[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4209":3,"related-4209":107},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":106},4209,"AI-Driven Smart Irrigation System Using Edge-Based Embedded Controllers","https:\u002F\u002Fdoi.org\u002F10.31838\u002Fece\u002F03.02.04","This study brings out a new AI-based smart irrigation device aimed at solving the increasing problem of water shortage and poor agricultural procedures using emulated edge-based controllers. This combination of environmental sensing, lightweight machine learning algorithms and actuation on microcontrollers enables the system to do real time, adaptive irrigation scheduling. Their implementation of important parts like Raspberry Pi 4 as an AI inference module and an STM32 microcontroller as an actuator controller accelerates the responsiveness, increases security, and saves energy consumption since there is no need in cloud computing. Using the local climatic and soil moisture data the machine learning model identifies optimum irrigation time and duration looking at various parameters such as temperature, humidity, rain and crop-specific thresholds. The whole decision-making process is automated and carried out at the edge that makes it functional even without interruption in areas with poor or no internet connection. This was done by conducting extensive field tests within the semi-arid agricultural areas to prove the efficacy of the recommended solution. The findings show that there is a considerable gain in the efficiency of resources, with up to 38 percent of reduction of water use and 20 percent of increase in the yield of the crops contrasted to the conventional time-based irrigation schemes. Also, the system averaged a latency of only 34millisecs and consumed power at less than 2.5W which became very handy in deployment in rural areas. TinyML and its compatibility with cheap edge hardware are scalable and sustainable, which creates an efficient path to modernize the irrigation infrastructure in the developing world. This article does not only highlight the revolutionary nature of embedded AI in precision agriculture, but it precondition the further innovative development of the field of data-driven farm management and autonomous farm management. The one suggested is one of the options that could be implemented to achieve environmental sustainability, enhance the productivity of agriculture, and make smart and local decisions to use water intelligently.","本研究提出了一种基于人工智能的新型智能灌溉装置，采用模拟边缘控制器，旨在解决日益严重的缺水问题和农业操作不当问题。该系统将环境传感、轻量级机器学习算法和微控制器上的执行器相结合，能够实现实时自适应灌溉调度。其关键部分的实现方式包括将树莓派4（Raspberry Pi 4）作为AI推理模块，将STM32微控制器作为执行器控制器，从而提升了响应速度、增强了安全性并降低了能耗，因为无需依赖云计算。利用当地气候和土壤湿度数据，机器学习模型根据温度、湿度、降雨和作物特定阈值等多种参数，确定最佳灌溉时间和时长。整个决策过程自动化并在边缘端执行，使其即使在互联网连接较差或无互联网连接的地区也能不间断运行。通过在半干旱农业区进行广泛的田间测试，验证了所推荐方案的有效性。研究结果表明，资源效率有显著提升，与传统基于时间的灌溉方案相比，用水量减少了高达38%，作物产量增加了20%。此外，该系统平均延迟仅为34毫秒，功耗低于2.5瓦，在农村地区部署中非常实用。TinyML及其与低成本边缘硬件的兼容性具有可扩展性和可持续性，为发展中国家灌溉基础设施的现代化开辟了一条高效路径。本文不仅强调了嵌入式AI在精准农业中的革命性，还为数据驱动农场管理和自主农场管理领域的进一步创新发展奠定了基础。所提出的方案是实现环境可持续性、提高农业生产力以及做出智能本地化决策以合理用水的一种可行选择。",null,"Progress in Electronics and Communication Engineering","https:\u002F\u002Fecejournals.in\u002Findex.php\u002FPECE\u002Farticle\u002Fview\u002F427","2026-10-19T00:00:00Z","论文",10,false,74,{"impact":18,"substance":19,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,13,3,1,"边缘AI灌溉系统实测节水38%、增产20%，方法新颖数据扎实，但论文发表于两周后、时效性偏弱，适合作为技术类精选。",[25],{"name":10,"url":11},[27,28,29,30,31],"智慧农业","农业人工智能","边缘计算","智能灌溉","精准农业",{"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":15,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,67,75,81],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},15,true,[34],[50,34],"灌溉",[30],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"I1",8,[],[31,57],"precision agriculture",[27,31],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":66},"I2",7,[],[64,65],"机器学习","machine learning",[28],{"code":68,"score":69,"supported":47,"title":70,"abstract":71,"tags":74},"H2",6,[],[72,73],"土壤","soil",[],{"code":76,"score":21,"supported":47,"title":77,"abstract":78,"tags":80},"H7",[],[79],"干旱",[],{"code":82,"score":22,"supported":15,"title":83,"abstract":84,"tags":85},"I5",[],[],[29],[87,88],"Raspberry Pi STM32 智能灌溉","TinyML 边缘计算 节水灌溉","RaspberryPiSTM32智能灌溉-4209",2,"10.31838\u002Fece\u002F03.02.04",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":22,"oa_url":11,"card":99,"direction":103,"ingested_from":105},"W7219822766",[95,97],{"name":96,"orcid":9},"Fahad Al-Jame",{"name":98,"orcid":9},"Perera Manthila",{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"提出基于边缘嵌入式控制器的AI智能灌溉系统，实现实时自适应灌溉调度。","Raspberry Pi 4做AI推理、STM32做执行控制，结合环境传感与轻量","半干旱区试验显示节水38%、增产20%，延迟34毫秒、功耗低于2.5W。","智慧农业 \u002F 农业物联网","可探索TinyML模型在多种作物与土壤气候下的泛化能力及低成本边缘节点组网协同。","openalex","2026-10-05T23:30:08.862496Z",{"total":69,"page":22,"page_size":69,"items":108},[109,201,279,360,448,510],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":13,"cover_url":9,"hotness":14,"is_selected":47,"score":117,"score_detail":118,"sources":122,"tags":124,"primary_topic":126,"secondary_topics":132,"classification":134,"search_phrases":171,"slug":174,"view_count":22,"doi":175,"paper":176,"created_at":200},4371,"Intelligent greenhouse irrigation control via multilayer perceptron neural network climate prediction integrated with fuzzy logic control","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2026.111587","Conventional irrigation methods rely on indoor sensor feedback, which fails to anticipate rapid microclimate fluctuations and often leads to over-irrigation or water stress. Additionally, traditional controllers used in the watering process lack the ability to adjust water flow rate dynamically, resulting in inefficient water usage and energy waste. Intelligent Greenhouse Irrigation Management (IGIM) remains a critical challenge for achieving sustainable, high-yield greenhouse agriculture. Precise irrigation management is especially important for crops like tomatoes, where both timing and quantity directly influence productivity and energy consumption. To address these challenges, this paper proposes a hybrid intelligent system that integrates a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with an Adaptive Fuzzy Logic Controller (AFLC). The MLP-ANN forecasts key indoor variables such as temperature, humidity, and soil moisture, achieving R² values of 0.999. The Root Mean Square Errors of 0.266 °C, 0.254%, and 0.808%, respectively. These predictions directly feed into the AFLC, which employs an adaptive rule base to dynamically adjust irrigation flow rates in real time (0–18 LPH), significantly enhancing water-use efficiency and reducing waste to save energy consumption in the watering process. The key novelty of this work is the direct integration of MLP-predicted microclimate variables into the antecedent of adaptive fuzzy rules, enabling the controller to anticipate and respond to climate variations before they affect crop conditions. Unlike conventional reactive or static rule-based systems, the proposed MLP-AFLC framework creates a closed-loop coupling between forecasting and actuation, allowing predictive, rather than threshold-based, irrigation decisions. By integrating predictive intelligence with adaptive control, the proposed MLP-AFLC framework provides a robust, autonomous, and sustainable solution for greenhouse management, advancing precision agriculture in highly dynamic environments.","传统灌溉方法依赖室内传感器反馈，无法预判微气候的快速波动，常导致过度灌溉或水分胁迫。此外，灌溉过程中使用的传统控制器缺乏动态调节流速的能力，造成水资源利用效率低下和能源浪费。智能温室灌溉管理（IGIM）仍是实现可持续、高产温室农业的关键挑战。对于番茄等作物而言，精准灌溉管理尤为重要，因为灌溉时机和水量直接影响产量和能耗。为解决这些挑战，本文提出了一种将多层感知器人工神经网络（MLP-ANN）与自适应模糊逻辑控制器（AFLC）相集成的混合智能系统。MLP-ANN预测温度、湿度和土壤湿度等关键室内变量，R²值达到0.999，均方根误差分别为0.266 °C、0.254%和0.808%。这些预测结果直接输入AFLC，后者采用自适应规则库实时动态调节灌溉流速（0–18 LPH），显著提高水资源利用效率并减少浪费，从而在灌溉过程中节约能耗。本工作的关键创新在于将MLP预测的微气候变量直接集成到自适应模糊规则的前件中，使控制器能够在气候变化影响作物状况之前进行预判和响应。与传统反应式或静态规则系统不同，所提出的MLP-AFLC框架在预测与执行之间建立了闭环耦合，实现了基于预测而非基于阈值的灌溉决策。通过将预测智能与自适应控制相结合，所提出的MLP-AFLC框架为温室管理提供了一种稳健、自主且可持续的解决方案，推动了高度动态环境下的精准农业发展。","Computers & Electrical Engineering","2026-10-06T00:00:00Z",81,{"impact":18,"substance":19,"depth":18,"authority":119,"freshness":120,"relevant":22,"comment":121},14,9,"提出MLP-ANN与自适应模糊控制融合的温室预测性灌溉框架，R²达0.999，方法新颖且数据可靠，对精准农业节水节能有实质参考价值。",[123],{"name":115,"url":112},[27,28,30,31,125],"温室种植",{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[133],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":15,"version":41,"method":42,"evidence":135},{"candidates":136,"agricultural_evidence":47},[137,142,148,152,156,162,167],{"code":33,"score":18,"supported":47,"title":138,"abstract":139,"tags":141},[34],[50,140,34],"水资源",[30],{"code":127,"score":46,"supported":47,"title":143,"abstract":145,"tags":147},[144],"greenhouse",[146,144],"温室",[125],{"code":53,"score":54,"supported":47,"title":149,"abstract":150,"tags":151},[],[31,57],[27,31],{"code":68,"score":69,"supported":47,"title":153,"abstract":154,"tags":155},[],[72,73],[],{"code":157,"score":21,"supported":47,"title":158,"abstract":159,"tags":161},"B1",[],[160],"番茄",[],{"code":76,"score":21,"supported":47,"title":163,"abstract":164,"tags":166},[],[165],"气候变化",[],{"code":60,"score":22,"supported":15,"title":168,"abstract":169,"tags":170},[],[],[28],[172,173],"MLP-AFLC 温室灌溉","神经网络 模糊控制 灌溉","MLP-AFLC温室灌溉-4371","10.1016\u002Fj.compeleceng.2026.111587",{"doi":175,"openalex_id":177,"authors":178,"venue":115,"cited_by_count":193,"oa_url":112,"card":194,"direction":199,"ingested_from":105},"W7220746994",[179,182,185,187,190],{"name":180,"orcid":181},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":183,"orcid":184},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":186,"orcid":9},"Abdelkader Mami",{"name":188,"orcid":189},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":191,"orcid":192},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",0,{"tldr":195,"method":196,"finding":197,"direction":103,"opportunity":198},"提出MLP-ANN预测温室微气候并驱动自适应模糊控制器，实现预测性智能灌溉。","MLP神经网络预测温湿度与土壤水分，结合自适应模糊逻辑控制流量。","预测R²达0.999，模糊控制器动态调节0-18 LPH流量，显著节水节能。","可探索多作物多环境迁移学习与边缘部署，降低预测模型算力需求。","农业人工智能与决策模型","2026-10-07T23:30:43.848308Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":208,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":209,"score_detail":210,"sources":215,"tags":217,"primary_topic":220,"secondary_topics":221,"classification":230,"search_phrases":256,"slug":259,"view_count":260,"doi":261,"paper":262,"created_at":278},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":211,"depth":212,"authority":213,"freshness":54,"relevant":22,"comment":214},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。",[216],{"name":207,"url":204},[27,28,218,30,31,219],"物联网","土壤监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[222,227],{"code":82,"slug":223,"name":224,"path":225,"parent_code":226},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":68,"slug":73,"name":228,"path":229,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":40,"locked":15,"version":41,"method":42,"evidence":231},{"candidates":232,"agricultural_evidence":47},[233,237,242,246,250],{"code":33,"score":18,"supported":47,"title":234,"abstract":235,"tags":236},[34],[50,140,34],[30],{"code":82,"score":46,"supported":47,"title":238,"abstract":240,"tags":241},[239],"IoT",[218,239],[218],{"code":68,"score":119,"supported":47,"title":243,"abstract":244,"tags":245},[73],[72,73],[],{"code":53,"score":54,"supported":47,"title":247,"abstract":248,"tags":249},[],[31,57],[27,31],{"code":60,"score":251,"supported":47,"title":252,"abstract":253,"tags":255},4,[],[254],"artificial intelligence",[28],[257,258],"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":261,"openalex_id":263,"authors":264,"venue":207,"cited_by_count":193,"oa_url":204,"card":273,"direction":103,"ingested_from":105},"W7212115545",[265,267,269,271],{"name":266,"orcid":9},"Gowri M.",{"name":268,"orcid":9},"Boomika M.",{"name":270,"orcid":9},"S. Rakshana",{"name":272,"orcid":9},"Rubali R.",{"tldr":274,"method":275,"finding":276,"direction":103,"opportunity":277},"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":280,"title":281,"url":282,"summary":283,"summary_zh":9,"content":9,"source_name":284,"source_url":282,"published_at":285,"category":13,"cover_url":9,"hotness":286,"is_selected":15,"score":16,"score_detail":287,"sources":289,"tags":299,"primary_topic":300,"secondary_topics":301,"classification":307,"search_phrases":339,"slug":342,"view_count":251,"doi":343,"paper":344,"created_at":359},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",55,{"impact":18,"substance":19,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":288},"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[290,291,294,296],{"name":284,"url":282},{"name":292,"url":293},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":292,"url":295},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":297,"url":298},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[27,28,29,218,30],{"code":82,"slug":223,"name":224,"path":225,"parent_code":226},[302,303],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":60,"slug":304,"name":305,"path":306,"parent_code":226},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":15,"version":41,"method":42,"evidence":308},{"candidates":309,"agricultural_evidence":47},[310,317,321,325,331,335],{"code":82,"score":311,"supported":47,"title":312,"abstract":314,"tags":316},27,[239,313],"edge computing",[315,239,313],"internet of things",[218,29],{"code":33,"score":213,"supported":47,"title":318,"abstract":319,"tags":320},[34],[34],[30],{"code":60,"score":213,"supported":47,"title":322,"abstract":323,"tags":324},[65],[65],[28],{"code":326,"score":21,"supported":47,"title":327,"abstract":328,"tags":330},"A8",[],[329],"food security",[],{"code":68,"score":21,"supported":47,"title":332,"abstract":333,"tags":334},[],[73],[],{"code":53,"score":22,"supported":15,"title":336,"abstract":337,"tags":338},[],[],[27],[340,341],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业智能灌溉边缘计算-1249","10.3389\u002Ffsufs.2026.1827416",{"doi":343,"openalex_id":345,"authors":346,"venue":284,"cited_by_count":193,"oa_url":353,"card":354,"direction":103,"ingested_from":105},"W7204189894",[347,350],{"name":348,"orcid":349},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":351,"orcid":352},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":355,"method":356,"finding":357,"direction":103,"opportunity":358},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":367,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":368,"score_detail":369,"sources":371,"tags":373,"primary_topic":375,"secondary_topics":376,"classification":379,"search_phrases":424,"slug":427,"view_count":193,"doi":428,"paper":429,"created_at":447},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":19,"depth":18,"authority":20,"freshness":120,"relevant":22,"comment":370},"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。",[372],{"name":366,"url":363},[27,28,218,31,374],"气候韧性",{"code":60,"slug":304,"name":305,"path":306,"parent_code":226},[377,378],{"code":82,"slug":223,"name":224,"path":225,"parent_code":226},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":15,"version":41,"method":42,"evidence":380},{"candidates":381,"agricultural_evidence":47},[382,390,395,399,403,408,412,418],{"code":60,"score":18,"supported":47,"title":383,"abstract":384,"tags":389},[254],[385,64,386,65,387,388],"深度学习","计算机视觉","deep learning","computer vision",[28],{"code":53,"score":391,"supported":47,"title":392,"abstract":393,"tags":394},16,[57],[31,57],[27,31],{"code":82,"score":14,"supported":47,"title":396,"abstract":397,"tags":398},[],[218,29,239,313],[218],{"code":33,"score":120,"supported":47,"title":400,"abstract":401,"tags":402},[],[50,140,34],[],{"code":76,"score":61,"supported":47,"title":404,"abstract":405,"tags":407},[],[165,406],"climate change",[374],{"code":68,"score":69,"supported":47,"title":409,"abstract":410,"tags":411},[],[72,73],[],{"code":413,"score":21,"supported":47,"title":414,"abstract":415,"tags":417},"A7",[],[416],"间作",[],{"code":419,"score":21,"supported":47,"title":420,"abstract":421,"tags":423},"F3",[],[422],"养分管理",[],[425,426],"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":428,"openalex_id":430,"authors":431,"venue":366,"cited_by_count":193,"oa_url":363,"card":442,"direction":103,"ingested_from":105},"W7223121456",[432,435,437,439],{"name":433,"orcid":434},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":436,"orcid":9},"Oluwadare OYEBODE",{"name":438,"orcid":9},"Iretiayo Adelaiye",{"name":440,"orcid":441},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":443,"method":444,"finding":445,"direction":199,"opportunity":446},"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。","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":449,"title":450,"url":451,"summary":452,"summary_zh":453,"content":9,"source_name":454,"source_url":451,"published_at":455,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":456,"score_detail":457,"sources":459,"tags":461,"primary_topic":464,"secondary_topics":465,"classification":467,"search_phrases":486,"slug":489,"view_count":193,"doi":490,"paper":491,"created_at":509},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":391,"substance":19,"depth":18,"authority":119,"freshness":54,"relevant":22,"comment":458},"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。",[460],{"name":454,"url":451},[27,28,30,462,463],"节水农业","光伏农业",{"code":82,"slug":223,"name":224,"path":225,"parent_code":226},[466],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":15,"version":41,"method":42,"evidence":468},{"candidates":469,"agricultural_evidence":47},[470,474,478,482],{"code":82,"score":212,"supported":47,"title":471,"abstract":472,"tags":473},[239],[218,315,239],[],{"code":33,"score":391,"supported":47,"title":475,"abstract":476,"tags":477},[34],[50,34],[30,462],{"code":60,"score":251,"supported":47,"title":479,"abstract":480,"tags":481},[],[254],[28],{"code":53,"score":22,"supported":15,"title":483,"abstract":484,"tags":485},[],[],[27],[487,488],"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":490,"openalex_id":492,"authors":493,"venue":454,"cited_by_count":193,"oa_url":503,"card":504,"direction":103,"ingested_from":105},"W7221571268",[494,497,500],{"name":495,"orcid":496},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":498,"orcid":499},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":501,"orcid":502},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":505,"method":506,"finding":507,"direction":103,"opportunity":508},"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":511,"title":512,"url":513,"summary":514,"summary_zh":515,"content":9,"source_name":516,"source_url":513,"published_at":455,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":517,"score_detail":518,"sources":521,"tags":523,"primary_topic":526,"secondary_topics":527,"classification":529,"search_phrases":560,"slug":563,"view_count":193,"doi":564,"paper":565,"created_at":576},4801,"CROPINTEL DATASET: A COMPREHENSIVE AGROENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS","https:\u002F\u002Fdoi.org\u002F10.30546\u002F09090.2025.210.005","The growing demand for sustainable agricultural intensification necessitates advanced tools for optimizing crop selection and resource management.This study presents an integrated analytical framework using machine learning to derive agro-environmental intelligence from the comprehensive CropIntel dataset.We evaluated a suite of over fifteen machine learning algorithms for three critical precision agriculture tasks: crop classification, seasonal irrigation requirement prediction, and yield potential forecasting.The results demonstrate the exceptional capability of tree-based models in this domain.Notably, Decision Tree, Bagging, and Gaussian Naïve Bayes classifiers achieved near-perfect accuracy (Acc≈99.7%) in identifying suitable crops based on climatic and edaphic conditions.For regression tasks, LightGBM and Histogram Gradient Boosting models proved most effective, explaining approximately 84% of the variance in irrigation needs (R²≈0.84)and about 70% of the variance in yield potential (R²≈0.696).These findings underscore the potential of machine learning to create powerful decision support systems that can guide crop selection, optimize water allocation, and provide reliable yield forecasts.This research contributes to a holistic, data-driven methodology that can enhance the efficiency and sustainability of agricultural practices.","对可持续农业集约化的需求日益增长，亟需先进工具来优化作物选择与资源管理。本研究提出一个集成的分析框架，利用机器学习从综合性的CropIntel数据集中提取农业环境智能信息。我们针对三个关键精准农业任务评估了超过十五种机器学习算法：作物分类、季节性灌溉需求预测和产量潜力预报。结果表明，基于树的模型在该领域具有卓越能力。值得注意的是，决策树、装袋法和高斯朴素贝叶斯分类器在基于气候和土壤条件识别适宜作物方面达到了近乎完美的准确率（Acc≈99.7%）。在回归任务中，LightGBM和直方图梯度提升模型最为有效，分别解释了约84%的灌溉需求方差（R²≈0.84）和约70%的产量潜力方差（R²≈0.696）。这些发现凸显了机器学习在构建强大决策支持系统方面的潜力，可指导作物选择、优化水资源分配并提供可靠的产量预报。本研究为一种整体性、数据驱动的方法论做出了贡献，有助于提升农业实践的效率与可持续性。","JOURNAL OF BAKU ENGINEERING UNIVERSITY- MATHEMATICS AND COMPUTER SCIENCE",72,{"impact":213,"substance":519,"depth":212,"authority":20,"freshness":120,"relevant":22,"comment":520},21,"构建CropIntel农业环境数据集并系统评测15种以上机器学习算法，在作物分类、灌溉需求与产量预测上给出可复现结论，方法扎实但属细分领域学术进展，产业影响有限。",[522],{"name":516,"url":513},[27,28,524,31,525],"产量预测","灌溉决策",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[528],{"code":60,"slug":304,"name":305,"path":306,"parent_code":226},{"status":40,"locked":15,"version":41,"method":42,"evidence":530},{"candidates":531,"agricultural_evidence":47},[532,536,541,545,549,555],{"code":33,"score":18,"supported":47,"title":533,"abstract":534,"tags":535},[34],[50,140,34],[525],{"code":60,"score":14,"supported":47,"title":537,"abstract":538,"tags":540},[],[64,65,539],"decision support",[28],{"code":53,"score":54,"supported":47,"title":542,"abstract":543,"tags":544},[],[31,57],[27,31],{"code":68,"score":21,"supported":47,"title":546,"abstract":547,"tags":548},[],[72],[],{"code":550,"score":21,"supported":47,"title":551,"abstract":552,"tags":554},"H8",[],[553],"可持续农业",[],{"code":556,"score":22,"supported":15,"title":557,"abstract":558,"tags":559},"I3",[],[],[524],[561,562],"CropIntel 数据集 作物分类","机器学习 灌溉需求 预测","CropIntel数据集作物分类-4801","10.30546\u002F09090.2025.210.005",{"doi":564,"openalex_id":566,"authors":567,"venue":516,"cited_by_count":193,"oa_url":513,"card":571,"direction":103,"ingested_from":105},"W7221521521",[568],{"name":569,"orcid":570},"Artughrul Gayibov","https:\u002F\u002Forcid.org\u002F0009-0009-7349-0286",{"tldr":572,"method":573,"finding":574,"direction":199,"opportunity":575},"基于CropIntel数据集，用15+种机器学习算法实现作物分类、灌溉需求预测与产量潜力预测。","CropIntel农业环境数据集，对比15+种机器学习算法，含树模型与贝叶斯分类","树模型分类准确率近99.7%，LightGBM等回归模型对灌溉需求R²约0.84、产量约0.70。","可探索多任务联合建模与模型可解释性，并将预测结果嵌入实时灌溉决策系统。","2026-10-13T23:30:08.688669Z"]