[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1035":3,"related-1035":113},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":33,"secondary_topics":39,"classification":52,"search_phrases":108,"slug":111,"view_count":22,"doi":8,"paper":8,"created_at":112},1035,"Application of Precision Agriculture and IoT-Based Smart Irrigation in Greenhouse Vegetable Production","https:\u002F\u002Fjournal-of-social-education.org\u002Findex.php\u002FJorunal\u002Farticle\u002Fview\u002F1226","原文标题:Application of Precision Agriculture and IoT-Based Smart Irrigation in Greenhouse Vegetable Production。作者:Zeeshan Ahmed、Muhammad Taimur Maqbool Muhammad、Abbas Khan Muhammad、Zain Ul Abideen、Abdul Haq。DOI: 10.63163\u002Fjpehss.v4i1.1226。该综述综合分析了精准农业(PA)和基于物联网(IoT)的智能灌溉在温室蔬菜生产中的集成,涉及土壤湿度、EC、pH、微气候监测器等物联网传感器与数据分析(AI\u002FML模型)及自动执行器在番茄、黄瓜、辣椒等作物实时资源管理中的整合。关键组件包括使用ZigBee\u002FLoRa协议的无线传感器网络、低延迟边缘\u002F雾计算和预测算法(如LSTM、SVM),可减少20-50%用水量,同时提高10-30%产量和养分效率。来自中国、欧洲和印度的案例展示了变量灌溉(VRI)和施肥系统的实际应用,实现高达40%的能源节约和最小化淋失。",null,"## Authors\n\n*    Zeeshan Ahmed  Department of Water Resources Management, Lasbela University of Agriculture, water and Marine Sciences, Uthal. zeesmile1999@gmail.com  Author \n*    Muhammad Taimur Maqbool  Department of Agronomy, University of Agriculture Faisalabad. taimuragronomist@gmail.com  Author \n*    Muhammad Abbas Khan  Department of Horticulture, Balochistan Agriculture College Quetta. muhammadabbaskhan1121@gmail.com  Author \n*    Zain Ul Abideen  Department of Agronomy, Agriculture University Peshawar zainkhanbajaur@gmail.com  Author \n*    Abdul Haq  Agriculture Engineer at Department of Agriculture research institute Balochistan Quetta. abdulhaqkakar54@gmail.com  Author \n\n## DOI:\n\n[https:\u002F\u002Fdoi.org\u002F10.63163\u002Fjpehss.v3i4.1226](https:\u002F\u002Fdoi.org\u002F10.63163\u002Fjpehss.v3i4.1226)\n## Abstract\n\nPrecision agriculture (PA) and Internet of Things (IoT)-based smart irrigation represent transformative technologies for optimizing greenhouse vegetable production, addressing global challenges like population growth (projected 9.7 billion by 2050), water scarcity, and climate variability. This review synthesizes the integration of IoT sensors (soil moisture, EC, pH, microclimate monitors) with data analytics (AI\u002FML models) and automated actuators for real-time resource management in crops such as tomatoes, cucumbers, and peppers. Key components include wireless sensor networks (WSNs) using protocols like ZigBee\u002FLoRa for data transmission, edge\u002Ffog computing for low-latency processing, and predictive algorithms (e.g., LSTM, SVM) that reduce water use by 20–50% while enhancing yield (10–30%) and nutrient efficiency. Case studies from China, Europe, and India demonstrate practical implementations, such as variable rate irrigation (VRI) and fertigation systems, achieving up to 40% energy savings and minimized leaching. Challenges encompass high initial costs, data security, and sensor accuracy in humid environments, with future directions emphasizing AI-driven digital twins, blockchain for traceability, and hybrid renewable energy integration for sustainable, resilient greenhouse systems.\n\n## Published\n\n2025-12-30\n\n## Issue\n\n## Section\n\nBiological Sciences","Journal of Physical Education Health and Social Sciences 2026, 4(1): 781-794","2026-08-21T16:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,8,2,1,"综述性论文，涵盖精准农业与IoT智能灌溉在温室蔬菜中的应用，信息较全面，但来源期刊权威性一般，且发表时间较早。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智能灌溉","精准农业","温室蔬菜","LSTM LoRa","变量灌溉","施肥系统",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[40,46],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":53,"locked":14,"version":54,"method":55,"evidence":56},"classified","2026-10-22.2","rules",{"candidates":57,"agricultural_evidence":60},[58,68,75,81,87,95,102],{"code":34,"score":59,"supported":60,"title":61,"abstract":63,"tags":67},17,true,[62],"vegetable",[36,64,65,66,62],"番茄","辣椒","黄瓜",[],{"code":47,"score":59,"supported":60,"title":69,"abstract":71,"tags":74},[70],"IoT",[72,73,70],"物联网","无线传感",[],{"code":41,"score":76,"supported":60,"title":77,"abstract":78,"tags":80},15,[42],[79,42],"灌溉",[27],{"code":82,"score":76,"supported":60,"title":83,"abstract":85,"tags":86},"I1",[84],"precision agriculture",[28,84],[28],{"code":88,"score":89,"supported":60,"title":90,"abstract":92,"tags":94},"B7",14,[91],"greenhouse",[93,91],"温室",[],{"code":96,"score":97,"supported":60,"title":98,"abstract":99,"tags":101},"F3",3,[],[100],"施肥",[],{"code":103,"score":97,"supported":60,"title":104,"abstract":105,"tags":107},"H2",[],[106],"土壤",[],[109,110],"变量灌溉 施肥系统 智能灌溉 温室蔬菜","变量灌溉 施肥系统","变量灌溉施肥系统智能灌溉温室蔬菜-1035","2026-08-28T00:07:02.279441Z",{"total":114,"page":22,"page_size":114,"items":115},6,[116,178,234,306,405,467],{"id":117,"title":118,"url":119,"summary":120,"summary_zh":121,"content":8,"source_name":122,"source_url":119,"published_at":123,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":124,"score_detail":125,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":134,"classification":136,"search_phrases":157,"slug":160,"view_count":22,"doi":161,"paper":162,"created_at":177},4296,"Smart Irrigation System for Precision Farming","https:\u002F\u002Fdoi.org\u002F10.17148\u002Fijarcce.2026.151010","Smart Irrigation System for Precision Farming is anIoT-based and sensor-driven approach designed to improve water management, crop productivity, and agricultural sustainability.Conventional irrigation practices often depend on fixed schedules or manual decisions, which can result in over-irrigation, under-irrigation, water wastage, and increased labour requirements.The proposed system integrates soil-moisture, temperature, humidity, and rainfall sensors with a microcontroller such as Arduino or ESP32 to continuously monitor field conditions in real time.Based on the collected data, the system automatically determines the irrigation requirement and controls water pumps or solenoid valves accordingly.Weather information can also be incorporated to avoid unnecessary irrigation during expected rainfall and to adapt watering according to environmental conditions.IoT connectivity enables farmers to remotely monitor soil and weather parameters through a web or mobile dashboard.The system provides a low-cost, scalable, and farmer-friendly solution that supports precision agriculture by delivering the right amount of water at the right time.Such intelligent irrigation can conserve water and energy, reduce manual effort, improve crop health, and contribute to sustainable agricultural development. IJARCCE","基于物联网和传感器的精准农业智能灌溉系统，旨在改善水资源管理、作物生产力和农业可持续性。传统灌溉方式往往依赖固定时间表或人工决策，可能导致过度灌溉、灌溉不足、水资源浪费以及劳动力需求增加。所提出的系统将土壤湿度、温度、湿度和降雨传感器与Arduino或ESP32等微控制器集成，以实时持续监测田间状况。基于采集的数据，系统自动判断灌溉需求，并相应控制水泵或电磁阀。系统还可纳入天气信息，以避免在预计降雨期间进行不必要的灌溉，并根据环境条件调整浇水。物联网连接使农民能够通过网络或移动仪表板远程监测土壤和天气参数。该系统提供了一种低成本、可扩展且对农民友好的解决方案，通过在恰当的时间提供恰当的水量来支持精准农业。这种智能灌溉能够节约水和能源、减少人工劳动、改善作物健康，并促进农业可持续发展。IJARCCE","IJARCCE","2026-10-05T00:00:00Z",49,{"impact":20,"substance":89,"depth":126,"authority":114,"freshness":20,"relevant":22,"comment":127},13,"面向精准农业的物联网智能灌溉系统论文，方法常规、缺乏大规模实测数据，属细分领域技术进展，公共价值有限。",[129],{"name":122,"url":119},[131,72,27,28,132],"智慧农业","传感器",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[135],{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"status":53,"locked":14,"version":54,"method":55,"evidence":137},{"candidates":138,"agricultural_evidence":60},[139,144,148,152],{"code":41,"score":18,"supported":60,"title":140,"abstract":141,"tags":143},[42],[79,142,42],"水资源",[27],{"code":82,"score":20,"supported":60,"title":145,"abstract":146,"tags":147},[],[28,84],[131,28],{"code":47,"score":20,"supported":60,"title":149,"abstract":150,"tags":151},[],[72,70],[132,72],{"code":103,"score":114,"supported":60,"title":153,"abstract":154,"tags":156},[],[106,155],"soil",[],[158,159],"智能灌溉系统 精准农业","物联网 土壤湿度传感器 灌溉","智能灌溉系统精准农业-4296","10.17148\u002Fijarcce.2026.151010",{"doi":161,"openalex_id":163,"authors":164,"venue":122,"cited_by_count":169,"oa_url":119,"card":170,"direction":174,"ingested_from":176},"W7220437851",[165,167],{"name":166,"orcid":8},"Mr. Bhagyesh N Chaudhari",{"name":168,"orcid":8},"Dr. Dinesh D Puri",0,{"tldr":171,"method":172,"finding":173,"direction":174,"opportunity":175},"基于物联网与传感器构建智能灌溉系统，实时监测土壤与气象并自动控制灌溉。","用Arduino\u002FESP32连接土壤湿度、温湿度、降雨传感器及物联网仪表盘。","系统可按需精准供水，节水节能、减少人工并提升作物健康与可持续性。","智慧农业 \u002F 农业物联网","可延伸至多源数据融合的灌溉决策优化、边缘智能控制及大田规模化验证。","openalex","2026-10-06T23:30:07.841226Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":8,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":186,"score_detail":187,"sources":190,"tags":192,"primary_topic":194,"secondary_topics":195,"classification":197,"search_phrases":218,"slug":221,"view_count":97,"doi":222,"paper":223,"created_at":233},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":19,"substance":188,"depth":59,"authority":17,"freshness":20,"relevant":22,"comment":189},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。",[191],{"name":184,"url":181},[131,72,27,28,193],"节水农业",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[196],{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"status":53,"locked":14,"version":54,"method":55,"evidence":198},{"candidates":199,"agricultural_evidence":60},[200,205,210,214],{"code":41,"score":201,"supported":60,"title":202,"abstract":203,"tags":204},19,[42],[79,142,42],[27,193],{"code":47,"score":18,"supported":60,"title":206,"abstract":207,"tags":209},[70],[72,208,70],"internet of things",[72],{"code":82,"score":20,"supported":60,"title":211,"abstract":212,"tags":213},[],[28,84],[131,28],{"code":103,"score":114,"supported":60,"title":215,"abstract":216,"tags":217},[],[106,155],[],[219,220],"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":222,"openalex_id":224,"authors":225,"venue":184,"cited_by_count":169,"oa_url":8,"card":228,"direction":174,"ingested_from":176},"W7213308969",[226],{"name":227,"orcid":8},"M. Rathamani",{"tldr":229,"method":230,"finding":231,"direction":174,"opportunity":232},"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":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":8,"source_name":240,"source_url":237,"published_at":241,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":242,"score_detail":243,"sources":246,"tags":248,"primary_topic":251,"secondary_topics":252,"classification":257,"search_phrases":283,"slug":286,"view_count":287,"doi":288,"paper":289,"created_at":305},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":18,"substance":244,"depth":59,"authority":17,"freshness":20,"relevant":22,"comment":245},20,"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。",[247],{"name":240,"url":237},[131,249,72,27,28,250],"农业人工智能","土壤监测",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[253,254],{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"code":103,"slug":155,"name":255,"path":256,"parent_code":45},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":53,"locked":14,"version":54,"method":55,"evidence":258},{"candidates":259,"agricultural_evidence":60},[260,264,268,272,276],{"code":41,"score":18,"supported":60,"title":261,"abstract":262,"tags":263},[42],[79,142,42],[27],{"code":47,"score":76,"supported":60,"title":265,"abstract":266,"tags":267},[70],[72,70],[72],{"code":103,"score":89,"supported":60,"title":269,"abstract":270,"tags":271},[155],[106,155],[],{"code":82,"score":20,"supported":60,"title":273,"abstract":274,"tags":275},[],[28,84],[131,28],{"code":277,"score":278,"supported":60,"title":279,"abstract":280,"tags":282},"I2",4,[],[281],"artificial intelligence",[249],[284,285],"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",5,"10.22214\u002Fijraset.2026.84727",{"doi":288,"openalex_id":290,"authors":291,"venue":240,"cited_by_count":169,"oa_url":237,"card":300,"direction":174,"ingested_from":176},"W7212115545",[292,294,296,298],{"name":293,"orcid":8},"Gowri M.",{"name":295,"orcid":8},"Boomika M.",{"name":297,"orcid":8},"S. Rakshana",{"name":299,"orcid":8},"Rubali R.",{"tldr":301,"method":302,"finding":303,"direction":174,"opportunity":304},"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":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":8,"source_name":312,"source_url":309,"published_at":313,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":314,"score_detail":315,"sources":319,"tags":321,"primary_topic":323,"secondary_topics":327,"classification":330,"search_phrases":380,"slug":383,"view_count":169,"doi":384,"paper":385,"created_at":404},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":18,"substance":316,"depth":18,"authority":126,"freshness":317,"relevant":22,"comment":318},22,9,"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。",[320],{"name":312,"url":309},[131,249,72,28,322],"气候韧性",{"code":277,"slug":324,"name":325,"path":326,"parent_code":51},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[328,329],{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":53,"locked":14,"version":54,"method":55,"evidence":331},{"candidates":332,"agricultural_evidence":60},[333,343,347,353,357,365,369,375],{"code":277,"score":18,"supported":60,"title":334,"abstract":335,"tags":342},[281],[336,337,338,339,340,341],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[249],{"code":82,"score":19,"supported":60,"title":344,"abstract":345,"tags":346},[84],[28,84],[131,28],{"code":47,"score":13,"supported":60,"title":348,"abstract":349,"tags":352},[],[72,350,70,351],"边缘计算","edge computing",[72],{"code":41,"score":317,"supported":60,"title":354,"abstract":355,"tags":356},[],[79,142,42],[],{"code":358,"score":359,"supported":60,"title":360,"abstract":361,"tags":364},"H7",7,[],[362,363],"气候变化","climate change",[322],{"code":103,"score":114,"supported":60,"title":366,"abstract":367,"tags":368},[],[106,155],[],{"code":370,"score":97,"supported":60,"title":371,"abstract":372,"tags":374},"A7",[],[373],"间作",[],{"code":96,"score":97,"supported":60,"title":376,"abstract":377,"tags":379},[],[378],"养分管理",[],[381,382],"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":384,"openalex_id":386,"authors":387,"venue":312,"cited_by_count":169,"oa_url":309,"card":398,"direction":174,"ingested_from":176},"W7223121456",[388,391,393,395],{"name":389,"orcid":390},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":392,"orcid":8},"Oluwadare OYEBODE",{"name":394,"orcid":8},"Iretiayo Adelaiye",{"name":396,"orcid":397},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":399,"method":400,"finding":401,"direction":402,"opportunity":403},"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",{"id":406,"title":407,"url":408,"summary":409,"summary_zh":410,"content":8,"source_name":411,"source_url":408,"published_at":412,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":413,"score_detail":414,"sources":416,"tags":418,"primary_topic":421,"secondary_topics":422,"classification":424,"search_phrases":443,"slug":446,"view_count":169,"doi":447,"paper":448,"created_at":466},5378,"IoT-Enabled Smart Irrigation Using ESP32 and LoRa for Precision Water Management in Connectivity-Limited Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204831","In this paper, a new method for irrigation systems in agriculture has been reported. This work aims to design and implement a smart irrigation system based on the ESP32 platform and Internet of Things (IoT) technologies for optimizing water usage management. With the rapid growth of Internet of Things (IoT) technologies, where devices are connected and can be controlled through the internet, it is now feasible to design smart irrigation systems based on real-time environmental data to deliver optimal water use with minimal human intervention. In Sahara areas, where communication networks and internet connections are lacking, alternative communication protocols are necessary for data transfer. LoRa technology based on the SX1278 transceiver provides long-range, low-power communication that can achieve multi-kilometer ranges (up to 10–15 km in rural areas) under ideal conditions: line-of-sight, high antenna height, low data rate, and open rural environments between distributed sensor nodes and a central gateway. However, this theoretical range cannot be assumed in practice. Field tests under standard farm deployment conditions (ground-level placement and vegetation interference) showed that the link was lost at approximately 120 m, which validates the system at the scale of a field plot. This project used Arduino (version 2.3.7) software to program the NodeMCU (ESP32) board to link the board with the Blynk cloud application. The project is used to monitor the temperature, humidity, and soil moisture and do the command for the irrigation system, with master and slave cards using LoRa technology as a communication tool to replace communication systems like WiFi or cellular networks in deserted or remote areas, which might not offer sufficient coverage or could be too expensive to build. To address this challenge, this paper presents the design and implementation of a smart irrigation system based on the ESP32 microcontroller and LoRa (SX1278) technology, with the long-term objective of helping in the modernization of traditional agriculture and environmental sustainability. Extending the coverage to larger fields and Sahara areas would require elevated antennas, optimized LoRa parameters (spreading factor and bandwidth), multi-hop relay nodes, or multiple gateways; these strategies are proposed as future work.","本文报道了一种农业灌溉系统的新方法。本工作旨在设计和实现一种基于ESP32平台和物联网（Internet of Things, IoT）技术的智能灌溉系统，以优化用水管理。随着物联网技术的快速发展，设备得以互联并可通过互联网进行控制，如今基于实时环境数据设计智能灌溉系统，以在最少人工干预下实现最优用水已成为可能。在撒哈拉地区，由于缺乏通信网络和互联网连接，数据传输需要采用替代通信协议。基于SX1278收发器的LoRa技术可提供远距离、低功耗通信，在理想条件下（视距传输、天线高度较高、低数据速率以及分布式传感器节点与中心网关之间开阔的农村环境），其通信距离可达数公里（农村地区可达10–15 km）。然而，这一理论距离在实际中并不能假定成立。在标准农田部署条件下（地面放置和植被干扰）进行的现场测试表明，链路在约120 m处即发生中断，这验证了系统在田块尺度上的可行性。本项目使用Arduino（版本2.3.7）软件对NodeMCU（ESP32）开发板进行编程，将开发板与Blynk云应用相连接。该项目用于监测温度、湿度和土壤水分，并向灌溉系统发送指令，主从卡采用LoRa技术作为通信工具，以替代WiFi或蜂窝网络等通信系统——这些系统在荒漠或偏远地区可能覆盖不足，或建设成本过高。为应对这一挑战，本文提出了基于ESP32微控制器和LoRa（SX1278）技术的智能灌溉系统的设计与实现，其长期目标是助力传统农业的现代化和环境可持续性。要将覆盖范围扩展至更大田块和撒哈拉地区，需要架高天线、优化LoRa参数（扩频因子和带宽）、采用多跳中继节点或部署多个网关；这些策略被提出作为未来工作。","Electronics","2026-10-19T00:00:00Z",67,{"impact":17,"substance":18,"depth":19,"authority":126,"freshness":20,"relevant":22,"comment":415},"面向无网络覆盖地区的ESP32+LoRa智能灌溉系统，含实地链路测试数据，方法有参考价值但属细分技术进展，未达每日精选门槛。",[417],{"name":411,"url":408},[131,72,28,419,420],"节水灌溉","LoRa",{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},[423],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":53,"locked":14,"version":54,"method":55,"evidence":425},{"candidates":426,"agricultural_evidence":60},[427,431,435,439],{"code":47,"score":18,"supported":60,"title":428,"abstract":429,"tags":430},[70],[72,208,70],[72],{"code":41,"score":76,"supported":60,"title":432,"abstract":433,"tags":434},[42],[79,42],[419],{"code":103,"score":114,"supported":60,"title":436,"abstract":437,"tags":438},[],[106,155],[],{"code":82,"score":21,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[131,28],[444,445],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":447,"openalex_id":449,"authors":450,"venue":411,"cited_by_count":169,"oa_url":408,"card":461,"direction":174,"ingested_from":176},"W7222959710",[451,453,455,458],{"name":452,"orcid":8},"Hanfoug Salah",{"name":454,"orcid":8},"Riad Saidi",{"name":456,"orcid":457},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":459,"orcid":460},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":462,"method":463,"finding":464,"direction":174,"opportunity":465},"基于ESP32与LoRa构建智能灌溉系统，在无网络覆盖区实现精准用水管理。","ESP32采集温湿度和土壤水分，经LoRa(SX1278)传输至网关并接入Bly","实际农田条件下LoRa链路仅约120米即中断，远低于理论公里级。","可研究多跳中继、天线高度与扩频参数优化，提升偏远农田LoRa组网覆盖与可靠性。","2026-10-20T23:30:08.464791Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":472,"content":8,"source_name":473,"source_url":470,"published_at":474,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":242,"score_detail":475,"sources":477,"tags":479,"primary_topic":483,"secondary_topics":484,"classification":486,"search_phrases":521,"slug":524,"view_count":169,"doi":525,"paper":526,"created_at":549},5077,"A Trust-Boundary Reference Architecture for Secure Cyber-Physical Agriculture: Design and Software-Based Proof-of-Concept for Smart Irrigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206545","Smart irrigation is an operational technology (OT) problem as much as an Internet of Things (IoT) one: software decisions act directly on valves, pumps, pressure and water delivery, so a spoofed reading or a replayed command has physical consequences. This paper presents a technology-neutral reference architecture that makes those consequences controllable. It is organised into seven functional layers and three cross-cutting planes, six trust-boundary classes that fix which properties must be re-established at each crossing, and an auditable crown-asset inventory with an explicit trust-level rubric. Two governing execution principles enforce this structure: local safety authority, which keeps final actuation permission at the field gateway, remote terminal unit (RTU) or programmable logic controller (PLC), and feedback-aware execution, which treats a command as complete only when independent physical feedback confirms it. Building on process-aware and safety-supervision ideas from industrial control, the architecture makes physical confirmation a mandatory completion condition at the local enforcement point, which catches a fully authorised command that is nonetheless physically unsafe, a failure mode that authorisation checks alone cannot detect. These properties are examined through a deterministic, openly available software proof-of-concept that verifies the design against its own acceptance criteria; it is not a field validation. The software executes six attack and failure scenarios plus one nominal-operation control against an intentionally unsecured negative control and a secure controller, under identical, controlled conditions: the negative control is compromised in all 6 attack scenarios and the secure controller in 0\u002F6, with 0\u002F1 false positives on legitimate operation. Analytically, the design costs 21 bytes and 50–60% additional Long Range Wide Area Network (LoRaWAN) time-on-air per record, and 78% of single-step perturbations of the trust-scoring inputs leave asset classification unchanged: the actuator class (A2) is completely stable, and the gateway\u002FRTU\u002FPLC class (A3) is stable under 6 of 7 admissible perturbations (86%), changing only when its credential\u002Fupdate authority reaches the trust-anchor threshold. A sensor-level mapping from sampling rate, measurement drift and physical fault modes to the edge-verification controls they dictate, provisions for secure time, counter recovery and the choice between fail-closed and fail-open safe states, an illustrative hypothetical worked deployment example, and a transfer of the model to greenhouse, fertigation, livestock-watering and cold-chain settings show how the architecture could be applied in practice.","智能灌溉既是一个物联网(IoT)问题，也是一个运营技术(OT)问题：软件决策直接作用于阀门、水泵、压力和水流输送，因此被欺骗的读数或被重放的命令会产生物理后果。本文提出了一种技术中立的参考架构，使这些后果可控。该架构由七个功能层和三个横切面、六类信任边界(规定每次跨越边界时必须重新建立哪些属性)，以及一份可审计的核心资产清单(配有明确的信任等级评定标准)组成。两条主导性执行原则保障了这一结构：本地安全授权，即将最终执行许可保留在田间网关、远程终端单元(RTU)或可编程逻辑控制器(PLC)处；以及反馈感知执行，即只有当独立的物理反馈予以确认时，才将命令视为完成。该架构以工业控制中的过程感知和安全监督思想为基础，将物理确认设为本地执行点的强制性完成条件，从而能够捕获完全获得授权但在物理上仍不安全的命令——这是仅靠授权检查无法检测到的失效模式。这些特性通过一个确定性、公开可用的软件概念验证进行了检验，该验证依据其自身的验收标准对设计进行评估；这不是现场验证。该软件在相同且受控的条件下，针对一个故意不设防护的阴性对照和一个安全控制器，执行了六种攻击与故障场景以及一项标称运行对照：阴性对照在所有6种攻击场景中均被攻破，而安全控制器为0\u002F6，对合法运行的误报为0\u002F1。分析表明，该设计每条记录额外增加21字节和50–60%的LoRaWAN(LoRaWAN)空中时间，且信任评分输入的78%单步扰动不会改变资产分类：执行器类(A2)完全稳定，网关\u002FRTU\u002FPLC类(A3)在7种可允许扰动中有6种保持稳定(86%)，仅当其凭证\u002F更新权限达到信任锚阈值时才会改变。还提出了从采样率、测量漂移和物理故障模式到其所决定的边缘验证控制的传感器级映射，以及安全时间、计数器恢复和故障关闭与故障打开安全状态之间选择的保障措施。","Sensors","2026-10-15T00:00:00Z",{"impact":17,"substance":316,"depth":18,"authority":89,"freshness":317,"relevant":22,"comment":476},"面向智慧灌溉的信任边界参考架构论文，方法新颖、含软件验证与量化开销数据，但属概念验证、非田间实证，产业影响有限，可作为技术前沿精选。",[478],{"name":473,"url":470},[480,131,481,27,482],"数字乡村","农业物联网","网络安全",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[485],{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"status":53,"locked":14,"version":54,"method":55,"evidence":487},{"candidates":488,"agricultural_evidence":60},[489,493,497,501,507,512,517],{"code":41,"score":76,"supported":60,"title":490,"abstract":491,"tags":492},[42],[79,42],[27],{"code":47,"score":13,"supported":60,"title":494,"abstract":495,"tags":496},[],[72,208,70],[481],{"code":88,"score":97,"supported":60,"title":498,"abstract":499,"tags":500},[],[91],[],{"code":502,"score":97,"supported":60,"title":503,"abstract":504,"tags":506},"C",[],[505],"livestock",[],{"code":96,"score":97,"supported":60,"title":508,"abstract":509,"tags":511},[],[510],"fertigation",[],{"code":513,"score":21,"supported":14,"title":514,"abstract":515,"tags":516},"I6",[],[],[480,482],{"code":82,"score":22,"supported":14,"title":518,"abstract":519,"tags":520},[],[],[131],[522,523],"智能灌溉 信任边界 参考架构","LoRaWAN 灌溉 安全","智能灌溉信任边界参考架构-5077","10.3390\u002Fs26206545",{"doi":525,"openalex_id":527,"authors":528,"venue":473,"cited_by_count":169,"oa_url":470,"card":544,"direction":174,"ingested_from":176},"W7222165876",[529,532,535,538,541],{"name":530,"orcid":531},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":533,"orcid":534},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":536,"orcid":537},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":539,"orcid":540},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":542,"orcid":543},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":545,"method":546,"finding":547,"direction":174,"opportunity":548},"提出面向智慧灌溉的安全参考架构，用软件验证可抵御重放与欺骗攻击。","七层三面参考架构、信任边界分类、确定性软件概念验证与LoRaWAN开销分析。","安全控制器在6种攻击场景下0次被攻破，负控制全部被攻破，且误报率为0。","可研究边缘验证控制与采样率、漂移、故障模式的映射，以及安全时间与失效安全状态选择。","2026-10-16T23:30:08.098924Z"]