[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5389":3,"related-5389":106},{"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":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":105},5389,"A Wideband Flexible Waterproof Antenna for Internet of Underground Things: Design, Characterization, and Field Validation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204815","The Internet of Underground Things (IoUT) is seen as a new technology that could revolutionize soil management, improving irrigation efficiency, soil and plant health, and food safety using reliable and resilient wireless connectivity. Rapid deployment of IoUT in precision agriculture requires flexible, waterproof antennas specifically designed for operation in soil. This study introduces a methodology for the design and characterization of waterproof, flexible antennas that maintain robust performance in soil across varying gravimetric water contents (GWC). Applying this methodology, a wideband, waterproof, and flexible antenna was designed and fabricated. Its parameters and characteristics were evaluated numerically and experimentally in soils with different GWCs to determine its suitability for IoUT applications. Within the GWC range of 10% to 20%, which is optimal for supporting plant health and water use, the antenna demonstrates a wide bandwidth of 0.6 to 1.8 GHz (|S11| \u003C −10 dB) and radiation efficiency ranging from 3.56% to 0.15% at 868 MHz. Experimental results obtained in free space, dry sand, and garden soil agree reasonably well with simulation predictions, showing that the proposed antenna is suitable for IoUT applications. Field validation demonstrated that the antenna prototype maintained its impedance response after two months of in-soil operation, indicating stable performance over the evaluated period.","地下物联网（Internet of Underground Things, IoUT）被视为一项可能彻底改变土壤管理的新技术，通过可靠且具有韧性的无线连接提升灌溉效率、土壤与植物健康以及食品安全。在精准农业中快速部署IoUT需要专为土壤环境运行而设计的柔性防水天线。本研究提出了一种防水柔性天线的设计与表征方法，使天线在不同重量含水率（gravimetric water content, GWC）的土壤中保持稳健性能。应用该方法，设计并制作了一款宽带防水柔性天线。在不同GWC的土壤中对其参数与特性进行了数值仿真与实验评估，以确定其用于IoUT应用的适用性。在10%至20%的GWC范围内（该范围最有利于维持植物健康与水分利用），天线在868 MHz处展现出0.6至1.8 GHz的宽带宽（|S11| \u003C −10 dB）以及3.56%至0.15%的辐射效率。在自由空间、干沙和花园土壤中获得的实验结果与仿真预测吻合良好，表明所提出的天线适用于IoUT应用。现场验证表明，天线原型在土壤中运行两个月后仍保持其阻抗响应，表明在所评估时段内性能稳定。",null,"Electronics","2026-10-19T00:00:00Z","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,"面向地下物联网的柔性防水天线设计与田间验证，方法新颖、数据扎实，对精准农业无线感知有参考价值，但属细分技术进展，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","物联网","土壤水分","地下物联网","柔性天线",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,67,74],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},7,true,[],[50,34],"土壤",[29],{"code":53,"score":46,"supported":47,"title":54,"abstract":55,"tags":58},"I1",[],[56,57],"精准农业","precision agriculture",[27],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":66},"E4",6,[],[64,65],"食品安全","food safety",[],{"code":68,"score":61,"supported":47,"title":69,"abstract":70,"tags":73},"H3",[],[71,72],"灌溉","irrigation",[],{"code":75,"score":76,"supported":47,"title":77,"abstract":78,"tags":79},"I5",4,[],[28],[28],[81,82],"IoUT 柔性防水天线","地下物联网 土壤水分","IoUT柔性防水天线-5389",0,"10.3390\u002Felectronics15204815",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":84,"oa_url":6,"card":98,"direction":102,"ingested_from":104},"W7222978032",[89,92,95],{"name":90,"orcid":91},"Nikolay Todorov Atanasov","https:\u002F\u002Forcid.org\u002F0000-0002-7707-0133",{"name":93,"orcid":94},"Blagovest Nikolaev Atanasov","https:\u002F\u002Forcid.org\u002F0009-0001-0145-5619",{"name":96,"orcid":97},"Gabriela Lachezarova Atanasova","https:\u002F\u002Forcid.org\u002F0000-0002-7177-8317",{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"设计并验证了一款用于地下物联网的宽带柔性防水天线。","采用数值仿真与实验测试，在不同含水率土壤中评估天线性能。","在10%-20%含水率下带宽0.6-1.8GHz，埋土两个月阻抗响应稳定。","智慧农业 \u002F 农业物联网","可探索天线辐射效率随土壤含水率下降的补偿机制，或结合土壤传感节点做长期田间组网验证。","openalex","2026-10-20T23:30:13.823825Z",{"total":61,"page":22,"page_size":61,"items":107},[108,190,245,302,375,441],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":9,"source_name":114,"source_url":111,"published_at":115,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":122,"tags":124,"primary_topic":127,"secondary_topics":133,"classification":140,"search_phrases":163,"slug":166,"view_count":167,"doi":168,"paper":169,"created_at":189},4427,"Development of Internet of Things-Based Soil Sensors for pH and Phosphorus Monitoring in Paddy Fields: Development, Accuracy, and Implementation Challenges","https:\u002F\u002Fdoi.org\u002F10.56581\u002Fijrerd.11.05.01-08","The development of Internet of Things (IoT) technology provides opportunities for faster and continuous soil monitoring to support precision agriculture.Monitoring soil pH and phosphorus is important in rice cultivation because both parameters are associated with soil chemical conditions and nutrient availability.This review aimed to examine the development of IoT-based soil sensors for monitoring soil pH and phosphorus, including sensor performance, factors affecting measurement results, and potential applications.A literature review was conducted on research and review articles published between 2019 and 2026.The literature was descriptively analyzed and synthesized based on technological development, sensor performance, and environmental and soil characteristics.The results indicate that pH and phosphorus sensors have been developed using various sensing approaches and have demonstrated promising performance under certain conditions; however, measurement results vary across parameters and soil conditions.Sensor performance is influenced by the sensing principle, soil characteristics, environmental conditions, calibration, and validation.IoT-based sensors have the potential to support real-time soil monitoring, but their application as quantitative measurement tools still requires validation and calibration under diverse soil conditions.","物联网（IoT）技术的发展为实现更快、连续性的土壤监测以支持精准农业提供了机遇。监测土壤pH和磷在水稻种植中具有重要意义，因为这两个参数均与土壤化学状况和养分有效性相关。本综述旨在考察用于监测土壤pH和磷的基于物联网的土壤传感器的发展现状，包括传感器性能、影响测量结果的因素以及潜在应用。本文对2019年至2026年间发表的研究论文和综述文章进行了文献综述。基于技术发展、传感器性能以及环境和土壤特征，对文献进行了描述性分析和综合。结果表明，pH和磷传感器已采用多种传感方法进行开发，并在特定条件下展现出良好性能；然而，测量结果因参数和土壤条件的不同而存在差异。传感器性能受传感原理、土壤特性、环境条件、校准和验证的影响。基于物联网的传感器具有支持实时土壤监测的潜力，但其作为定量测量工具的应用仍需在不同土壤条件下进行验证和校准。","International journal of recent engineering research and development.","2026-10-06T00:00:00Z",62,{"impact":17,"substance":118,"depth":119,"authority":120,"freshness":21,"relevant":22,"comment":121},18,15,9,"综述性论文，系统梳理IoT土壤pH与磷传感器的发展、精度与落地挑战，对智慧农业土壤监测方向有参考价值，但非突破性成果。",[123],{"name":114,"url":111},[27,28,56,125,126],"土壤监测","水稻种植",{"code":128,"slug":129,"name":130,"path":131,"parent_code":132},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[134,139],{"code":75,"slug":135,"name":136,"path":137,"parent_code":138},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":141},{"candidates":142,"agricultural_evidence":47},[143,149,154,159],{"code":75,"score":118,"supported":47,"title":144,"abstract":146,"tags":148},[145],"internet of things",[28,145,147],"IoT",[28],{"code":128,"score":119,"supported":47,"title":150,"abstract":152,"tags":153},[151],"paddy",[130,129],[126],{"code":33,"score":155,"supported":47,"title":156,"abstract":157,"tags":158},14,[34],[50,34],[],{"code":53,"score":21,"supported":47,"title":160,"abstract":161,"tags":162},[],[56,57],[27,56],[164,165],"物联网 土壤传感器 稻田","土壤pH 监测","物联网土壤传感器稻田-4427",2,"10.56581\u002Fijrerd.11.05.01-08",{"doi":168,"openalex_id":170,"authors":171,"venue":114,"cited_by_count":84,"oa_url":111,"card":184,"direction":102,"ingested_from":104},"W7220841659",[172,174,177,179,182],{"name":173,"orcid":9},"Fariha Laqania",{"name":175,"orcid":176},"Oviyanti Mulyani","https:\u002F\u002Forcid.org\u002F0000-0003-3941-8896",{"name":178,"orcid":9},"Ade Setiawan",{"name":180,"orcid":181},"Pujawati Suryatmana","https:\u002F\u002Forcid.org\u002F0000-0002-4012-8645",{"name":183,"orcid":9},"Wahyu Daradjat",{"tldr":185,"method":186,"finding":187,"direction":102,"opportunity":188},"综述2019-2026年稻田pH与磷的物联网土壤传感器发展、精度及实施挑战。","文献综述，按技术发展、传感器性能与土壤环境特征归纳分析。","传感器在特定条件下表现良好，但精度受原理、土壤、环境与校准影响，定量应用仍需验证。","稻田复杂土壤条件下pH\u002F磷传感器的原位校准与多源数据融合验证是待突破的研究空白。","2026-10-08T23:30:13.316611Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":202,"tags":204,"primary_topic":207,"secondary_topics":208,"classification":210,"search_phrases":228,"slug":231,"view_count":84,"doi":232,"paper":233,"created_at":244},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":200,"depth":155,"authority":61,"freshness":21,"relevant":22,"comment":201},16,"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。",[203],{"name":196,"url":193},[205,27,28,125,206],"数字农业","农业传感器",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[209],{"code":75,"slug":135,"name":136,"path":137,"parent_code":138},{"status":40,"locked":14,"version":41,"method":42,"evidence":211},{"candidates":212,"agricultural_evidence":47},[213,217,224],{"code":33,"score":155,"supported":47,"title":214,"abstract":215,"tags":216},[34],[50,34],[],{"code":75,"score":218,"supported":47,"title":219,"abstract":220,"tags":223},11,[],[28,221,145,147,222],"无线传感","wireless sensor",[206,28],{"code":53,"score":21,"supported":47,"title":225,"abstract":226,"tags":227},[],[56,57],[205,27],[229,230],"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":232,"openalex_id":234,"authors":235,"venue":196,"cited_by_count":84,"oa_url":238,"card":239,"direction":102,"ingested_from":104},"W7220426775",[236],{"name":237,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 12 2025\u002FSmart Soil Monitoring Systems 245-258.pdf",{"tldr":240,"method":241,"finding":242,"direction":102,"opportunity":243},"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":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":251,"source_url":248,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":262,"classification":264,"search_phrases":287,"slug":290,"view_count":291,"doi":292,"paper":293,"created_at":301},3771,"IOT-BASED SOIL CONDITION ASSESSMENT IN SMART AGRICULTURE: A REVIEW OF INDICATORS, COMPOSITE INDICES AND DECISION-SUPPORT SYSTEMS","https:\u002F\u002Fdoi.org\u002F10.17683\u002Fijomam\u002Fissue25\u002Fv1.24","Soil condition assessment is increasingly supported by mechatronic sensing and Internet of Things (IoT) technologies that complement laboratory and field evaluation with continuous and distributed measurements.This review focuses on the complete information chain required for IoT-based soil assessment: indicator selection, sensing and communication, data-quality control, normalization, composite index construction and decision support.Physical, chemical and biological indicators are considered together, while their suitability for continuous monitoring is differentiated.Soil moisture, temperature and electrical conductivity are readily integrated into IoT platforms; pH and nutrient sensing require more rigorous calibration, whereas organic matter, structural and biological indicators often remain periodic laboratory or field inputs.A compact processing framework is proposed in which sensor records are validated, filtered and normalized before aggregation into physical, chemical and biological sub-indices and a conceptual Soil Condition Index (SCI).The SCI is then linked to transparent rule-based alerts and a modular end-toend architecture connecting sensing nodes, communication, database services and user interfaces.The proposed framework is intended as an interpretation and early-warning layer rather than an autonomous agronomic decision-making system.Its practical application requires local calibration, independent validation and explicit reporting of data completeness.An experimental photovoltaicpowered IoT prototype is also presented to illustrate the practical implementation of the multidepth sensing, acquisition and 4G communication layers.The review highlights persistent gaps in multi-parameter integration, sensor-data quality control, local calibration and the transformation of raw measurements into transparent decision-support outputs.","土壤状况评估正日益得到机电传感与物联网（IoT）技术的支持，这些技术通过连续和分布式测量补充了实验室与田间评价。本综述聚焦于基于物联网的土壤评估所需的完整信息链：指标选择、传感与通信、数据质量控制、归一化、综合指数构建及决策支持。物理、化学和生物指标被一并考虑，同时对其适用于连续监测的程度加以区分。土壤水分、温度和电导率易于集成到物联网平台中；pH和养分传感需要更严格的校准，而有机质、结构及生物指标通常仍依赖周期性的实验室或田间输入。本文提出一个紧凑的处理框架，其中传感器记录在聚合为物理、化学和生物子指数以及一个概念性土壤状况指数（SCI）之前，先经过验证、滤波和归一化。随后，SCI与透明的基于规则的警报以及一个连接传感节点、通信、数据库服务和用户界面的模块化端到端架构相链接。所提出的框架旨在作为解释与预警层，而非自主的农艺决策系统。其实际应用需要本地校准、独立验证以及数据完整性的明确报告。本文还展示了一个实验性光伏供电物联网原型，以说明多深度传感、采集和4G通信层的实际实现。本综述强调了在多参数集成、传感器数据质量控制、本地校准以及将原始测量转化为透明决策支持输出方面持续存在的差距。","International Journal of Mechatronics and Applied Mechanics","2026-09-28T00:00:00Z",69,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":255},"系统梳理物联网土壤评估的指标体系与决策支持框架，方法链条完整，但属综述性论文，实践影响有限。",[257],{"name":251,"url":248},[27,28,259,260,125],"决策支持","传感器",{"code":75,"slug":135,"name":136,"path":137,"parent_code":138},[263],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":265},{"candidates":266,"agricultural_evidence":47},[267,272,276,281],{"code":75,"score":268,"supported":47,"title":269,"abstract":270,"tags":271},19,[147],[28,145,147],[260,28],{"code":33,"score":155,"supported":47,"title":273,"abstract":274,"tags":275},[34],[50,34],[],{"code":53,"score":120,"supported":47,"title":277,"abstract":279,"tags":280},[278],"smart agriculture",[],[27],{"code":282,"score":76,"supported":47,"title":283,"abstract":284,"tags":286},"I2",[],[285],"decision support",[259],[288,289],"IoT 土壤评估 智慧农业","土壤状况指数 SCI","IoT土壤评估智慧农业-3771",5,"10.17683\u002Fijomam\u002Fissue25\u002Fv1.24",{"doi":292,"openalex_id":294,"authors":295,"venue":251,"cited_by_count":84,"oa_url":248,"card":296,"direction":102,"ingested_from":104},"W7214635996",[],{"tldr":297,"method":298,"finding":299,"direction":102,"opportunity":300},"综述物联网土壤评估全信息链，提出土壤状况指数与决策支持框架。","文献综述，整合物理化学生物指标、传感通信、数据质控与复合指数构建。","水分温度电导率易集成，pH养分需校准，有机质生物指标仍依赖实验室。","多参数融合、传感器数据质控、本地校准及透明决策输出仍是研究空白。","2026-09-29T23:30:09.167877Z",{"id":303,"title":304,"url":305,"summary":306,"summary_zh":307,"content":9,"source_name":308,"source_url":305,"published_at":309,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":310,"score_detail":311,"sources":314,"tags":316,"primary_topic":320,"secondary_topics":323,"classification":325,"search_phrases":352,"slug":355,"view_count":76,"doi":356,"paper":357,"created_at":374},3190,"Comparing Irrigation Identification Methods in Colorado: Limitations of Downscaled SMAP Soil Moisture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183223","With limited freshwater resources and growing water demands, it is imperative to identify and monitor water needs. Agricultural irrigation is the world’s largest water user, comprising 45–90% of freshwater withdrawals. Identifying and tracking changes in irrigated land is necessary for sustainable water management and forecasting agricultural water needs and patterns; however, the low resolution of available remote sensing observations hinders field-scale analysis. Downscaling soil moisture observations has been offered as a solution to this problem. Our study compares the performance of five irrigation identification methods using a newly developed downscaled deep soil moisture extrapolation method used to estimate Soil Moisture Active Passive (SMAP) soil moisture (SM) at a spatial resolution of 400 m for 5 cm, 20 cm, and 50 cm depths. Using this data along with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation and Landsat Normalized Difference Vegetation Index (NDVI), we evaluate these methods with respect to crop type, irrigation type, and observation depth on agricultural fields in Colorado using the irrigation maps provided by the Colorado Decision Support System from 2015 through 2024. With no crop\u002Firrigation type–observation depth combination exceeding an F1 score of 0.149 or MCC value of 0.163, we find that none of the methods can accurately identify irrigated land regardless of crop type, irrigation type, and observation depth. Because these methods succeeded in earlier small-area studies, and because the classified maps resolved into large, spatially uniform blocks, we interpret this as a limitation specific to field-scale detection over large, heterogeneous regions rather than a defect in the dataset. These findings highlight a limitation of this downscaled SM dataset and raise the question of whether other downscaled soil moisture products share this limitation.","在淡水资源有限且用水需求不断增长的背景下，识别和监测水资源需求势在必行。农业灌溉是全球最大的用水部门，占淡水取水量的45%–90%。识别和追踪灌溉土地的变化对于可持续水资源管理以及预测农业用水需求和模式至关重要；然而，现有遥感观测的低分辨率阻碍了田块尺度的分析。土壤湿度观测数据的降尺度被提出作为解决这一问题的方法。本研究比较了五种灌溉识别方法的性能，所用数据基于新开发的降尺度深层土壤湿度外推方法，用于估算土壤湿度主动被动（SMAP）卫星在400 m空间分辨率下5 cm、20 cm和50 cm深度的土壤湿度（SM）。利用该数据以及气候灾害组红外降水与站点数据（CHIRPS）降水和Landsat归一化植被指数（NDVI），我们结合科罗拉多决策支持系统提供的2015年至2024年灌溉地图，在科罗拉多州的农田上就作物类型、灌溉类型和观测深度对这些方法进行了评估。在没有任何作物\u002F灌溉类型–观测深度组合的F1分数超过0.149或MCC值超过0.163的情况下，我们发现无论作物类型、灌溉类型和观测深度如何，这些方法均无法准确识别灌溉土地。由于这些方法在早期小区域研究中取得了成功，且分类地图呈现为大的、空间均一的斑块，我们将此解释为大规模异质区域上田块尺度检测所特有的局限性，而非数据集本身的缺陷。这些发现凸显了该降尺度SM数据集的局限性，并提出其他降尺度土壤湿度产品是否也存在这一局限性的问题。","Remote Sensing","2026-09-19T00:00:00Z",75,{"impact":17,"substance":312,"depth":118,"authority":155,"freshness":120,"relevant":22,"comment":313},22,"该研究通过大区域对比实验揭示降尺度SMAP土壤水分在田块尺度灌溉识别上的局限，方法严谨、结论明确，对农业遥感与水资源管理有参考价值，但属细分领域学术进展，公共影响有限。",[315],{"name":308,"url":305},[27,317,318,29,319],"遥感","水资源管理","灌溉识别",{"code":68,"slug":72,"name":321,"path":322,"parent_code":37},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[324],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":326},{"candidates":327,"agricultural_evidence":47},[328,333,337,343,348],{"code":68,"score":118,"supported":47,"title":329,"abstract":330,"tags":332},[72],[71,331,72],"水资源",[318],{"code":33,"score":119,"supported":47,"title":334,"abstract":335,"tags":336},[34],[50,34],[29],{"code":338,"score":46,"supported":47,"title":339,"abstract":340,"tags":342},"I3",[],[317,341],"remote sensing",[317],{"code":282,"score":344,"supported":47,"title":345,"abstract":346,"tags":347},3,[],[285],[],{"code":53,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[27],[353,354],"SMAP 土壤水分 灌溉识别","科罗拉多 灌溉制图 遥感","SMAP土壤水分灌溉识别-3190","10.3390\u002Frs18183223",{"doi":356,"openalex_id":358,"authors":359,"venue":308,"cited_by_count":84,"oa_url":305,"card":368,"direction":372,"ingested_from":104},"W7213934204",[360,363,365],{"name":361,"orcid":362},"Annelise M. Turman","https:\u002F\u002Forcid.org\u002F0009-0000-2392-3631",{"name":364,"orcid":9},"Bin Fang",{"name":366,"orcid":367},"V. Vijaya Lakshmi","https:\u002F\u002Forcid.org\u002F0000-0001-9522-7897",{"tldr":369,"method":370,"finding":371,"direction":372,"opportunity":373},"比较五种灌溉识别方法，发现降尺度SMAP土壤湿度在科罗拉多田间尺度无法准确识别灌溉。","用400米降尺度SMAP土壤湿度、CHIRPS降水和Landsat NDVI，对","所有方法F1最高仅0.149，无法准确识别灌溉地，归因于大区域异质性而非数据缺陷。","农业遥感与作物表型","可检验其他降尺度土壤湿度产品是否同样受大区域异质性限制，并探索融合多源数据提升田间尺度灌溉识别。","2026-09-22T23:30:26.627420Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":380,"content":9,"source_name":381,"source_url":378,"published_at":382,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":386,"tags":388,"primary_topic":392,"secondary_topics":393,"classification":396,"search_phrases":421,"slug":424,"view_count":291,"doi":425,"paper":426,"created_at":440},3158,"Internet of things-based smart irrigation system using soil moisture and weather data","https:\u002F\u002Fdoi.org\u002F10.12928\u002Ftelkomnika.v24i5.27889","Water wastage in agriculture remains a significant challenge due to irrigation practices that often rely on fixed schedules rather than actual field conditions. This study presents an internet of things (IoT)-based smart irrigation system designed to improve water-use efficiency through real-time monitoring and automated irrigation control. The system integrates a capacitive soil moisture sensor with weather information obtained from an online application programming interface (API), while all data processing is performed locally on a Raspberry Pi edge device. A rule-based decision mechanism is used to classify soil conditions into dry, optimal, and wet categories and to determine appropriate irrigation actions based on soil moisture levels and rainfall forecasts. The system was implemented using low-cost and readily available components and tested under controlled conditions with soil moisture levels ranging from approximately 0% to above 85%. Experimental results showed consistent classification of critical dry, optimal, and critical wet conditions, enabling appropriate irrigation responses under different scenarios. In addition, email notifications were generated only during critical conditions, while no alerts were triggered under optimal moisture levels, demonstrating stable and reliable operation. The proposed system provides a practical and cost-effective solution for supporting efficient irrigation management and sustainable agricultural practices.","农业中的水资源浪费仍然是一项重大挑战，因为灌溉实践往往依赖固定时间表，而非实际田间条件。本研究提出了一种基于物联网（IoT）的智能灌溉系统，旨在通过实时监测和自动灌溉控制来提高用水效率。该系统将电容式土壤湿度传感器与从在线应用程序编程接口（API）获取的天气信息相结合，同时所有数据处理均在Raspberry Pi边缘设备上本地完成。系统采用基于规则的决策机制，将土壤状况分为干燥、适宜和湿润三类，并根据土壤湿度水平和降雨预报确定适当的灌溉措施。该系统使用低成本和易于获取的组件实现，并在受控条件下进行了测试，土壤湿度水平范围约为0%至85%以上。实验结果表明，系统能够一致地分类临界干燥、适宜和临界湿润状况，从而在不同情景下实现适当的灌溉响应。此外，电子邮件通知仅在临界状况下生成，而在适宜湿度水平下未触发任何警报，表明系统运行稳定可靠。所提出的系统为支持高效灌溉管理和可持续农业实践提供了一种实用且具有成本效益的解决方案。","TELKOMNIKA (Telecommunication Computing Electronics and Control)","2026-09-21T00:00:00Z",58,{"impact":21,"substance":200,"depth":155,"authority":17,"freshness":21,"relevant":22,"comment":385},"低成本物联网智能灌溉系统，方法清晰、结论可靠，对节水农业有实用参考价值，但属常规技术验证类论文，影响范围有限。",[387],{"name":381,"url":378},[27,28,389,390,391],"智能灌溉","土壤墒情","节水农业",{"code":68,"slug":72,"name":321,"path":322,"parent_code":37},[394,395],{"code":75,"slug":135,"name":136,"path":137,"parent_code":138},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":397},{"candidates":398,"agricultural_evidence":47},[399,403,407,411,417],{"code":68,"score":268,"supported":47,"title":400,"abstract":401,"tags":402},[72],[71,331,72],[389,391],{"code":75,"score":118,"supported":47,"title":404,"abstract":405,"tags":406},[145],[28,145,147],[28],{"code":33,"score":119,"supported":47,"title":408,"abstract":409,"tags":410},[34],[50,34],[390],{"code":412,"score":344,"supported":47,"title":413,"abstract":414,"tags":416},"H8",[],[415],"可持续农业",[],{"code":53,"score":22,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[27],[422,423],"IoT 智能灌溉 土壤湿度","Raspberry Pi 边缘计算 灌溉","IoT智能灌溉土壤湿度-3158","10.12928\u002Ftelkomnika.v24i5.27889",{"doi":425,"openalex_id":427,"authors":428,"venue":381,"cited_by_count":84,"oa_url":378,"card":435,"direction":102,"ingested_from":104},"W7213907872",[429,431,433],{"name":430,"orcid":9},"Zakarie Abdi Mohamud",{"name":432,"orcid":9},"Rozeha Binti A. Rashid",{"name":434,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":436,"method":437,"finding":438,"direction":102,"opportunity":439},"基于物联网与土壤湿度及天气数据，实现低成本自动灌溉决策系统。","电容式土壤湿度传感器、天气API、Raspberry Pi边缘计算与规则决策。","系统能准确分类干、适宜、湿状态，仅在临界条件触发灌溉与邮件通知。","可引入机器学习预测土壤湿度动态，优化规则阈值并扩展至多作物多区域验证。","2026-09-22T23:30:10.956765Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":9,"source_name":447,"source_url":444,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":310,"score_detail":449,"sources":451,"tags":453,"primary_topic":455,"secondary_topics":456,"classification":459,"search_phrases":483,"slug":486,"view_count":76,"doi":487,"paper":488,"created_at":504},2140,"AIOT in Predictive Agriculture - IoT and AI Integration for Real-Time Soil Monitoring and Smart Irrigation in Predictive Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84727","Modern agriculture faces severe challenges due to climate volatility, accelerating groundwater depletion, and the global imperative to maximize crop production on diminishing arable land. Traditional irrigation frameworks rely predominantly on static schedules or reactive threshold switching, leading to substantial water waste, energy inefficiencies, and suboptimal crop yields. To overcome these limitations, this paper proposes an end-to-end Artificial Intelligence of Things (AIoT) framework designed for real-time multi-parameter soil tracking and predictive smart irrigation. The system architecture deploys low-power IoT field nodes driven by ESP32 microcontrollers, integrated with capacitive soil moisture sensors, environmental sensors, and soil pH probes that stream telemetry data over lightweight MQTT protocols. To transition from reactive monitoring to proactive resource allocation, a cloud-based predictive engine utilizes Long Short-Term Memory (LSTM) neural networks to forecast 24- to-48-hour soil moisture depletion dynamics based on historical moisture profiles and localized meteorological factors. Experimental validation across a 90-day testbed demonstrates that the proposed predictive framework achieves a to reduction in total water consumption while maintaining optimal volumetric soil water content. Furthermore, deep-sleep dynamic power profiling confirms node energy autonomy of up to 219 days on a single battery charge, presenting a scalable, sustainable, and economically viable solution for precision agriculture.","现代农业正面临气候波动、地下水加速枯竭以及全球在日益减少的耕地上最大化作物产量的迫切需求等严峻挑战。传统灌溉框架主要依赖静态调度或反应式阈值切换，导致大量水资源浪费、能源效率低下以及作物产量欠优。为克服这些局限，本文提出了一种端到端的人工智能物联网（AIoT）框架，专为实时多参数土壤监测与预测性智能灌溉而设计。该系统架构部署了由ESP32微控制器驱动的低功耗物联网田间节点，集成了电容式土壤水分传感器、环境传感器和土壤pH探头，通过轻量级MQTT协议传输遥测数据。为实现从反应式监测向主动式资源分配的转变，基于云的预测引擎利用长短期记忆（LSTM）神经网络，根据历史水分剖面和局部气象因素，预测24至48小时的土壤水分消耗动态。在为期90天的测试平台上进行的实验验证表明，所提出的预测框架在保持最优土壤体积含水量的同时，实现了总用水量的降低。此外，深度睡眠动态功耗分析证实，节点在单次电池充电下可实现长达219天的能量自主运行，为精准农业提供了一种可扩展、可持续且经济可行的解决方案。","International Journal for Research in Applied Science and Engineering Technology","2026-09-10T00:00:00Z",{"impact":118,"substance":18,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":450},"AIoT+LSTM 预测灌溉的完整实证研究，90 天试验与节水、能耗数据扎实，对精准农业落地有参考价值，但期刊层级与影响范围偏细分领域。",[452],{"name":447,"url":444},[27,454,28,389,56,125],"农业人工智能",{"code":68,"slug":72,"name":321,"path":322,"parent_code":37},[457,458],{"code":75,"slug":135,"name":136,"path":137,"parent_code":138},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":460},{"candidates":461,"agricultural_evidence":47},[462,466,470,474,478],{"code":68,"score":118,"supported":47,"title":463,"abstract":464,"tags":465},[72],[71,331,72],[389],{"code":75,"score":119,"supported":47,"title":467,"abstract":468,"tags":469},[147],[28,147],[28],{"code":33,"score":155,"supported":47,"title":471,"abstract":472,"tags":473},[34],[50,34],[],{"code":53,"score":21,"supported":47,"title":475,"abstract":476,"tags":477},[],[56,57],[27,56],{"code":282,"score":76,"supported":47,"title":479,"abstract":480,"tags":482},[],[481],"artificial intelligence",[454],[484,485],"农业人工智能 土壤监测 智慧农业 智能灌溉","农业人工智能 土壤监测","农业人工智能土壤监测智慧农业智能灌溉-2140","10.22214\u002Fijraset.2026.84727",{"doi":487,"openalex_id":489,"authors":490,"venue":447,"cited_by_count":84,"oa_url":444,"card":499,"direction":102,"ingested_from":104},"W7212115545",[491,493,495,497],{"name":492,"orcid":9},"Gowri M.",{"name":494,"orcid":9},"Boomika M.",{"name":496,"orcid":9},"S. Rakshana",{"name":498,"orcid":9},"Rubali R.",{"tldr":500,"method":501,"finding":502,"direction":102,"opportunity":503},"提出AIoT框架，用LSTM预测土壤湿度实现智能灌溉，节水并延长节点续航。","ESP32节点+电容湿度\u002FpH传感器，MQTT上云，LSTM预测24-48小时湿","90天试验节水显著，土壤含水量保持最优，单次电池续航达219天。","可探索多作物多气候下LSTM泛化能力，及边缘端轻量预测模型降低云依赖。","2026-09-11T23:30:10.173013Z"]