[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2777":3,"related-2777":100},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":99},2777,"Satellite Positioning-Based Precision Irrigation and Crop Health Monitoring System","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug1577","Precision agriculture requires timely information on spatial variations in soil and environmental conditions to improve irrigation efficiency and crop productivity. This paper presents a low-cost satellite positioning-based precision irrigation and crop health monitoring system using an ESP32 microcontroller, soil-moisture sensor, DHT22 temperature and relative-humidity sensor, light-intensity sensor, NPK sensor, pH sensor, and NEO-6 GPS receiver. The system acquires soil, environmental, nutrient, and positional data and associates each measurement with its geographical coordinates. Soil moisture is used as the primary parameter for irrigation control, while temperature, relative humidity, light intensity, NPK, and pH provide supplementary indicators of crop and soil conditions. The GPS receiver enables spatial tagging of measurements for location-specific field monitoring and irrigation decisions. The results demonstrate sensor-based irrigation operation, spatial variability in field conditions, GPS positioning within approximately 3–5 m under open-sky conditions, and 29.3% reduction in water consumption compared with fixed-time irrigation. The proposed system provides a low-cost platform for spatially informed irrigation and crop-condition monitoring, with potential application in small- and medium-scale agricultural fields.","精准农业需要及时获取土壤和环境条件的空间变异信息，以提高灌溉效率和作物生产力。本文提出了一种基于低成本卫星定位的精准灌溉与作物健康监测系统，采用ESP32微控制器、土壤湿度传感器、DHT22温湿度传感器、光照强度传感器、NPK传感器、pH传感器和NEO-6 GPS接收器。该系统采集土壤、环境、养分和位置数据，并将每次测量与其地理坐标关联。土壤湿度作为灌溉控制的主要参数，而温度、相对湿度、光照强度、NPK和pH则提供作物和土壤条件的辅助指标。GPS接收器实现测量的空间标记，以支持特定位置的田间监测和灌溉决策。结果表明，基于传感器的灌溉操作可行，田间条件存在空间变异，在开阔天空条件下GPS定位精度约为3–5 m，与固定时间灌溉相比用水量减少了29.3%。所提出的系统为空间信息驱动的灌溉和作物状况监测提供了一个低成本平台，具有在中小型农田中应用的潜力。",null,"International Journal of Innovative Science and Research Technology (IJISRT)","2026-09-15T00:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,6,9,1,"低成本ESP32+GPS精准灌溉系统，实测节水29.3%，方法具体数据可信，但属实验室\u002F小规模验证，产业影响有限，可作为智慧农业技术案例入选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","物联网","精准灌溉","土壤监测","卫星定位",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,51,59,66],{"code":33,"score":19,"supported":46,"title":47,"abstract":48,"tags":50},true,[34],[49,34],"灌溉",[29],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":58},"I1",7,[],[56,57],"精准农业","precision agriculture",[27],{"code":60,"score":20,"supported":46,"title":61,"abstract":62,"tags":65},"H2",[],[63,64],"土壤","soil",[],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"I5",[],[],[28],[72,73],"卫星定位 土壤监测 智慧农业 精准灌溉","卫星定位 土壤监测","卫星定位土壤监测智慧农业精准灌溉-2777",3,"10.38124\u002Fijisrt\u002F26aug1577",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":90,"oa_url":91,"card":92,"direction":96,"ingested_from":98},"W7213362841",[80,82,84,86,88],{"name":81,"orcid":9},"O. A. Akintola",{"name":83,"orcid":9},"A. N. Lawal",{"name":85,"orcid":9},"O. B. Goodtalk",{"name":87,"orcid":9},"J.O. Ayankale",{"name":89,"orcid":9},"I. Mafiana",0,"https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG1577.pdf",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"开发基于ESP32和GPS的低成本精准灌溉与作物健康监测系统，实现空间标记与节水。","ESP32、土壤湿度、DHT22、光照、NPK、pH、NEO-6 GPS，以土壤","GPS定位精度3-5米，相比定时灌溉节水29.3%，并揭示田间空间变异。","智慧农业 \u002F 农业物联网","可研究多传感器融合的智能决策算法，或结合低功耗广域网实现大田远程监测与精准灌溉。","openalex","2026-09-17T23:30:18.174616Z",{"total":20,"page":22,"page_size":20,"items":101},[102,183,294,387,463,526],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":121,"secondary_topics":122,"classification":131,"search_phrases":160,"slug":163,"view_count":164,"doi":165,"paper":166,"created_at":182},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":112,"depth":113,"authority":114,"freshness":17,"relevant":22,"comment":115},20,17,12,"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。",[117],{"name":108,"url":105},[27,119,28,120,56,30],"农业人工智能","智能灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[123,128],{"code":67,"slug":124,"name":125,"path":126,"parent_code":127},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":60,"slug":64,"name":129,"path":130,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":40,"locked":14,"version":41,"method":42,"evidence":132},{"candidates":133,"agricultural_evidence":46},[134,139,144,149,153],{"code":33,"score":18,"supported":46,"title":135,"abstract":136,"tags":138},[34],[49,137,34],"水资源",[120],{"code":67,"score":19,"supported":46,"title":140,"abstract":142,"tags":143},[141],"IoT",[28,141],[28],{"code":60,"score":145,"supported":46,"title":146,"abstract":147,"tags":148},14,[64],[63,64],[],{"code":52,"score":17,"supported":46,"title":150,"abstract":151,"tags":152},[],[56,57],[27,56],{"code":154,"score":155,"supported":46,"title":156,"abstract":157,"tags":159},"I2",4,[],[158],"artificial intelligence",[119],[161,162],"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":165,"openalex_id":167,"authors":168,"venue":108,"cited_by_count":90,"oa_url":105,"card":177,"direction":96,"ingested_from":98},"W7212115545",[169,171,173,175],{"name":170,"orcid":9},"Gowri M.",{"name":172,"orcid":9},"Boomika M.",{"name":174,"orcid":9},"S. Rakshana",{"name":176,"orcid":9},"Rubali R.",{"tldr":178,"method":179,"finding":180,"direction":96,"opportunity":181},"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":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":195,"tags":197,"primary_topic":200,"secondary_topics":201,"classification":207,"search_phrases":264,"slug":267,"view_count":90,"doi":268,"paper":269,"created_at":293},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":18,"substance":193,"depth":18,"authority":114,"freshness":21,"relevant":22,"comment":194},22,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[196],{"name":189,"url":186},[27,119,198,199,29],"遥感","作物水分胁迫",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[202,206],{"code":154,"slug":203,"name":204,"path":205,"parent_code":127},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":67,"slug":124,"name":125,"path":126,"parent_code":127},{"status":40,"locked":14,"version":41,"method":42,"evidence":208},{"candidates":209,"agricultural_evidence":46},[210,214,220,225,231,237,244,251,258],{"code":33,"score":19,"supported":46,"title":211,"abstract":212,"tags":213},[34],[49,34],[29],{"code":154,"score":13,"supported":46,"title":215,"abstract":216,"tags":219},[],[217,218,158],"机器学习","machine learning",[119],{"code":67,"score":21,"supported":46,"title":221,"abstract":222,"tags":224},[],[28,223,141],"internet of things",[],{"code":52,"score":53,"supported":46,"title":226,"abstract":227,"tags":230},[],[228,229],"数字孪生","digital twin",[27],{"code":232,"score":53,"supported":46,"title":233,"abstract":234,"tags":236},"I3",[],[198,235],"remote sensing",[198],{"code":238,"score":20,"supported":46,"title":239,"abstract":240,"tags":243},"A2",[],[241,242],"小麦","wheat",[],{"code":245,"score":20,"supported":46,"title":246,"abstract":247,"tags":250},"A3",[],[248,249],"玉米","maize",[],{"code":252,"score":20,"supported":46,"title":253,"abstract":254,"tags":257},"A5",[],[255,256],"马铃薯","potato",[],{"code":259,"score":75,"supported":46,"title":260,"abstract":261,"tags":263},"B2",[],[262],"葡萄",[],[265,266],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":268,"openalex_id":270,"authors":271,"venue":189,"cited_by_count":90,"oa_url":186,"card":287,"direction":96,"ingested_from":98},"W7223088921",[272,275,278,281,284],{"name":273,"orcid":274},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":276,"orcid":277},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":279,"orcid":280},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":282,"orcid":283},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":285,"orcid":286},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":288,"method":289,"finding":290,"direction":291,"opportunity":292},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":300,"source_url":297,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":301,"score_detail":302,"sources":305,"tags":307,"primary_topic":309,"secondary_topics":310,"classification":313,"search_phrases":362,"slug":365,"view_count":90,"doi":366,"paper":367,"created_at":386},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)",80,{"impact":18,"substance":193,"depth":18,"authority":303,"freshness":21,"relevant":22,"comment":304},13,"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。",[306],{"name":300,"url":297},[27,119,28,56,308],"气候韧性",{"code":154,"slug":203,"name":204,"path":205,"parent_code":127},[311,312],{"code":67,"slug":124,"name":125,"path":126,"parent_code":127},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":314},{"candidates":315,"agricultural_evidence":46},[316,324,329,335,339,346,350,356],{"code":154,"score":18,"supported":46,"title":317,"abstract":318,"tags":323},[158],[319,217,320,218,321,322],"深度学习","计算机视觉","deep learning","computer vision",[119],{"code":52,"score":325,"supported":46,"title":326,"abstract":327,"tags":328},16,[57],[56,57],[27,56],{"code":67,"score":13,"supported":46,"title":330,"abstract":331,"tags":334},[],[28,332,141,333],"边缘计算","edge computing",[28],{"code":33,"score":21,"supported":46,"title":336,"abstract":337,"tags":338},[],[49,137,34],[],{"code":340,"score":53,"supported":46,"title":341,"abstract":342,"tags":345},"H7",[],[343,344],"气候变化","climate change",[308],{"code":60,"score":20,"supported":46,"title":347,"abstract":348,"tags":349},[],[63,64],[],{"code":351,"score":75,"supported":46,"title":352,"abstract":353,"tags":355},"A7",[],[354],"间作",[],{"code":357,"score":75,"supported":46,"title":358,"abstract":359,"tags":361},"F3",[],[360],"养分管理",[],[363,364],"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":366,"openalex_id":368,"authors":369,"venue":300,"cited_by_count":90,"oa_url":297,"card":380,"direction":96,"ingested_from":98},"W7223121456",[370,373,375,377],{"name":371,"orcid":372},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":374,"orcid":9},"Oluwadare OYEBODE",{"name":376,"orcid":9},"Iretiayo Adelaiye",{"name":378,"orcid":379},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":381,"method":382,"finding":383,"direction":384,"opportunity":385},"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":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":394,"category":12,"cover_url":9,"hotness":395,"is_selected":14,"score":396,"score_detail":397,"sources":400,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":410,"search_phrases":440,"slug":443,"view_count":90,"doi":444,"paper":445,"created_at":462},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":193,"substance":398,"depth":113,"authority":303,"freshness":90,"relevant":22,"comment":399},21,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[401,402],{"name":393,"url":390},{"name":393,"url":403},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,119,405,406,29],"农业物联网","区块链溯源",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[409],{"code":67,"slug":124,"name":125,"path":126,"parent_code":127},{"status":40,"locked":14,"version":41,"method":42,"evidence":411},{"candidates":412,"agricultural_evidence":46},[413,417,421,425,431,436],{"code":33,"score":19,"supported":46,"title":414,"abstract":415,"tags":416},[34],[49,34],[29],{"code":67,"score":13,"supported":46,"title":418,"abstract":419,"tags":420},[],[28,223,141],[405],{"code":154,"score":155,"supported":46,"title":422,"abstract":423,"tags":424},[],[158],[119],{"code":426,"score":75,"supported":46,"title":427,"abstract":428,"tags":430},"G2",[],[429],"小农户",[],{"code":432,"score":22,"supported":14,"title":433,"abstract":434,"tags":435},"E4",[],[],[406],{"code":52,"score":22,"supported":14,"title":437,"abstract":438,"tags":439},[],[],[27],[441,442],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":444,"openalex_id":446,"authors":447,"venue":393,"cited_by_count":90,"oa_url":390,"card":456,"direction":96,"ingested_from":98},"W7223034914",[448,451,453],{"name":449,"orcid":450},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":452,"orcid":9},"Dr.T.Baskar",{"name":454,"orcid":455},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":457,"method":458,"finding":459,"direction":460,"opportunity":461},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":464,"title":465,"url":466,"summary":467,"summary_zh":468,"content":9,"source_name":469,"source_url":466,"published_at":470,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":471,"score_detail":472,"sources":474,"tags":476,"primary_topic":479,"secondary_topics":480,"classification":482,"search_phrases":502,"slug":505,"view_count":90,"doi":506,"paper":507,"created_at":525},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":114,"substance":18,"depth":325,"authority":303,"freshness":17,"relevant":22,"comment":473},"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。",[475],{"name":469,"url":466},[27,28,56,477,478],"节水灌溉","LoRa",{"code":67,"slug":124,"name":125,"path":126,"parent_code":127},[481],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":483},{"candidates":484,"agricultural_evidence":46},[485,489,493,497],{"code":67,"score":18,"supported":46,"title":486,"abstract":487,"tags":488},[141],[28,223,141],[28],{"code":33,"score":19,"supported":46,"title":490,"abstract":491,"tags":492},[34],[49,34],[477],{"code":60,"score":20,"supported":46,"title":494,"abstract":495,"tags":496},[],[63,64],[],{"code":52,"score":498,"supported":14,"title":499,"abstract":500,"tags":501},2,[],[],[27,56],[503,504],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":506,"openalex_id":508,"authors":509,"venue":469,"cited_by_count":90,"oa_url":466,"card":520,"direction":96,"ingested_from":98},"W7222959710",[510,512,514,517],{"name":511,"orcid":9},"Hanfoug Salah",{"name":513,"orcid":9},"Riad Saidi",{"name":515,"orcid":516},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":518,"orcid":519},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":521,"method":522,"finding":523,"direction":96,"opportunity":524},"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":527,"title":528,"url":529,"summary":530,"summary_zh":531,"content":9,"source_name":532,"source_url":529,"published_at":533,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":534,"score_detail":535,"sources":537,"tags":539,"primary_topic":541,"secondary_topics":542,"classification":543,"search_phrases":572,"slug":575,"view_count":498,"doi":576,"paper":577,"created_at":594},4810,"Smart water management in agriculture: convergence of sensors, imaging, AI, precision irrigation, and robotics systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrwa.2026.1973897","Agricultural irrigation systems can bear the heavy losses of water through runoff, evaporation, and deep percolation. Crop water requirements vary with crop species, growth stage, soil properties, evapotranspiration, weather, and plant water status; therefore, irrigation scheduling based on a single parameter is insufficient. Conventional water management systems often rely on uniform application, fixed scheduling, and limited real-time information, which overlooks spatial and temporal variation in soil, crop, and weather conditions and can result in water loss, over-irrigation, and under-irrigation. This review highlights studies on the integration of AI-driven sensing and imaging technologies, IoT-based precision irrigation, variable-rate technologies, digital twins, and agricultural robotics for crop water management. These approaches integrate field, crop, weather, and spatial information to support water-demand assessment, crop-stress monitoring, irrigation scheduling, site-specific application, and automated water management. The reviewed studies report water savings, improved water-use efficiency, crop performance, and more site-specific irrigation and input application across different crops and irrigation systems. The review also highlights the limitations of these systems, including sensing reliability, model transferability, connectivity, interoperability, cybersecurity, investment costs, and environmental effects. Overall, the integration of these technologies provides potential for timely and field-specific irrigation decisions and supports efficient and climate-resilient water management in agriculture.","农业灌溉系统会因径流、蒸发和深层渗漏而承受严重的水量损失。作物需水量随作物种类、生长阶段、土壤性质、蒸散量、天气和植株水分状况而变化；因此，仅基于单一参数的灌溉调度是不够的。传统的水管理系统往往依赖均匀灌溉、固定调度和有限的实时信息，忽视了土壤、作物和天气条件的时空变异，可能导致水分损失、过度灌溉和灌溉不足。本文综述了将人工智能驱动的传感与成像技术、基于物联网的精准灌溉、变量灌溉技术、数字孪生和农业机器人集成应用于作物水分管理的研究。这些方法整合了田块、作物、天气和空间信息，以支持需水评估、作物胁迫监测、灌溉调度、定点变量灌溉和自动化水分管理。所综述的研究报告了在不同作物和灌溉系统中实现的节水、水分利用效率提升、作物表现改善以及更具田块针对性的灌溉和投入品施用。本文还指出了这些系统的局限性，包括传感可靠性、模型可迁移性、连通性、互操作性、网络安全、投资成本和环境影响。总体而言，这些技术的集成可为及时且针对具体田块的灌溉决策提供可能，并支持农业中高效且具有气候韧性的水分管理。","Frontiers in Water","2026-10-12T00:00:00Z",78,{"impact":18,"substance":112,"depth":18,"authority":303,"freshness":21,"relevant":22,"comment":536},"系统综述AI传感、物联网精准灌溉与农业机器人集成，方法视角新、结论可靠，对智慧农业水资源管理有参考价值。",[538],{"name":532,"url":529},[27,119,540,228,29],"农业机器人",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":544},{"candidates":545,"agricultural_evidence":46},[546,551,555,559,563,568],{"code":33,"score":18,"supported":46,"title":547,"abstract":548,"tags":550},[34],[49,34,549],"evapotranspiration",[29],{"code":60,"score":20,"supported":46,"title":552,"abstract":553,"tags":554},[],[63,64],[],{"code":67,"score":20,"supported":46,"title":556,"abstract":557,"tags":558},[],[28,141],[],{"code":52,"score":164,"supported":46,"title":560,"abstract":561,"tags":562},[],[228],[228,27],{"code":564,"score":155,"supported":46,"title":565,"abstract":566,"tags":567},"F6",[],[540],[540],{"code":154,"score":22,"supported":14,"title":569,"abstract":570,"tags":571},[],[],[119],[573,574],"精准灌溉 传感器 人工智能","农业机器人 水资源管理","精准灌溉传感器人工智能-4810","10.3389\u002Ffrwa.2026.1973897",{"doi":576,"openalex_id":578,"authors":579,"venue":532,"cited_by_count":90,"oa_url":588,"card":589,"direction":96,"ingested_from":98},"W7221563526",[580,583,585],{"name":581,"orcid":582},"Sachin Ghanghas","https:\u002F\u002Forcid.org\u002F0000-0001-9231-2527",{"name":584,"orcid":9},"Naveen Sihag",{"name":586,"orcid":587},"Parveen Dhanger","https:\u002F\u002Forcid.org\u002F0000-0001-5250-0275","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fwater\u002Farticles\u002F10.3389\u002Ffrwa.2026.1973897\u002Fpdf",{"tldr":590,"method":591,"finding":592,"direction":96,"opportunity":593},"综述AI传感、成像、物联网精准灌溉、数字孪生与机器人集成用于作物水分管理。","综述融合AI传感成像、IoT精准灌溉、变量技术、数字孪生与农业机器人。","集成方案可节水、提升水分利用效率与作物表现，但存在传感可靠性、模型迁移等局限。","可研究多源传感与数字孪生融合的跨区域模型迁移及低成本、安全互操作的灌溉决策系统。","2026-10-13T23:30:10.375774Z"]