[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3158":3,"related-3158":112},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":49,"search_phrases":88,"slug":91,"view_count":92,"doi":93,"paper":94,"created_at":111},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。",null,"TELKOMNIKA (Telecommunication Computing Electronics and Control)","2026-09-21T00:00:00Z","论文",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,16,14,12,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","物联网","智能灌溉","土壤墒情","节水农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[38,43],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,63,70,76,83],{"code":32,"score":56,"supported":57,"title":58,"abstract":59,"tags":62},19,true,[33],[60,61,33],"灌溉","水资源",[28,30],{"code":44,"score":64,"supported":57,"title":65,"abstract":67,"tags":69},18,[66],"internet of things",[27,66,68],"IoT",[27],{"code":39,"score":71,"supported":57,"title":72,"abstract":73,"tags":75},15,[40],[74,40],"土壤",[29],{"code":77,"score":78,"supported":57,"title":79,"abstract":80,"tags":82},"H8",3,[],[81],"可持续农业",[],{"code":84,"score":21,"supported":14,"title":85,"abstract":86,"tags":87},"I1",[],[],[26],[89,90],"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":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":103,"oa_url":6,"card":104,"direction":108,"ingested_from":110},"W7213907872",[97,99,101],{"name":98,"orcid":9},"Zakarie Abdi Mohamud",{"name":100,"orcid":9},"Rozeha Binti A. Rashid",{"name":102,"orcid":9},"Yazid Abubakar Sufyan",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-22T23:30:10.956765Z",{"total":113,"page":21,"page_size":113,"items":114},6,[115,187,245,308,361,423],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":128,"tags":130,"primary_topic":134,"secondary_topics":135,"classification":138,"search_phrases":165,"slug":168,"view_count":92,"doi":169,"paper":170,"created_at":186},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",75,{"impact":64,"substance":125,"depth":126,"authority":20,"freshness":17,"relevant":21,"comment":127},20,17,"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。",[129],{"name":121,"url":118},[26,131,27,28,132,133],"农业人工智能","精准农业","土壤监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[136,137],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":139},{"candidates":140,"agricultural_evidence":57},[141,145,149,153,158],{"code":32,"score":64,"supported":57,"title":142,"abstract":143,"tags":144},[33],[60,61,33],[28],{"code":44,"score":71,"supported":57,"title":146,"abstract":147,"tags":148},[68],[27,68],[27],{"code":39,"score":19,"supported":57,"title":150,"abstract":151,"tags":152},[40],[74,40],[],{"code":84,"score":17,"supported":57,"title":154,"abstract":155,"tags":157},[],[132,156],"precision agriculture",[26,132],{"code":159,"score":160,"supported":57,"title":161,"abstract":162,"tags":164},"I2",4,[],[163],"artificial intelligence",[131],[166,167],"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":169,"openalex_id":171,"authors":172,"venue":121,"cited_by_count":103,"oa_url":118,"card":181,"direction":108,"ingested_from":110},"W7212115545",[173,175,177,179],{"name":174,"orcid":9},"Gowri M.",{"name":176,"orcid":9},"Boomika M.",{"name":178,"orcid":9},"S. Rakshana",{"name":180,"orcid":9},"Rubali R.",{"tldr":182,"method":183,"finding":184,"direction":108,"opportunity":185},"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",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":199,"tags":201,"primary_topic":202,"secondary_topics":203,"classification":205,"search_phrases":226,"slug":229,"view_count":230,"doi":231,"paper":232,"created_at":244},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":19,"depth":197,"authority":92,"freshness":13,"relevant":21,"comment":198},13,"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。",[200],{"name":193,"url":190},[26,27,28,29,30],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[204],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":206},{"candidates":207,"agricultural_evidence":57},[208,212,217,222],{"code":44,"score":64,"supported":57,"title":209,"abstract":210,"tags":211},[68],[27,66,68],[27],{"code":32,"score":213,"supported":57,"title":214,"abstract":215,"tags":216},11,[],[60,61,33],[28,30],{"code":39,"score":218,"supported":57,"title":219,"abstract":220,"tags":221},7,[],[74,40],[29],{"code":84,"score":21,"supported":14,"title":223,"abstract":224,"tags":225},[],[],[26],[227,228],"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",2,"10.61132\u002Fjupiter.v4i5.1609",{"doi":231,"openalex_id":233,"authors":234,"venue":193,"cited_by_count":103,"oa_url":190,"card":239,"direction":108,"ingested_from":110},"W7213462460",[235,237],{"name":236,"orcid":9},"Rifki Aldiansyah",{"name":238,"orcid":9},"Dani Sasmoko",{"tldr":240,"method":241,"finding":242,"direction":108,"opportunity":243},"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":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":257,"tags":259,"primary_topic":261,"secondary_topics":262,"classification":264,"search_phrases":293,"slug":296,"view_count":21,"doi":9,"paper":297,"created_at":307},4578,"Automatic irrigation system-1","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fview\u002F4718","Agriculture is one of the major consumers of freshwater, and inefficient irrigation can result in significant water loss. Traditional irrigation methods often depend on manual observation and fixed watering schedules, which may lead to over-irrigation or under-irrigation. An automatic irrigation system provides a technological solution by monitoring soil moisture and automatically supplying water according to the requirements of the crop. This research paper presents the design and working principle of an automatic irrigation system based on soil-moisture sensing, a microcontroller, a relay, and a water pump. The proposed system continuously measures the moisture level of the soil. When the moisture falls below a predefined threshold, the controller activates the water pump. Once sufficient moisture is restored, the pump is automatically switched off. The system can additionally incorporate Internet of Things (IoT) technology, allowing farmers to monitor soil and environmental conditions remotely through a mobile or web interface. Previous research has demonstrated the potential of sensor-based irrigation automation to improve water-use efficiency. For example, a soil-moisture-based automated basin irrigation system reported irrigation application efficiency of up to 86.6%. I More recent systems combine soil moisture, temperature, humidity, weather information, IoT connectivity, and machine learning to improve irrigation scheduling. Wiley Online Library+1 The proposed system is therefore a low-cost and scalable approach toward precision agriculture, water conservation, reduced labor requirements, and sustainable agricultural production.","农业是淡水的主要消耗领域之一，低效灌溉会造成严重的水资源浪费。传统灌溉方式往往依赖人工观察和固定灌溉计划，可能导致过度灌溉或灌溉不足。自动灌溉系统通过监测土壤湿度并根据作物需求自动供水，提供了一种技术解决方案。本文介绍了一种基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统的设计和工作原理。该系统持续测量土壤湿度。当湿度低于预设阈值时，控制器启动水泵。当湿度恢复到充足水平后，水泵自动关闭。该系统还可集成物联网（IoT）技术，使农民能够通过移动端或网页界面远程监测土壤和环境状况。已有研究表明，基于传感器的灌溉自动化在提高用水效率方面具有潜力。例如，一项基于土壤湿度的自动化畦灌系统报告称，其灌溉施用效率可达86.6%。I 较新的系统则结合土壤湿度、温度、湿度、天气信息、物联网连接和机器学习来改进灌溉调度。Wiley Online Library+1 因此，该系统是一种低成本且可扩展的方法，有助于实现精准农业、节水、减少劳动力需求以及可持续农业生产。","International Journal of Engineering Research and Science & Technology","2026-10-07T00:00:00Z",34,{"impact":113,"substance":17,"depth":255,"authority":92,"freshness":113,"relevant":21,"comment":256},9,"论文仅泛述土壤湿度传感+单片机+水泵的自动灌溉原理，方法无新颖性、无实验数据，来源为普通工程期刊，公共价值有限，不建议进入每日精选。",[258],{"name":251,"url":248},[26,27,29,30,260],"自动灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[263],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":265},{"candidates":266,"agricultural_evidence":57},[267,271,275,279,283,289],{"code":32,"score":64,"supported":57,"title":268,"abstract":269,"tags":270},[33],[60,61,33],[30],{"code":44,"score":13,"supported":57,"title":272,"abstract":273,"tags":274},[],[27,66,68],[27],{"code":39,"score":218,"supported":57,"title":276,"abstract":277,"tags":278},[],[74,40],[29],{"code":84,"score":218,"supported":57,"title":280,"abstract":281,"tags":282},[],[132,156],[26],{"code":159,"score":113,"supported":57,"title":284,"abstract":285,"tags":288},[],[286,287],"机器学习","machine learning",[],{"code":77,"score":78,"supported":57,"title":290,"abstract":291,"tags":292},[],[81],[],[294,295],"自动灌溉系统 土壤湿度传感器","IoT 精准灌溉 节水","自动灌溉系统土壤湿度传感器-4578",{"doi":9,"openalex_id":298,"authors":299,"venue":251,"cited_by_count":103,"oa_url":248,"card":302,"direction":108,"ingested_from":110},"W7221294404",[300],{"name":301,"orcid":9},"Pooja Rajendra Sangole,Shravani Datta Dhomane ,Shreyas nivrutti thete ,amol Dinakar laware,Sumit sudan dalu,Kalpesh Gopal Agrawal,Piyush Sitaram Ghore",{"tldr":303,"method":304,"finding":305,"direction":108,"opportunity":306},"设计基于土壤湿度传感、微控制器和继电器的自动灌溉系统，按需供水。","土壤湿度传感器、微控制器、继电器、水泵，可扩展IoT远程监控。","基于传感器的自动灌溉可提升水分利用效率，灌溉效率可达86.6%。","可结合气象数据与机器学习优化灌溉阈值，并开展田间长期节水增产验证。","2026-10-10T23:30:15.205190Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":9,"source_name":314,"source_url":311,"published_at":315,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":316,"score_detail":317,"sources":320,"tags":322,"primary_topic":323,"secondary_topics":324,"classification":326,"search_phrases":345,"slug":348,"view_count":78,"doi":349,"paper":350,"created_at":360},2645,"IOT AND WEATHER DATA-DRIVEN SMART IRRIGATION FOR WATER-EFFICIENT CROP PRODUCTION","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fjcise\u002F2\u002F3\u002F012","Agriculture is one of the major consumers of freshwater, and inefficient irrigation practices can lead to significant water wastage, reduced crop productivity, and increased cultivation costs. Conventional irrigation methods often depend on fixed schedules or manual decisions and may not adequately respond to changing soil and weather conditions. This study proposes an IoT and Weather Data-Driven Smart Irrigation System for Water-Efficient Crop Production that enables intelligent and automated irrigation based on real-time field conditions. The proposed system integrates Internet of Things (IoT) sensors such as soil-moisture, temperature, humidity, and water-level sensors with external weather information including rainfall probability and temperature forecasts. The collected data are transmitted to a central monitoring platform using an IoT-enabled controller. Based on soil moisture levels and prevailing or predicted weather conditions, the system determines the appropriate timing and duration of irrigation. When adequate soil moisture or sufficient rainfall is expected, irrigation is reduced or postponed, thereby avoiding unnecessary water consumption. The proposed system was developed and evaluated using selected agricultural crops under controlled field conditions. Its performance was assessed in terms of water consumption, irrigation efficiency, soil moisture management, crop growth, and system reliability. Experimental results demonstrate a water savings of up to 35% compared to conventional schedule-based irrigation, alongside an 18.2% improvement in soil moisture stability. The study demonstrates how the integration of IoT and weather data can support precision agriculture, sustainable water management, and improved agricultural productivity.","农业是淡水的主要消耗者之一，低效的灌溉方式可能导致严重的水资源浪费、作物生产力下降以及种植成本增加。传统灌溉方法通常依赖固定时间表或人工决策，可能无法充分应对不断变化的土壤和天气条件。本研究提出了一种基于物联网与天气数据的智能灌溉系统，用于节水型作物生产，该系统能够根据实时田间条件实现智能化和自动化灌溉。所提出的系统将物联网（IoT）传感器（如土壤湿度、温度、湿度和水位传感器）与外部天气信息（包括降雨概率和温度预报）相结合。采集的数据通过支持物联网的控制器传输至中央监控平台。系统根据土壤湿度水平以及当前或预测的天气条件，确定适当的灌溉时间和时长。当土壤湿度充足或预计有足够降雨时，灌溉会减少或推迟，从而避免不必要的水资源消耗。所提出的系统在受控田间条件下选用特定农作物进行了开发和评估。其性能从耗水量、灌溉效率、土壤湿度管理、作物生长和系统可靠性等方面进行了评估。实验结果表明，与传统基于时间表的灌溉方式相比，该系统可节水高达35%，同时土壤湿度稳定性提高了18.2%。该研究展示了物联网与天气数据的融合如何支持精准农业、可持续水资源管理以及农业生产力的提升。","RCHUB JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE)","2026-09-15T00:00:00Z",74,{"impact":18,"substance":318,"depth":126,"authority":20,"freshness":17,"relevant":21,"comment":319},21,"该论文提出物联网与气象数据驱动的智能灌溉系统，实测节水35%、土壤湿度稳定性提升18.2%，方法新颖且数据可靠，对智慧农业节水管理有实质参考价值，值得进入每日精选。",[321],{"name":314,"url":311},[26,27,28,132,30],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[325],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":327},{"candidates":328,"agricultural_evidence":57},[329,333,337,341],{"code":32,"score":56,"supported":57,"title":330,"abstract":331,"tags":332},[33],[60,61,33],[28,30],{"code":44,"score":64,"supported":57,"title":334,"abstract":335,"tags":336},[68],[27,66,68],[27],{"code":84,"score":17,"supported":57,"title":338,"abstract":339,"tags":340},[],[132,156],[26,132],{"code":39,"score":113,"supported":57,"title":342,"abstract":343,"tags":344},[],[74,40],[],[346,347],"智慧农业 智能灌溉 精准农业 节水农业","智慧农业 智能灌溉","智慧农业智能灌溉精准农业节水农业-2645","10.65725\u002Fjcise\u002F2\u002F3\u002F012",{"doi":349,"openalex_id":351,"authors":352,"venue":314,"cited_by_count":103,"oa_url":9,"card":355,"direction":108,"ingested_from":110},"W7213308969",[353],{"name":354,"orcid":9},"M. Rathamani",{"tldr":356,"method":357,"finding":358,"direction":108,"opportunity":359},"提出物联网与天气数据驱动的智能灌溉系统，按实时土壤与气象条件自动决策灌溉。","集成土壤湿度、温湿度、水位传感器与降雨概率、温度预报，经物联网控制器决策。","相比定时灌溉节水达35%，土壤湿度稳定性提升18.2%。","可探索多源气象预报误差下的灌溉决策鲁棒性，及跨作物、跨区域的节水模型泛化。","2026-09-16T23:30:12.064334Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":367,"source_url":364,"published_at":368,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":369,"score_detail":370,"sources":373,"tags":375,"primary_topic":377,"secondary_topics":378,"classification":380,"search_phrases":399,"slug":402,"view_count":103,"doi":403,"paper":404,"created_at":422},4888,"A MATLAB-based AI-IoT simulation framework for LSTM-driven smart solar irrigation and renewable energy management","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74935-0","Smart irrigation offers a great avenue for better water use efficiency, and farming sustainability. However, most of the studies conducted so far tend to concentrate on the monitoring systems and platforms of the Internet of Things (IoT) or on the prediction models based on Artificial Intelligence (AI) (but not on both). To address this gap, this research study proposes an Artificial Intelligence (AI)-Internet of Things (IoT) system to predict the water consumption requirements and control the energy produced by a PV system along with a battery State of Charge (SOC) management system, Maximum Power Point Tracking (MPPT) algorithm, adaptive pump-speed control, and conceptual IoT communication architecture. A dataset of real environmental data from the NASA POWER database was used to test the framework for Egypt’s New Delta region. The LSTM model was able to classify the irrigation pump operating states with an accuracy of 96.11%, a recall of 0.9820, a precision of 0.9100, and an F1-score of 0.9446. It had a better performance than the two models, Artificial Neural Network (ANN) and Support Vector Machine (SVM) when compared and robustness testing with various independent training runs showed a stable performance in predicting. An estimated annual water-saving rate of 28.57% was obtained relative to the assumed conventional fixed-schedule baseline, while motor energy use was reduced by 74.74% through adaptive pump-speed control. A sensitivity analysis was also performed, showing that the model was stable to moderate variations in environmental and operating conditions. Combined, these findings suggest that the use of AI-based modelling for renewable energy management in the context of an irrigation simulation model built in MATLAB could be a valuable asset in improving irrigation efficiency and lowering water and energy use, which is a promising start for future implementation of hardware versions of intelligent, solar-powered irrigation devices.","智能灌溉为提高用水效率和农业可持续性提供了重要途径。然而，迄今为止大多数研究倾向于集中在物联网（IoT）的监测系统和平台，或基于人工智能（AI）的预测模型（但很少两者兼顾）。为弥补这一空白，本研究提出了一种人工智能（AI）-物联网（IoT）系统，用于预测用水需求并控制光伏系统产生的能量，同时包含电池荷电状态（SOC）管理系统、最大功率点跟踪（MPPT）算法、自适应泵速控制以及概念性物联网通信架构。研究采用NASA POWER数据库中的真实环境数据集，在埃及新三角洲地区对该框架进行了测试。LSTM模型能够对灌溉泵运行状态进行分类，准确率达到96.11%，召回率为0.9820，精确率为0.9100，F1分数为0.9446。与人工神经网络（ANN）和支持向量机（SVM）两个模型相比，该模型表现更优，且通过多次独立训练运行进行的鲁棒性测试表明其在预测方面具有稳定性能。相对于假设的传统固定调度基线，估计年节水率为28.57%，而通过自适应泵速控制，电机能耗降低了74.74%。研究还进行了敏感性分析，表明该模型对环境和运行条件的中等程度变化具有稳定性。综合来看，这些发现表明，在MATLAB中构建的灌溉仿真模型背景下，将基于AI的建模用于可再生能源管理，可能是提高灌溉效率、降低水和能源消耗的有价值手段，这为未来实现智能太阳能灌溉设备的硬件版本提供了一个有前景的开端。","Scientific Reports","2026-10-12T00:00:00Z",78,{"impact":18,"substance":371,"depth":64,"authority":19,"freshness":17,"relevant":21,"comment":372},22,"该研究提出AI-IoT融合的太阳能灌溉仿真框架，LSTM预测精度达96.11%，节水28.57%、节能74.74%，方法新颖且数据可靠，对智慧农业与农业能源管理有参考价值。",[374],{"name":367,"url":364},[26,131,28,30,376],"光伏农业",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[379],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":381},{"candidates":382,"agricultural_evidence":57},[383,387,391,395],{"code":44,"score":126,"supported":57,"title":384,"abstract":385,"tags":386},[68],[27,66,68],[],{"code":32,"score":18,"supported":57,"title":388,"abstract":389,"tags":390},[33],[60,33],[28,30],{"code":159,"score":160,"supported":57,"title":392,"abstract":393,"tags":394},[],[163],[131],{"code":84,"score":21,"supported":14,"title":396,"abstract":397,"tags":398},[],[],[26],[400,401],"MATLAB AI-IoT 太阳能灌溉","LSTM 智能灌溉 埃及新三角洲","MATLABAI-IoT太阳能灌溉-4888","10.1038\u002Fs41598-026-74935-0",{"doi":403,"openalex_id":405,"authors":406,"venue":367,"cited_by_count":103,"oa_url":416,"card":417,"direction":108,"ingested_from":110},"W7221571268",[407,410,413],{"name":408,"orcid":409},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":411,"orcid":412},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":414,"orcid":415},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":418,"method":419,"finding":420,"direction":108,"opportunity":421},"提出MATLAB中AI-IoT仿真框架，用LSTM预测灌溉需水并管理光伏与电池。","NASA POWER环境数据，LSTM对比ANN\u002FSVM，含MPPT与自适应泵速","LSTM分类准确率96.11%，节水28.57%，电机能耗降74.74%。","可延伸至真实硬件部署与多区域验证，并融合实时气象与土壤墒情数据。","2026-10-14T23:30:13.060384Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":251,"source_url":426,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":429,"score_detail":430,"sources":432,"tags":434,"primary_topic":435,"secondary_topics":436,"classification":438,"search_phrases":465,"slug":468,"view_count":78,"doi":9,"paper":469,"created_at":478},4577,"Automatic irrigation system-2","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fview\u002F4719","Agriculture is one of the major consumers of freshwater, and inefficient irrigation can result in significant water loss. Traditional irrigation methods often depend on manual observation and fixed watering schedules, which may lead to over-irrigation or under-irrigation. An automatic irrigation system provides a technological solution by monitoring soil moisture and automatically supplying water according to the requirements of the crop. This research paper presents the design and working principle of an automatic irrigation system based on soilmoisture sensing, a microcontroller, a relay, and a water pump. The proposed system continuously measures the moisture level of the soil. When the moisture falls below a predefined threshold, the controller activates the water pump. Once sufficient moisture is restored, the pump is automatically switched off. The system can additionally incorporate Internet of Things (IoT) technology, allowing farmers to monitor soil and environmental conditions remotely through a mobile or web interface. Previous research has demonstrated the potential of sensor-based irrigation automation to improve water-use efficiency. For example, a soil-moisture-based automated basin irrigation system reported irrigation application efficiency of up to 86.6%. I More recent systems combine soil moisture, temperature, humidity, weather information, IoT connectivity, and machine learning to improve irrigation scheduling. Wiley Online Library+1 The proposed system is therefore a low-cost and scalable approach toward precision agriculture, water conservation, reduced labor requirements, and sustainable agricultural production.","农业是淡水的主要消耗领域之一，低效灌溉会造成大量水资源浪费。传统灌溉方法往往依赖人工观察和固定灌溉计划，可能导致过度灌溉或灌溉不足。自动灌溉系统通过监测土壤湿度并根据作物需求自动供水，提供了一种技术解决方案。本文介绍了一种基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统的设计和工作原理。该系统持续测量土壤湿度水平。当湿度低于预设阈值时，控制器启动水泵。当湿度恢复到足够水平后，水泵自动关闭。该系统还可集成物联网（IoT）技术，使农民能够通过移动端或网页界面远程监测土壤和环境状况。已有研究表明，基于传感器的灌溉自动化具有提高水资源利用效率的潜力。例如，一项基于土壤湿度的自动化畦灌系统报告称，其灌溉施用效率可达86.6%。近期系统则结合土壤湿度、温度、湿度、天气信息、物联网连接和机器学习来优化灌溉调度。因此，所提出的系统是一种低成本、可扩展的方案，有助于实现精准农业、水资源保护、减少劳动力需求以及可持续农业生产。",42,{"impact":17,"substance":13,"depth":20,"authority":113,"freshness":113,"relevant":21,"comment":431},"主题契合智慧农业，但属通用性论文介绍，方法无新意、来源权威性一般，不建议进入每日精选。",[433],{"name":251,"url":426},[26,27,132,29,260],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[437],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":439},{"candidates":440,"agricultural_evidence":57},[441,445,449,453,457,461],{"code":32,"score":126,"supported":57,"title":442,"abstract":443,"tags":444},[33],[60,61,33],[],{"code":44,"score":13,"supported":57,"title":446,"abstract":447,"tags":448},[],[27,66,68],[27],{"code":84,"score":17,"supported":57,"title":450,"abstract":451,"tags":452},[],[132,156],[26,132],{"code":39,"score":218,"supported":57,"title":454,"abstract":455,"tags":456},[],[74,40],[29],{"code":159,"score":113,"supported":57,"title":458,"abstract":459,"tags":460},[],[286,287],[],{"code":77,"score":78,"supported":57,"title":462,"abstract":463,"tags":464},[],[81],[],[466,467],"自动灌溉系统 土壤湿度 物联网","土壤墒情 智慧农业 精准农业 自动灌溉","自动灌溉系统土壤湿度物联网-4577",{"doi":9,"openalex_id":470,"authors":471,"venue":251,"cited_by_count":103,"oa_url":426,"card":473,"direction":108,"ingested_from":110},"W7221285464",[472],{"name":301,"orcid":9},{"tldr":474,"method":475,"finding":476,"direction":108,"opportunity":477},"设计基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统，实现按需供水。","土壤湿度传感器、微控制器、继电器、水泵，可选IoT远程监控。","系统在湿度低于阈值时自动启泵，恢复后停泵，低成本可扩展，提升水分利用效率。","可结合机器学习与气象数据优化灌溉阈值，并开展多作物田间验证与能耗优化。","2026-10-10T23:30:15.132034Z"]