[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4302":3,"related-4302":161},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":49,"search_phrases":133,"slug":136,"view_count":137,"doi":138,"paper":139,"created_at":160},4302,"BIG DATA IN AGRONOMY: TECHNOLOGIES, APPLICATIONS, BENEFITS AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.4238\u002Fk6hcgw77","Big Data is becoming an important component of modern agronomy by enabling the integration, processing and analysis of large, diverse and continuously generated datasets for improved agricultural decision-making. Agricultural Big Data is generated from multiple sources, including remote sensing satellites, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, field observations and historical databases. The integration of these data sources with Geographic Information Systems (GIS), cloud computing, Artificial Intelligence (AI) and Machine Learning (ML) enables the identification of spatial and temporal patterns that are difficult to detect using conventional approaches. Big Data applications in agronomy include climate and weather prediction, pest and disease management, soil health and nutrient management, irrigation scheduling, crop yield prediction, precision agriculture, crop insurance and weed management. Examples from India and other regions demonstrate the application of satellite imagery, UAV-based sensing, IoT systems, crop-growth models and machine-learning algorithms for field-scale and regional agricultural decision making. Big Data analytics can improve resource-use efficiency, support predictive and real-time monitoring, strengthen agricultural risk assessment and facilitate site-specific management. However, effective implementation is constrained by challenges such as high infrastructure and operational costs, data quality and interoperability issues, data privacy and ownership concerns, limited rural digital infrastructure, inadequate technical skills and increasing requirements for data storage and computing. Therefore, the effective integration of Big Data with agronomic knowledge and decision-support systems can contribute to more precise, efficient and sustainable agricultural production while addressing emerging challenges related to climate variability, resource scarcity and food security.","大数据正成为现代农学的重要组成部分，它能够整合、处理和分析大规模、多样化且持续生成的数据集，从而改进农业决策。农业大数据来源于多种渠道，包括遥感卫星、无人机（UAV）、物联网（IoT）传感器、气象站、农业机械、田间观测和历史数据库。将这些数据源与地理信息系统（GIS）、云计算、人工智能（AI）和机器学习（ML）相结合，能够识别传统方法难以发现的空间和时间模式。大数据在农学中的应用包括气候与天气预测、病虫害管理、土壤健康与养分管理、灌溉调度、作物产量预测、精准农业、作物保险和杂草管理。来自印度及其他地区的案例展示了卫星影像、基于无人机的遥感、物联网系统、作物生长模型和机器学习算法在田块尺度和区域农业决策中的应用。大数据分析可以提高资源利用效率，支持预测性和实时监测，加强农业风险评估，并促进因地制宜的管理。然而，有效实施受到诸多挑战的制约，如高昂的基础设施和运营成本、数据质量和互操作性问题、数据隐私和所有权担忧、农村数字基础设施有限、技术技能不足以及数据存储和计算需求不断增加。因此，将大数据与农学知识和决策支持系统有效整合，有助于实现更精准、高效和可持续的农业生产，同时应对气候变异性、资源稀缺和粮食安全等方面的新兴挑战。",null,"Genetics and Molecular Research","2026-10-05T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"系统综述农业大数据技术、应用与挑战，信息密度高，对智慧农业决策有参考价值，但属综述性论文，非突破性成果。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","农业大数据","精准农业","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39,44],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":45,"slug":46,"name":47,"path":48,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":56},[55,63,71,78,84,92,100,107,114,120,127],{"code":33,"score":13,"supported":56,"title":57,"abstract":58,"tags":62},true,[],[59,60,61],"机器学习","machine learning","artificial intelligence",[28],{"code":40,"score":64,"supported":56,"title":65,"abstract":66,"tags":70},9,[],[67,68,69],"地理信息","GIS","geographic information",[],{"code":45,"score":64,"supported":56,"title":72,"abstract":73,"tags":77},[],[74,75,76],"物联网","internet of things","IoT",[],{"code":79,"score":21,"supported":56,"title":80,"abstract":81,"tags":83},"I1",[],[30,82],"precision agriculture",[27,30],{"code":85,"score":86,"supported":56,"title":87,"abstract":88,"tags":91},"I3",7,[],[89,90],"遥感","remote sensing",[31],{"code":93,"score":94,"supported":56,"title":95,"abstract":96,"tags":99},"A8",6,[],[97,98],"粮食安全","food security",[],{"code":101,"score":94,"supported":56,"title":102,"abstract":103,"tags":106},"H2",[],[104,105],"土壤","soil",[],{"code":108,"score":94,"supported":56,"title":109,"abstract":110,"tags":113},"H3",[],[111,112],"灌溉","irrigation",[],{"code":115,"score":116,"supported":56,"title":117,"abstract":118,"tags":119},"I6",4,[],[29],[29],{"code":121,"score":122,"supported":56,"title":123,"abstract":124,"tags":126},"F3",3,[],[125],"养分管理",[],{"code":128,"score":122,"supported":56,"title":129,"abstract":130,"tags":132},"F5",[],[131],"农业机械",[],[134,135],"农业大数据 遥感 物联网","机器学习 作物产量预测","农业大数据遥感物联网-4302",0,"10.4238\u002Fk6hcgw77",{"doi":138,"openalex_id":140,"authors":141,"venue":10,"cited_by_count":137,"oa_url":6,"card":152,"direction":158,"ingested_from":159},"W7220374836",[142,144,146,148,150],{"name":143,"orcid":9},"Kodavali Aswini",{"name":145,"orcid":9},"Raju Marimuthu",{"name":147,"orcid":9},"Guru Gurusamy",{"name":149,"orcid":9},"Kannan Pandian",{"name":151,"orcid":9},"Anandham Rangasamy",{"tldr":153,"method":154,"finding":155,"direction":156,"opportunity":157},"综述农学大数据的技术、应用、效益与挑战，强调多源数据融合支撑精准农业决策。","整合遥感、无人机、物联网、GIS、云计算与AI\u002FML的多源农业数据分析。","大数据可提升资源效率与实时监测，但受成本、数据质量、隐私和农村数字基建制约。","农业人工智能与决策模型","可研究低成本边缘计算与数据互操作标准，破解小农户场景下大数据落地瓶颈。","智慧农业 \u002F 农业物联网","openalex","2026-10-06T23:30:11.880721Z",{"total":94,"page":22,"page_size":94,"items":162},[163,237,331,389,447,523],{"id":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":9,"source_name":169,"source_url":166,"published_at":170,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":171,"score_detail":172,"sources":176,"tags":178,"primary_topic":179,"secondary_topics":180,"classification":183,"search_phrases":218,"slug":221,"view_count":116,"doi":222,"paper":223,"created_at":236},1724,"Integrating Artificial Intelligence and IoT for Precision Farming: A Review of Agriculture 4.0 Technologies, Applications, and Challenges","https:\u002F\u002Fdoi.org\u002F10.67564\u002Firassjms.v3.i9.0186","Agriculture 4.0 marks a decisive shift from mechanized and chemically intensive farming toward a data-driven, automated, and connected model of food production. At the center of this shift lies the convergence of artificial intelligence (AI) and the Internet of Things (IoT), which together allow farms to sense, interpret, and act on field conditions with a precision that manual methods cannot match. This review synthesizes the current body of literature on AI-IoT integration in precision farming, covering the core enabling technologies — smart sensors, unmanned aerial vehicles (UAVs), geographic information systems (GIS), edge and cloud computing, and block chain-based traceability — and the layered architecture through which they combine into functioning smart-farming systems. Drawing on peer-reviewed studies published largely between 2019 and 2025, the review examines representative applications in smart irrigation, crop-disease detection, yield prediction, and supply-chain traceability, consolidates the recurring barriers reported across this literature, and provides a detailed, yearordered comparison of technique, dataset, performance metrics, and reported limitations across ten representative studies. The review concludes by outlining research gaps — particularly around affordable edge-AI models, interoperable data standards, and region-specific validation in smallholder contexts such as India — that merit attention in future work.","农业4.0标志着从机械化、化学密集型农业向数据驱动、自动化、互联的食品生产模式的决定性转变。这一转变的核心在于人工智能（AI）与物联网（IoT）的融合，二者共同使农场能够以人工方法无法企及的精度感知、解读并响应田间条件。本综述综合了当前关于精准农业中AI-IoT整合的文献体系，涵盖核心使能技术——智能传感器、无人机（UAVs）、地理信息系统（GIS）、边缘与云计算，以及基于区块链的可追溯性——以及它们组合成功能性智慧农业系统的分层架构。基于主要发表于2019年至2025年间的同行评审研究，本综述考察了智能灌溉、作物病害检测、产量预测和供应链可追溯性中的代表性应用，整合了文献中反复出现的障碍，并对十项代表性研究进行了按年份排序的技术、数据集、性能指标及报告局限性的详细比较。综述最后指出了研究空白——特别是围绕经济实惠的边缘AI模型、互操作性数据标准，以及在印度等小农情境下的区域特定验证——这些值得在未来工作中予以关注。","IRASS Journal of Multidisciplinary Studies","2026-09-04T00:00:00Z",70,{"impact":17,"substance":173,"depth":17,"authority":13,"freshness":174,"relevant":22,"comment":175},22,2,"综述AI与IoT在精准农业中的集成应用，涵盖技术、应用与挑战，对行业有参考价值，但时效性较低。",[177],{"name":169,"url":166},[27,28,74,30],{"code":45,"slug":46,"name":47,"path":48,"parent_code":37},[181,182],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":184},{"candidates":185,"agricultural_evidence":56},[186,190,195,199,203,207,212],{"code":45,"score":17,"supported":56,"title":187,"abstract":188,"tags":189},[76],[74,75,76],[74],{"code":33,"score":191,"supported":56,"title":192,"abstract":193,"tags":194},12,[61],[61],[28],{"code":40,"score":64,"supported":56,"title":196,"abstract":197,"tags":198},[],[67,68,69],[],{"code":79,"score":21,"supported":56,"title":200,"abstract":201,"tags":202},[],[27,30],[27,30],{"code":108,"score":94,"supported":56,"title":204,"abstract":205,"tags":206},[],[111,112],[],{"code":128,"score":122,"supported":56,"title":208,"abstract":209,"tags":211},[],[210],"机械化",[],{"code":213,"score":122,"supported":56,"title":214,"abstract":215,"tags":217},"G2",[],[216],"smallholder",[],[219,220],"农业人工智能 智慧农业 精准农业 物联网","农业人工智能 智慧农业","农业人工智能智慧农业精准农业物联网-1724","10.67564\u002Firassjms.v3.i9.0186",{"doi":222,"openalex_id":224,"authors":225,"venue":169,"cited_by_count":137,"oa_url":230,"card":231,"direction":158,"ingested_from":159},"W7208758888",[226,228],{"name":227,"orcid":9},"G Swetha",{"name":229,"orcid":9},"Prof T Anuradha","https:\u002F\u002Firasspublisher.com\u002Fassets\u002Farticles\u002F1788493097.pdf",{"tldr":232,"method":233,"finding":234,"direction":158,"opportunity":235},"综述AI与IoT在精准农业中的集成技术、应用与挑战，提出未来研究方向。","系统综述2019-2025年文献，分析架构、应用案例及十项代表性研究。","AI-IoT集成提升农业效率，但存在成本、互操作性和小农验证等挑战。","研究空白包括低成本边缘AI模型、数据标准互操作及小农场景的区域验证，可探索适应性方案。","2026-09-05T23:30:09.434649Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":244,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":248,"tags":250,"primary_topic":252,"secondary_topics":253,"classification":259,"search_phrases":307,"slug":310,"view_count":137,"doi":311,"paper":312,"created_at":330},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":17,"substance":173,"depth":17,"authority":20,"freshness":64,"relevant":22,"comment":247},"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。",[249],{"name":243,"url":240},[27,28,74,30,251],"气候韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[254,255],{"code":45,"slug":46,"name":47,"path":48,"parent_code":37},{"code":108,"slug":112,"name":256,"path":257,"parent_code":258},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":50,"locked":14,"version":51,"method":52,"evidence":260},{"candidates":261,"agricultural_evidence":56},[262,270,275,281,286,293,297,303],{"code":33,"score":17,"supported":56,"title":263,"abstract":264,"tags":269},[61],[265,59,266,60,267,268],"深度学习","计算机视觉","deep learning","computer vision",[28],{"code":79,"score":271,"supported":56,"title":272,"abstract":273,"tags":274},16,[82],[30,82],[27,30],{"code":45,"score":13,"supported":56,"title":276,"abstract":277,"tags":280},[],[74,278,76,279],"边缘计算","edge computing",[74],{"code":108,"score":64,"supported":56,"title":282,"abstract":283,"tags":285},[],[111,284,112],"水资源",[],{"code":287,"score":86,"supported":56,"title":288,"abstract":289,"tags":292},"H7",[],[290,291],"气候变化","climate change",[251],{"code":101,"score":94,"supported":56,"title":294,"abstract":295,"tags":296},[],[104,105],[],{"code":298,"score":122,"supported":56,"title":299,"abstract":300,"tags":302},"A7",[],[301],"间作",[],{"code":121,"score":122,"supported":56,"title":304,"abstract":305,"tags":306},[],[125],[],[308,309],"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":311,"openalex_id":313,"authors":314,"venue":243,"cited_by_count":137,"oa_url":240,"card":325,"direction":158,"ingested_from":159},"W7223121456",[315,318,320,322],{"name":316,"orcid":317},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":319,"orcid":9},"Oluwadare OYEBODE",{"name":321,"orcid":9},"Iretiayo Adelaiye",{"name":323,"orcid":324},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":326,"method":327,"finding":328,"direction":156,"opportunity":329},"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":332,"title":333,"url":334,"summary":335,"summary_zh":336,"content":9,"source_name":337,"source_url":334,"published_at":338,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":339,"score_detail":340,"sources":342,"tags":344,"primary_topic":347,"secondary_topics":348,"classification":350,"search_phrases":371,"slug":374,"view_count":122,"doi":375,"paper":376,"created_at":388},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":191,"substance":17,"depth":271,"authority":20,"freshness":64,"relevant":22,"comment":341},"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[343],{"name":337,"url":334},[27,28,345,31,346],"农业物联网","作物估产",{"code":45,"slug":46,"name":47,"path":48,"parent_code":37},[349],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":351},{"candidates":352,"agricultural_evidence":56},[353,357,363,367],{"code":45,"score":17,"supported":56,"title":354,"abstract":355,"tags":356},[76],[74,278,75,76,279],[345],{"code":79,"score":358,"supported":56,"title":359,"abstract":361,"tags":362},15,[360],"smart agriculture",[30,82],[27],{"code":33,"score":13,"supported":56,"title":364,"abstract":365,"tags":366},[],[265,59,60,267,61],[28],{"code":85,"score":174,"supported":14,"title":368,"abstract":369,"tags":370},[],[],[346,31],[372,373],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809","10.1007\u002Fs12083-026-02299-3",{"doi":375,"openalex_id":377,"authors":378,"venue":337,"cited_by_count":137,"oa_url":334,"card":383,"direction":158,"ingested_from":159},"W7221560555",[379,381],{"name":380,"orcid":9},"Keerthana T",{"name":382,"orcid":9},"Janarthanan R",{"tldr":384,"method":385,"finding":386,"direction":158,"opportunity":387},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":9,"source_name":395,"source_url":392,"published_at":396,"category":12,"cover_url":9,"hotness":397,"is_selected":14,"score":398,"score_detail":399,"sources":402,"tags":406,"primary_topic":408,"secondary_topics":409,"classification":411,"search_phrases":426,"slug":429,"view_count":22,"doi":9,"paper":430,"created_at":446},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":21,"substance":400,"depth":20,"authority":94,"freshness":94,"relevant":22,"comment":401},14,"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。",[403,404],{"name":395,"url":392},{"name":395,"url":405},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[27,28,345,30,407],"低功耗通信",{"code":45,"slug":46,"name":47,"path":48,"parent_code":37},[410],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":412},{"candidates":413,"agricultural_evidence":56},[414,418,422],{"code":45,"score":17,"supported":56,"title":415,"abstract":416,"tags":417},[76],[74,278,75,76,279],[345],{"code":33,"score":13,"supported":56,"title":419,"abstract":420,"tags":421},[],[59,60,61],[28],{"code":79,"score":21,"supported":56,"title":423,"abstract":424,"tags":425},[],[30,82],[27,30],[427,428],"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":9,"openalex_id":431,"authors":432,"venue":395,"cited_by_count":137,"oa_url":392,"card":441,"direction":158,"ingested_from":159},"W7221414266",[433,435,437,439],{"name":434,"orcid":9},"Shital Chavan",{"name":436,"orcid":9},"Anam Bedrekar",{"name":438,"orcid":9},"Sanika Babar",{"name":440,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":442,"method":443,"finding":444,"direction":158,"opportunity":445},"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":448,"title":449,"url":450,"summary":451,"summary_zh":452,"content":9,"source_name":453,"source_url":450,"published_at":454,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":462,"secondary_topics":468,"classification":471,"search_phrases":504,"slug":507,"view_count":22,"doi":508,"paper":509,"created_at":522},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":271,"substance":18,"depth":19,"authority":191,"freshness":64,"relevant":22,"comment":457},"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。",[459],{"name":453,"url":450},[27,28,74,30,461],"棉花病害",{"code":463,"slug":464,"name":465,"path":466,"parent_code":467},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[469,470],{"code":45,"slug":46,"name":47,"path":48,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":472},{"candidates":473,"agricultural_evidence":56},[474,478,484,489,493,500],{"code":45,"score":358,"supported":56,"title":475,"abstract":476,"tags":477},[76],[74,76],[74],{"code":463,"score":400,"supported":56,"title":479,"abstract":481,"tags":483},[480],"cotton",[482,480],"棉花",[],{"code":79,"score":485,"supported":56,"title":486,"abstract":487,"tags":488},11,[],[27,30,82],[27,30],{"code":33,"score":13,"supported":56,"title":490,"abstract":491,"tags":492},[],[265,266,267,61,268],[28],{"code":494,"score":94,"supported":56,"title":495,"abstract":496,"tags":499},"F4",[],[497,498],"农药","pesticide",[],{"code":101,"score":94,"supported":56,"title":501,"abstract":502,"tags":503},[],[104,105],[],[505,506],"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":508,"openalex_id":510,"authors":511,"venue":453,"cited_by_count":137,"oa_url":450,"card":517,"direction":158,"ingested_from":159},"W7221340722",[512,514],{"name":513,"orcid":9},"Deepak R Khadse",{"name":515,"orcid":516},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":518,"method":519,"finding":520,"direction":158,"opportunity":521},"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":524,"title":525,"url":526,"summary":527,"summary_zh":528,"content":9,"source_name":453,"source_url":526,"published_at":529,"category":12,"cover_url":9,"hotness":397,"is_selected":14,"score":530,"score_detail":531,"sources":533,"tags":538,"primary_topic":541,"secondary_topics":542,"classification":549,"search_phrases":585,"slug":588,"view_count":22,"doi":589,"paper":590,"created_at":600},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":173,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":532},"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。",[534,535],{"name":453,"url":526},{"name":536,"url":537},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,28,539,345,30,540],"农业机器人","自动驾驶农机",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[543,548],{"code":128,"slug":544,"name":545,"path":546,"parent_code":547},"machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":45,"slug":46,"name":47,"path":48,"parent_code":37},{"status":50,"locked":14,"version":51,"method":52,"evidence":550},{"candidates":551,"agricultural_evidence":56},[552,556,562,566,570,575,580],{"code":33,"score":17,"supported":56,"title":553,"abstract":554,"tags":555},[61],[59,266,60,61,268],[28],{"code":128,"score":19,"supported":56,"title":557,"abstract":559,"tags":561},[558],"agricultural machinery",[131,560,558],"收割机",[],{"code":45,"score":13,"supported":56,"title":563,"abstract":564,"tags":565},[],[74,75,76],[345],{"code":85,"score":94,"supported":56,"title":567,"abstract":568,"tags":569},[],[89,90],[],{"code":79,"score":571,"supported":56,"title":572,"abstract":573,"tags":574},5,[],[30],[27,30],{"code":494,"score":122,"supported":56,"title":576,"abstract":577,"tags":579},[],[578],"除草",[],{"code":581,"score":22,"supported":14,"title":582,"abstract":583,"tags":584},"F6",[],[],[539],[586,587],"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":589,"openalex_id":591,"authors":592,"venue":453,"cited_by_count":137,"oa_url":526,"card":595,"direction":158,"ingested_from":159},"W7221138004",[593],{"name":594,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":596,"method":597,"finding":598,"direction":158,"opportunity":599},"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"]