[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4298":3,"related-4298":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":67,"doi":75,"paper":76,"created_at":99},4298,"Smart Agriculture Using ESP32","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.48s.39","Agriculture is an important sector that requires continuous monitoring of crop health and field conditions to improve productivity and reduce crop loss.Traditional plant disease identification mainly depends on manual observation by farmers or agricultural experts, which may be time-consuming, less accurate, and difficult to perform continuously in large fields.In this paper, we propose a smart agriculture system using ESP32, ESP32-CAM, and Convolutional Neural Network (CNN) based leaf disease detection to support real-time crop monitoring.The proposed system monitors environmental parameters such as soil moisture, water level, temperature, pump status, and solenoid valve status through an interactive web dashboard.Leaf images are captured using an ESP32-CAM or uploaded manually to the system, and the trained CNN model classifies the disease category from the input image.The system further provides disease description, preventive steps, and treatment recommendations to assist farmers in taking timely action.The integration of IoT-based environmental monitoring and AI-based disease detection improves the efficiency of crop health analysis and supports smart decisionmaking in agriculture.","农业是一个重要的产业，需要持续监测作物健康和田间状况，以提高生产力并减少作物损失。传统的植物病害识别主要依赖农民或农业专家的肉眼观察，这种方式可能耗时、准确性较低，并且难以在大面积农田中持续进行。本文提出了一种智能农业系统，利用ESP32、ESP32-CAM和基于卷积神经网络（CNN）的叶片病害检测来支持实时作物监测。所提出的系统通过交互式网页仪表盘监测土壤湿度、水位、温度、水泵状态和电磁阀状态等环境参数。叶片图像通过ESP32-CAM采集或手动上传至系统，训练好的CNN模型对输入图像进行病害类别分类。该系统进一步提供病害描述、预防步骤和治疗建议，以帮助农民及时采取行动。基于物联网的环境监测与基于人工智能的病害检测相结合，提高了作物健康分析的效率，并支持农业中的智能决策。",null,"International Journal of Drug Delivery Technology","2026-10-05T00:00:00Z","论文",10,false,47,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,14,13,4,1,"ESP32+CNN 的作物病害检测系统方案，技术路线常规、期刊权威性弱，属细分技术进展，未达每日精选门槛。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","物联网","精准农业","病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57,65],{"code":45,"score":19,"supported":46,"title":47,"abstract":49,"tags":50},"I1",true,[48],"smart agriculture",[48],[26,29],{"code":32,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},7,[],[28,55],"IoT",[28],{"code":58,"score":59,"supported":46,"title":60,"abstract":61,"tags":64},"H2",6,[],[62,63],"土壤","soil",[],{"code":66,"score":67,"supported":14,"title":68,"abstract":69,"tags":70},"I2",2,[],[],[27,30],[72,73],"ESP32 智慧农业","ESP32-CAM 叶片病害检测","ESP32智慧农业-4298","10.25258\u002Fijddt.16.48s.39",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7220453212",[79,81,83,85,87,89],{"name":80,"orcid":9},"R. Jayalakshmi",{"name":82,"orcid":9},"R. G. Suresh Kumar",{"name":84,"orcid":9},"Bharath P",{"name":86,"orcid":9},"Harishvarsakthi A",{"name":88,"orcid":9},"Abhimanyu Dinesan",{"name":90,"orcid":9},"Mohammed Fayid Faizal",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"提出基于ESP32与CNN的智能农业系统，实现环境监测与叶片病害实时检测。","ESP32、ESP32-CAM采集环境与图像数据，CNN分类叶片病害，Web仪表","IoT环境监测与AI病害检测集成可提升作物健康分析效率并支持智能决策。","智慧农业 \u002F 农业物联网","可研究边缘端轻量CNN部署与多病害实时识别，降低对云端和人工的依赖。","openalex","2026-10-06T23:30:08.152961Z",{"total":59,"page":21,"page_size":59,"items":101},[102,213,285,364,424,479],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":120,"secondary_topics":124,"classification":132,"search_phrases":188,"slug":191,"view_count":91,"doi":192,"paper":193,"created_at":212},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":112,"substance":113,"depth":112,"authority":19,"freshness":114,"relevant":21,"comment":115},18,22,9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[117],{"name":108,"url":105},[26,27,28,29,119],"气候韧性",{"code":66,"slug":121,"name":122,"path":123,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[125,126],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":133},{"candidates":134,"agricultural_evidence":46},[135,146,152,158,164,171,175,182],{"code":66,"score":112,"supported":46,"title":136,"abstract":138,"tags":145},[137],"artificial intelligence",[139,140,141,142,143,144],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[27],{"code":45,"score":147,"supported":46,"title":148,"abstract":150,"tags":151},16,[149],"precision agriculture",[29,149],[26,29],{"code":32,"score":13,"supported":46,"title":153,"abstract":154,"tags":157},[],[28,155,55,156],"边缘计算","edge computing",[28],{"code":127,"score":114,"supported":46,"title":159,"abstract":160,"tags":163},[],[161,162,128],"灌溉","水资源",[],{"code":165,"score":52,"supported":46,"title":166,"abstract":167,"tags":170},"H7",[],[168,169],"气候变化","climate change",[119],{"code":58,"score":59,"supported":46,"title":172,"abstract":173,"tags":174},[],[62,63],[],{"code":176,"score":177,"supported":46,"title":178,"abstract":179,"tags":181},"A7",3,[],[180],"间作",[],{"code":183,"score":177,"supported":46,"title":184,"abstract":185,"tags":187},"F3",[],[186],"养分管理",[],[189,190],"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":192,"openalex_id":194,"authors":195,"venue":108,"cited_by_count":91,"oa_url":105,"card":206,"direction":96,"ingested_from":98},"W7223121456",[196,199,201,203],{"name":197,"orcid":198},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":200,"orcid":9},"Oluwadare OYEBODE",{"name":202,"orcid":9},"Iretiayo Adelaiye",{"name":204,"orcid":205},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":207,"method":208,"finding":209,"direction":210,"opportunity":211},"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":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":220,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":221,"score_detail":222,"sources":224,"tags":226,"primary_topic":228,"secondary_topics":229,"classification":230,"search_phrases":261,"slug":264,"view_count":177,"doi":265,"paper":266,"created_at":284},4877,"IoT-Enabled Autonomous Agriculture Monitoring Rover for Smart Farming: A Comprehensive Literature Review","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85108","The rapid development of the Internet of Things (IoT), autonomous robotics, artificial intelligence (AI), embedded systems, wireless communication, and precision agriculture has created new opportunities for improving agricultural productivity and resource utilization. Conventional agricultural monitoring relies extensively on manual field inspection, which is time-consuming, labor-intensive, and often unable to provide continuous information about crop and soil conditions. Autonomous agricultural rovers provide an alternative approach by enabling mobile sensing, real-time environmental monitoring, crop observation, navigation, and precision agricultural operations. This paper presents a comprehensive literature review of IoT-enabled autonomous agricultural rovers for smart farming, with particular emphasis on recent developments reported in IEEE literature. Existing research is analyzed according to rover architecture, sensing technologies, navigation techniques, communication technologies, IoT platforms, agricultural applications, AI integration, and autonomy levels. Recent studies have demonstrated the feasibility of ESP32-based IoT rovers, ROS-based agricultural monitoring robots, autonomous navigation systems, precision irrigation platforms, soil-monitoring robots, and AI-enabled agricultural rovers. However, several limitations remain, including restricted autonomous navigation in unstructured environments, limited multi-parameter sensing, communication reliability in rural areas, high computational requirements for AI, energy limitations, and insufficient integration of monitoring with autonomous decision-making. Based on the reviewed literature, a research gap is identified for a low-cost, modular, IoT-enabled autonomous agricultural monitoring rover capable of simultaneously performing soil and environmental sensing, crop monitoring, obstacle avoidance, real-time wireless communication, location-aware operation, and intelligent decision support.","物联网（IoT）、自主机器人、人工智能（AI）、嵌入式系统、无线通信和精准农业的快速发展，为提升农业生产率和资源利用率创造了新的机遇。传统农业监测在很大程度上依赖人工田间巡检，这种方式耗时、费力，且往往无法持续提供作物和土壤状况的信息。自主农业巡田机器人通过实现移动传感、实时环境监测、作物观测、导航和精准农业作业，提供了一种替代方案。本文对面向智慧农业的物联网赋能自主农业巡田机器人进行了全面的文献综述，特别关注IEEE文献中报道的最新进展。现有研究按照巡田机器人架构、传感技术、导航技术、通信技术、物联网平台、农业应用、人工智能集成和自主性水平进行了分析。近期研究已证明基于ESP32的物联网巡田机器人、基于ROS的农业监测机器人、自主导航系统、精准灌溉平台、土壤监测机器人和人工智能赋能农业巡田机器人的可行性。然而，仍存在若干局限，包括非结构化环境中的自主导航受限、多参数传感能力有限、农村地区通信可靠性不足、人工智能的高计算需求、能量限制，以及监测与自主决策的集成不充分。基于所综述的文献，本文识别出一个研究空白，即缺乏一种低成本、模块化、物联网赋能的自主农业监测巡田机器人，能够同时执行土壤与环境传感、作物监测、避障、实时无线通信、位置感知作业和智能决策支持。","International Journal for Research in Applied Science and Engineering Technology","2026-10-13T00:00:00Z",52,{"impact":17,"substance":18,"depth":147,"authority":59,"freshness":17,"relevant":21,"comment":223},"综述性论文，系统梳理IoT自主农业巡检机器人架构与瓶颈，但来源期刊影响力有限，属细分领域参考价值。",[225],{"name":219,"url":216},[26,27,227,28,29],"农业机器人",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":231},{"candidates":232,"agricultural_evidence":46},[233,238,243,248,252,256],{"code":32,"score":112,"supported":46,"title":234,"abstract":235,"tags":237},[55],[28,236,55],"internet of things",[28],{"code":45,"score":239,"supported":46,"title":240,"abstract":241,"tags":242},11,[],[26,29,149],[26,29],{"code":66,"score":52,"supported":46,"title":244,"abstract":245,"tags":247},[],[137,246],"decision support",[27],{"code":58,"score":59,"supported":46,"title":249,"abstract":250,"tags":251},[],[62,63],[],{"code":127,"score":59,"supported":46,"title":253,"abstract":254,"tags":255},[],[161,128],[],{"code":257,"score":21,"supported":14,"title":258,"abstract":259,"tags":260},"F6",[],[],[227],[262,263],"ESP32 ROS 农业机器人","农业人工智能 农业机器人 智慧农业 精准农业","ESP32ROS农业机器人-4877","10.22214\u002Fijraset.2026.85108",{"doi":265,"openalex_id":267,"authors":268,"venue":219,"cited_by_count":91,"oa_url":216,"card":279,"direction":96,"ingested_from":98},"W7221682750",[269,271,273,275,277],{"name":270,"orcid":9},"Chaitra A S",{"name":272,"orcid":9},"Venugopal Gowda V",{"name":274,"orcid":9},"Vinay H",{"name":276,"orcid":9},"Jeevan T",{"name":278,"orcid":9},"Puneeth M",{"tldr":280,"method":281,"finding":282,"direction":96,"opportunity":283},"综述了物联网赋能的自主农业监测机器人，分析架构、传感、导航与AI集成，指出低成本模块化系统的研究空白","文献综述，聚焦IEEE近期研究，按架构、传感、导航、通信、AI与自主等级分类分析","现有系统在非结构化导航、多参数传感、农村通信、AI算力与能耗上存在局限，缺乏监测与决策一体化。","可研发低成本模块化IoT自主巡田机器人，融合多传感、避障、实时通信与智能决策，填补集成空白。","2026-10-14T23:30:08.717386Z",{"id":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":9,"source_name":291,"source_url":288,"published_at":292,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":293,"score_detail":294,"sources":299,"tags":301,"primary_topic":303,"secondary_topics":309,"classification":312,"search_phrases":345,"slug":348,"view_count":21,"doi":349,"paper":350,"created_at":363},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":147,"substance":295,"depth":296,"authority":297,"freshness":114,"relevant":21,"comment":298},20,17,12,"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。",[300],{"name":291,"url":288},[26,27,28,29,302],"棉花病害",{"code":304,"slug":305,"name":306,"path":307,"parent_code":308},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[310,311],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":66,"slug":121,"name":122,"path":123,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":313},{"candidates":314,"agricultural_evidence":46},[315,320,326,330,334,341],{"code":32,"score":316,"supported":46,"title":317,"abstract":318,"tags":319},15,[55],[28,55],[28],{"code":304,"score":18,"supported":46,"title":321,"abstract":323,"tags":325},[322],"cotton",[324,322],"棉花",[],{"code":45,"score":239,"supported":46,"title":327,"abstract":328,"tags":329},[],[26,29,149],[26,29],{"code":66,"score":13,"supported":46,"title":331,"abstract":332,"tags":333},[],[139,141,143,137,144],[27],{"code":335,"score":59,"supported":46,"title":336,"abstract":337,"tags":340},"F4",[],[338,339],"农药","pesticide",[],{"code":58,"score":59,"supported":46,"title":342,"abstract":343,"tags":344},[],[62,63],[],[346,347],"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":349,"openalex_id":351,"authors":352,"venue":291,"cited_by_count":91,"oa_url":288,"card":358,"direction":96,"ingested_from":98},"W7221340722",[353,355],{"name":354,"orcid":9},"Deepak R Khadse",{"name":356,"orcid":357},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":359,"method":360,"finding":361,"direction":96,"opportunity":362},"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":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":9,"source_name":370,"source_url":367,"published_at":371,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":372,"score_detail":373,"sources":375,"tags":377,"primary_topic":379,"secondary_topics":380,"classification":386,"search_phrases":406,"slug":409,"view_count":67,"doi":410,"paper":411,"created_at":423},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":112,"substance":295,"depth":112,"authority":19,"freshness":114,"relevant":21,"comment":374},"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。",[376],{"name":370,"url":367},[26,27,28,378,29],"数字孪生",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[381,382],{"code":66,"slug":121,"name":122,"path":123,"parent_code":36},{"code":165,"slug":383,"name":384,"path":385,"parent_code":131},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":39,"locked":14,"version":40,"method":41,"evidence":387},{"candidates":388,"agricultural_evidence":46},[389,393,397,401],{"code":32,"score":112,"supported":46,"title":390,"abstract":391,"tags":392},[236],[28,155,236,55,156],[28],{"code":45,"score":297,"supported":46,"title":394,"abstract":395,"tags":396},[],[29,378,149],[378,26,29],{"code":66,"score":297,"supported":46,"title":398,"abstract":399,"tags":400},[137],[137],[27],{"code":165,"score":114,"supported":46,"title":402,"abstract":403,"tags":405},[],[168,169,404],"climate resilience",[],[407,408],"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","10.1016\u002Fj.nxsust.2026.100535",{"doi":410,"openalex_id":412,"authors":413,"venue":370,"cited_by_count":91,"oa_url":367,"card":417,"direction":96,"ingested_from":98},"W7216004668",[414],{"name":415,"orcid":416},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":418,"method":419,"finding":420,"direction":421,"opportunity":422},"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":425,"title":426,"url":427,"summary":428,"summary_zh":429,"content":9,"source_name":430,"source_url":427,"published_at":431,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":432,"score_detail":433,"sources":436,"tags":438,"primary_topic":440,"secondary_topics":441,"classification":443,"search_phrases":458,"slug":461,"view_count":21,"doi":462,"paper":463,"created_at":478},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",76,{"impact":147,"substance":434,"depth":296,"authority":19,"freshness":114,"relevant":21,"comment":435},21,"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。",[437],{"name":430,"url":427},[26,27,28,29,439],"植物病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[442],{"code":66,"slug":121,"name":122,"path":123,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":444},{"candidates":445,"agricultural_evidence":46},[446,450,454],{"code":32,"score":316,"supported":46,"title":447,"abstract":448,"tags":449},[55],[28,55],[28],{"code":45,"score":19,"supported":46,"title":451,"abstract":452,"tags":453},[48],[29],[26,29],{"code":66,"score":17,"supported":46,"title":455,"abstract":456,"tags":457},[],[139,143],[27,439],[459,460],"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":462,"openalex_id":464,"authors":465,"venue":430,"cited_by_count":91,"oa_url":9,"card":473,"direction":96,"ingested_from":98},"W7214976120",[466,469,471],{"name":467,"orcid":468},"Chitoor Venkat Rao Ajay Kumar","https:\u002F\u002Forcid.org\u002F0009-0001-4082-5416",{"name":470,"orcid":9},"C. Anbuananth",{"name":472,"orcid":9},"Pandi Chiranjeevi",{"tldr":474,"method":475,"finding":476,"direction":96,"opportunity":477},"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":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":9,"source_name":485,"source_url":482,"published_at":486,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":432,"score_detail":487,"sources":489,"tags":491,"primary_topic":493,"secondary_topics":494,"classification":496,"search_phrases":538,"slug":541,"view_count":177,"doi":542,"paper":543,"created_at":568},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。","The Open Agriculture Journal","2026-09-11T00:00:00Z",{"impact":112,"substance":434,"depth":296,"authority":297,"freshness":17,"relevant":21,"comment":488},"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。",[490],{"name":485,"url":482},[26,27,28,29,492],"作物推荐",{"code":66,"slug":121,"name":122,"path":123,"parent_code":36},[495],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":497},{"candidates":498,"agricultural_evidence":46},[499,503,507,511,518,522,526,532],{"code":66,"score":112,"supported":46,"title":500,"abstract":501,"tags":502},[137],[139,140,142,143],[27],{"code":45,"score":147,"supported":46,"title":504,"abstract":505,"tags":506},[48],[29,149],[26,29],{"code":32,"score":316,"supported":46,"title":508,"abstract":509,"tags":510},[55],[28,55],[28],{"code":512,"score":59,"supported":46,"title":513,"abstract":514,"tags":517},"A1",[],[515,516],"水稻","rice",[],{"code":58,"score":59,"supported":46,"title":519,"abstract":520,"tags":521},[],[62,63],[],{"code":165,"score":59,"supported":46,"title":523,"abstract":524,"tags":525},[],[168,169],[],{"code":527,"score":177,"supported":46,"title":528,"abstract":529,"tags":531},"H8",[],[530],"可持续农业",[],{"code":533,"score":177,"supported":46,"title":534,"abstract":535,"tags":537},"I6",[],[536],"agricultural data",[],[539,540],"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":542,"openalex_id":544,"authors":545,"venue":485,"cited_by_count":91,"oa_url":482,"card":563,"direction":96,"ingested_from":98},"W7212307638",[546,548,551,554,557,560],{"name":547,"orcid":9},"Shreya Sriram",{"name":549,"orcid":550},"Prajeesh C B","https:\u002F\u002Forcid.org\u002F0000-0002-8404-8583",{"name":552,"orcid":553},"Delphin Raj Kesari Mary","https:\u002F\u002Forcid.org\u002F0000-0002-8989-7090",{"name":555,"orcid":556},"Anju S. Pillai","https:\u002F\u002Forcid.org\u002F0000-0001-5298-6789",{"name":558,"orcid":559},"R. V.","https:\u002F\u002Forcid.org\u002F0000-0002-0699-2990",{"name":561,"orcid":562},"V. M. Manikandan","https:\u002F\u002Forcid.org\u002F0000-0001-6903-7563",{"tldr":564,"method":565,"finding":566,"direction":96,"opportunity":567},"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。","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。","2026-09-13T23:30:09.745236Z"]