[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2510":3,"related-2510":98},{"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":38,"search_phrases":70,"slug":73,"view_count":74,"doi":75,"paper":76,"created_at":97},2510,"A Low-Power, IP68-Compliant, BLE-Based Multi-Station Smart Irrigation Control Unit for Agricultural Applications","https:\u002F\u002Fdoi.org\u002F10.47115\u002Fbsagriculture.2004639","This study designs, develops, and evaluates the performance of a low-power, Bluetooth Low Energy (BLE) based, IP68 protection-rated, multi-station smart irrigation control unit for agricultural irrigation applications. The system is built on an STM32F103 microcontroller-based electronic control board and can manage 6 independent irrigation stations. Irrigation is automatically controlled by evaluating environmental data obtained from rain, frost, and wind sensors, and it is designed to be compatible with different types of rain sensors. Thanks to the user-definable cycle & soak algorithm, irrigation is performed in specific cycles, and a waiting period is applied after each cycle to allow the soil to absorb the water, aiming to increase water use efficiency. The control unit can be managed remotely from mobile devices compatible with common mobile operating systems via a mobile application using BLE technology, up to approximately 15 m; irrigation schedules can be created, past irrigation records can be viewed, and manual control operations can be performed remotely. The system is designed to operate with a 9V alkaline battery (550 mAh), offering approximately 6 months of continuous operation with a single battery and approximately 1 year with two batteries. The improved control unit, with its IP68 protection class housing, provides high durability against harsh environmental conditions. Its multi-station structure, low power consumption, and user-friendly features offer an integrated solution for smart irrigation applications.","本研究设计、开发并评估了一种用于农业灌溉应用的低功耗、基于低功耗蓝牙（BLE）、具有IP68防护等级的多站智能灌溉控制单元的性能。该系统构建于基于STM32F103微控制器的电子控制板上，可管理6个独立的灌溉站。通过评估来自雨量、霜冻和风速传感器的环境数据来自动控制灌溉，并设计为兼容不同类型的雨量传感器。借助用户可定义的循环与浸润算法，灌溉按特定周期进行，每个周期后设置等待期以使土壤吸收水分，旨在提高水资源利用效率。该控制单元可通过使用BLE技术的移动应用程序，从兼容常见移动操作系统的移动设备上进行远程管理，通信距离可达约15米；可创建灌溉计划、查看历史灌溉记录，并远程执行手动控制操作。该系统设计为使用9V碱性电池（550 mAh）运行，单节电池可连续运行约6个月，两节电池可连续运行约1年。改进后的控制单元采用IP68防护等级外壳，对恶劣环境条件具有高耐久性。其多站结构、低功耗和用户友好特性为智能灌溉应用提供了一体化解决方案。",null,"Black Sea Journal of Agriculture","2026-09-14T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,9,1,"面向农业灌溉的低功耗BLE多站控制单元，方法完整、指标具体，但属细分技术方案，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","智能灌溉","农业传感器","节水农业","低功耗物联网",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,60,65],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},19,true,[33],[49,50,33],"灌溉","水资源",[27,29],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":59},"H2",6,[],[57,58],"土壤","soil",[],{"code":61,"score":21,"supported":14,"title":62,"abstract":63,"tags":64},"I1",[],[],[26],{"code":66,"score":21,"supported":14,"title":67,"abstract":68,"tags":69},"I5",[],[],[28],[71,72],"低功耗物联网 农业传感器 智慧农业 智能灌溉","低功耗物联网 农业传感器","低功耗物联网农业传感器智慧农业智能灌溉-2510",4,"10.47115\u002Fbsagriculture.2004639",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":88,"oa_url":89,"card":90,"direction":94,"ingested_from":96},"W7212619308",[79,82,85],{"name":80,"orcid":81},"Samet Eyicil","https:\u002F\u002Forcid.org\u002F0009-0003-4651-6897",{"name":83,"orcid":84},"Yavuz Turan","https:\u002F\u002Forcid.org\u002F0000-0001-8537-3989",{"name":86,"orcid":87},"İbrahim Keleş","https:\u002F\u002Forcid.org\u002F0000-0001-8252-2635",0,"https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6270760",{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"设计并评估了一款低功耗、IP68防护、基于BLE的六站智能灌溉控制单元。","基于STM32F103与BLE，结合雨霜风传感器及循环浸泡算法，用9V电池供电。","单电池续航约6个月，双电池约1年，BLE控制距离约15米，IP68外壳耐恶劣环境。","智慧农业 \u002F 农业物联网","可探索多传感器融合的灌溉决策与太阳能供电，延长野外部署寿命并提升节水效率。","openalex","2026-09-15T23:30:08.453960Z",{"total":54,"page":21,"page_size":54,"items":99},[100,175,247,309,384,448],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":114,"tags":116,"primary_topic":119,"secondary_topics":124,"classification":126,"search_phrases":151,"slug":154,"view_count":88,"doi":155,"paper":156,"created_at":174},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":19,"substance":110,"depth":18,"authority":111,"freshness":112,"relevant":21,"comment":113},22,14,8,"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。",[115],{"name":106,"url":103},[26,117,27,29,118],"农业人工智能","光伏农业",{"code":66,"slug":120,"name":121,"path":122,"parent_code":123},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[125],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":127},{"candidates":128,"agricultural_evidence":46},[129,137,141,147],{"code":66,"score":130,"supported":46,"title":131,"abstract":133,"tags":136},17,[132],"IoT",[134,135,132],"物联网","internet of things",[],{"code":32,"score":19,"supported":46,"title":138,"abstract":139,"tags":140},[33],[49,33],[27,29],{"code":142,"score":74,"supported":46,"title":143,"abstract":144,"tags":146},"I2",[],[145],"artificial intelligence",[117],{"code":61,"score":21,"supported":14,"title":148,"abstract":149,"tags":150},[],[],[26],[152,153],"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":155,"openalex_id":157,"authors":158,"venue":106,"cited_by_count":88,"oa_url":168,"card":169,"direction":94,"ingested_from":96},"W7221571268",[159,162,165],{"name":160,"orcid":161},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":163,"orcid":164},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":166,"orcid":167},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":170,"method":171,"finding":172,"direction":94,"opportunity":173},"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":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":106,"source_url":178,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":186,"tags":188,"primary_topic":190,"secondary_topics":191,"classification":196,"search_phrases":224,"slug":227,"view_count":54,"doi":228,"paper":229,"created_at":246},4567,"An embedded deep learning–fuzzy logic framework for intelligent and automated irrigation in smart farming","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72706-5","Water scarcity and the need to improve resource efficiency have made intelligent and automated irrigation management an important research challenge in smart farming. This study proposes IrrigaDL-FL, a low-cost automated smart irrigation framework based on the internet of things, integrating intelligent data routing with adaptive irrigation decision-making. The proposed framework consists of two complementary components. First, a Deep Learning–Fuzzy Logic (DL-FL) framework is integrated with the Open Shortest Path First (OSPF) protocol to enhance data routing efficiency in the network. The DL-FL framework combines these heterogeneous characteristics to identify suitable next-hop nodes and establish energy-efficient routing paths. Second, irrigation decision-making is performed using a lightweight Convolutional Neural Network–Fuzzy Logic Controller (CNN-FLC). The CNN-FLC processes sensor-derived and crop-related information, including air temperature, relative humidity, soil moisture, evapotranspiration rate, crop growth coefficient, crop water requirement, planting date, cumulative rainfall, recent rainfall, irrigation history, crop growth stage, and the difference between actual and target soil moisture. These inputs are used to characterize crop hydration stress and dynamically determine the appropriate irrigation program and sprinkler activation duration. The proposed IrrigaDL-FL framework is evaluated through simulations with different numbers of sensor nodes over predefined routing rounds and is compared with several existing approaches. The results demonstrate that IrrigaDL-FL achieves superior performance in terms of network lifetime, residual energy, energy variance, number of transmitted packets, and average path length.","水资源短缺与提高资源效率的需求，使智能化和自动化灌溉管理成为智慧农业中的重要研究挑战。本研究提出IrrigaDL-FL，一种基于物联网的低成本自动化智能灌溉框架，将智能数据路由与自适应灌溉决策相结合。该框架由两个互补部分组成。首先，将深度学习-模糊逻辑（DL-FL）框架与开放最短路径优先（OSPF）协议集成，以提高网络中的数据路由效率。DL-FL框架结合这些异构特征来识别合适的下一跳节点并建立节能路由路径。其次，灌溉决策通过轻量级卷积神经网络-模糊逻辑控制器（CNN-FLC）执行。CNN-FLC处理来自传感器的信息和作物相关信息，包括空气温度、相对湿度、土壤湿度、蒸散速率、作物生长系数、作物需水量、种植日期、累计降雨量、近期降雨量、灌溉历史、作物生长阶段以及实际土壤湿度与目标土壤湿度之间的差值。这些输入用于表征作物水分胁迫，并动态确定合适的灌溉方案和喷灌器启动时长。所提出的IrrigaDL-FL框架通过在预定义路由轮次下对不同数量传感器节点进行仿真评估，并与若干现有方法进行比较。结果表明，IrrigaDL-FL在网络寿命、剩余能量、能量方差、传输数据包数量和平均路径长度方面均取得了更优性能。","2026-10-08T00:00:00Z",77,{"impact":19,"substance":184,"depth":18,"authority":111,"freshness":112,"relevant":21,"comment":185},21,"提出低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由与灌溉决策，方法新颖、仿真验证充分，对智慧农业节水研究有参考价值。",[187],{"name":106,"url":178},[26,117,189,27,29],"农业物联网",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[192],{"code":142,"slug":193,"name":194,"path":195,"parent_code":123},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":197},{"candidates":198,"agricultural_evidence":46},[199,204,211,216,220],{"code":32,"score":45,"supported":46,"title":200,"abstract":201,"tags":203},[33],[49,50,33,202],"evapotranspiration",[27,29],{"code":142,"score":205,"supported":46,"title":206,"abstract":208,"tags":210},15,[207],"deep learning",[209,207],"深度学习",[117],{"code":66,"score":212,"supported":46,"title":213,"abstract":214,"tags":215},7,[],[134,135],[189],{"code":53,"score":54,"supported":46,"title":217,"abstract":218,"tags":219},[],[57,58],[],{"code":61,"score":74,"supported":46,"title":221,"abstract":222,"tags":223},[],[26],[26],[225,226],"IrrigaDL-FL 智能灌溉","CNN-FLC 灌溉决策","IrrigaDL-FL智能灌溉-4567","10.1038\u002Fs41598-026-72706-5",{"doi":228,"openalex_id":230,"authors":231,"venue":106,"cited_by_count":88,"oa_url":240,"card":241,"direction":94,"ingested_from":96},"W7221040139",[232,234,236,238],{"name":233,"orcid":9},"Ze Luo",{"name":235,"orcid":9},"Zhuo Yang",{"name":237,"orcid":9},"Yi Hu",{"name":239,"orcid":9},"Jun Wang","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72706-5_reference.pdf",{"tldr":242,"method":243,"finding":244,"direction":94,"opportunity":245},"提出IrrigaDL-FL低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由和灌溉决策。","用CNN-模糊逻辑控制器处理多源传感器与作物数据，结合OSPF协议做节能路由。","在仿真中网络寿命、剩余能量、能耗方差、传输包数和平均路径长度均优于现有方法。","可探索真实农田部署下多作物、多气候场景的泛化能力与边缘计算资源约束优化。","2026-10-10T23:30:11.306685Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":9,"source_name":253,"source_url":254,"published_at":255,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":256,"score_detail":257,"sources":259,"tags":261,"primary_topic":263,"secondary_topics":264,"classification":265,"search_phrases":293,"slug":296,"view_count":74,"doi":297,"paper":298,"created_at":308},4164,"SMART IRRIGATION STRATEGY RECOMMENDED SYSTEM USING ML","https:\u002F\u002Fdoi.org\u002F10.64751\u002Fqbkrtp46","Water used for irrigation forms the largest share of freshwater consumption in agriculture, and much of it is wasted because fields are watered on a fixed schedule rather than according to the actual needs of the crop. Over-irrigation leaches nutrients, raises pumping cost, and can damage roots, while under-irrigation reduces yield during critical growth stages. This paper presents a Smart Irrigation Strategy Recommendation System that uses soil and weather sensing together with machine learning to recommend when to irrigate, how much water to apply, and which irrigation method suits the field and crop. The sensing part of the system consists of field nodes built around an ESP32 microcontroller that read a capacitive soil moisture sensor at two depths, a soil temperature probe, and an air temperature and humidity sensor. Each node samples these values at regular intervals, averages several readings to reduce noise, and sends them over Wi-Fi or a LoRa link to a gateway. The nodes are powered by a small solar panel and battery, and they spend most of their time in deep sleep to extend battery life. Weather forecast data for the location, including expected rainfall, temperature, and wind speed, is obtained from a public service and merged with the sensor readings. The machine learning part of the system operates in two stages. In the first stage, a regression model predicts the soil moisture level expected over the next twenty-four hours using current sensor readings, reference evapotranspiration computed from weather data, crop type, and growth stage. Linear Regression, Random Forest, Gradient Boosting, and Support Vector Regression were compared for this task using RMSE and MAE. In the second stage, a classification model recommends one of several irrigation actions, namely no irrigation, light irrigation, normal irrigation, or deferral because rain is expected. Decision Tree, Random Forest, K-Nearest Neighbours, and XGBoost classifiers were compared using accuracy, precision, recall, and F1-score. The recommendations are presented through a web dashboard that shows live soil moisture for each field zone, the predicted moisture curve, the recommended action with the estimated water volume and pump running time, and a record of past irrigation events. The system can also switch a relay-controlled pump or solenoid valve automatically when the farmer chooses automatic mode, while manual confirmation remains the default. In trials using collected and public agronomic data, the Random Forest models gave the best results, and the recommended schedules used noticeably less water than a fixed daily schedule while keeping moisture within the desired range. The system is intended for small and medium farms, kitchen gardens, polyhouses, and institutional campuses where water availability is limited and labour for manual checking is scarce. It combines inexpensive electronics, low-power wireless communication, and interpretable machine learning in a single practical tool. Future work includes crop-specific models trained on local field trials, integration of satellite vegetation indices, and support for drip fertigation scheduling.","农业灌溉用水占农业淡水消耗量的最大份额，而其中大量水资源被浪费，原因在于农田按固定日程灌溉，而非根据作物的实际需求进行灌溉。过度灌溉会淋失养分、增加抽水成本，并可能损伤根系；而灌溉不足则会在关键生长期降低产量。本文提出了一种智能灌溉策略推荐系统，利用土壤与气象传感技术结合机器学习，推荐何时灌溉、灌多少水，以及哪种灌溉方式适合该田块和作物。系统的传感部分由基于ESP32微控制器构建的田间节点组成，这些节点读取两个深度的电容式土壤水分传感器、一个土壤温度探头以及一个空气温湿度传感器。每个节点按固定间隔采样这些数值，对多次读数取平均以降低噪声，并通过Wi-Fi或LoRa链路发送至网关。节点由小型太阳能板和电池供电，大部分时间处于深度睡眠状态以延长电池寿命。该地点的天气预报数据（包括预期降雨量、温度和风速）从公共服务获取，并与传感器读数融合。系统的机器学习部分分两个阶段运行。第一阶段，回归模型利用当前传感器读数、根据气象数据计算的参考蒸散量、作物类型和生长阶段，预测未来二十四小时内预期的土壤水分水平。针对该任务，使用RMSE和MAE对线性回归、随机森林、梯度提升和支持向量回归进行了比较。第二阶段，分类模型推荐若干灌溉措施之一，即不灌溉、轻度灌溉、正常灌溉或因预期降雨而推迟灌溉。使用准确率、精确率、召回率和F1分数对决策树、随机森林、K近邻和XGBoost分类器进行了比较。推荐结果通过一个网页仪表板呈现，显示每个田块分区的实时土壤水分、预测水分曲线、推荐措施及估计水量和泵运行时间，以及过往灌溉事件记录。当农民选择自动模式时，系统还可自动切换继电器控制的泵或电磁阀，而手动确认仍为默认方式。在利用收集数据和公共农业","International Journal of AI Electronics and Nexus Energy","https:\u002F\u002Fzesterapublications.com\u002Fjournals\u002Findex.php\u002Fijaene\u002Farticle\u002Fview\u002F1085","2026-10-02T00:00:00Z",56,{"impact":112,"substance":18,"depth":19,"authority":54,"freshness":112,"relevant":21,"comment":258},"论文提出基于ESP32传感与机器学习两阶段模型的智能灌溉推荐系统，方法完整、对比实验充分，但属学术研究且期刊影响力有限，公共价值偏细分领域。",[260],{"name":253,"url":254},[26,117,27,262,29],"土壤墒情",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":266},{"candidates":267,"agricultural_evidence":46},[268,272,276,282,289],{"code":32,"score":45,"supported":46,"title":269,"abstract":270,"tags":271},[33],[49,50,33,202],[27,29],{"code":53,"score":212,"supported":46,"title":273,"abstract":274,"tags":275},[],[57,58],[262],{"code":142,"score":212,"supported":46,"title":277,"abstract":278,"tags":281},[],[279,280],"机器学习","machine learning",[117],{"code":283,"score":284,"supported":46,"title":285,"abstract":286,"tags":288},"F3",3,[],[287],"fertigation",[],{"code":61,"score":21,"supported":14,"title":290,"abstract":291,"tags":292},[],[],[26],[294,295],"ESP32 土壤湿度 智能灌溉","机器学习 灌溉决策 推荐系统","ESP32土壤湿度智能灌溉-4164","10.64751\u002Fqbkrtp46",{"doi":297,"openalex_id":299,"authors":300,"venue":253,"cited_by_count":88,"oa_url":254,"card":303,"direction":94,"ingested_from":96},"W7218574975",[301],{"name":302,"orcid":9},"1 Dr.M.Mahesh, 2 Kesagoni Akshika, 3 Bandari Sandeep, 4 Dumpati Akshitha, 5 Thadem Pranay",{"tldr":304,"method":305,"finding":306,"direction":94,"opportunity":307},"提出基于ESP32传感与两阶段机器学习的智能灌溉推荐系统，预测土壤湿度并推荐灌溉动作。","ESP32土壤温湿度传感+LoRa\u002FWiFi，回归预测湿度、分类推荐灌溉，比较多","两阶段ML可预测未来24小时土壤湿度并给出灌溉建议，支持自动或手动控制水泵。","可探索多深度土壤传感与天气预报融合的轻量级边缘ML模型，降低部署成本并提升推荐精度。","2026-10-04T23:30:08.843055Z",{"id":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":9,"source_name":315,"source_url":312,"published_at":316,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":317,"sources":321,"tags":323,"primary_topic":325,"secondary_topics":326,"classification":333,"search_phrases":358,"slug":361,"view_count":112,"doi":362,"paper":363,"created_at":383},3355,"AI and Sustainable Agriculture Through Cost–Benefit Analysis of Smart Irrigation Systems","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412813","The advancing role of Artificial Intelligence (AI) and its application in agriculture have disrupted traditional agricultural practices, with smart irrigation systems representing one of the leading technologies enabling sustainable agriculture. Smart irrigation systems utilize real–time data, machine learning algorithms, and predictive analytics to better optimize irrigation water use, limit wasted resources, and improve the yields of crop products. The proposed research will assess the economic and environmental impacts of AI smart irrigation systems with a full costs–benefits analysis. The proposed research considers both the capital cost and operating cost of smart irrigation systems and compares these traditional irrigation practices while also examining the long–term benefits of potential water savings from Smart Irrigation Systems, expanded agricultural production, and reduced human labour. This will give context for measuring the impacts of Smart Irrigation Systems on farm businesses, including both opportunities and barriers to adoption. Additionally, using a formal literature review to lock down existing research and surveys of irrigation farmers to collect a field data set will provide the proposed researchers a collective sample to measure the efficacy of AI smart irrigation systems, identify barriers, compare opportunities, and measure performance under differing climate and soil properties. The research will find high and substantial respective levels of benefits from the implementation of AI–based smart systems, particularly in water–stressed systems with positive impacts on farm profitability, private, and environmental conservation. This research is essential for informing stakeholders of actions and the delivery of AI–enabled solutions in support of more sustainable agricultural practices.","人工智能（Artificial Intelligence, AI）的不断发展及其在农业中的应用已经颠覆了传统的农业实践，其中智能灌溉系统是实现可持续农业的领先技术之一。智能灌溉系统利用实时数据、机器学习算法和预测分析，更好地优化灌溉用水、减少资源浪费并提高作物产量。拟议研究将通过全面的成本效益分析，评估AI智能灌溉系统的经济和环境影响。该研究将综合考虑智能灌溉系统的资本成本和运营成本，并将其与传统灌溉实践进行比较，同时考察智能灌溉系统在潜在节水、扩大农业生产和减少人力劳动方面的长期效益。这将为衡量智能灌溉系统对农场经营的影响提供背景，包括采用的机会和障碍。此外，通过正式文献综述锁定现有研究成果，并对灌溉农户进行调查以收集实地数据集，将为拟议研究者提供一个集体样本，用以衡量AI智能灌溉系统的效能、识别障碍、比较机会，并评估在不同气候和土壤条件下的表现。研究发现，实施基于AI的智能系统可带来显著且可观的效益，尤其是在水资源紧张的地区，对农场盈利能力、私人利益和环境保护均有积极影响。这项研究对于向利益相关者通报行动方案以及推动AI赋能解决方案以支持更可持续的农业实践至关重要。","AgEcon Search (University of Minnesota, USA)","2026-09-23T00:00:00Z",{"impact":18,"substance":318,"depth":18,"authority":319,"freshness":112,"relevant":21,"comment":320},20,13,"该研究以成本效益分析评估AI智能灌溉的经济与环境效益，方法系统、结论具参考价值，但属学术论文而非政策或产业事件，适合作为智慧农业主题的深度补充。",[322],{"name":315,"url":312},[26,117,27,29,324],"成本效益分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[327,332],{"code":328,"slug":329,"name":330,"path":331,"parent_code":36},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"code":142,"slug":193,"name":194,"path":195,"parent_code":123},{"status":39,"locked":14,"version":40,"method":41,"evidence":334},{"candidates":335,"agricultural_evidence":46},[336,340,346,350,354],{"code":32,"score":45,"supported":46,"title":337,"abstract":338,"tags":339},[33],[49,50,33],[27,29],{"code":328,"score":111,"supported":46,"title":341,"abstract":343,"tags":345},[342],"sustainable agriculture",[344,342],"可持续农业",[],{"code":142,"score":13,"supported":46,"title":347,"abstract":348,"tags":349},[],[279,280,145],[117],{"code":53,"score":54,"supported":46,"title":351,"abstract":352,"tags":353},[],[57,58],[],{"code":61,"score":21,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[26],[359,360],"AI 智能灌溉 成本效益","AgEcon Search 智能灌溉","AI智能灌溉成本效益-3355","10.22004\u002Fag.econ.412813",{"doi":362,"openalex_id":364,"authors":365,"venue":315,"cited_by_count":88,"oa_url":312,"card":378,"direction":94,"ingested_from":96},"W7214115789",[366,368,370,372,374,376],{"name":367,"orcid":9},"Venkata Suman Jami",{"name":369,"orcid":9},"Purushotham Prasad Kalisetti",{"name":371,"orcid":9},"Sampath Dakshina Murthy A.",{"name":373,"orcid":9},"Gurunadha R.",{"name":375,"orcid":9},"Hema Mamidipaka",{"name":377,"orcid":9},"Gurrapu Omprakash",{"tldr":379,"method":380,"finding":381,"direction":94,"opportunity":382},"通过成本效益分析评估AI智能灌溉系统的经济与环境影响。","文献综述结合灌溉农户调查数据，进行成本效益分析。","AI智能灌溉在水资源紧张地区效益显著，提升利润并促进环保。","可针对不同气候土壤条件，量化AI灌溉的长期采纳障碍与推广机制。","2026-09-24T23:30:10.558578Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":9,"source_name":390,"source_url":387,"published_at":391,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":392,"score_detail":393,"sources":395,"tags":397,"primary_topic":398,"secondary_topics":399,"classification":404,"search_phrases":427,"slug":430,"view_count":431,"doi":432,"paper":433,"created_at":447},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":112,"substance":19,"depth":111,"authority":17,"freshness":112,"relevant":21,"comment":394},"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。",[396],{"name":390,"url":387},[26,134,27,262,29],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[400,401],{"code":66,"slug":120,"name":121,"path":122,"parent_code":123},{"code":53,"slug":58,"name":402,"path":403,"parent_code":36},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":39,"locked":14,"version":40,"method":41,"evidence":405},{"candidates":406,"agricultural_evidence":46},[407,411,415,419,423],{"code":32,"score":45,"supported":46,"title":408,"abstract":409,"tags":410},[33],[49,50,33],[27,29],{"code":66,"score":18,"supported":46,"title":412,"abstract":413,"tags":414},[135],[134,135,132],[134],{"code":53,"score":205,"supported":46,"title":416,"abstract":417,"tags":418},[58],[57,58],[262],{"code":328,"score":284,"supported":46,"title":420,"abstract":421,"tags":422},[],[344],[],{"code":61,"score":21,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[26],[428,429],"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":432,"openalex_id":434,"authors":435,"venue":390,"cited_by_count":88,"oa_url":387,"card":442,"direction":94,"ingested_from":96},"W7213907872",[436,438,440],{"name":437,"orcid":9},"Zakarie Abdi Mohamud",{"name":439,"orcid":9},"Rozeha Binti A. Rashid",{"name":441,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":443,"method":444,"finding":445,"direction":94,"opportunity":446},"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":449,"title":450,"url":451,"summary":452,"summary_zh":453,"content":9,"source_name":454,"source_url":451,"published_at":455,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":456,"score_detail":457,"sources":459,"tags":461,"primary_topic":462,"secondary_topics":463,"classification":465,"search_phrases":485,"slug":488,"view_count":489,"doi":490,"paper":491,"created_at":503},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":112,"substance":111,"depth":319,"authority":431,"freshness":13,"relevant":21,"comment":458},"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。",[460],{"name":454,"url":451},[26,134,27,262,29],{"code":66,"slug":120,"name":121,"path":122,"parent_code":123},[464],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":466},{"candidates":467,"agricultural_evidence":46},[468,472,477,481],{"code":66,"score":18,"supported":46,"title":469,"abstract":470,"tags":471},[132],[134,135,132],[134],{"code":32,"score":473,"supported":46,"title":474,"abstract":475,"tags":476},11,[],[49,50,33],[27,29],{"code":53,"score":212,"supported":46,"title":478,"abstract":479,"tags":480},[],[57,58],[262],{"code":61,"score":21,"supported":14,"title":482,"abstract":483,"tags":484},[],[],[26],[486,487],"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":490,"openalex_id":492,"authors":493,"venue":454,"cited_by_count":88,"oa_url":451,"card":498,"direction":94,"ingested_from":96},"W7213462460",[494,496],{"name":495,"orcid":9},"Rifki Aldiansyah",{"name":497,"orcid":9},"Dani Sasmoko",{"tldr":499,"method":500,"finding":501,"direction":94,"opportunity":502},"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"]