[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1270":3,"related-1270":89},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":88},1270,"Tendencias en agricultura y silvicultura de precisión: un análisis bibliométrico de la integración de datos, el apoyo a la toma de decisiones y la sostenibilidad","https:\u002F\u002Fdoi.org\u002F10.69639\u002Farandu.v13i3.2452","Este estudio analiza las tendencias y la estructura conceptual de la investigación sobre agricultura y silvicultura de precisión, con énfasis en la integración de datos, los sistemas de apoyo a la toma de decisiones y la sostenibilidad. Se desarrolló un análisis bibliométrico de documentos indexados en Web of Science y Scopus, recuperados mediante una estrategia de búsqueda orientada a identificar estudios relacionados con tecnologías digitales, inteligencia artificial, teledetección, IoT, modelización predictiva y gestión sostenible. El proceso de selección documental siguió criterios de depuración basados en PRISMA, clasificación por cuartiles y revisión de metadatos, obteniéndose un corpus final de 411 documentos publicados entre 2003 y 2026. Los resultados evidencian un crecimiento acelerado de la producción científica, con una alta concentración de literatura reciente y una estructura temática organizada en torno a temas motores, básicos, de nicho y emergentes. Los clústeres de precision agriculture y smart agriculture se identificaron como núcleos articuladores del campo, mientras que inteligencia artificial, IoT y seguridad alimentaria funcionaron como bases transversales. Asimismo, la agricultura digital, la gobernanza tecnológica, el aprendizaje por refuerzo y las redes neuronales convolucionales aparecen como líneas especializadas o en desarrollo. Se concluye que el mapa temático permite reconocer patrones de centralidad y densidad que diferencian tendencias consolidadas, especializadas y emergentes, aportando una visión ordenada para orientar futuras investigaciones sobre sistemas agroforestales inteligentes, adaptativos y sostenibles.",null,"Arandu-UTIC.","2026-08-29T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":16,"authority":12,"freshness":18,"relevant":19,"comment":20},18,20,3,1,"基于411篇文献的计量分析，揭示精准农业与林业研究趋势，对智慧农业领域有参考价值。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","农业人工智能","农业遥感","可持续发展","精准农业",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":45},[42,51,56,61],{"code":43,"score":44,"supported":45,"title":46,"abstract":47,"tags":50},"I1",8,true,[],[48,49],"smart agriculture","precision agriculture",[24,28],{"code":30,"score":18,"supported":45,"title":52,"abstract":53,"tags":55},[],[54],"IoT",[],{"code":57,"score":19,"supported":13,"title":58,"abstract":59,"tags":60},"I2",[],[],[25],{"code":62,"score":19,"supported":13,"title":63,"abstract":64,"tags":65},"I3",[],[],[26],[67,68],"农业人工智能 可持续发展 农业遥感 智慧农业","农业人工智能 可持续发展","农业人工智能可持续发展农业遥感智慧农业-1270",2,"10.69639\u002Farandu.v13i3.2452",{"doi":71,"openalex_id":73,"authors":74,"venue":9,"cited_by_count":78,"oa_url":79,"card":80,"direction":86,"ingested_from":87},"W7204668132",[75],{"name":76,"orcid":77},"Carlos Arturo Carvajal Chávez","https:\u002F\u002Forcid.org\u002F0000-0002-2781-6953",0,"https:\u002F\u002Frevista.utic.edu.py\u002Frevista.ojs\u002Findex.php\u002Frevistas\u002Farticle\u002Fdownload\u002F2452\u002F3991",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"通过文献计量分析，梳理精准农业与林业的研究趋势、主题结构和热点方向。","基于Web of Science和Scopus，采用PRISMA筛选，对411篇","精准农业和智慧农业是核心主题，AI、IoT和食品安全为基础，数字农业、强化学习等为新兴方向。","农业人工智能与决策模型","可深入探索强化学习与卷积神经网络在智能农林业系统中的应用，结合数据集成与可持续性，填补新兴技术整合研究的空白。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:13.088508Z",{"total":90,"page":19,"page_size":90,"items":91},6,[92,196,301,372,430,508],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":8,"source_name":98,"source_url":95,"published_at":99,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":101,"sources":107,"tags":109,"primary_topic":112,"secondary_topics":113,"classification":114,"search_phrases":165,"slug":168,"view_count":18,"doi":169,"paper":170,"created_at":195},2775,"Digital-enabled life cycle thinking for sustainable food systems: aligning with sustainable development goals","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-63273-w","The increasing complexity of global food systems, coupled with climate change, resource scarcity, and socio-economic pressures, necessitates advanced sustainability assessment approaches. While life cycle thinking (LCT) provides a comprehensive framework, its practical application is constrained by data limitations, methodological uncertainty, and the limited ability of conventional models to capture dynamic operational conditions. This study proposes a digitally integrated LCT framework that combines artificial intelligence (AI), blockchain, internet of things (IoT), and remote sensing to enhance data accuracy, transparency, and real-time decision-making in food supply chains. A key contribution of this research is the introduction of two novel constructs, the digital trust index (DTI) and proof-of-climate impact (PCI) which quantify stakeholder trust and provide verifiable evidence linking digital interventions to measurable environmental outcomes. The DTI is conceptualized as a composite measure of stakeholder trust, integrating both digital infrastructure capabilities and governance-based trust mechanisms. Scenario analysis demonstrates that integrated digital and governance systems can achieve up to 34% climate impact reduction (CIR), while Monte Carlo simulation results indicate an average CIR of approximately 20%, confirming the robustness of the framework under uncertainty. The study reveals that SDG 12 (23%) and SDG 9 (20%) dominate digital LCT research in food systems, followed by SDG 2 and SDG 13, while biodiversity-focused goals (SDG 14 & SDG 15) receive less attention. The proposed SDG oriented framework integrates digital technologies across LCT phases, enabling real-time and transparent sustainability assessment while enhancing the monitoring of biodiversity impacts. The findings highlight that digitalization, when coupled with governance and sustainable development goal-oriented metrics, enables more transparent, adaptive, and impact-driven sustainability assessment in food systems.","全球粮食系统日益复杂，加之气候变化、资源稀缺和社会经济压力，迫切需要先进的可持续性评估方法。尽管生命周期思维（LCT）提供了全面的框架，但其实际应用受到数据限制、方法学不确定性以及传统模型捕捉动态运行条件能力有限的制约。本研究提出了一种数字化集成的LCT框架，将人工智能（AI）、区块链、物联网（IoT）和遥感相结合，以提升食品供应链中的数据准确性、透明度和实时决策能力。本研究的一项关键贡献是引入了两个新颖构念——数字信任指数（DTI）和气候影响证明（PCI），用以量化利益相关者信任，并提供可验证的证据，将数字干预与可衡量的环境成果相联系。DTI被概念化为利益相关者信任的综合度量，整合了数字基础设施能力和基于治理的信任机制。情景分析表明，集成的数字与治理系统可实现高达34%的气候影响减排（CIR），而蒙特卡洛模拟结果显示平均CIR约为20%，证实了该框架在不确定性下的稳健性。研究揭示，SDG 12（23%）和SDG 9（20%）在食品系统中的数字LCT研究中占主导地位，其次是SDG 2和SDG 13，而聚焦生物多样性的目标（SDG 14和SDG 15）受到的关注较少。所提出的面向SDG的框架将数字技术整合到LCT各阶段，实现实时、透明的可持续性评估，同时增强对生物多样性影响的监测。研究结果表明，数字化在与治理和面向可持续发展目标的指标相结合时，能够在食品系统中实现更透明、更具适应性和以影响为导向的可持续性评估。","Scientific Reports","2026-09-15T00:00:00Z",86,{"impact":102,"substance":103,"depth":104,"authority":105,"freshness":44,"relevant":19,"comment":106},22,23,19,14,"提出数字信任指数与气候影响证明等新构念，量化数字技术对食物系统可持续性的减排贡献，方法新颖、结论可靠，对农业信息化与绿色转型具有较强参考价值。",[108],{"name":98,"url":95},[24,25,26,27,110,111],"区块链溯源","食物系统",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":115},{"candidates":116,"agricultural_evidence":45},[117,124,131,138,145,151,156,161],{"code":30,"score":118,"supported":45,"title":119,"abstract":120,"tags":123},9,[],[121,122,54],"物联网","internet of things",[],{"code":62,"score":125,"supported":45,"title":126,"abstract":127,"tags":130},7,[],[128,129],"遥感","remote sensing",[26],{"code":132,"score":90,"supported":45,"title":133,"abstract":134,"tags":137},"H6",[],[135,136],"生物多样性","biodiversity",[],{"code":139,"score":90,"supported":45,"title":140,"abstract":141,"tags":144},"H7",[],[142,143],"气候变化","climate change",[],{"code":57,"score":146,"supported":45,"title":147,"abstract":148,"tags":150},4,[],[149],"artificial intelligence",[25],{"code":152,"score":19,"supported":13,"title":153,"abstract":154,"tags":155},"E2",[],[],[111],{"code":157,"score":19,"supported":13,"title":158,"abstract":159,"tags":160},"E4",[],[],[110],{"code":43,"score":19,"supported":13,"title":162,"abstract":163,"tags":164},[],[],[24],[166,167],"农业人工智能 区块链溯源 可持续发展 农业遥感","农业人工智能 区块链溯源","农业人工智能区块链溯源可持续发展农业遥感-2775","10.1038\u002Fs41598-026-63273-w",{"doi":169,"openalex_id":171,"authors":172,"venue":98,"cited_by_count":78,"oa_url":188,"card":189,"direction":86,"ingested_from":87},"W7213239181",[173,175,178,180,182,185],{"name":174,"orcid":8},"S. U. Parvathy",{"name":176,"orcid":177},"Vysakh Kani Kolil","https:\u002F\u002Forcid.org\u002F0000-0003-2035-3439",{"name":179,"orcid":8},"Raghu Raman",{"name":181,"orcid":8},"Nripendra P. Rana",{"name":183,"orcid":184},"Sasangan Ramanathan","https:\u002F\u002Forcid.org\u002F0000-0002-7459-0934",{"name":186,"orcid":187},"Krishnashree Achuthan","https:\u002F\u002Forcid.org\u002F0000-0003-2618-0882","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-63273-w_reference.pdf",{"tldr":190,"method":191,"finding":192,"direction":193,"opportunity":194},"提出融合AI、区块链、物联网与遥感的数字生命周期思维框架，用于食品系统可持续性评估。","整合AI、区块链、IoT与遥感，构建数字信任指数和气候影响证明，用情景分析与蒙特","数字与治理结合可使气候影响降低最高34%，平均约20%；SDG12和SDG9主导相关研究。","农业绿色发展与碳","可探索数字信任指数与气候影响证明在农业食品供应链中的实证验证及生物多样性目标监测。","2026-09-17T23:30:15.308110Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":8,"source_name":202,"source_url":199,"published_at":203,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":204,"score_detail":205,"sources":208,"tags":210,"primary_topic":212,"secondary_topics":216,"classification":224,"search_phrases":277,"slug":280,"view_count":78,"doi":281,"paper":282,"created_at":300},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":16,"substance":102,"depth":16,"authority":206,"freshness":118,"relevant":19,"comment":207},13,"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。",[209],{"name":202,"url":199},[24,25,121,28,211],"气候韧性",{"code":57,"slug":213,"name":214,"path":215,"parent_code":34},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[217,218],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"code":219,"slug":220,"name":221,"path":222,"parent_code":223},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":37,"locked":13,"version":38,"method":39,"evidence":225},{"candidates":226,"agricultural_evidence":45},[227,237,242,248,254,258,265,271],{"code":57,"score":16,"supported":45,"title":228,"abstract":229,"tags":236},[149],[230,231,232,233,234,235],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[25],{"code":43,"score":238,"supported":45,"title":239,"abstract":240,"tags":241},16,[49],[28,49],[24,28],{"code":30,"score":12,"supported":45,"title":243,"abstract":244,"tags":247},[],[121,245,54,246],"边缘计算","edge computing",[121],{"code":219,"score":118,"supported":45,"title":249,"abstract":250,"tags":253},[],[251,252,220],"灌溉","水资源",[],{"code":139,"score":125,"supported":45,"title":255,"abstract":256,"tags":257},[],[142,143],[211],{"code":259,"score":90,"supported":45,"title":260,"abstract":261,"tags":264},"H2",[],[262,263],"土壤","soil",[],{"code":266,"score":18,"supported":45,"title":267,"abstract":268,"tags":270},"A7",[],[269],"间作",[],{"code":272,"score":18,"supported":45,"title":273,"abstract":274,"tags":276},"F3",[],[275],"养分管理",[],[278,279],"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":281,"openalex_id":283,"authors":284,"venue":202,"cited_by_count":78,"oa_url":199,"card":295,"direction":86,"ingested_from":87},"W7223121456",[285,288,290,292],{"name":286,"orcid":287},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":289,"orcid":8},"Oluwadare OYEBODE",{"name":291,"orcid":8},"Iretiayo Adelaiye",{"name":293,"orcid":294},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":296,"method":297,"finding":298,"direction":84,"opportunity":299},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":8,"source_name":307,"source_url":304,"published_at":308,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":309,"score_detail":310,"sources":312,"tags":314,"primary_topic":316,"secondary_topics":317,"classification":318,"search_phrases":348,"slug":351,"view_count":18,"doi":352,"paper":353,"created_at":371},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":44,"substance":105,"depth":238,"authority":90,"freshness":44,"relevant":19,"comment":311},"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。",[313],{"name":307,"url":304},[24,25,315,121,28],"农业机器人",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":319},{"candidates":320,"agricultural_evidence":45},[321,325,330,335,339,343],{"code":30,"score":16,"supported":45,"title":322,"abstract":323,"tags":324},[54],[121,122,54],[121],{"code":43,"score":326,"supported":45,"title":327,"abstract":328,"tags":329},11,[],[24,28,49],[24,28],{"code":57,"score":125,"supported":45,"title":331,"abstract":332,"tags":334},[],[149,333],"decision support",[25],{"code":259,"score":90,"supported":45,"title":336,"abstract":337,"tags":338},[],[262,263],[],{"code":219,"score":90,"supported":45,"title":340,"abstract":341,"tags":342},[],[251,220],[],{"code":344,"score":19,"supported":13,"title":345,"abstract":346,"tags":347},"F6",[],[],[315],[349,350],"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":352,"openalex_id":354,"authors":355,"venue":307,"cited_by_count":78,"oa_url":304,"card":366,"direction":86,"ingested_from":87},"W7221682750",[356,358,360,362,364],{"name":357,"orcid":8},"Chaitra A S",{"name":359,"orcid":8},"Venugopal Gowda V",{"name":361,"orcid":8},"Vinay H",{"name":363,"orcid":8},"Jeevan T",{"name":365,"orcid":8},"Puneeth M",{"tldr":367,"method":368,"finding":369,"direction":86,"opportunity":370},"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":373,"title":374,"url":375,"summary":376,"summary_zh":377,"content":8,"source_name":378,"source_url":375,"published_at":379,"category":11,"cover_url":8,"hotness":380,"is_selected":13,"score":381,"score_detail":382,"sources":384,"tags":388,"primary_topic":391,"secondary_topics":392,"classification":394,"search_phrases":409,"slug":412,"view_count":19,"doi":8,"paper":413,"created_at":429},4672,"AI-BASED ENERGY EFFICIENT COMMUNICATION IN IOT SYSTEM","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F309","The rapid growth of the Internet of Things (IoT) has led to a massive increase in the number of connected devices across various domains such as smart homes, healthcare, agriculture, and industrial automation. As these devices are often battery-powered and deployed in remote or large scale environments, energy efficiency has become a critical concern in IoT communication systems. Traditional IoT networks typically operate using fixed transmission power levels, which do not adapt to changing environmental or network conditions, resulting in unnecessary energy consumption and reduced battery life.To address these limitations, there is a growing need for intelligent and adaptive communication mechanisms that can dynamically optimize power usage without compromising reliability. In this context, Artificial Intelligence (AI)-based adaptive transmission power control has emerged as a promising solution. This review paper presents an analysis of AI-driven techniques that enable IoT devices to adjust their transmission power based on real-time conditions and network requirements.The study explores approaches such as machine learning-based prediction models, which utilize parameters like Received Signal Strength Indicator (RSSI), distance, interference levels, and packet loss rates to determine optimal transmission power. Additionally, the role of edge computing is examined, enabling faster decision-making with reduced latency. The integration of Low Power Wide Area Network (LPWAN) technologies is also discussed for supporting long-range and energy-efficient communication.Furthermore, the paper highlights how adaptive power control improves network performance by reducing energy wastage, minimizing interference, and extending device lifetime. These benefits are especially useful in applications like smart cities, precision agriculture, and industrial automation.Despite these advantages, challenges such as implementation complexity, resource constraints, and data privacy concerns remain. The paper also outlines future research directions, including the development of lightweight AI models and improved integration with emerging technologies.In conclusion, AIbased adaptive transmission power control significantly enhances energy efficiency and communication reliability, making it essential for the sustainable development of next- generation IoT systems. It also supports scalable and cost-effective deployment of IoT networks in diverse real- world environments.","物联网（IoT）的快速发展使得智能家居、医疗健康、农业和工业自动化等各个领域的联网设备数量大幅增加。由于这些设备通常由电池供电，且部署在偏远或大规模环境中，能效已成为物联网通信系统中的关键问题。传统物联网网络通常采用固定传输功率运行，无法适应不断变化的环境或网络条件，导致不必要的能耗和电池寿命缩短。为解决这些局限，人们日益需要能够在不影响可靠性的前提下动态优化功率使用的智能自适应通信机制。在此背景下，基于人工智能（AI）的自适应传输功率控制已成为一种有前景的解决方案。本综述论文分析了使物联网设备能够根据实时条件和网络需求调整传输功率的AI驱动技术。研究探讨了基于机器学习的预测模型等方法，这些方法利用接收信号强度指示（RSSI）、距离、干扰水平和丢包率等参数来确定最优传输功率。此外，还考察了边缘计算的作用，其能够以更低延迟实现更快决策。论文还讨论了低功耗广域网（LPWAN）技术的集成，以支持长距离和节能通信。进一步地，本文强调了自适应功率控制如何通过减少能量浪费、最小化干扰和延长设备寿命来改善网络性能。这些优势在智慧城市、精准农业和工业自动化等应用中尤为有用。尽管具有这些优势，实施复杂性、资源约束和数据隐私问题等挑战仍然存在。论文还概述了未来研究方向，包括开发轻量级AI模型以及与新兴技术的改进集成。总之，基于AI的自适应传输功率控制显著提升了能效和通信可靠性，使其成为下一代物联网系统可持续发展的关键。它还支持物联网网络在多样化现实环境中的可扩展且具有成本效益的部署。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",25,47,{"impact":44,"substance":105,"depth":206,"authority":90,"freshness":90,"relevant":19,"comment":383},"综述性论文，方法层面有参考价值，但非农业专属突破，且期刊权威性一般，仅作主题聚合素材。",[385,386],{"name":378,"url":375},{"name":378,"url":387},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[24,25,389,28,390],"农业物联网","低功耗通信",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[393],{"code":57,"slug":213,"name":214,"path":215,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":395},{"candidates":396,"agricultural_evidence":45},[397,401,405],{"code":30,"score":16,"supported":45,"title":398,"abstract":399,"tags":400},[54],[121,245,122,54,246],[389],{"code":57,"score":12,"supported":45,"title":402,"abstract":403,"tags":404},[],[231,233,149],[25],{"code":43,"score":44,"supported":45,"title":406,"abstract":407,"tags":408},[],[28,49],[24,28],[410,411],"AI 物联网 传输功率控制","LPWAN 精准农业","AI物联网传输功率控制-4672",{"doi":8,"openalex_id":414,"authors":415,"venue":378,"cited_by_count":78,"oa_url":375,"card":424,"direction":86,"ingested_from":87},"W7221414266",[416,418,420,422],{"name":417,"orcid":8},"Shital Chavan",{"name":419,"orcid":8},"Anam Bedrekar",{"name":421,"orcid":8},"Sanika Babar",{"name":423,"orcid":8},"Prof. M. V. Naiknavare",{"tldr":425,"method":426,"finding":427,"direction":86,"opportunity":428},"综述AI自适应发射功率控制提升IoT通信能效，面向智慧农业等场景。","综述机器学习预测模型、边缘计算与LPWAN技术，基于RSSI等参数。","AI自适应功率控制可降低能耗、减少干扰并延长设备寿命，但存在实现复杂与隐私挑战。","可研究面向农田的轻量级AI功率控制模型，结合土壤墒情与作物需水优化能耗。","2026-10-11T23:30:16.397155Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":8,"source_name":436,"source_url":433,"published_at":437,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":438,"score_detail":439,"sources":443,"tags":445,"primary_topic":447,"secondary_topics":453,"classification":456,"search_phrases":489,"slug":492,"view_count":19,"doi":493,"paper":494,"created_at":507},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":238,"substance":17,"depth":440,"authority":441,"freshness":118,"relevant":19,"comment":442},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。",[444],{"name":436,"url":433},[24,25,121,28,446],"棉花病害",{"code":448,"slug":449,"name":450,"path":451,"parent_code":452},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[454,455],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"code":57,"slug":213,"name":214,"path":215,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":457},{"candidates":458,"agricultural_evidence":45},[459,464,470,474,478,485],{"code":30,"score":460,"supported":45,"title":461,"abstract":462,"tags":463},15,[54],[121,54],[121],{"code":448,"score":105,"supported":45,"title":465,"abstract":467,"tags":469},[466],"cotton",[468,466],"棉花",[],{"code":43,"score":326,"supported":45,"title":471,"abstract":472,"tags":473},[],[24,28,49],[24,28],{"code":57,"score":12,"supported":45,"title":475,"abstract":476,"tags":477},[],[230,232,234,149,235],[25],{"code":479,"score":90,"supported":45,"title":480,"abstract":481,"tags":484},"F4",[],[482,483],"农药","pesticide",[],{"code":259,"score":90,"supported":45,"title":486,"abstract":487,"tags":488},[],[262,263],[],[490,491],"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":493,"openalex_id":495,"authors":496,"venue":436,"cited_by_count":78,"oa_url":433,"card":502,"direction":86,"ingested_from":87},"W7221340722",[497,499],{"name":498,"orcid":8},"Deepak R Khadse",{"name":500,"orcid":501},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":503,"method":504,"finding":505,"direction":86,"opportunity":506},"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":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":8,"source_name":436,"source_url":511,"published_at":514,"category":11,"cover_url":8,"hotness":380,"is_selected":13,"score":515,"score_detail":516,"sources":518,"tags":523,"primary_topic":525,"secondary_topics":526,"classification":534,"search_phrases":570,"slug":573,"view_count":19,"doi":574,"paper":575,"created_at":585},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",81,{"impact":102,"substance":17,"depth":16,"authority":206,"freshness":44,"relevant":19,"comment":517},"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。",[519,520],{"name":436,"url":511},{"name":521,"url":522},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[24,25,315,389,28,524],"自动驾驶农机",{"code":57,"slug":213,"name":214,"path":215,"parent_code":34},[527,533],{"code":528,"slug":529,"name":530,"path":531,"parent_code":532},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":535},{"candidates":536,"agricultural_evidence":45},[537,541,548,552,556,561,566],{"code":57,"score":16,"supported":45,"title":538,"abstract":539,"tags":540},[149],[231,232,233,149,235],[25],{"code":528,"score":440,"supported":45,"title":542,"abstract":544,"tags":547},[543],"agricultural machinery",[545,546,543],"农业机械","收割机",[],{"code":30,"score":12,"supported":45,"title":549,"abstract":550,"tags":551},[],[121,122,54],[389],{"code":62,"score":90,"supported":45,"title":553,"abstract":554,"tags":555},[],[128,129],[],{"code":43,"score":557,"supported":45,"title":558,"abstract":559,"tags":560},5,[],[28],[24,28],{"code":479,"score":18,"supported":45,"title":562,"abstract":563,"tags":565},[],[564],"除草",[],{"code":344,"score":19,"supported":13,"title":567,"abstract":568,"tags":569},[],[],[315],[571,572],"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":574,"openalex_id":576,"authors":577,"venue":436,"cited_by_count":78,"oa_url":511,"card":580,"direction":86,"ingested_from":87},"W7221138004",[578],{"name":579,"orcid":8},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":581,"method":582,"finding":583,"direction":86,"opportunity":584},"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"]