[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4007":3,"related-4007":101},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":77,"slug":80,"view_count":81,"doi":82,"paper":83,"created_at":100},4007,"Development of an Iot-Based Automated Plant Monitoring and Watering System Using Two-Input Sugeno Fuzzy Logic","https:\u002F\u002Fdoi.org\u002F10.59688\u002Frdmmvz13","Manual plant irrigation often leads to inefficient water usage and delayed responses to changing environmental conditions. This study aims to develop and implement an Internet of Things (IoT)-based monitoring and automatic irrigation system using ESP32 and ESP32-CAM. The proposed system continuously monitors air temperature, humidity, soil moisture, and light intensity while simultaneously capturing plant images for remote observation through a web-based interface. A zero-order Sugeno Fuzzy Logic algorithm is employed to determine irrigation duration based on soil moisture and light intensity inputs, enabling adaptive watering decisions according to environmental conditions. Sensor data and captured images are transmitted to a remote server and displayed through a monitoring website for real-time access. Outdoor testing demonstrated that the system was able to continuously monitor environmental parameters, perform adaptive irrigation based on fuzzy inference results, successfully transmit sensor data to the database, and synchronize captured images with the web platform. The experimental results indicate that the proposed system can provide reliable monitoring and automated irrigation functions, supporting the implementation of IoT-based smart agriculture applications.","人工灌溉往往导致用水效率低下，并且对不断变化的环境条件响应滞后。本研究旨在开发并实现一个基于物联网（IoT）的监测与自动灌溉系统，采用ESP32和ESP32-CAM。所提出的系统持续监测空气温度、湿度、土壤湿度和光照强度，同时采集植物图像，通过基于网页的界面进行远程观察。系统采用零阶Sugeno模糊逻辑算法，根据土壤湿度和光照强度输入确定灌溉时长，从而依据环境条件实现自适应浇水决策。传感器数据和采集的图像被传输至远程服务器，并通过监测网站显示，以供实时访问。户外测试表明，该系统能够持续监测环境参数，基于模糊推理结果执行自适应灌溉，成功将传感器数据传输至数据库，并使采集的图像与网络平台同步。实验结果表明，所提出的系统能够提供可靠的监测和自动灌溉功能，支持基于物联网的智慧农业应用的实施。",null,"BULLETIN OF NETWORK ENGINEER AND INFORMATICS","2026-10-02T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":13,"relevant":20,"comment":21},8,18,15,1,"基于ESP32与Sugeno模糊逻辑的IoT自动灌溉系统研究，方法具体、实验完整，对智慧农业应用有参考价值，但属细分技术论文，公共影响有限。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","物联网","智能灌溉","环境监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,50,58,64,72],{"code":31,"score":18,"supported":44,"title":45,"abstract":47,"tags":49},true,[46],"IoT",[27,48,46],"internet of things",[27],{"code":51,"score":52,"supported":44,"title":53,"abstract":54,"tags":57},"H3",7,[],[55,56],"灌溉","irrigation",[28],{"code":59,"score":52,"supported":44,"title":60,"abstract":61,"tags":63},"I1",[],[25,62],"smart agriculture",[25],{"code":65,"score":66,"supported":44,"title":67,"abstract":68,"tags":71},"H2",6,[],[69,70],"土壤","soil",[],{"code":73,"score":20,"supported":14,"title":74,"abstract":75,"tags":76},"I2",[],[],[26],[78,79],"ESP32 自动灌溉 系统","Sugeno 模糊逻辑 灌溉","ESP32自动灌溉系统-4007",2,"10.59688\u002Frdmmvz13",{"doi":82,"openalex_id":84,"authors":85,"venue":10,"cited_by_count":92,"oa_url":6,"card":93,"direction":97,"ingested_from":99},"W7215131860",[86,88,90],{"name":87,"orcid":9},"Muhammad Viki Agung Prakoso",{"name":89,"orcid":9},"Ahmad Taqwa",{"name":91,"orcid":9},"Nasron",0,{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"开发基于ESP32与ESP32-CAM的物联网植物监测与自动灌溉系统，用Sugeno模糊逻辑决策浇水","ESP32\u002FESP32-CAM采集温湿度、土壤水分、光照与图像，零阶Sugeno","系统可连续监测环境参数、自适应灌溉，并成功将数据与图像同步至网页平台。","智慧农业 \u002F 农业物联网","可引入作物生长模型或机器学习优化模糊规则，并结合作物图像实现缺水胁迫的视觉诊断。","openalex","2026-10-02T23:30:11.514000Z",{"total":66,"page":20,"page_size":66,"items":102},[103,194,266,347,437,500],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":119,"tags":121,"primary_topic":123,"secondary_topics":127,"classification":133,"search_phrases":171,"slug":174,"view_count":81,"doi":175,"paper":176,"created_at":193},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",72,{"impact":113,"substance":114,"depth":115,"authority":116,"freshness":117,"relevant":20,"comment":118},12,21,17,13,9,"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[120],{"name":109,"url":106},[25,26,122,27,28],"机器学习",{"code":51,"slug":56,"name":124,"path":125,"parent_code":126},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[128,132],{"code":73,"slug":129,"name":130,"path":131,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":134},{"candidates":135,"agricultural_evidence":44},[136,141,146,150,154,161,167],{"code":51,"score":18,"supported":44,"title":137,"abstract":138,"tags":140},[56],[55,139,56],"水资源",[28],{"code":73,"score":116,"supported":44,"title":142,"abstract":144,"tags":145},[143],"artificial intelligence",[122],[26,122],{"code":31,"score":117,"supported":44,"title":147,"abstract":148,"tags":149},[46],[],[27],{"code":65,"score":66,"supported":44,"title":151,"abstract":152,"tags":153},[],[69,70],[],{"code":155,"score":156,"supported":44,"title":157,"abstract":158,"tags":160},"H7",3,[],[159],"干旱",[],{"code":162,"score":156,"supported":44,"title":163,"abstract":164,"tags":166},"H8",[],[165],"可持续农业",[],{"code":59,"score":20,"supported":14,"title":168,"abstract":169,"tags":170},[],[],[25],[172,173],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":175,"openalex_id":177,"authors":178,"venue":109,"cited_by_count":92,"oa_url":106,"card":188,"direction":97,"ingested_from":99},"W7213546497",[179,182,185],{"name":180,"orcid":181},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":183,"orcid":184},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":186,"orcid":187},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":189,"method":190,"finding":191,"direction":97,"opportunity":192},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":206,"tags":208,"primary_topic":211,"secondary_topics":212,"classification":217,"search_phrases":243,"slug":246,"view_count":247,"doi":248,"paper":249,"created_at":265},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":204,"depth":115,"authority":113,"freshness":17,"relevant":20,"comment":205},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。",[207],{"name":200,"url":197},[25,26,27,28,209,210],"精准农业","土壤监测",{"code":51,"slug":56,"name":124,"path":125,"parent_code":126},[213,214],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":65,"slug":70,"name":215,"path":216,"parent_code":126},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":38,"locked":14,"version":39,"method":40,"evidence":218},{"candidates":219,"agricultural_evidence":44},[220,224,228,233,238],{"code":51,"score":18,"supported":44,"title":221,"abstract":222,"tags":223},[56],[55,139,56],[28],{"code":31,"score":19,"supported":44,"title":225,"abstract":226,"tags":227},[46],[27,46],[27],{"code":65,"score":229,"supported":44,"title":230,"abstract":231,"tags":232},14,[70],[69,70],[],{"code":59,"score":17,"supported":44,"title":234,"abstract":235,"tags":237},[],[209,236],"precision agriculture",[25,209],{"code":73,"score":239,"supported":44,"title":240,"abstract":241,"tags":242},4,[],[143],[26],[244,245],"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":248,"openalex_id":250,"authors":251,"venue":200,"cited_by_count":92,"oa_url":197,"card":260,"direction":97,"ingested_from":99},"W7212115545",[252,254,256,258],{"name":253,"orcid":9},"Gowri M.",{"name":255,"orcid":9},"Boomika M.",{"name":257,"orcid":9},"S. Rakshana",{"name":259,"orcid":9},"Rubali R.",{"tldr":261,"method":262,"finding":263,"direction":97,"opportunity":264},"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":267,"title":268,"url":269,"summary":270,"summary_zh":9,"content":9,"source_name":271,"source_url":269,"published_at":272,"category":12,"cover_url":9,"hotness":273,"is_selected":14,"score":274,"score_detail":275,"sources":278,"tags":288,"primary_topic":290,"secondary_topics":291,"classification":294,"search_phrases":326,"slug":329,"view_count":239,"doi":330,"paper":331,"created_at":346},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",55,74,{"impact":18,"substance":276,"depth":18,"authority":116,"freshness":156,"relevant":20,"comment":277},22,"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。",[279,280,283,285],{"name":271,"url":269},{"name":281,"url":282},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":281,"url":284},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":286,"url":287},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[25,26,289,27,28],"边缘计算",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[292,293],{"code":51,"slug":56,"name":124,"path":125,"parent_code":126},{"code":73,"slug":129,"name":130,"path":131,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":295},{"candidates":296,"agricultural_evidence":44},[297,303,307,312,318,322],{"code":31,"score":298,"supported":44,"title":299,"abstract":301,"tags":302},27,[46,300],"edge computing",[48,46,300],[27,289],{"code":51,"score":113,"supported":44,"title":304,"abstract":305,"tags":306},[56],[56],[28],{"code":73,"score":113,"supported":44,"title":308,"abstract":310,"tags":311},[309],"machine learning",[309],[26],{"code":313,"score":156,"supported":44,"title":314,"abstract":315,"tags":317},"A8",[],[316],"food security",[],{"code":65,"score":156,"supported":44,"title":319,"abstract":320,"tags":321},[],[70],[],{"code":59,"score":20,"supported":14,"title":323,"abstract":324,"tags":325},[],[],[25],[327,328],"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":330,"openalex_id":332,"authors":333,"venue":271,"cited_by_count":92,"oa_url":340,"card":341,"direction":97,"ingested_from":99},"W7204189894",[334,337],{"name":335,"orcid":336},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":338,"orcid":339},"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":342,"method":343,"finding":344,"direction":97,"opportunity":345},"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":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":354,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":355,"score_detail":356,"sources":358,"tags":360,"primary_topic":362,"secondary_topics":363,"classification":366,"search_phrases":412,"slug":415,"view_count":92,"doi":416,"paper":417,"created_at":436},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":276,"depth":18,"authority":116,"freshness":117,"relevant":20,"comment":357},"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。",[359],{"name":353,"url":350},[25,26,27,209,361],"气候韧性",{"code":73,"slug":129,"name":130,"path":131,"parent_code":35},[364,365],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":51,"slug":56,"name":124,"path":125,"parent_code":126},{"status":38,"locked":14,"version":39,"method":40,"evidence":367},{"candidates":368,"agricultural_evidence":44},[369,377,382,386,390,396,400,406],{"code":73,"score":18,"supported":44,"title":370,"abstract":371,"tags":376},[143],[372,122,373,309,374,375],"深度学习","计算机视觉","deep learning","computer vision",[26],{"code":59,"score":378,"supported":44,"title":379,"abstract":380,"tags":381},16,[236],[209,236],[25,209],{"code":31,"score":13,"supported":44,"title":383,"abstract":384,"tags":385},[],[27,289,46,300],[27],{"code":51,"score":117,"supported":44,"title":387,"abstract":388,"tags":389},[],[55,139,56],[],{"code":155,"score":52,"supported":44,"title":391,"abstract":392,"tags":395},[],[393,394],"气候变化","climate change",[361],{"code":65,"score":66,"supported":44,"title":397,"abstract":398,"tags":399},[],[69,70],[],{"code":401,"score":156,"supported":44,"title":402,"abstract":403,"tags":405},"A7",[],[404],"间作",[],{"code":407,"score":156,"supported":44,"title":408,"abstract":409,"tags":411},"F3",[],[410],"养分管理",[],[413,414],"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":416,"openalex_id":418,"authors":419,"venue":353,"cited_by_count":92,"oa_url":350,"card":430,"direction":97,"ingested_from":99},"W7223121456",[420,423,425,427],{"name":421,"orcid":422},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":424,"orcid":9},"Oluwadare OYEBODE",{"name":426,"orcid":9},"Iretiayo Adelaiye",{"name":428,"orcid":429},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":431,"method":432,"finding":433,"direction":434,"opportunity":435},"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":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":9,"source_name":443,"source_url":440,"published_at":444,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":445,"sources":447,"tags":449,"primary_topic":452,"secondary_topics":453,"classification":454,"search_phrases":484,"slug":487,"view_count":92,"doi":488,"paper":489,"created_at":499},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering","2026-10-16T00:00:00Z",{"impact":378,"substance":114,"depth":115,"authority":113,"freshness":117,"relevant":20,"comment":446},"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[448],{"name":443,"url":440},[25,26,27,450,451],"作物胁迫","遥感监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":455},{"candidates":456,"agricultural_evidence":44},[457,461,468,472,476,480],{"code":31,"score":18,"supported":44,"title":458,"abstract":459,"tags":460},[46],[27,48,46],[27],{"code":462,"score":52,"supported":44,"title":463,"abstract":464,"tags":467},"I3",[],[465,466],"遥感","remote sensing",[451],{"code":65,"score":66,"supported":44,"title":469,"abstract":470,"tags":471},[],[69,70],[],{"code":51,"score":66,"supported":44,"title":473,"abstract":474,"tags":475},[],[55,56],[],{"code":59,"score":20,"supported":14,"title":477,"abstract":478,"tags":479},[],[],[25],{"code":73,"score":20,"supported":14,"title":481,"abstract":482,"tags":483},[],[],[26],[485,486],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":488,"openalex_id":490,"authors":491,"venue":443,"cited_by_count":92,"oa_url":440,"card":494,"direction":97,"ingested_from":99},"W7222730658",[492],{"name":493,"orcid":9},"Sunitha Thankappan",{"tldr":495,"method":496,"finding":497,"direction":97,"opportunity":498},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z",{"id":501,"title":502,"url":503,"summary":504,"summary_zh":505,"content":9,"source_name":506,"source_url":503,"published_at":507,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":508,"score_detail":509,"sources":511,"tags":513,"primary_topic":516,"secondary_topics":517,"classification":519,"search_phrases":538,"slug":541,"view_count":92,"doi":542,"paper":543,"created_at":561},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":378,"substance":276,"depth":18,"authority":229,"freshness":17,"relevant":20,"comment":510},"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。",[512],{"name":506,"url":503},[25,26,28,514,515],"节水农业","光伏农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[518],{"code":51,"slug":56,"name":124,"path":125,"parent_code":126},{"status":38,"locked":14,"version":39,"method":40,"evidence":520},{"candidates":521,"agricultural_evidence":44},[522,526,530,534],{"code":31,"score":115,"supported":44,"title":523,"abstract":524,"tags":525},[46],[27,48,46],[],{"code":51,"score":378,"supported":44,"title":527,"abstract":528,"tags":529},[56],[55,56],[28,514],{"code":73,"score":239,"supported":44,"title":531,"abstract":532,"tags":533},[],[143],[26],{"code":59,"score":20,"supported":14,"title":535,"abstract":536,"tags":537},[],[],[25],[539,540],"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":542,"openalex_id":544,"authors":545,"venue":506,"cited_by_count":92,"oa_url":555,"card":556,"direction":97,"ingested_from":99},"W7221571268",[546,549,552],{"name":547,"orcid":548},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":550,"orcid":551},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":553,"orcid":554},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":557,"method":558,"finding":559,"direction":97,"opportunity":560},"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"]