[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4578":3,"related-4578":111},{"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":44,"search_phrases":93,"slug":96,"view_count":21,"doi":9,"paper":97,"created_at":110},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。",null,"International Journal of Engineering Research and Science & Technology","2026-10-07T00:00:00Z","论文",10,false,34,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},6,8,9,5,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","物联网","土壤墒情","节水农业","自动灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,58,64,72,79,86],{"code":32,"score":51,"supported":52,"title":53,"abstract":54,"tags":57},18,true,[33],[55,56,33],"灌溉","水资源",[29],{"code":39,"score":13,"supported":52,"title":59,"abstract":60,"tags":63},[],[27,61,62],"internet of things","IoT",[27],{"code":65,"score":66,"supported":52,"title":67,"abstract":68,"tags":71},"H2",7,[],[69,70],"土壤","soil",[28],{"code":73,"score":66,"supported":52,"title":74,"abstract":75,"tags":78},"I1",[],[76,77],"精准农业","precision agriculture",[26],{"code":80,"score":17,"supported":52,"title":81,"abstract":82,"tags":85},"I2",[],[83,84],"机器学习","machine learning",[],{"code":87,"score":88,"supported":52,"title":89,"abstract":90,"tags":92},"H8",3,[],[91],"可持续农业",[],[94,95],"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":98,"authors":99,"venue":10,"cited_by_count":102,"oa_url":6,"card":103,"direction":107,"ingested_from":109},"W7221294404",[100],{"name":101,"orcid":9},"Pooja Rajendra Sangole,Shravani Datta Dhomane ,Shreyas nivrutti thete ,amol Dinakar laware,Sumit sudan dalu,Kalpesh Gopal Agrawal,Piyush Sitaram Ghore",0,{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"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%。","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-10T23:30:15.205190Z",{"total":17,"page":21,"page_size":17,"items":112},[113,171,239,296,359,412],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":10,"source_url":116,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":123,"tags":125,"primary_topic":126,"secondary_topics":127,"classification":129,"search_phrases":157,"slug":160,"view_count":88,"doi":9,"paper":161,"created_at":170},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":18,"substance":13,"depth":121,"authority":17,"freshness":17,"relevant":21,"comment":122},12,"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。",[124],{"name":10,"url":116},[26,27,76,28,30],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[128],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":130},{"candidates":131,"agricultural_evidence":52},[132,137,141,145,149,153],{"code":32,"score":133,"supported":52,"title":134,"abstract":135,"tags":136},17,[33],[55,56,33],[],{"code":39,"score":13,"supported":52,"title":138,"abstract":139,"tags":140},[],[27,61,62],[27],{"code":73,"score":18,"supported":52,"title":142,"abstract":143,"tags":144},[],[76,77],[26,76],{"code":65,"score":66,"supported":52,"title":146,"abstract":147,"tags":148},[],[69,70],[28],{"code":80,"score":17,"supported":52,"title":150,"abstract":151,"tags":152},[],[83,84],[],{"code":87,"score":88,"supported":52,"title":154,"abstract":155,"tags":156},[],[91],[],[158,159],"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":162,"authors":163,"venue":10,"cited_by_count":102,"oa_url":116,"card":165,"direction":107,"ingested_from":109},"W7221285464",[164],{"name":101,"orcid":9},{"tldr":166,"method":167,"finding":168,"direction":107,"opportunity":169},"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",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":9,"source_name":177,"source_url":174,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":179,"score_detail":180,"sources":184,"tags":186,"primary_topic":188,"secondary_topics":189,"classification":194,"search_phrases":219,"slug":222,"view_count":20,"doi":223,"paper":224,"created_at":238},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":18,"substance":181,"depth":182,"authority":121,"freshness":18,"relevant":21,"comment":183},16,14,"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。",[185],{"name":177,"url":174},[26,27,187,28,29],"智能灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[190,191],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":65,"slug":70,"name":192,"path":193,"parent_code":36},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":45,"locked":14,"version":46,"method":47,"evidence":195},{"candidates":196,"agricultural_evidence":52},[197,202,206,211,215],{"code":32,"score":198,"supported":52,"title":199,"abstract":200,"tags":201},19,[33],[55,56,33],[187,29],{"code":39,"score":51,"supported":52,"title":203,"abstract":204,"tags":205},[61],[27,61,62],[27],{"code":65,"score":207,"supported":52,"title":208,"abstract":209,"tags":210},15,[70],[69,70],[28],{"code":87,"score":88,"supported":52,"title":212,"abstract":213,"tags":214},[],[91],[],{"code":73,"score":21,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[26],[220,221],"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":223,"openalex_id":225,"authors":226,"venue":177,"cited_by_count":102,"oa_url":174,"card":233,"direction":107,"ingested_from":109},"W7213907872",[227,229,231],{"name":228,"orcid":9},"Zakarie Abdi Mohamud",{"name":230,"orcid":9},"Rozeha Binti A. Rashid",{"name":232,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":234,"method":235,"finding":236,"direction":107,"opportunity":237},"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":240,"title":241,"url":242,"summary":243,"summary_zh":244,"content":9,"source_name":245,"source_url":242,"published_at":246,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":247,"score_detail":248,"sources":251,"tags":253,"primary_topic":254,"secondary_topics":255,"classification":257,"search_phrases":277,"slug":280,"view_count":281,"doi":282,"paper":283,"created_at":295},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":18,"substance":182,"depth":249,"authority":20,"freshness":13,"relevant":21,"comment":250},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。",[252],{"name":245,"url":242},[26,27,187,28,29],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[256],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":258},{"candidates":259,"agricultural_evidence":52},[260,264,269,273],{"code":39,"score":51,"supported":52,"title":261,"abstract":262,"tags":263},[62],[27,61,62],[27],{"code":32,"score":265,"supported":52,"title":266,"abstract":267,"tags":268},11,[],[55,56,33],[187,29],{"code":65,"score":66,"supported":52,"title":270,"abstract":271,"tags":272},[],[69,70],[28],{"code":73,"score":21,"supported":14,"title":274,"abstract":275,"tags":276},[],[],[26],[278,279],"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":282,"openalex_id":284,"authors":285,"venue":245,"cited_by_count":102,"oa_url":242,"card":290,"direction":107,"ingested_from":109},"W7213462460",[286,288],{"name":287,"orcid":9},"Rifki Aldiansyah",{"name":289,"orcid":9},"Dani Sasmoko",{"tldr":291,"method":292,"finding":293,"direction":107,"opportunity":294},"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":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":9,"source_name":302,"source_url":299,"published_at":303,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":304,"score_detail":305,"sources":307,"tags":309,"primary_topic":312,"secondary_topics":313,"classification":315,"search_phrases":340,"slug":343,"view_count":344,"doi":345,"paper":346,"created_at":358},3472,"Solar- Powered and Performance Evaluation of a Smart Irrigation System","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijemt.vol.12.no8.2026.pg158.171","Experiment was carried out on how to generate and developed solar powered pumping system for smart irrigation. Solar power energy materials used consist of photovoltaic panel, solar charge controller, batteries, and inverter, distribution pipe, voltmeter, wiring accessories and water pump. The coupled energy materials were set at an angle of 150 South - east. The generated voltage recorded during the day time from the solar energy were recorded in two parts; from solar panel and voltage controller. The two solar panels generated an average solar energy of 24.63v, PV panel and 10.23v charge controller. Within 10 hours set for solar energy assessment from the panels,from 9:00 am to 6:00pm, PV panels recorded voltage that ranged between 19.3 to 30.2, while Charge controller recorded from 5.1 to 16.0v.The voltage kept increasing from 9:0am until it reached a peak at 3:0pm, and started declining at 4:0pm to the lowest voltage at 6: 00pm, when the sun rays have gone dim.The charge controller allows current into battery and protect and regulate its charge and discharge. The pump efficiency was tested using variation in pumping time (sec), which was set at 10,20, 30,40,50 and 60 seconds. The flow rate (L\u002Fsec) remained constant at 5 L\u002Fsec, because the battery was at full charge state. The pumping unit was coupled to an automated soil moisture-based drip irrigation system, developed using smart irrigation system. Internet of Things (IoT) components used were, Arduino, Veroboard and Timer Switch, Soil Moisture Sensor, Relay Module and Water Pump. The experimental plots were laid out in an improvised green house. Med emitters were used as irrigation drippers. Moisture sensor was set to trigger irrigation when soil moisture was below50%. The, solar energy generated, sustained pumping water required for the Smart irrigation system throughout the irrigation scheduled period to harvest time.","本研究旨在探讨如何构建和开发用于智能灌溉的太阳能驱动抽水系统。所使用的太阳能能源材料包括光伏板、太阳能充电控制器、电池、逆变器、输水管道、电压表、接线附件和水泵。将耦合的能源材料以东南方向150°角安装。白天从太阳能中产生的电压记录分为两部分：来自太阳能板和电压控制器。两块太阳能板的平均太阳能发电量为24.63V，光伏板为24.63V，充电控制器为10.23V。在用于评估太阳能板发电量的10小时时段内（上午9：00至下午6：00），光伏板记录的电压范围为19.3至30.2V，而充电控制器记录的电压范围为5.1至16.0V。电压从上午9：00起持续上升，直至下午3：00达到峰值，下午4：00开始下降，至下午6：00阳光变暗时降至最低电压。充电控制器允许电流进入电池，并保护和调节其充放电。通过改变抽水时间（秒）测试水泵效率，设定时间为10、20、30、40、50和60秒。由于电池处于满充状态，流量（L\u002Fs）保持恒定在5L\u002Fs。抽水装置与基于土壤湿度的自动化滴灌系统耦合，该系统采用智能灌溉系统开发。所使用的物联网（IoT）组件包括Arduino、万用板（Veroboard）和定时开关、土壤湿度传感器、继电器模块和水泵。试验小区布置在简易温室中。采用中流量滴头作为灌溉滴头。湿度传感器设定为当土壤湿度低于50%时触发灌溉。所产生的太阳能在整个灌溉计划期间直至收获期，持续维持智能灌溉系统所需抽水量。","INTERNATIONAL JOURNAL OF ENGINEERING AND MODERN TECHNOLOGY","2026-09-23T00:00:00Z",49,{"impact":18,"substance":182,"depth":249,"authority":17,"freshness":18,"relevant":21,"comment":306},"太阳能驱动智能灌溉系统的实测数据与IoT集成方案有一定参考价值，但期刊层级偏低、影响范围有限，暂不建议进入每日精选。",[308],{"name":302,"url":299},[26,27,310,28,311],"节水灌溉","太阳能灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[314],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":316},{"candidates":317,"agricultural_evidence":52},[318,322,326,330,336],{"code":32,"score":207,"supported":52,"title":319,"abstract":320,"tags":321},[33],[55,33],[310],{"code":39,"score":13,"supported":52,"title":323,"abstract":324,"tags":325},[],[27,61,62],[27],{"code":65,"score":66,"supported":52,"title":327,"abstract":328,"tags":329},[],[69,70],[28],{"code":331,"score":88,"supported":52,"title":332,"abstract":333,"tags":335},"B7",[],[334],"温室",[],{"code":73,"score":21,"supported":14,"title":337,"abstract":338,"tags":339},[],[],[26],[341,342],"太阳能 智能灌溉 系统","土壤湿度 滴灌 物联网","太阳能智能灌溉系统-3472",4,"10.56201\u002Fijemt.vol.12.no8.2026.pg158.171",{"doi":345,"openalex_id":347,"authors":348,"venue":302,"cited_by_count":102,"oa_url":352,"card":353,"direction":107,"ingested_from":109},"W7214070923",[349],{"name":350,"orcid":351},"Malum Japhet Flayin","https:\u002F\u002Forcid.org\u002F0000-0002-9400-2546","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJEMT\u002FVOL. 12 NO. 8 2026\u002FSolar- Powered and Performance Evaluation 158-171.pdf",{"tldr":354,"method":355,"finding":356,"direction":107,"opportunity":357},"研发并评估太阳能供电的智能滴灌系统，利用土壤湿度传感器自动触发灌溉。","光伏板、充电控制器、电池、Arduino与土壤湿度传感器，温室试验。","光伏板日均电压24.63V，泵流量稳定5L\u002Fs，系统可全天候维持灌溉至收获。","可研究多传感器融合与能量管理优化，提升阴雨天续航及灌溉决策精度。","2026-09-25T23:30:10.959076Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":371,"tags":373,"primary_topic":374,"secondary_topics":375,"classification":377,"search_phrases":396,"slug":399,"view_count":88,"doi":400,"paper":401,"created_at":411},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":181,"substance":369,"depth":133,"authority":121,"freshness":18,"relevant":21,"comment":370},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。",[372],{"name":365,"url":362},[26,27,187,76,29],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[376],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":378},{"candidates":379,"agricultural_evidence":52},[380,384,388,392],{"code":32,"score":198,"supported":52,"title":381,"abstract":382,"tags":383},[33],[55,56,33],[187,29],{"code":39,"score":51,"supported":52,"title":385,"abstract":386,"tags":387},[62],[27,61,62],[27],{"code":73,"score":18,"supported":52,"title":389,"abstract":390,"tags":391},[],[76,77],[26,76],{"code":65,"score":17,"supported":52,"title":393,"abstract":394,"tags":395},[],[69,70],[],[397,398],"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":400,"openalex_id":402,"authors":403,"venue":365,"cited_by_count":102,"oa_url":9,"card":406,"direction":107,"ingested_from":109},"W7213308969",[404],{"name":405,"orcid":9},"M. Rathamani",{"tldr":407,"method":408,"finding":409,"direction":107,"opportunity":410},"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":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":418,"source_url":415,"published_at":419,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":420,"score_detail":421,"sources":424,"tags":426,"primary_topic":429,"secondary_topics":433,"classification":436,"search_phrases":485,"slug":488,"view_count":102,"doi":489,"paper":490,"created_at":509},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":51,"substance":422,"depth":51,"authority":249,"freshness":19,"relevant":21,"comment":423},22,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[425],{"name":418,"url":415},[26,427,27,76,428],"农业人工智能","气候韧性",{"code":80,"slug":430,"name":431,"path":432,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[434,435],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":437},{"candidates":438,"agricultural_evidence":52},[439,448,452,458,462,469,473,479],{"code":80,"score":51,"supported":52,"title":440,"abstract":442,"tags":447},[441],"artificial intelligence",[443,83,444,84,445,446],"深度学习","计算机视觉","deep learning","computer vision",[427],{"code":73,"score":181,"supported":52,"title":449,"abstract":450,"tags":451},[77],[76,77],[26,76],{"code":39,"score":13,"supported":52,"title":453,"abstract":454,"tags":457},[],[27,455,62,456],"边缘计算","edge computing",[27],{"code":32,"score":19,"supported":52,"title":459,"abstract":460,"tags":461},[],[55,56,33],[],{"code":463,"score":66,"supported":52,"title":464,"abstract":465,"tags":468},"H7",[],[466,467],"气候变化","climate change",[428],{"code":65,"score":17,"supported":52,"title":470,"abstract":471,"tags":472},[],[69,70],[],{"code":474,"score":88,"supported":52,"title":475,"abstract":476,"tags":478},"A7",[],[477],"间作",[],{"code":480,"score":88,"supported":52,"title":481,"abstract":482,"tags":484},"F3",[],[483],"养分管理",[],[486,487],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":489,"openalex_id":491,"authors":492,"venue":418,"cited_by_count":102,"oa_url":415,"card":503,"direction":107,"ingested_from":109},"W7223121456",[493,496,498,500],{"name":494,"orcid":495},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":497,"orcid":9},"Oluwadare OYEBODE",{"name":499,"orcid":9},"Iretiayo Adelaiye",{"name":501,"orcid":502},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":504,"method":505,"finding":506,"direction":507,"opportunity":508},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z"]