodeMCU（ESP32）开发板进行编程，将开发板与Blynk云应用相连接。该项目用于监测温度、湿度和土壤水分，并向灌溉系统发送指令，主从卡采用LoRa技术作为通信工具，以替代WiFi或蜂窝网络等通信系统——这些系统在荒漠或偏远地区可能覆盖不足，或建设成本过高。为应对这一挑战，本文提出了基于ESP32微控制器和LoRa（SX1278）技术的智能灌溉系统的设计与实现，其长期目标是助力传统农业的现代化和环境可持续性。要将覆盖范围扩展至更大田块和撒哈拉地区，需要架高天线、优化LoRa参数（扩频因子和带宽）、采用多跳中继节点或部署多个网关；这些策略被提出作为未来工作。","Electronics","2026-10-19T00:00:00Z",67,{"impact":17,"substance":96,"depth":179,"authority":20,"freshness":21,"relevant":22,"comment":410},"面向无网络覆盖地区的ESP32+LoRa智能灌溉系统，含实地链路测试数据，方法有参考价值但属细分技术进展，未达每日精选门槛。",[412],{"name":406,"url":403},[27,29,138,414,415],"节水灌溉","LoRa",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[418],{"code":129,"slug":133,"name":254,"path":255,"parent_code":190},{"status":40,"locked":14,"version":41,"method":42,"evidence":420},{"candidates":421,"agricultural_evidence":47},[422,426,430,434],{"code":33,"score":96,"supported":47,"title":423,"abstract":424,"tags":425},[49],[29,52,49],[29],{"code":129,"score":97,"supported":47,"title":427,"abstract":428,"tags":429},[133],[132,133],[414],{"code":142,"score":84,"supported":47,"title":431,"abstract":432,"tags":433},[],[145,146],[],{"code":55,"score":65,"supported":14,"title":435,"abstract":436,"tags":437},[],[],[27,138],[439,440],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":442,"openalex_id":444,"authors":445,"venue":406,"cited_by_count":74,"oa_url":403,"card":456,"direction":79,"ingested_from":81},"W7222959710",[446,448,450,453],{"name":447,"orcid":9},"Hanfoug Salah",{"name":449,"orcid":9},"Riad Saidi",{"name":451,"orcid":452},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":454,"orcid":455},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":457,"method":458,"finding":459,"direction":79,"opportunity":460},"基于ESP32与LoRa构建智能灌溉系统，在无网络覆盖区实现精准用水管理。","ESP32采集温湿度和土壤水分，经LoRa(SX1278)传输至网关并接入Bly","实际农田条件下LoRa链路仅约120米即中断，远低于理论公里级。","可研究多跳中继、天线高度与扩频参数优化，提升偏远农田LoRa组网覆盖与可靠性。","2026-10-20T23:30:08.464791Z",{"id":463,"title":464,"url":465,"summary":466,"summary_zh":467,"content":9,"source_name":468,"source_url":465,"published_at":469,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":470,"sources":472,"tags":474,"primary_topic":478,"secondary_topics":479,"classification":480,"search_phrases":497,"slug":500,"view_count":74,"doi":501,"paper":502,"created_at":516},5309,"A Multi-Layer Contextual Model for Managing Data Overload in Smart Hydroponics","https:\u002F\u002Fdoi.org\u002F10.32890\u002Fjict2026.25.4.4","The widespread use of the Internet of Things (IoT) in hydroponic agriculture allows continuous monitoring of environmental conditions and provides real-time information to support crop management. However, the increasing amount of sensor data generated makes the monitoring process more complex as users need to interpret multiple environmental parameters simultaneously. Existing hydroponic monitoring systems focus on data collection and visualization, while the management and information delivery aspects to reduce data overload are still limited. This study proposes a multi-layer contextual model to manage environmental information in an intelligent hydroponic monitoring system. The model consists of five interconnected layers (Power, Sensor, Communication, Database and Visualization), which support the process of acquisition, validation, management and contextual data delivery. To validate the proposed model, a solar-powered IoT hydroponic monitoring system integrated with an Augmented Reality (AR) application was developed based on Situation Awareness Theory. Using the System Usability Scale (SUS), the prototype was assessed by three domain experts. The results achieved an average usability score of 85.83, which reflects excellent usability. The results indicate that the proposed model improves organisation and presentation of environmental information by prioritising contextually relevant data rather than displaying all available sensor readings at once. This enables users to identify important environmental conditions efficiently and supports more effective monitoring. This approach helps to reduce data overload and provides a practical framework that can support the development of IoT-based smart agriculture applications in the future through the integration of anomaly detection techniques and predictive analytics.","物联网（IoT）在水培农业中的广泛使用使得环境条件的持续监测成为可能，并提供实时信息以支持作物管理。然而，传感器数据量的不断增加使监测过程更加复杂，因为用户需要同时解读多个环境参数。现有的水培监测系统侧重于数据采集与可视化，而在减少数据过载的管理与信息传递方面仍然有限。本研究提出一种多层上下文模型，用于管理智能水培监测系统中的环境信息。该模型由五个相互关联的层级（供电、传感器、通信、数据库和可视化）组成，支持数据的采集、验证、管理和上下文传递过程。为验证所提出的模型，基于态势感知理论开发了一套集成了增强现实（AR）应用的太阳能物联网水培监测系统。采用系统可用性量表（SUS）由三位领域专家对原型进行评估，结果获得平均可用性得分85.83，反映出优异的可用性。结果表明，所提出的模型通过优先呈现上下文相关的数据而非一次性显示所有可用的传感器读数，改善了环境信息的组织与呈现方式。这使用户能够高效识别重要的环境条件，并支持更有效的监测。该方法有助于减少数据过载，并提供了一个实用框架，未来可通过集成异常检测技术和预测分析来支持基于物联网的智慧农业应用的开发。","Journal of Information and Communication Technology","2026-10-18T00:00:00Z",{"impact":17,"substance":18,"depth":179,"authority":20,"freshness":320,"relevant":22,"comment":471},"提出五层情境化模型，并用太阳能物联网与AR原型验证，SUS 85.83，方法新颖、结论可靠，但属单点原型研究，产业影响有限。",[473],{"name":468,"url":465},[27,29,475,476,477],"水培","增强现实","数据过载",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":481},{"candidates":482,"agricultural_evidence":47},[483,487,491],{"code":33,"score":13,"supported":47,"title":484,"abstract":485,"tags":486},[],[29,52,49],[29],{"code":55,"score":56,"supported":47,"title":488,"abstract":489,"tags":490},[],[27,59],[27],{"code":492,"score":292,"supported":47,"title":493,"abstract":494,"tags":496},"B7",[],[495],"hydroponic",[475],[498,499],"智能水培 物联网 监测系统","增强现实 水培 数据过载","智能水培物联网监测系统-5309","10.32890\u002Fjict2026.25.4.4",{"doi":501,"openalex_id":503,"authors":504,"venue":468,"cited_by_count":74,"oa_url":465,"card":511,"direction":79,"ingested_from":81},"W7222865525",[505,508],{"name":506,"orcid":507},"Ruziana Mohamad Rasli","https:\u002F\u002Forcid.org\u002F0009-0006-7187-513X",{"name":509,"orcid":510},"Sobihatun Nur Abdul Salam","https:\u002F\u002Forcid.org\u002F0000-0002-6751-9187",{"tldr":512,"method":513,"finding":514,"direction":79,"opportunity":515},"提出五层情境模型管理水培IoT环境数据，结合AR应用减少数据过载。","五层情境模型+太阳能IoT水培监测系统+AR应用，SUS量表由3位专家评估。","原型SUS均分85.83，情境化优先展示关键数据可有效缓解数据过载。","可延伸至异常检测与预测分析融合，并开展多用户大规模实证验证。","2026-10-19T23:30:07.917393Z"]