[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4877":3,"related-4877":114},{"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":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":113},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。",null,"International Journal for Research in Applied Science and Engineering Technology","2026-10-13T00:00:00Z","论文",10,false,52,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,14,16,6,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业机器人","物联网","精准农业",{"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},[44,52,59,67,74,81],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},18,true,[48],"IoT",[29,50,48],"internet of things",[29],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":58},"I1",11,[],[26,30,57],"precision agriculture",[26,30],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"I2",7,[],[64,65],"artificial intelligence","decision support",[27],{"code":68,"score":20,"supported":46,"title":69,"abstract":70,"tags":73},"H2",[],[71,72],"土壤","soil",[],{"code":75,"score":20,"supported":46,"title":76,"abstract":77,"tags":80},"H3",[],[78,79],"灌溉","irrigation",[],{"code":82,"score":21,"supported":14,"title":83,"abstract":84,"tags":85},"F6",[],[],[28],[87,88],"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",3,"10.22214\u002Fijraset.2026.85108",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":105,"oa_url":6,"card":106,"direction":110,"ingested_from":112},"W7221682750",[95,97,99,101,103],{"name":96,"orcid":9},"Chaitra A S",{"name":98,"orcid":9},"Venugopal Gowda V",{"name":100,"orcid":9},"Vinay H",{"name":102,"orcid":9},"Jeevan T",{"name":104,"orcid":9},"Puneeth M",0,{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-14T23:30:08.717386Z",{"total":20,"page":21,"page_size":20,"items":115},[116,220,299,382,453,523],{"id":117,"title":118,"url":119,"summary":120,"summary_zh":121,"content":9,"source_name":122,"source_url":119,"published_at":123,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":124,"score_detail":125,"sources":130,"tags":132,"primary_topic":134,"secondary_topics":138,"classification":144,"search_phrases":195,"slug":198,"view_count":105,"doi":199,"paper":200,"created_at":219},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":45,"substance":126,"depth":45,"authority":127,"freshness":128,"relevant":21,"comment":129},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。",[131],{"name":122,"url":119},[26,27,29,30,133],"气候韧性",{"code":60,"slug":135,"name":136,"path":137,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[139,140],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":75,"slug":79,"name":141,"path":142,"parent_code":143},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":145},{"candidates":146,"agricultural_evidence":46},[147,157,161,167,172,179,183,189],{"code":60,"score":45,"supported":46,"title":148,"abstract":149,"tags":156},[64],[150,151,152,153,154,155],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[27],{"code":53,"score":19,"supported":46,"title":158,"abstract":159,"tags":160},[57],[30,57],[26,30],{"code":32,"score":13,"supported":46,"title":162,"abstract":163,"tags":166},[],[29,164,48,165],"边缘计算","edge computing",[29],{"code":75,"score":128,"supported":46,"title":168,"abstract":169,"tags":171},[],[78,170,79],"水资源",[],{"code":173,"score":61,"supported":46,"title":174,"abstract":175,"tags":178},"H7",[],[176,177],"气候变化","climate change",[133],{"code":68,"score":20,"supported":46,"title":180,"abstract":181,"tags":182},[],[71,72],[],{"code":184,"score":90,"supported":46,"title":185,"abstract":186,"tags":188},"A7",[],[187],"间作",[],{"code":190,"score":90,"supported":46,"title":191,"abstract":192,"tags":194},"F3",[],[193],"养分管理",[],[196,197],"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":199,"openalex_id":201,"authors":202,"venue":122,"cited_by_count":105,"oa_url":119,"card":213,"direction":110,"ingested_from":112},"W7223121456",[203,206,208,210],{"name":204,"orcid":205},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":207,"orcid":9},"Oluwadare OYEBODE",{"name":209,"orcid":9},"Iretiayo Adelaiye",{"name":211,"orcid":212},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":214,"method":215,"finding":216,"direction":217,"opportunity":218},"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":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":9,"source_name":226,"source_url":223,"published_at":227,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":228,"score_detail":229,"sources":234,"tags":236,"primary_topic":238,"secondary_topics":244,"classification":247,"search_phrases":280,"slug":283,"view_count":21,"doi":284,"paper":285,"created_at":298},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":19,"substance":230,"depth":231,"authority":232,"freshness":128,"relevant":21,"comment":233},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。",[235],{"name":226,"url":223},[26,27,29,30,237],"棉花病害",{"code":239,"slug":240,"name":241,"path":242,"parent_code":243},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[245,246],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":60,"slug":135,"name":136,"path":137,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":248},{"candidates":249,"agricultural_evidence":46},[250,255,261,265,269,276],{"code":32,"score":251,"supported":46,"title":252,"abstract":253,"tags":254},15,[48],[29,48],[29],{"code":239,"score":18,"supported":46,"title":256,"abstract":258,"tags":260},[257],"cotton",[259,257],"棉花",[],{"code":53,"score":54,"supported":46,"title":262,"abstract":263,"tags":264},[],[26,30,57],[26,30],{"code":60,"score":13,"supported":46,"title":266,"abstract":267,"tags":268},[],[150,152,154,64,155],[27],{"code":270,"score":20,"supported":46,"title":271,"abstract":272,"tags":275},"F4",[],[273,274],"农药","pesticide",[],{"code":68,"score":20,"supported":46,"title":277,"abstract":278,"tags":279},[],[71,72],[],[281,282],"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":284,"openalex_id":286,"authors":287,"venue":226,"cited_by_count":105,"oa_url":223,"card":293,"direction":110,"ingested_from":112},"W7221340722",[288,290],{"name":289,"orcid":9},"Deepak R Khadse",{"name":291,"orcid":292},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":294,"method":295,"finding":296,"direction":110,"opportunity":297},"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":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":9,"source_name":226,"source_url":302,"published_at":305,"category":12,"cover_url":9,"hotness":306,"is_selected":14,"score":307,"score_detail":308,"sources":310,"tags":315,"primary_topic":318,"secondary_topics":319,"classification":327,"search_phrases":366,"slug":369,"view_count":21,"doi":370,"paper":371,"created_at":381},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","2026-10-09T00:00:00Z",25,81,{"impact":126,"substance":230,"depth":45,"authority":127,"freshness":17,"relevant":21,"comment":309},"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[311,312],{"name":226,"url":302},{"name":313,"url":314},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[26,27,28,316,30,317],"农业物联网","自动驾驶农机",{"code":60,"slug":135,"name":136,"path":137,"parent_code":36},[320,326],{"code":321,"slug":322,"name":323,"path":324,"parent_code":325},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":328},{"candidates":329,"agricultural_evidence":46},[330,334,341,345,352,357,362],{"code":60,"score":45,"supported":46,"title":331,"abstract":332,"tags":333},[64],[151,152,153,64,155],[27],{"code":321,"score":231,"supported":46,"title":335,"abstract":337,"tags":340},[336],"agricultural machinery",[338,339,336],"农业机械","收割机",[],{"code":32,"score":13,"supported":46,"title":342,"abstract":343,"tags":344},[],[29,50,48],[316],{"code":346,"score":20,"supported":46,"title":347,"abstract":348,"tags":351},"I3",[],[349,350],"遥感","remote sensing",[],{"code":53,"score":353,"supported":46,"title":354,"abstract":355,"tags":356},5,[],[30],[26,30],{"code":270,"score":90,"supported":46,"title":358,"abstract":359,"tags":361},[],[360],"除草",[],{"code":82,"score":21,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[28],[367,368],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":370,"openalex_id":372,"authors":373,"venue":226,"cited_by_count":105,"oa_url":302,"card":376,"direction":110,"ingested_from":112},"W7221138004",[374],{"name":375,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":377,"method":378,"finding":379,"direction":110,"opportunity":380},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":383,"title":384,"url":385,"summary":386,"summary_zh":387,"content":9,"source_name":388,"source_url":385,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":389,"score_detail":390,"sources":392,"tags":394,"primary_topic":396,"secondary_topics":397,"classification":398,"search_phrases":429,"slug":432,"view_count":105,"doi":433,"paper":434,"created_at":452},4572,"Design and development of IoT based solar power agriculture robot","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44430-026-00040-6","Agriculture in developing economies such as Pakistan remains heavily dependent on manual labor and single-purpose, grid or fuel-powered machinery that is often unaffordable for smallholder farmers. This paper presents the design, fabrication, and experimental evaluation of a multifunctional, solar-powered, Internet of Things (IoT)-enabled agricultural robot capable of performing soil cultivation, seed placement, irrigation, and crop cutting within a single integrated platform. The system architecture is built around an ESP32 microcontroller that interfaces with a DHT11 temperature\u002Fhumidity sensor, an HC-SR04 ultrasonic obstacle sensor, an ESP32-CAM module for visual field monitoring, an L298N dual H-bridge motor driver, and relay-controlled actuators, with energy supplied by a 20 W photovoltaic panel, a 12 V\u002F7.2 Ah battery, and a battery management system (BMS). Mechanical subsystems comprising a tooth-type cultivator, a soil-leveling roller, a servo-actuated seed dispenser, a DC pump-based irrigation unit, and a blade-type cutter were designed in CAD, fabricated, and integrated onto a four-wheel-drive chassis. Engineering calculations were used to size the power budget, battery backup, drive-motor torque, structural factor of safety, seeding rate, and irrigation flow. Field trials show a typical operating power demand of 24.4 W against an average usable solar output of 11–12 W, a battery backup of approximately 3.5 h, The experimental results demonstrated satisfactory performance of the proposed agricultural robot, achieving an average seed-placement accuracy of 74.45% over 11 experimental trials. The developed system also successfully performed soil digging, irrigation, crop cutting, obstacle detection, and real-time IoT-based remote monitoring, demonstrating its suitability for sustainable precision agriculture.","巴基斯坦等发展中经济体的农业仍然高度依赖人工劳动以及单一用途的电网或燃料驱动机械，而这类机械往往是小农户无力承担的。本文介绍了一种多功能、太阳能供电、支持物联网（IoT）的农业机器人的设计、制造与实验评估，该机器人能够在单一集成平台上完成土壤耕作、播种、灌溉和作物收割。系统架构以ESP32微控制器为核心，连接DHT11温湿度传感器、HC-SR04超声波避障传感器、用于田间视觉监测的ESP32-CAM模块、L298N双H桥电机驱动器以及继电器控制执行器，能源由20 W光伏板、12 V\u002F7.2 Ah电池和电池管理系统（BMS）提供。机械子系统包括齿式耕作器、平土辊、舵机驱动排种器、基于直流泵的灌溉单元和刀片式切割器，经CAD设计、制造并集成到四轮驱动底盘上。通过工程计算确定了功率预算、电池续航、驱动电机扭矩、结构安全系数、播种速率和灌溉流量。田间试验表明，典型工作功率需求为24.4 W，而平均可用太阳能输出为11–12 W，电池续航约为3.5 h。实验结果表明，所提出的农业机器人性能令人满意，在11次实验 trials 中实现了74.45%的平均播种精度。所开发的系统还成功完成了挖土、灌溉、作物切割、障碍物检测以及基于物联网的实时远程监测，表明其适用于可持续精准农业。","Discover Robotics",56,{"impact":17,"substance":45,"depth":251,"authority":20,"freshness":128,"relevant":21,"comment":391},"面向小农户的多功能太阳能物联网农业机器人论文，方法集成度高、有实测数据，但属单篇学术成果，公共影响有限，可作为智慧农业技术前沿收录。",[393],{"name":388,"url":385},[26,28,29,30,395],"光伏农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":399},{"candidates":400,"agricultural_evidence":46},[401,405,409,414,421,425],{"code":32,"score":45,"supported":46,"title":402,"abstract":403,"tags":404},[48],[29,50,48],[29],{"code":53,"score":17,"supported":46,"title":406,"abstract":407,"tags":408},[],[30,57],[26,30],{"code":82,"score":61,"supported":46,"title":410,"abstract":411,"tags":413},[],[28,412],"agricultural robot",[28],{"code":415,"score":20,"supported":46,"title":416,"abstract":417,"tags":420},"G2",[],[418,419],"小农户","smallholder",[],{"code":68,"score":20,"supported":46,"title":422,"abstract":423,"tags":424},[],[71,72],[],{"code":75,"score":20,"supported":46,"title":426,"abstract":427,"tags":428},[],[78,79],[],[430,431],"太阳能农业机器人 ESP32","IoT 农业机器人 播种精度","太阳能农业机器人ESP32-4572","10.1007\u002Fs44430-026-00040-6",{"doi":433,"openalex_id":435,"authors":436,"venue":388,"cited_by_count":105,"oa_url":385,"card":447,"direction":110,"ingested_from":112},"W7221126513",[437,439,441,443,445],{"name":438,"orcid":9},"Mohammad Ali",{"name":440,"orcid":9},"Sana Ullah",{"name":442,"orcid":9},"Asad Ullah",{"name":444,"orcid":9},"M. Danyal Khan",{"name":446,"orcid":9},"Mubasher Hassan",{"tldr":448,"method":449,"finding":450,"direction":110,"opportunity":451},"设计并试验了一款太阳能物联网多功能农业机器人，可耕地、播种、灌溉和收割。","基于ESP32、DHT11、超声波、ESP32-CAM和20W光伏板，CAD制造","作业功耗24.4W，电池续航约3.5小时，11次试验平均播种精度74.45%。","太阳能供电与作业功耗不匹配，可研究能量优化调度或混合供电提升续航与播种精度。","2026-10-10T23:30:13.752841Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":9,"content":9,"source_name":458,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":459,"score_detail":460,"sources":463,"tags":465,"primary_topic":467,"secondary_topics":468,"classification":469,"search_phrases":510,"slug":513,"view_count":21,"doi":9,"paper":514,"created_at":522},4476,"智慧农业演进支撑欧洲绿色协议：物联网、人工智能与机器人可持续精准农业综述","https:\u002F\u002Fakjournals.com\u002Fview\u002Fjournals\u002F446\u002Faop\u002Farticle-10.1556-446.2026.00313\u002Farticle-10.1556-446.2026.00313.xml","Nyéki Anikó（生物工程与精准技术系）发表智慧农业演进综述，基于2000—2025年Scopus、Web of Science和Google Scholar数据库的同行评审研究；提出政策驱动的数字农业生态管理（PDAM）框架，建立基于欧盟目标（农药、养分、土壤、生物多样性和气候）过去趋势的可适应指标；最有效的工具支持GNSS机器控制、变量率应用和遥感，而AI决策支持工具、自主除草和数字孪生田间验证仍较弱；本研究为IoT、AI和机器人在欧洲可持续精准农业的整合应用提供政策与技术参考。","Progress in Agricultural Engineering Sciences 2026",71,{"impact":45,"substance":230,"depth":45,"authority":127,"freshness":461,"relevant":21,"comment":462},2,"基于2000—2025年文献的智慧农业综述，提出PDAM政策框架并指出AI决策与数字孪生验证短板，具政策与技术参考价值，但非国内动态且时效性弱。",[464],{"name":458,"url":456},[26,27,28,30,466],"欧洲绿色协议",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":470},{"candidates":471,"agricultural_evidence":46},[472,479,483,487,494,498,502,506],{"code":53,"score":473,"supported":46,"title":474,"abstract":475,"tags":478},27,[26,30],[26,30,476,477],"数字农业","数字孪生",[26,30],{"code":32,"score":54,"supported":46,"title":480,"abstract":481,"tags":482},[29],[48],[],{"code":270,"score":20,"supported":46,"title":484,"abstract":485,"tags":486},[],[273,360],[],{"code":488,"score":20,"supported":46,"title":489,"abstract":490,"tags":493},"H6",[],[491,492],"生物多样性","农业生态",[],{"code":68,"score":90,"supported":46,"title":495,"abstract":496,"tags":497},[],[71],[],{"code":346,"score":90,"supported":46,"title":499,"abstract":500,"tags":501},[],[349],[],{"code":82,"score":21,"supported":14,"title":503,"abstract":504,"tags":505},[],[],[28],{"code":60,"score":21,"supported":14,"title":507,"abstract":508,"tags":509},[],[],[27],[511,512],"Nyéki Anikó 精准农业","欧洲绿色协议 数字农业生态管理","NyékiAnikó精准农业-4476",{"doi":9,"openalex_id":9,"authors":515,"venue":9,"cited_by_count":105,"oa_url":9,"card":516,"direction":110,"ingested_from":521},[],{"tldr":517,"method":518,"finding":519,"direction":110,"opportunity":520},"综述2000—2025年智慧农业演进，提出政策驱动的数字农业生态管理框架以支撑欧洲绿色协议。","基于Scopus、WoS、Google Scholar文献综述，构建PDAM框架","GNSS机器控制、变量率施用与遥感最有效；AI决策、自主除草与数字孪生田间验证仍薄弱。","AI决策支持、自主除草与数字孪生的田间验证不足，是精准农业落地研究的重点空白。","agent","2026-10-09T00:05:02.372715Z",{"id":524,"title":525,"url":526,"summary":527,"summary_zh":528,"content":9,"source_name":529,"source_url":526,"published_at":530,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":531,"score_detail":532,"sources":535,"tags":537,"primary_topic":539,"secondary_topics":540,"classification":541,"search_phrases":561,"slug":564,"view_count":461,"doi":565,"paper":566,"created_at":586},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。","International Journal of Drug Delivery Technology","2026-10-05T00:00:00Z",47,{"impact":17,"substance":18,"depth":127,"authority":533,"freshness":17,"relevant":21,"comment":534},4,"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。",[536],{"name":529,"url":526},[26,27,29,30,538],"病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":542},{"candidates":543,"agricultural_evidence":46},[544,549,553,557],{"code":53,"score":127,"supported":46,"title":545,"abstract":547,"tags":548},[546],"smart agriculture",[546],[26,30],{"code":32,"score":61,"supported":46,"title":550,"abstract":551,"tags":552},[],[29,48],[29],{"code":68,"score":20,"supported":46,"title":554,"abstract":555,"tags":556},[],[71,72],[],{"code":60,"score":461,"supported":14,"title":558,"abstract":559,"tags":560},[],[],[27,538],[562,563],"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":565,"openalex_id":567,"authors":568,"venue":529,"cited_by_count":105,"oa_url":526,"card":581,"direction":110,"ingested_from":112},"W7220453212",[569,571,573,575,577,579],{"name":570,"orcid":9},"R. Jayalakshmi",{"name":572,"orcid":9},"R. G. Suresh Kumar",{"name":574,"orcid":9},"Bharath P",{"name":576,"orcid":9},"Harishvarsakthi A",{"name":578,"orcid":9},"Abhimanyu Dinesan",{"name":580,"orcid":9},"Mohammed Fayid Faizal",{"tldr":582,"method":583,"finding":584,"direction":110,"opportunity":585},"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。","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。","2026-10-06T23:30:08.152961Z"]