[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2302":3,"related-2302":143},{"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":44,"search_phrases":109,"slug":112,"view_count":98,"doi":113,"paper":114,"created_at":142},2302,"AgriSphere: A Smart Agriculture Framework Integrating IoT and Artificial Intelligence for Adaptive Crop Selection","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315503187260909160504","Introduction Rapid climate change, soil degradation, and changing environmental conditions make crop selection difficult for farmers. This study proposes an IoT- and AI-based framework to recommend suitable crops using current soil conditions and future weather forecasts. It also identifies the key environmental factors influencing crop selection. Methods A three-layer architecture was designed with data collection, communication, and data processing modules. Real-time data on nitrogen, phosphorus, potassium, pH, temperature, and humidity were collected through IoT sensors and combined with rainfall and historical agricultural data. A dataset containing multiple environmental features and 22 crop classes was used for model development. Machine learning and deep learning methods, including Random Forest, XGBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Decision Trees (DT), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM), were applied for classification and forecasting. Performance was evaluated using accuracy, F1-Score, MAE, RMSE, and R 2 . Pareto analysis was also performed to identify the most influential parameters. Results Random Forest and CNN achieved the highest classification accuracy of 99.54% with an F1-Score of 0.995, while XGBoost also performed strongly with 99.32% accuracy. Regression analysis showed that ensemble models outperformed linear models. Pareto analysis revealed that rainfall, humidity, and potassium were the most influential factors in crop recommendation. In a real-time case study, the framework recommended rice as the most suitable crop for the given input conditions. Discussion The results show that integrating IoT sensing with AI-based forecasting supports proactive crop planning before sowing and improves sustainable farming decisions under changing climate conditions. Conclusion The proposed framework effectively combines real-time monitoring, predictive analytics, and intelligent crop recommendation, offering a practical foundation for scalable precision agriculture systems.","引言 快速的气候变化、土壤退化以及不断变化的环境条件使农民难以进行作物选择。本研究提出了一种基于物联网（IoT）和人工智能（AI）的框架，利用当前土壤条件和未来天气预报来推荐适宜的作物。研究还识别了影响作物选择的关键环境因素。方法 设计了一个三层架构，包括数据采集、通信和数据处理模块。通过物联网传感器采集氮、磷、钾、pH值、温度和湿度的实时数据，并结合降雨量和历史农业数据。使用包含多个环境特征和22种作物类别的数据集进行模型开发。应用机器学习和深度学习方法进行分类和预测，包括随机森林（Random Forest）、XGBoost、K近邻（KNN）、支持向量机（SVM）、卷积神经网络（CNN）、决策树（DT）、深度神经网络（DNN）和长短期记忆网络（LSTM）。采用准确率、F1分数、MAE、RMSE和R²评估性能。同时进行帕累托分析以识别最具影响力的参数。结果 随机森林和CNN取得了最高的分类准确率99.54%，F1分数为0.995，XGBoost也表现强劲，准确率为99.32%。回归分析表明，集成模型优于线性模型。帕累托分析显示，降雨量、湿度和钾是作物推荐中影响最大的因素。在实时案例研究中，该框架推荐水稻为给定输入条件下最适宜的作物。讨论 结果表明，将物联网感知与基于人工智能的预测相结合，有助于在播种前进行主动的作物规划，并在气候变化条件下改善可持续农业决策。结论 所提出的框架有效结合了实时监测、预测分析和智能作物推荐，为可扩展的精准农业系统提供了实用基础。",null,"The Open Agriculture Journal","2026-09-11T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,21,17,12,8,1,"该论文提出IoT与AI融合的智能选种框架，多模型对比与实时案例验证充分，方法新颖且结论可靠，对智慧农业精准种植有较高参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","物联网","精准农业","作物推荐",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,60,68,74,82,89,96,103],{"code":33,"score":17,"supported":51,"title":52,"abstract":54,"tags":59},true,[53],"artificial intelligence",[55,56,57,58],"深度学习","机器学习","machine learning","deep learning",[28],{"code":61,"score":62,"supported":51,"title":63,"abstract":65,"tags":67},"I1",16,[64],"smart agriculture",[30,66],"precision agriculture",[27,30],{"code":40,"score":69,"supported":51,"title":70,"abstract":72,"tags":73},15,[71],"IoT",[29,71],[29],{"code":75,"score":76,"supported":51,"title":77,"abstract":78,"tags":81},"A1",6,[],[79,80],"水稻","rice",[],{"code":83,"score":76,"supported":51,"title":84,"abstract":85,"tags":88},"H2",[],[86,87],"土壤","soil",[],{"code":90,"score":76,"supported":51,"title":91,"abstract":92,"tags":95},"H7",[],[93,94],"气候变化","climate change",[],{"code":97,"score":98,"supported":51,"title":99,"abstract":100,"tags":102},"H8",3,[],[101],"可持续农业",[],{"code":104,"score":98,"supported":51,"title":105,"abstract":106,"tags":108},"I6",[],[107],"agricultural data",[],[110,111],"农业人工智能 作物推荐 智慧农业 精准农业","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业精准农业-2302","10.2174\u002F0118743315503187260909160504",{"doi":113,"openalex_id":115,"authors":116,"venue":10,"cited_by_count":134,"oa_url":6,"card":135,"direction":139,"ingested_from":141},"W7212307638",[117,119,122,125,128,131],{"name":118,"orcid":9},"Shreya Sriram",{"name":120,"orcid":121},"Prajeesh C B","https:\u002F\u002Forcid.org\u002F0000-0002-8404-8583",{"name":123,"orcid":124},"Delphin Raj Kesari Mary","https:\u002F\u002Forcid.org\u002F0000-0002-8989-7090",{"name":126,"orcid":127},"Anju S. Pillai","https:\u002F\u002Forcid.org\u002F0000-0001-5298-6789",{"name":129,"orcid":130},"R. V.","https:\u002F\u002Forcid.org\u002F0000-0002-0699-2990",{"name":132,"orcid":133},"V. M. Manikandan","https:\u002F\u002Forcid.org\u002F0000-0001-6903-7563",0,{"tldr":136,"method":137,"finding":138,"direction":139,"opportunity":140},"提出IoT与AI融合框架，结合实时土壤数据与天气预报推荐适宜作物。","三层IoT架构采集NPK、pH等数据，用RF、CNN等8种模型分类22种作物。","RF与CNN分类准确率达99.54%，降雨、湿度和钾是作物选择最关键因素。","智慧农业 \u002F 农业物联网","可探索多源遥感与边缘计算融合，实现小农户低成本、可解释的实时作物推荐系统。","openalex","2026-09-13T23:30:09.745236Z",{"total":76,"page":22,"page_size":76,"items":144},[145,243,321,383,436,498],{"id":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":9,"source_name":151,"source_url":148,"published_at":152,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":153,"score_detail":154,"sources":159,"tags":161,"primary_topic":163,"secondary_topics":164,"classification":172,"search_phrases":218,"slug":221,"view_count":134,"doi":222,"paper":223,"created_at":242},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":17,"substance":155,"depth":17,"authority":156,"freshness":157,"relevant":22,"comment":158},22,13,9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[160],{"name":151,"url":148},[27,28,29,30,162],"气候韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[165,166],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":167,"slug":168,"name":169,"path":170,"parent_code":171},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":173},{"candidates":174,"agricultural_evidence":51},[175,181,185,191,197,202,206,212],{"code":33,"score":17,"supported":51,"title":176,"abstract":177,"tags":180},[53],[55,56,178,57,58,179],"计算机视觉","computer vision",[28],{"code":61,"score":62,"supported":51,"title":182,"abstract":183,"tags":184},[66],[30,66],[27,30],{"code":40,"score":13,"supported":51,"title":186,"abstract":187,"tags":190},[],[29,188,71,189],"边缘计算","edge computing",[29],{"code":167,"score":157,"supported":51,"title":192,"abstract":193,"tags":196},[],[194,195,168],"灌溉","水资源",[],{"code":90,"score":198,"supported":51,"title":199,"abstract":200,"tags":201},7,[],[93,94],[162],{"code":83,"score":76,"supported":51,"title":203,"abstract":204,"tags":205},[],[86,87],[],{"code":207,"score":98,"supported":51,"title":208,"abstract":209,"tags":211},"A7",[],[210],"间作",[],{"code":213,"score":98,"supported":51,"title":214,"abstract":215,"tags":217},"F3",[],[216],"养分管理",[],[219,220],"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":222,"openalex_id":224,"authors":225,"venue":151,"cited_by_count":134,"oa_url":148,"card":236,"direction":139,"ingested_from":141},"W7223121456",[226,229,231,233],{"name":227,"orcid":228},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":230,"orcid":9},"Oluwadare OYEBODE",{"name":232,"orcid":9},"Iretiayo Adelaiye",{"name":234,"orcid":235},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":237,"method":238,"finding":239,"direction":240,"opportunity":241},"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":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":255,"tags":257,"primary_topic":259,"secondary_topics":265,"classification":268,"search_phrases":302,"slug":305,"view_count":22,"doi":306,"paper":307,"created_at":320},4660,"IoT-Based Automatic Cotton Leaf Disease Detection, Infection Percentage Estimation, and Treatment Recommendation Using Raspberry PI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280692","Cotton is one of the most important commercial crops worldwide, and its productivity is significantly affected by various leaf diseases such as bacterial blight, alter aria leaf spot, Cercospora leaf spot, powdery mildew, and leaf curl disease. Early and accurate identification of these diseases is essential to minimize crop losses and improve yield. Traditional disease diagnosis relies on manual field inspection by experts, which is time-consuming, labour-intensive, and often impractical for large agricultural fields. The proposed system integrates a Raspberry Pi5, a high-resolution camera module, environmental sensors, and wireless communication technologies to create an intelligent crop monitoring platform. Images of cotton leaves are captured using the Raspberry Pi camera and processed through computer vision techniques for leaf segmentation and feature extraction. A lightweight deep learning model based on MobileNetV2 is employed to automatically identify different cotton leaf diseases. In addition to disease classification, image segmentation techniques are used to calculate the percentage of infected leaf area, enabling the system to determine the severity level of infection. The system also collects environmental parameters such as temperature, humidity, soil moisture, and light intensity using IoT sensors. These parameters, along with disease diagnosis results, are transmitted to a cloud-based dashboard for real-time monitoring and analysis. Automated alerts can be sent to farmers through SMS or mobile notifications when severe infections are detected. Experimental evaluation is expected to achieve disease classification accuracy above 93%, infection area estimation error below 5%, and real-time inference within one second per image on the Raspberry Pi platform. The proposed solution offers a cost-effective, portable, and scalable approach for precision agriculture by combining artificial intelligence, image processing, and IoT technologies. The system enables early disease detection, accurate infection assessment, timely treatment recommendations, reduced pesticide usage, and improved cotton crop productivity, making it a valuable tool for smart farming applications.","棉花是全球最重要的经济作物之一，其产量受到多种叶部病害的显著影响，如细菌性枯萎病（bacterial blight）、链格孢叶斑病（alternaria leaf spot）、尾孢叶斑病（Cercospora leaf spot）、白粉病（powdery mildew）和卷叶病（leaf curl disease）。早期准确识别这些病害对于减少作物损失和提高产量至关重要。传统的病害诊断依赖专家进行人工田间巡检，这种方式耗时、费力，且对于大面积农田往往不切实际。所提出的系统集成了Raspberry Pi5、高分辨率摄像头模块、环境传感器和无线通信技术，构建了一个智能作物监测平台。利用Raspberry Pi摄像头采集棉花叶片图像，并通过计算机视觉技术进行叶片分割和特征提取。采用基于MobileNetV2的轻量级深度学习模型自动识别不同的棉花叶部病害。除病害分类外，还利用图像分割技术计算受感染叶面积百分比，使系统能够确定感染的严重程度。该系统还通过物联网传感器采集温度、湿度、土壤水分和光照强度等环境参数。这些参数连同病害诊断结果一起传输至基于云端的仪表板，用于实时监测和分析。当检测到严重感染时，可通过短信或移动通知向农民发送自动警报。实验评估预期在Raspberry Pi平台上实现高于93%的病害分类准确率、低于5%的感染面积估计误差，以及每张图像一秒以内的实时推理。所提出的解决方案结合了人工智能、图像处理和物联网技术，为精准农业提供了一种经济高效、便携且可扩展的方法。该系统能够实现病害早期检测、准确感染评估、及时治疗建议、减少农药使用并提高棉花作物产量，使其成为智慧农业应用中的一种有价值的工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",74,{"impact":62,"substance":253,"depth":19,"authority":20,"freshness":157,"relevant":22,"comment":254},20,"基于树莓派与轻量深度学习的棉花叶病检测与物联网监测方案，方法具体、指标明确，对智慧植保有参考价值。",[256],{"name":249,"url":246},[27,28,29,30,258],"棉花病害",{"code":260,"slug":261,"name":262,"path":263,"parent_code":264},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[266,267],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":269},{"candidates":270,"agricultural_evidence":51},[271,275,282,287,291,298],{"code":40,"score":69,"supported":51,"title":272,"abstract":273,"tags":274},[71],[29,71],[29],{"code":260,"score":276,"supported":51,"title":277,"abstract":279,"tags":281},14,[278],"cotton",[280,278],"棉花",[],{"code":61,"score":283,"supported":51,"title":284,"abstract":285,"tags":286},11,[],[27,30,66],[27,30],{"code":33,"score":13,"supported":51,"title":288,"abstract":289,"tags":290},[],[55,178,58,53,179],[28],{"code":292,"score":76,"supported":51,"title":293,"abstract":294,"tags":297},"F4",[],[295,296],"农药","pesticide",[],{"code":83,"score":76,"supported":51,"title":299,"abstract":300,"tags":301},[],[86,87],[],[303,304],"Raspberry Pi 棉花病害检测","MobileNetV2 棉花叶病识别","RaspberryPi棉花病害检测-4660","10.5281\u002Fzenodo.23280692",{"doi":306,"openalex_id":308,"authors":309,"venue":249,"cited_by_count":134,"oa_url":246,"card":315,"direction":139,"ingested_from":141},"W7221340722",[310,312],{"name":311,"orcid":9},"Deepak R Khadse",{"name":313,"orcid":314},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":316,"method":317,"finding":318,"direction":139,"opportunity":319},"基于树莓派与物联网，实现棉花叶病自动检测、感染率估算与防治推荐。","树莓派5+摄像头+环境传感器，MobileNetV2分类与图像分割，云端看板。","预期分类准确率超93%，感染面积误差低于5%，单图推理1秒内。","可探索边缘端轻量模型与多病害严重度分级、精准施药决策的闭环验证。","2026-10-11T23:30:07.876353Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":327,"source_url":324,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":332,"tags":334,"primary_topic":336,"secondary_topics":337,"classification":343,"search_phrases":364,"slug":367,"view_count":368,"doi":369,"paper":370,"created_at":382},4058,"Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100535","Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge computing, and Digital Twins provide unprecedented opportunities to transform sustainability management from reactive monitoring into intelligent, predictive, and autonomous decision-making. IoT enables continuous environmental sensing, AI extracts knowledge from large heterogeneous datasets, edge intelligence supports low-latency analytics, while Digital Twins create dynamic virtual representations of physical systems capable of simulation and optimization. This critical integrative review examines recent developments in AI-enabled sustainability across smart energy systems, intelligent water management, precision agriculture, transportation, smart buildings, circular economy, and climate resilience. Rather than reviewing these technologies independently, the paper analyzes their interactions within an integrated digital ecosystem and discusses emerging topics including Green AI, carbon-aware computing, explainable AI, federated learning, cybersecurity, and sustainability governance. The review method combines a structured multi-database search with targeted evidence and standards updates through June 2026. Building upon the reviewed literature, the paper introduces the Sustainability Intelligence Framework (SIF), a conceptual seven-layer architecture integrating IoT sensing, edge intelligence, AI analytics, Digital Twins, decision intelligence, and sustainability assessment within a continuous feedback cycle. The proposed framework provides a systems-level perspective for designing adaptive, resilient, and resource-efficient infrastructures capable of supporting future sustainable development. The synthesis identifies continuous, auditable sustainability assessment (Layer 7), rather than the underlying six-layer IoT\u002Fcyber–physical stack, as the framework’s distinct contribution. It also shows that reported benefits remain difficult to compare because field validation, system boundaries, rebound effects, and lifecycle digital burdens are inconsistently reported.","可持续性已成为二十一世纪具有决定性意义的科学与社会挑战之一。气候变化、快速城市化、人口增长、能源需求上升以及自然资源减少，要求决策必须持续、自适应，并由可靠的环境智能提供支撑。传统监测系统往往依赖周期性测量和孤立分析工具，已不足以管理高度动态的环境系统。人工智能（AI）、物联网（IoT）、边缘计算和数字孪生（Digital Twins）的最新进展，为将可持续性管理从被动监测转变为智能、预测性和自主决策提供了前所未有的机遇。物联网能够实现持续环境感知，人工智能可从大规模异构数据集中提取知识，边缘智能支持低时延分析，而数字孪生则创建物理系统的动态虚拟表示，以支持仿真与优化。这篇批判性综合综述考察了AI赋能可持续性在智能能源系统、智能水管理、精准农业、交通、智能建筑、循环经济和气候韧性等领域的最新进展。本文并未孤立地评述这些技术，而是分析它们在集成数字生态系统中的相互作用，并讨论绿色AI、碳感知计算、可解释AI、联邦学习、网络安全和可持续性治理等新兴议题。综述方法结合了结构化多数据库检索，并通过截至2026年6月的针对性证据与标准更新加以补充。在所评述文献的基础上，本文提出可持续性智能框架（Sustainability Intelligence Framework, SIF），这是一个概念性七层架构，在持续反馈循环中整合物联网感知、边缘智能、AI分析、数字孪生、决策智能和可持续性评估。所提出的框架为设计能够支持未来可持续发展的自适应、韧性和资源高效型基础设施提供了系统层面视角。该综合指出，框架的独特贡献在于持续、可审计的可持续性评估（第7层），而非底层六层物联网\u002F网络—物理技术栈。研究还表明，由于现场验证、","Next Sustainability","2026-10-02T00:00:00Z",78,{"impact":17,"substance":253,"depth":17,"authority":156,"freshness":157,"relevant":22,"comment":331},"该综述提出七层可持续智能框架，系统整合AI、物联网与数字孪生，对智慧农业与气候韧性基础设施具有较高参考价值，但偏概念性、缺乏实证数据。",[333],{"name":327,"url":324},[27,28,29,335,30],"数字孪生",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[338,339],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":90,"slug":340,"name":341,"path":342,"parent_code":171},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":45,"locked":14,"version":46,"method":47,"evidence":344},{"candidates":345,"agricultural_evidence":51},[346,351,355,359],{"code":40,"score":17,"supported":51,"title":347,"abstract":349,"tags":350},[348],"internet of things",[29,188,348,71,189],[29],{"code":61,"score":20,"supported":51,"title":352,"abstract":353,"tags":354},[],[30,335,66],[335,27,30],{"code":33,"score":20,"supported":51,"title":356,"abstract":357,"tags":358},[53],[53],[28],{"code":90,"score":157,"supported":51,"title":360,"abstract":361,"tags":363},[],[93,94,362],"climate resilience",[],[365,366],"农业人工智能 数字孪生 智慧农业 精准农业","农业人工智能 数字孪生","农业人工智能数字孪生智慧农业精准农业-4058",2,"10.1016\u002Fj.nxsust.2026.100535",{"doi":369,"openalex_id":371,"authors":372,"venue":327,"cited_by_count":134,"oa_url":324,"card":376,"direction":139,"ingested_from":141},"W7216004668",[373],{"name":374,"orcid":375},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":377,"method":378,"finding":379,"direction":380,"opportunity":381},"综述AI、物联网与数字孪生融合的可持续性智能，提出七层SIF框架。","多数据库结构化检索，整合IoT、边缘计算、AI与数字孪生文献。","持续可审计的可持续性评估是框架核心贡献，但实地验证仍难比较。","其他","农业场景下SIF七层框架的田间验证与可审计碳\u002F资源评估方法尚属空白。","2026-10-03T23:30:09.950820Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":391,"sources":393,"tags":395,"primary_topic":397,"secondary_topics":398,"classification":400,"search_phrases":415,"slug":418,"view_count":22,"doi":419,"paper":420,"created_at":435},3885,"InResKolArno_SCO: An Efficient Plant Disease Classification Framework for IoT-Enabled Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.1142\u002Fs0218001426500503","This study proposes a hybrid approach for plant disease classification, integrating Swarm Cray Optimization (SCO) with the InResKolArno, a deep learning model, to address challenges like environmental sensitivity, high computational demands, and limited resources in IoT environments. The process begins with collecting a plant disease recognition dataset, followed by image preprocessing steps such as nearest neighbor interpolation and normalization to improve quality. Disease regions are isolated using color-based thresholding for segmentation. Feature extraction is carried out using deep convolutional neural networks (DCNN), specifically VGG16 and GoogLeNet, to extract high-level leaf features. The InResKolArno classifier, enhanced by SCO (a combination of Particle Swarm Optimization and Crayfish Optimization), is employed to categorize the leaves into three classes: healthy, rust, and powdery mildew. This method improves classification accuracy and reduces data transmission costs in IoT-based precision farming systems. The proposed model achieves high performance with an accuracy of 99.41% and a Positive Predictive Value (PPV) of 99.69%, identifying plant diseases in just 28 seconds. This hybrid model provides superior computational efficiency, making it suitable for real-time deployment in smart farming applications, effectively identifying plant diseases for early intervention and better crop management.","本研究提出了一种植物病害分类的混合方法，将群虾优化算法（Swarm Cray Optimization，SCO）与深度学习模型InResKolArno相结合，以应对物联网环境中的环境敏感性、高计算需求和资源受限等挑战。该流程首先收集植物病害识别数据集，随后进行图像预处理步骤，如最近邻插值和归一化，以提高图像质量。利用基于颜色的阈值分割方法分离病害区域。采用深度卷积神经网络（DCNN），具体为VGG16和GoogLeNet，进行特征提取，以获取叶片的高层特征。经SCO（粒子群优化与螯虾优化的结合）增强的InResKolArno分类器用于将叶片分为三类：健康、锈病和白粉病。该方法提高了分类准确率，并降低了基于物联网的精准农业系统中的数据传输成本。所提出的模型实现了高性能，准确率达99.41%，阳性预测值（PPV）达99.69%，仅需28秒即可识别植物病害。该混合模型具有优越的计算效率，适用于智能农业应用中的实时部署，能够有效识别植物病害，从而实现早期干预和更好的作物管理。","International Journal of Pattern Recognition and Artificial Intelligence","2026-09-30T00:00:00Z",{"impact":62,"substance":18,"depth":19,"authority":156,"freshness":157,"relevant":22,"comment":392},"提出SCO优化InResKolArno的植物病害分类框架，准确率99.41%、28秒识别，面向IoT智慧农业实时部署，方法新颖且数据可靠，值得入选。",[394],{"name":389,"url":386},[27,28,29,30,396],"植物病害识别",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[399],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":401},{"candidates":402,"agricultural_evidence":51},[403,407,411],{"code":40,"score":69,"supported":51,"title":404,"abstract":405,"tags":406},[71],[29,71],[29],{"code":61,"score":156,"supported":51,"title":408,"abstract":409,"tags":410},[64],[30],[27,30],{"code":33,"score":21,"supported":51,"title":412,"abstract":413,"tags":414},[],[55,58],[28,396],[416,417],"InResKolArno SCO 植物病害分类","VGG16 GoogLeNet 病害识别","InResKolArnoSCO植物病害分类-3885","10.1142\u002Fs0218001426500503",{"doi":419,"openalex_id":421,"authors":422,"venue":389,"cited_by_count":134,"oa_url":9,"card":430,"direction":139,"ingested_from":141},"W7214976120",[423,426,428],{"name":424,"orcid":425},"Chitoor Venkat Rao Ajay Kumar","https:\u002F\u002Forcid.org\u002F0009-0001-4082-5416",{"name":427,"orcid":9},"C. Anbuananth",{"name":429,"orcid":9},"Pandi Chiranjeevi",{"tldr":431,"method":432,"finding":433,"direction":139,"opportunity":434},"提出InResKolArno_SCO混合框架，用于物联网智慧农业中的植物病害分类。","VGG16与GoogLeNet提取特征，结合粒子群与螯虾优化算法优化分类器。","在健康、锈病、白粉病三分类上准确率达99.41%，仅需28秒。","可探索轻量化模型在边缘设备上的部署，并扩展至更多病害与田间复杂环境验证。","2026-10-01T23:30:09.723646Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":443,"sources":445,"tags":447,"primary_topic":449,"secondary_topics":450,"classification":452,"search_phrases":480,"slug":483,"view_count":22,"doi":484,"paper":485,"created_at":497},3877,"Machine learning classification techniques for smart agriculture: a review of disease detection and crop recommendation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1878006","The adoption of Internet of Things (IoT) and Machine Learning (ML) in agriculture presents a revolutionary step toward efficient agricultural practices using data to address global issues surrounding food security. This review conducted a systematic search of the Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases for articles published between January 2023 and January 2026. It used keyword combinations like “IoT and machine learning and agriculture”, “crop recommendation”, “plant disease detection”, and “precision farming”. From the initial pool of articles found, 30 articles met the criteria for empirical methodology, quantitative performance reporting, and clear dataset descriptions. These were chosen for detailed analysis. It was discovered that the ensemble, as well as the traditional machine learning methods, such as Random Forest, Support Vector Machine (SVM), and Decision Trees, produce highly accurate predictions of the crop yields with an accuracy of 99.9%. Meanwhile, Convolutional Neural Networks (CNN) and ResNet-based deep learning models detect diseases with an accuracy above 98% while Gated Recurrent Units (GRU) and Long-Short Term Memory (LSTM) time-series models provide forecasts of weather conditions with an accuracy ranging from 94 to 98%, due to the complex nature of the environment dynamics. The application of IoT systems for precision agriculture produced a significant improvement in terms of resource utilization, resulting in 60–96% of water savings, 25% increased yield, and 36–38% reduced herbicide use. Although these advancements have been made, wider application is still hindered by high costs of implementation, lack of technological skills among farmers, data privacy concerns, issues of scalability within different agricultural settings, and consistent internet connection in rural areas. The current paper evaluates the use of IoT and ML in the agriculture sector from diverse data sources and studies to reveal the immense benefits offered by the two systems in enhancing farming activities.","物联网（IoT）与机器学习（ML）在农业中的应用标志着向高效农业实践迈出了革命性的一步，即利用数据来解决全球粮食安全相关问题。本综述对Scopus、IEEE Xplore、ScienceDirect和SpringerLink数据库中2023年1月至2026年1月间发表的文章进行了系统性检索，使用了“物联网与机器学习与农业”“作物推荐”“植物病害检测”和“精准农业”等关键词组合。从初步检索到的文章池中，30篇文章符合实证方法、定量性能报告和清晰数据集描述的标准，被选入进行详细分析。研究发现，集成方法以及随机森林、支持向量机（SVM）和决策树等传统机器学习方法对作物产量的预测精度高达99.9%。同时，卷积神经网络（CNN）和基于ResNet的深度学习模型检测病害的准确率超过98%，而门控循环单元（GRU）和长短期记忆（LSTM）时间序列模型由于环境动态的复杂性，对天气状况的预测准确率在94%至98%之间。物联网系统在精准农业中的应用在资源利用方面产生了显著改善，实现了60%至96%的节水、25%的产量提升以及36%至38%的除草剂用量减少。尽管取得了这些进展，更广泛的应用仍受到实施成本高昂、农民缺乏技术技能、数据隐私顾虑、不同农业场景下的可扩展性问题以及农村地区互联网连接不稳定等因素的制约。本文通过来自不同数据来源和研究，评估了物联网与机器学习在农业领域的应用，揭示了这两个系统在提升农业活动方面所带来的巨大效益。","Frontiers in Artificial Intelligence",{"impact":17,"substance":253,"depth":19,"authority":156,"freshness":21,"relevant":22,"comment":444},"系统综述整合30篇实证研究，量化了IoT与机器学习在作物推荐、病害检测和精准灌溉上的性能与节水增产效益，对智慧农业技术选型有参考价值。",[446],{"name":442,"url":439},[27,28,448,30,31],"病虫害识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[451],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":453},{"candidates":454,"agricultural_evidence":51},[455,460,464,468,475],{"code":33,"score":456,"supported":51,"title":457,"abstract":458,"tags":459},19,[57],[55,56,57,58],[28,448],{"code":61,"score":62,"supported":51,"title":461,"abstract":462,"tags":463},[64],[30,66],[27,30],{"code":40,"score":157,"supported":51,"title":465,"abstract":466,"tags":467},[],[29,348,71],[],{"code":469,"score":76,"supported":51,"title":470,"abstract":471,"tags":474},"A8",[],[472,473],"粮食安全","food security",[],{"code":292,"score":98,"supported":51,"title":476,"abstract":477,"tags":479},[],[478],"除草",[],[481,482],"农业人工智能 病虫害识别 作物推荐 智慧农业","农业人工智能 病虫害识别","农业人工智能病虫害识别作物推荐智慧农业-3877","10.3389\u002Ffrai.2026.1878006",{"doi":484,"openalex_id":486,"authors":487,"venue":442,"cited_by_count":134,"oa_url":439,"card":492,"direction":139,"ingested_from":141},"W7214889993",[488,490],{"name":489,"orcid":9},"M. Umamaheswari",{"name":491,"orcid":9},"Kavitha D",{"tldr":493,"method":494,"finding":495,"direction":240,"opportunity":496},"综述2023-2026年30篇文献，评估物联网与机器学习在作物病害检测和推荐中的应用。","系统检索Scopus等四个数据库，筛选30篇实证研究进行性能与数据集分析。","集成学习与SVM等预测产量准确率达99.9%，CNN病害检测超98%，IoT节水60-96%。","可研究低成本、低连接依赖的边缘ML模型，解决小农户部署与数据隐私问题。","2026-10-01T23:30:09.158196Z",{"id":499,"title":500,"url":501,"summary":502,"summary_zh":503,"content":9,"source_name":504,"source_url":501,"published_at":505,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":506,"score_detail":507,"sources":509,"tags":511,"primary_topic":513,"secondary_topics":514,"classification":519,"search_phrases":539,"slug":541,"view_count":368,"doi":542,"paper":543,"created_at":556},2509,"Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722922","Crop recommendation systems that integrate soil and climate data with artificial intelligence (AI) have expanded rapidly since 2020, spanning classical machine learning (ML), ensemble methods, deep learning, explainable AI (XAI), and Internet of Things (IoT)-enabled sensing. This review critically synthesizes 35 sources — 33 peer-reviewed journal articles and conference papers plus 2 preprints retained only for background context — comprising 12 studies verified in full against their primary text and 23 studies verified at the bibliographic level, to examine what has been attempted, which data and algorithms have been used, and how reliable the reported results are. The reviewed literature shows convergent use of a narrow feature set (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and recurring near-ceiling accuracy, including cases at or above 98% [1], [5], [6] and, in one case, a reported 1.00 across accuracy, precision, recall, and F1-score following class-balancing [7]. Cross-examination of dataset descriptions across studies reveals inconsistent provenance and documentation: structurally similar seven-feature datasets are described with different national contexts and reported sample sizes ranging from 2,100 to 3,000 records [5], [6], [9]. Validation practice is dominated by random hold-out or k-fold cross-validation; among the studies examined in full, only one tested spatial cross-validation, reporting a substantial performance decline (AUC 0.89 to 0.55–0.62) relative to random-split results [4]. Explainable AI and uncertainty quantification remain minority practices in the reviewed literature. A prior conference proceedings paper self-described as a","自2020年以来，将土壤和气候数据与人工智能（AI）相结合的作物推荐系统迅速扩展，涵盖经典机器学习（ML）、集成方法、深度学习、可解释人工智能（XAI）以及物联网（IoT）赋能的传感技术。本综述批判性地综合了35个来源——33篇同行评审期刊论文和会议论文，以及2篇仅用于背景参考的预印本——其中包括12项经全文核实的研究和23项经书目层面核实的研究，以考察已尝试的研究方向、所使用的数据和算法，以及所报告结果的可靠性。所综述的文献显示，特征集使用趋同且范围狭窄（氮、磷、钾、温度、湿度、pH和降雨量），准确率反复接近上限，包括达到或超过98%的案例[1], [5], [6]，以及一例在类别平衡后准确率、精确率、召回率和F1分数均报告为1.00的研究[7]。对各项研究中数据集描述的交叉审查揭示了来源和文档记录的不一致：结构相似的七特征数据集被描述为不同的国家背景，报告的样本量从2,100到3,000条记录不等[5], [6], [9]。验证实践以随机留出法或k折交叉验证为主；在经全文审查的研究中，仅有一项测试了空间交叉验证，报告称相对于随机划分结果，性能显著下降（AUC从0.89降至0.55–0.62）[4]。可解释人工智能和不确定性量化在所综述文献中仍属少数实践。一篇先前会议论文集中自述为","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",61,{"impact":20,"substance":17,"depth":62,"authority":76,"freshness":157,"relevant":22,"comment":508},"系统综述揭示作物推荐模型普遍存在数据集来源混乱与验证方法单一（仅一项空间交叉验证即大幅掉点）的问题，对智慧农业AI落地有实质警示价值，但期刊影响力有限。",[510],{"name":504,"url":501},[27,28,30,31,512],"土壤数据",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[515,518],{"code":83,"slug":87,"name":516,"path":517,"parent_code":171},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":520},{"candidates":521,"agricultural_evidence":51},[522,527,531,535],{"code":33,"score":523,"supported":51,"title":524,"abstract":525,"tags":526},34,[57,58,53],[55,56,57,58,53],[28],{"code":83,"score":69,"supported":51,"title":528,"abstract":529,"tags":530},[87],[86,87],[512],{"code":61,"score":13,"supported":51,"title":532,"abstract":533,"tags":534},[64],[],[27,30],{"code":40,"score":157,"supported":51,"title":536,"abstract":537,"tags":538},[],[29,348,71],[],[540,111],"农业人工智能 作物推荐 土壤数据 智慧农业","农业人工智能作物推荐土壤数据智慧农业-2509","10.64388\u002Firev10i3-1722922",{"doi":542,"openalex_id":544,"authors":545,"venue":504,"cited_by_count":134,"oa_url":550,"card":551,"direction":139,"ingested_from":141},"W7212594050",[546,548],{"name":547,"orcid":9},"Snehal Sanjay Raut",{"name":549,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722922.pdf",{"tldr":552,"method":553,"finding":554,"direction":240,"opportunity":555},"综述AI作物推荐研究，指出高准确率多源于数据与验证缺陷。","系统综述35篇文献，对比ML、DL、XAI与IoT方法及验证方式。","常用7特征数据集来源不一，随机验证致准确率虚高，空间验证性能骤降。","需建立标准化数据集并推广空间交叉验证与不确定性量化，提升模型真实泛化能力。","2026-09-15T23:30:08.397298Z"]