[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5554":3,"related-5554":90},{"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":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":89},5554,"Smart Agriculture using IoT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531185","The Smart Agriculture Using IoT system uses different sensors installed in the agricultural field to collect data about soil and environmental conditions. The collected data is sent to a microcontroller, such as an ESP32 or Arduino with a suitable communication module. When the soil moisture level becomes lower than the required level, the system can automatically switch on the water pump. When sufficient moisture is detected, the pump switches off. Farmers can also monitor field conditions through a mobile application or web dashboard. This system reduces manual work and helps farmers use water and other resources efficiently.","基于物联网的智慧农业系统利用安装在农田中的各类传感器采集土壤与环境状况数据。采集到的数据被传送至微控制器，如ESP32或配备合适通信模块的Arduino。当土壤湿度低于所需水平时，系统可自动开启水泵；当检测到湿度充足时，水泵则关闭。农民还可通过移动应用程序或网页仪表盘监测田间状况。该系统减少了人工劳作，帮助农民高效利用水资源及其他资源。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",10,false,49,{"impact":17,"substance":18,"depth":13,"authority":19,"freshness":13,"relevant":20,"comment":21},8,12,9,1,"论文介绍基于ESP32\u002FArduino的土壤湿度自动灌溉与远程监测方案，属常规技术实现，方法新颖性有限，但主题贴合智慧农业且时效性强，可作为一般资讯收录。",[23],{"name":10,"url":6},[25,26,27,28],"智慧农业","农业物联网","智能灌溉","土壤墒情",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":45},[42,50,56,64],{"code":43,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},"I1",15,true,[47],"smart agriculture",[25,47],[25],{"code":30,"score":44,"supported":45,"title":51,"abstract":53,"tags":55},[52],"IoT",[54,52],"物联网",[26],{"code":57,"score":58,"supported":45,"title":59,"abstract":60,"tags":63},"H2",7,[],[61,62],"土壤","soil",[28],{"code":65,"score":66,"supported":45,"title":67,"abstract":68,"tags":70},"H3",4,[],[69],"水资源",[27],[72,73],"ESP32 土壤湿度 自动灌溉","农业物联网 移动端监测","ESP32土壤湿度自动灌溉-5554",0,"10.5281\u002Fzenodo.23531185",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":75,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7223137846",[80],{"name":81,"orcid":9},"Prachi Khairnar",{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"基于物联网传感器实现农田土壤湿度自动监测与灌溉控制。","部署土壤与环境传感器，经ESP32\u002FArduino采集数据并联动水泵。","系统可自动启停水泵并通过手机或网页端远程监控，节水省工。","智慧农业 \u002F 农业物联网","可引入多源传感融合与预测性灌溉决策，弥补仅靠阈值控制的局限。","openalex","2026-10-22T11:20:18.299106Z",{"total":91,"page":20,"page_size":91,"items":92},6,[93,190,269,337,392,455],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":107,"tags":109,"primary_topic":112,"secondary_topics":117,"classification":119,"search_phrases":161,"slug":164,"view_count":75,"doi":165,"paper":166,"created_at":189},5077,"A Trust-Boundary Reference Architecture for Secure Cyber-Physical Agriculture: Design and Software-Based Proof-of-Concept for Smart Irrigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206545","Smart irrigation is an operational technology (OT) problem as much as an Internet of Things (IoT) one: software decisions act directly on valves, pumps, pressure and water delivery, so a spoofed reading or a replayed command has physical consequences. This paper presents a technology-neutral reference architecture that makes those consequences controllable. It is organised into seven functional layers and three cross-cutting planes, six trust-boundary classes that fix which properties must be re-established at each crossing, and an auditable crown-asset inventory with an explicit trust-level rubric. Two governing execution principles enforce this structure: local safety authority, which keeps final actuation permission at the field gateway, remote terminal unit (RTU) or programmable logic controller (PLC), and feedback-aware execution, which treats a command as complete only when independent physical feedback confirms it. Building on process-aware and safety-supervision ideas from industrial control, the architecture makes physical confirmation a mandatory completion condition at the local enforcement point, which catches a fully authorised command that is nonetheless physically unsafe, a failure mode that authorisation checks alone cannot detect. These properties are examined through a deterministic, openly available software proof-of-concept that verifies the design against its own acceptance criteria; it is not a field validation. The software executes six attack and failure scenarios plus one nominal-operation control against an intentionally unsecured negative control and a secure controller, under identical, controlled conditions: the negative control is compromised in all 6 attack scenarios and the secure controller in 0\u002F6, with 0\u002F1 false positives on legitimate operation. Analytically, the design costs 21 bytes and 50–60% additional Long Range Wide Area Network (LoRaWAN) time-on-air per record, and 78% of single-step perturbations of the trust-scoring inputs leave asset classification unchanged: the actuator class (A2) is completely stable, and the gateway\u002FRTU\u002FPLC class (A3) is stable under 6 of 7 admissible perturbations (86%), changing only when its credential\u002Fupdate authority reaches the trust-anchor threshold. A sensor-level mapping from sampling rate, measurement drift and physical fault modes to the edge-verification controls they dictate, provisions for secure time, counter recovery and the choice between fail-closed and fail-open safe states, an illustrative hypothetical worked deployment example, and a transfer of the model to greenhouse, fertigation, livestock-watering and cold-chain settings show how the architecture could be applied in practice.","智能灌溉既是一个物联网(IoT)问题，也是一个运营技术(OT)问题：软件决策直接作用于阀门、水泵、压力和水流输送，因此被欺骗的读数或被重放的命令会产生物理后果。本文提出了一种技术中立的参考架构，使这些后果可控。该架构由七个功能层和三个横切面、六类信任边界(规定每次跨越边界时必须重新建立哪些属性)，以及一份可审计的核心资产清单(配有明确的信任等级评定标准)组成。两条主导性执行原则保障了这一结构：本地安全授权，即将最终执行许可保留在田间网关、远程终端单元(RTU)或可编程逻辑控制器(PLC)处；以及反馈感知执行，即只有当独立的物理反馈予以确认时，才将命令视为完成。该架构以工业控制中的过程感知和安全监督思想为基础，将物理确认设为本地执行点的强制性完成条件，从而能够捕获完全获得授权但在物理上仍不安全的命令——这是仅靠授权检查无法检测到的失效模式。这些特性通过一个确定性、公开可用的软件概念验证进行了检验，该验证依据其自身的验收标准对设计进行评估；这不是现场验证。该软件在相同且受控的条件下，针对一个故意不设防护的阴性对照和一个安全控制器，执行了六种攻击与故障场景以及一项标称运行对照：阴性对照在所有6种攻击场景中均被攻破，而安全控制器为0\u002F6，对合法运行的误报为0\u002F1。分析表明，该设计每条记录额外增加21字节和50–60%的LoRaWAN(LoRaWAN)空中时间，且信任评分输入的78%单步扰动不会改变资产分类：执行器类(A2)完全稳定，网关\u002FRTU\u002FPLC类(A3)在7种可允许扰动中有6种保持稳定(86%)，仅当其凭证\u002F更新权限达到信任锚阈值时才会改变。还提出了从采样率、测量漂移和物理故障模式到其所决定的边缘验证控制的传感器级映射，以及安全时间、计数器恢复和故障关闭与故障打开安全状态之间选择的保障措施。","Sensors","2026-10-15T00:00:00Z",75,{"impact":18,"substance":103,"depth":104,"authority":105,"freshness":19,"relevant":20,"comment":106},22,18,14,"面向智慧灌溉的信任边界参考架构论文，方法新颖、含软件验证与量化开销数据，但属概念验证、非田间实证，产业影响有限，可作为技术前沿精选。",[108],{"name":99,"url":96},[110,25,26,27,111],"数字乡村","网络安全",{"code":65,"slug":113,"name":114,"path":115,"parent_code":116},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[118],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":120},{"candidates":121,"agricultural_evidence":45},[122,127,132,139,145,151,157],{"code":65,"score":44,"supported":45,"title":123,"abstract":124,"tags":126},[113],[125,113],"灌溉",[27],{"code":30,"score":13,"supported":45,"title":128,"abstract":129,"tags":131},[],[54,130,52],"internet of things",[26],{"code":133,"score":134,"supported":45,"title":135,"abstract":136,"tags":138},"B7",3,[],[137],"greenhouse",[],{"code":140,"score":134,"supported":45,"title":141,"abstract":142,"tags":144},"C",[],[143],"livestock",[],{"code":146,"score":134,"supported":45,"title":147,"abstract":148,"tags":150},"F3",[],[149],"fertigation",[],{"code":152,"score":153,"supported":14,"title":154,"abstract":155,"tags":156},"I6",2,[],[],[110,111],{"code":43,"score":20,"supported":14,"title":158,"abstract":159,"tags":160},[],[],[25],[162,163],"智能灌溉 信任边界 参考架构","LoRaWAN 灌溉 安全","智能灌溉信任边界参考架构-5077","10.3390\u002Fs26206545",{"doi":165,"openalex_id":167,"authors":168,"venue":99,"cited_by_count":75,"oa_url":96,"card":184,"direction":86,"ingested_from":88},"W7222165876",[169,172,175,178,181],{"name":170,"orcid":171},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":173,"orcid":174},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":176,"orcid":177},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":179,"orcid":180},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":182,"orcid":183},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":185,"method":186,"finding":187,"direction":86,"opportunity":188},"提出面向智慧灌溉的安全参考架构，用软件验证可抵御重放与欺骗攻击。","七层三面参考架构、信任边界分类、确定性软件概念验证与LoRaWAN开销分析。","安全控制器在6种攻击场景下0次被攻破，负控制全部被攻破，且误报率为0。","可研究边缘验证控制与采样率、漂移、故障模式的映射，以及安全时间与失效安全状态选择。","2026-10-16T23:30:08.098924Z",{"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":101,"score_detail":198,"sources":202,"tags":204,"primary_topic":207,"secondary_topics":208,"classification":215,"search_phrases":250,"slug":253,"view_count":66,"doi":9,"paper":254,"created_at":268},4076,"Automated System for Plant Health Monitoring and Stress Diagnosis Using IoT and Machine Learning","https:\u002F\u002Fwajes.isroset.org\u002Findex.php\u002Fj\u002Farticle\u002Fview\u002F208","Irrigation decisions on smallholder farms are still taken subjectively, and both root-zone dehydration and over-saturation measurably depress yield, making low-cost automated plant-stress diagnosis an urgent requirement in water-scarce agricultural regions. This study designs, implements and validates a decentralized system that closes the control loop between field sensing and irrigation actuation by combining Internet of Things (IoT) hardware with a cloud-hosted predictive Machine Learning (ML) model. The edge node is built on an ESP32 system-on-chip integrated with a calibrated capacitive soil-moisture probe and a DHT22 temperature and humidity array. Serialized telemetry is transmitted over a non-blocking, encrypted wireless channel to a Flask web service, where a 150-tree Random Forest classifier trained on 5,000 labelled multi-sensor records assigns the plant to one of three physiological states Optimal, Temperature Stress or Dehydration Stress and returns an actuator control bit that drives a micro-irrigation pump. An engineered Evapotranspiration Proxy Index couples ambient temperature and relative humidity into a single transpiration-sensitive feature. Capacitive calibration was validated against the gravimetric standard on ten soil cores, yielding a Pearson correlation coefficient of 0.948. Over a continuous forty-eight-hour open-air evaluation the classifier achieved an overall accuracy of 96.8%, with 98.2% precision and 97.9% recall on the critical Dehydration Stress class, and a mean end-to-end round-trip latency of 1.84 seconds. Deep-sleep duty cycling reduced the mean node current to 0.65 mA, corresponding to an estimated 159 days of unattended operation on a single 2,500 mAh cell. The results demonstrate that a low-cost edge–cloud architecture can deliver dependable, timely and energy-sustainable irrigation control for micro-precision agriculture without server-grade hardware.","小农户的灌溉决策仍依赖主观判断，而根区脱水和过度饱和均会显著降低产量，这使得低成本自动化植物胁迫诊断成为缺水农业地区的迫切需求。本研究设计、实现并验证了一种去中心化系统，通过将物联网（IoT）硬件与云端预测机器学习（ML）模型相结合，闭合了田间传感与灌溉执行之间的控制回路。边缘节点基于ESP32片上系统构建，集成了经过校准的电容式土壤水分探头和DHT22温湿度传感器阵列。序列化遥测数据通过非阻塞加密无线信道传输至Flask Web服务，在此处，一个基于5,000条带标签多传感器记录训练的150棵树的随机森林分类器将植物判定为三种生理状态之一——最优、温度胁迫或脱水胁迫——并返回驱动微灌泵的执行器控制位。所设计的蒸散代理指数将环境温度和相对湿度耦合为单一蒸腾敏感特征。电容校准在十个土芯上与重量法标准进行验证，得到皮尔逊相关系数为0.948。在连续四十八小时的户外评估中，分类器总体准确率达到96.8%，在关键的脱水胁迫类别上精确率为98.2%、召回率为97.9%，平均端到端往返延迟为1.84秒。深度睡眠占空比控制将节点平均电流降至0.65 mA，相当于在单节2,500 mAh电池上估计可无人值守运行159天。结果表明，低成本边缘—云架构无需服务器级硬件即可为微精准农业提供可靠、及时且能源可持续的灌溉控制。","World Academics Journal of Engineering Sciences","2026-09-30T00:00:00Z",{"impact":199,"substance":103,"depth":104,"authority":200,"freshness":91,"relevant":20,"comment":201},16,13,"低成本边缘-云架构实现植物胁迫自动诊断与灌溉闭环，数据扎实、结论可靠，对智慧农业节水灌溉有参考价值。",[203],{"name":196,"url":193},[25,205,26,206,28],"农业人工智能","精准灌溉",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[209,214],{"code":210,"slug":211,"name":212,"path":213,"parent_code":34},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":65,"slug":113,"name":114,"path":115,"parent_code":116},{"status":37,"locked":14,"version":38,"method":39,"evidence":216},{"candidates":217,"agricultural_evidence":45},[218,222,228,233,237,243],{"code":30,"score":104,"supported":45,"title":219,"abstract":220,"tags":221},[52],[54,130,52],[26],{"code":210,"score":44,"supported":45,"title":223,"abstract":225,"tags":227},[224],"machine learning",[226,224],"机器学习",[205],{"code":65,"score":13,"supported":45,"title":229,"abstract":230,"tags":232},[],[125,113,231],"evapotranspiration",[206],{"code":57,"score":58,"supported":45,"title":234,"abstract":235,"tags":236},[],[61,62],[28],{"code":43,"score":58,"supported":45,"title":238,"abstract":239,"tags":242},[],[240,241],"精准农业","precision agriculture",[25],{"code":244,"score":91,"supported":45,"title":245,"abstract":246,"tags":249},"G2",[],[247,248],"小农户","smallholder",[],[251,252],"ESP32 土壤湿度 灌溉 机器学习","随机森林 植物胁迫 诊断","ESP32土壤湿度灌溉机器学习-4076",{"doi":9,"openalex_id":255,"authors":256,"venue":196,"cited_by_count":75,"oa_url":193,"card":263,"direction":86,"ingested_from":88},"W7217836036",[257,259,261],{"name":258,"orcid":9},"Christian Athanas Mwakyusa",{"name":260,"orcid":9},"Allen Deogratius Mwalekwa",{"name":262,"orcid":9},"Gift Geofrey Mushi",{"tldr":264,"method":265,"finding":266,"direction":86,"opportunity":267},"设计并验证了基于ESP32与随机森林的物联网闭环灌溉系统，实现植物胁迫自动诊断。","ESP32、电容式土壤湿度传感器、DHT22、随机森林分类器、Flask云服务。","48小时测试分类准确率96.8%，脱水胁迫召回率97.9%，节点续航约159天。","可探索多作物泛化、极端环境鲁棒性及边缘端轻量化模型部署以降低云依赖。","2026-10-03T23:30:18.857006Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":9,"source_name":275,"source_url":272,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":280,"tags":282,"primary_topic":284,"secondary_topics":285,"classification":290,"search_phrases":316,"slug":319,"view_count":320,"doi":321,"paper":322,"created_at":336},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":17,"substance":199,"depth":105,"authority":18,"freshness":17,"relevant":20,"comment":279},"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。",[281],{"name":275,"url":272},[25,54,27,28,283],"节水农业",{"code":65,"slug":113,"name":114,"path":115,"parent_code":116},[286,287],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"code":57,"slug":62,"name":288,"path":289,"parent_code":116},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":37,"locked":14,"version":38,"method":39,"evidence":291},{"candidates":292,"agricultural_evidence":45},[293,298,302,306,312],{"code":65,"score":294,"supported":45,"title":295,"abstract":296,"tags":297},19,[113],[125,69,113],[27,283],{"code":30,"score":104,"supported":45,"title":299,"abstract":300,"tags":301},[130],[54,130,52],[54],{"code":57,"score":44,"supported":45,"title":303,"abstract":304,"tags":305},[62],[61,62],[28],{"code":307,"score":134,"supported":45,"title":308,"abstract":309,"tags":311},"H8",[],[310],"可持续农业",[],{"code":43,"score":20,"supported":14,"title":313,"abstract":314,"tags":315},[],[],[25],[317,318],"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",5,"10.12928\u002Ftelkomnika.v24i5.27889",{"doi":321,"openalex_id":323,"authors":324,"venue":275,"cited_by_count":75,"oa_url":272,"card":331,"direction":86,"ingested_from":88},"W7213907872",[325,327,329],{"name":326,"orcid":9},"Zakarie Abdi Mohamud",{"name":328,"orcid":9},"Rozeha Binti A. Rashid",{"name":330,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":332,"method":333,"finding":334,"direction":86,"opportunity":335},"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":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":9,"source_name":343,"source_url":340,"published_at":344,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":351,"secondary_topics":352,"classification":354,"search_phrases":374,"slug":377,"view_count":153,"doi":378,"paper":379,"created_at":391},2767,"Prototype Sistem Kendali Penyiraman Tanaman Otomatis Berbasis IoT dengan Mode Manual dan Otomatis Menggunakan ESP32 dan Platform Blynk","https:\u002F\u002Fdoi.org\u002F10.61132\u002Fjupiter.v4i5.1609","The increasing scarcity of water resources and the need for optimal soil moisture levels for plant growth have driven the development of smart irrigation systems based on the Internet of Things (IoT). This study presents a prototype of an automatic plant watering control system using an ESP32 microcontroller equipped with a capacitive soil moisture sensor, relay module, and I2C LCD interface. The system is designed with two operating modes: an automatic mode that independently activates the water pump based on soil moisture thresholds, and a manual mode that allows users to control the pump directly through the Blynk application on a smartphone. Sensor data is transmitted to the Blynk platform in real-time via a WiFi connection using virtual pin communication protocols. Test results show that the system is capable of reading soil moisture values with good accuracy, responding to changes in soil conditions in less than 2 seconds, and simultaneously displaying moisture status and pump conditions on both the LCD and the Blynk application. This system is expected to serve as a solution for efficient water use in urban and household agriculture, supporting the concept of sustainable and energy-efficient farming.","水资源日益稀缺以及植物生长对最佳土壤湿度的需求，推动了基于物联网（IoT）的智能灌溉系统的发展。本研究提出了一种自动植物浇水控制系统的原型，该系统采用ESP32微控制器，并配备电容式土壤湿度传感器、继电器模块和I2C液晶显示接口。系统设计了两种运行模式：自动模式根据土壤湿度阈值独立启动水泵，手动模式允许用户通过智能手机上的Blynk应用程序直接控制水泵。传感器数据通过WiFi连接，利用虚拟引脚通信协议实时传输至Blynk平台。测试结果表明，该系统能够以良好的精度读取土壤湿度值，在2秒内响应土壤条件变化，并同时在液晶显示屏和Blynk应用程序上显示湿度状态和水泵运行情况。该系统有望为城市和家庭农业中的高效用水提供解决方案，支持可持续和节能农业的理念。","Jupiter Publikasi Ilmu Keteknikan Industri Teknik Elektro dan Informatika","2026-09-17T00:00:00Z",50,{"impact":17,"substance":105,"depth":200,"authority":320,"freshness":13,"relevant":20,"comment":347},"基于ESP32与Blynk的自动灌溉原型系统，方法常规、规模有限，属细分技术验证，公共价值与权威性一般，不宜进入每日精选。",[349],{"name":343,"url":340},[25,54,27,28,283],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[353],{"code":65,"slug":113,"name":114,"path":115,"parent_code":116},{"status":37,"locked":14,"version":38,"method":39,"evidence":355},{"candidates":356,"agricultural_evidence":45},[357,361,366,370],{"code":30,"score":104,"supported":45,"title":358,"abstract":359,"tags":360},[52],[54,130,52],[54],{"code":65,"score":362,"supported":45,"title":363,"abstract":364,"tags":365},11,[],[125,69,113],[27,283],{"code":57,"score":58,"supported":45,"title":367,"abstract":368,"tags":369},[],[61,62],[28],{"code":43,"score":20,"supported":14,"title":371,"abstract":372,"tags":373},[],[],[25],[375,376],"土壤墒情 智慧农业 智能灌溉 节水农业","土壤墒情 智慧农业","土壤墒情智慧农业智能灌溉节水农业-2767","10.61132\u002Fjupiter.v4i5.1609",{"doi":378,"openalex_id":380,"authors":381,"venue":343,"cited_by_count":75,"oa_url":340,"card":386,"direction":86,"ingested_from":88},"W7213462460",[382,384],{"name":383,"orcid":9},"Rifki Aldiansyah",{"name":385,"orcid":9},"Dani Sasmoko",{"tldr":387,"method":388,"finding":389,"direction":86,"opportunity":390},"基于ESP32和Blynk开发了带手动\u002F自动模式的自动浇水原型系统。","ESP32、电容式土壤湿度传感器、继电器、I2C LCD与Blynk物联网平台。","系统能准确读取土壤湿度，响应时间小于2秒，并实时同步显示状态。","可扩展多传感器融合与自适应阈值算法，提升灌溉决策的精准性和节能性。","2026-09-17T23:30:10.351496Z",{"id":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":9,"source_name":398,"source_url":395,"published_at":399,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":400,"score_detail":401,"sources":404,"tags":406,"primary_topic":407,"secondary_topics":408,"classification":410,"search_phrases":439,"slug":442,"view_count":153,"doi":443,"paper":444,"created_at":454},2652,"ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: TRANSFORMING TEACHING, LEARNING, AND STUDENT ENGAGEMENT","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fijhlt\u002F1\u002F2\u002F001","Efficient irrigation management is essential for sustainable agriculture, particularly in the context of increasing freshwater scarcity and the growing imperative to optimize crop productivity. Conventional irrigation practices rely predominantly on fixed time schedules or manual field assessments, which frequently induce over-irrigation, root-zone nutrient leaching, under-irrigation water stress, and substantial resource inefficiency. This paper proposes an Intelligent Irrigation Management System that integrates Internet of Things (IoT) sensing architectures, multi-parameter environmental telemetry, and supervised machine learning (ML) algorithms to facilitate dynamic, data-driven, and automated irrigation control. The proposed system continuously acquires real-time field data—including soil moisture, ambient temperature, relative humidity, soil temperature, and rainfall—via deployed sensor nodes managed by an ESP32 microcontroller pipeline. The telemetry stream is transmitted through low-power communication channels to a centralized processing engine, where a Random Forest classification model evaluates multidimensional soil-environmental interactions to predict immediate irrigation requirements. The predicted states feed into an automated actuation layer that directly modulates a solenoid-valve and water-pump relay, forming a closed-loop feedback pipeline. Evaluated against traditional threshold-based and schedule-driven approaches, the proposed IoT-ML framework demonstrates superior operational responsiveness, minimizes unnecessary water application, and offers a robust, scalable architectural template for modern precision agriculture.","高效灌溉管理对可持续农业至关重要，尤其是在淡水日益稀缺、优化作物生产力需求不断增长的背景下。传统灌溉实践主要依赖固定时间表或人工田间评估，这常常导致过度灌溉、根区养分淋失、灌溉不足引起的水分胁迫以及严重的资源低效。本文提出了一种智能灌溉管理系统，该系统集成了物联网（IoT）感知架构、多参数环境遥测以及监督式机器学习（ML）算法，以实现动态、数据驱动和自动化的灌溉控制。所提出的系统通过由ESP32微控制器管道管理的部署传感器节点，持续采集实时田间数据——包括土壤湿度、环境温度、相对湿度、土壤温度和降雨量。遥测数据流通过低功耗通信信道传输至集中处理引擎，其中随机森林分类模型评估多维土壤-环境相互作用，以预测即时灌溉需求。预测状态输入自动执行层，直接调节电磁阀和水泵继电器，形成闭环反馈管道。与传统基于阈值和时间表驱动的方法相比，所提出的IoT-ML框架展现出更优的运行响应能力，最大限度地减少了不必要的灌溉用水，并为现代精准农业提供了一种稳健、可扩展的架构模板。","INTERNATIONAL JOURNAL OF HUMANITIES AND LEARNING TECHNOLOGY INNOVATION (IJHLT)","2026-09-15T00:00:00Z",72,{"impact":199,"substance":104,"depth":402,"authority":200,"freshness":17,"relevant":20,"comment":403},17,"论文提出IoT与随机森林融合的闭环智能灌溉系统，方法完整、数据驱动，对节水农业有参考价值，但标题与摘要主题不符需核实。",[405],{"name":398,"url":395},[25,26,226,27,240],{"code":210,"slug":211,"name":212,"path":213,"parent_code":34},[409],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":411},{"candidates":412,"agricultural_evidence":45},[413,418,422,426,430,434],{"code":210,"score":44,"supported":45,"title":414,"abstract":416,"tags":417},[415],"artificial intelligence",[226,224],[226],{"code":30,"score":13,"supported":45,"title":419,"abstract":420,"tags":421},[],[54,130,52],[26],{"code":43,"score":17,"supported":45,"title":423,"abstract":424,"tags":425},[],[240,241],[25,240],{"code":65,"score":58,"supported":45,"title":427,"abstract":428,"tags":429},[],[125,113],[27],{"code":57,"score":91,"supported":45,"title":431,"abstract":432,"tags":433},[],[61,62],[],{"code":307,"score":91,"supported":45,"title":435,"abstract":436,"tags":438},[],[310,437],"sustainable agriculture",[],[440,441],"农业物联网 智慧农业 智能灌溉 机器学习","农业物联网 智慧农业","农业物联网智慧农业智能灌溉机器学习-2652","10.65725\u002Fijhlt\u002F1\u002F2\u002F001",{"doi":443,"openalex_id":445,"authors":446,"venue":398,"cited_by_count":75,"oa_url":9,"card":449,"direction":86,"ingested_from":88},"W7213277724",[447],{"name":448,"orcid":9},"M. Rathamani",{"tldr":450,"method":451,"finding":452,"direction":86,"opportunity":453},"提出融合物联网传感与随机森林的智能灌溉系统，实现数据驱动的自动灌溉控制。","ESP32传感器节点采集土壤温湿度等数据，随机森林分类预测灌溉需求。","相比传统定时或阈值方法，该系统响应更优、减少不必要灌溉，可扩展性强。","可探索多模态数据融合与边缘智能，提升灌溉决策的实时性与泛化能力。","2026-09-16T23:30:16.194086Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":9,"content":9,"source_name":460,"source_url":458,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":467,"tags":469,"primary_topic":471,"secondary_topics":472,"classification":474,"search_phrases":501,"slug":502,"view_count":134,"doi":503,"paper":504,"created_at":518},1258,"Integration of IoT and Sensor Technologies for Sustainable Smart Irrigation Systems: Trends, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.31272\u002Fajece.35","Efficient water management is a persistent challenge in modern agriculture, especially in arid and semi-arid regions. The development and adoption of advanced soil sensor technologies are essential for optimising water use and supporting sustainable, high-yield agricultural systems. This systematic review compares primary soil sensor methodologies and synthesises findings from 156 peer-reviewed studies published between 2015 and 2025. The review assesses dielectric techniques, including time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR), as well as capacitive sensors, tensiometers, neutron probes, and instruments for measuring salinity and pH. Meta-analytical results show that TDR systems achieve high accuracy (pooled RMSE: 0.016 m³\u002Fm³) but are associated with significant costs ($500–$1200 per unit). In contrast, low-cost capacitive sensors can provide acceptable accuracy (RMSE: 0.045 m³\u002Fm³) when rigorously calibrated. LoRaWAN (Long Range Wide Area Network) is identified as the most effective communication protocol for agricultural Internet of Things (IoT) applications, with transmission ranges of 2–15 km and battery lifespans of 2–10 years. The use of machine learning methods improves irrigation scheduling accuracy by 15–30%, and artificial intelligence (AI) systems can achieve water savings of 35–50%. Key research gaps include the absence of standardised calibration procedures, as 67% of studies lack comprehensive validation, limited long-term stability assessments, with only 23% reporting data beyond six months, and insufficient investigation of multi-sensor data fusion. The review concludes by identifying future research priorities to support the development of robust and scalable innovative irrigation systems for sustainable agriculture.","Academic Journal of Electrical and Computer Engineering","2026-08-30T00:00:00Z",81,{"impact":103,"substance":464,"depth":465,"authority":200,"freshness":153,"relevant":20,"comment":466},24,20,"系统综述整合156项研究，对比多种土壤传感器与通信协议，数据详实，对智慧灌溉技术发展有重要参考价值。",[468],{"name":460,"url":458},[25,26,226,27,470],"传感器",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[473],{"code":65,"slug":113,"name":114,"path":115,"parent_code":116},{"status":37,"locked":14,"version":38,"method":39,"evidence":475},{"candidates":476,"agricultural_evidence":45},[477,481,485,489,493,497],{"code":30,"score":199,"supported":45,"title":478,"abstract":479,"tags":480},[52],[130,52],[470,26],{"code":65,"score":18,"supported":45,"title":482,"abstract":483,"tags":484},[113],[113],[27],{"code":210,"score":58,"supported":45,"title":486,"abstract":487,"tags":488},[],[224,415],[226],{"code":57,"score":134,"supported":45,"title":490,"abstract":491,"tags":492},[],[62],[],{"code":307,"score":134,"supported":45,"title":494,"abstract":495,"tags":496},[],[437],[],{"code":43,"score":20,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[25],[440,441],"农业物联网智慧农业智能灌溉机器学习-1258","10.31272\u002Fajece.35",{"doi":503,"openalex_id":505,"authors":506,"venue":460,"cited_by_count":75,"oa_url":458,"card":513,"direction":86,"ingested_from":88},"W7204712150",[507,509,511],{"name":508,"orcid":9},"Omar Talib Khazraji",{"name":510,"orcid":9},"Marwan J. Hussein",{"name":512,"orcid":9},"Ahmed M. Almawla",{"tldr":514,"method":515,"finding":516,"direction":86,"opportunity":517},"系统综述2015-2025年土壤传感器与IoT在智能灌溉中的应用，评估技术、成本与性能，指出研究空白","系统综述156篇研究，比较TDR、FDR、电容等传感器，结合LoRaWAN和机器","TDR精度高但贵，电容传感器性价比好；LoRaWAN最优；AI节水35-50%；缺乏标准化校准和长期","研究空白：多传感器数据融合、标准化校准协议、长期稳定性评估，可开发低成本高精度融合系统。","2026-09-01T04:03:11.465487Z"]