[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5386":3,"related-5386":83},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":56,"slug":59,"view_count":15,"doi":60,"paper":61,"created_at":82},5386,"From Ancient Records to Agentic Intelligence: A Comprehensive Historical Review of Data Science and Its Role in Intelligent Internet of Things Analytics","https:\u002F\u002Fdoi.org\u002F10.58496\u002Fbjiot\u002F2026\u002F014","Since the era of the Mesopotamia between 2900 BCE - 2350 BCE, the goals of “data science-like” approaches have always revolved around collecting fragments of information and trying to make insights out of such information. Data Science was not clearly termed as Data Science from the onset. Moreover, there exists several questions about Data Science just been a polished name for Statistics. Therefore, there is a need to look carefully at how Data Science has evolved over the years and where it is currently. The study defines Data Science as a multidisciplinary field that integrates statistics, mathematics, computer science, domain expertise, and ethics. This is important because, it will give Data Scientists and the Data Science enthusiasts a clear understanding of this discipline. In addition, the paper demonstrates how Data Science transforms massive Internet of Things (IoT) data streams into actionable insights through predictive analytics, anomaly detection, explainable artificial intelligence, cybersecurity analytics, digital twins, and intelligent decision support. The findings reveal that the evolution of Data Science reflects a transition from descriptive statistical analysis to autonomous, explainable, and trustworthy intelligent systems capable of supporting real-time decision-making across diverse domains. This work provides a unified historical perspective, highlights emerging research directions, and offers a valuable reference for researchers, educators, and practitioners seeking to understand the past, present, and future direction of Data Science.","自公元前2900年至公元前2350年的美索不达米亚时代以来，“类数据科学”方法的目标始终围绕收集信息碎片并试图从中提炼洞见。数据科学从一开始并未被明确命名为数据科学。此外，关于数据科学是否只是统计学的一个精致名称，仍存在若干疑问。因此，有必要仔细审视数据科学多年来的演变历程及其当前所处的位置。本研究将数据科学定义为一个融合统计学、数学、计算机科学、领域专业知识与伦理学的多学科领域。这一定义具有重要意义，因为它能使数据科学家和数据科学爱好者对该学科形成清晰的认识。此外，本文展示了数据科学如何通过预测分析、异常检测、可解释人工智能、网络安全分析、数字孪生和智能决策支持，将海量物联网（IoT）数据流转化为可付诸行动的洞见。研究结果表明，数据科学的演变反映了从描述性统计分析向自主、可解释且可信的智能系统的转变，这些系统能够支持跨领域的实时决策。本研究提供了统一的历史视角，突出了新兴研究方向，并为试图理解数据科学过去、现在与未来方向的研究者、教育者和从业者提供了有价值的参考。",null,"Babylonian Journal of Internet of Things","2026-10-18T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为数据科学通史综述，仅泛泛提及物联网分析，未涉及农业场景，与三农及农业信息化无实质关联。",[19],{"name":10,"url":6},[21,22,23],"农业人工智能","物联网","数据科学",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":55},[31,40,48],{"code":32,"score":33,"supported":34,"title":35,"abstract":37,"tags":39},"I5",18,true,[36],"internet of things",[22,36,38],"IoT",[22],{"code":41,"score":42,"supported":34,"title":43,"abstract":44,"tags":47},"I2",7,[],[45,46],"artificial intelligence","decision support",[21],{"code":49,"score":50,"supported":34,"title":51,"abstract":52,"tags":54},"I1",3,[],[53],"数字孪生",[],"农业相关性需要复核",[57,58],"农业人工智能 数据科学 物联网","农业人工智能 数据科学","农业人工智能数据科学物联网-5386","10.58496\u002Fbjiot\u002F2026\u002F014",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":15,"oa_url":73,"card":74,"direction":80,"ingested_from":81},"W7222906119",[64,67,70],{"name":65,"orcid":66},"Aamo Iorliam","https:\u002F\u002Forcid.org\u002F0000-0001-8238-9686",{"name":68,"orcid":69},"Iveren Blessing Iorliam","https:\u002F\u002Forcid.org\u002F0000-0002-9973-6151",{"name":71,"orcid":72},"Yahaya Isah Shehu","https:\u002F\u002Forcid.org\u002F0000-0001-8924-9344","https:\u002F\u002Fmesopotamian.press\u002Fjournals\u002Findex.php\u002FBJIoT\u002Farticle\u002Fdownload\u002F1197\u002F1231",{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"综述数据科学从古代记录到智能体智能的历史演进及其在物联网分析中的作用。","历史文献综述，梳理数据科学跨学科定义与物联网分析技术。","数据科学已从描述性统计转向自主、可解释、可信的实时智能决策系统。","农业人工智能与决策模型","可将智能体智能与可解释AI引入农业物联网实时决策，探索可信自主农事决策系统。","智慧农业 \u002F 农业物联网","openalex","2026-10-20T23:30:10.332642Z",{"total":84,"page":85,"page_size":84,"items":86},6,1,[87,189,296,361,431,501],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":9,"source_name":93,"source_url":90,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":95,"score_detail":96,"sources":101,"tags":103,"primary_topic":107,"secondary_topics":112,"classification":113,"search_phrases":175,"slug":178,"view_count":15,"doi":179,"paper":180,"created_at":188},5556,"AI Powered IoT-Based System for Weed and Pest Control in Agriculture Using Deep Learning","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.76s.32","Weeds, pest attacks, labour shortage and over-dependence on chemical pesticides are the serious challenges faced by the agricultural sector in the wake of ever growing weed infestation.Manual weed and pest control has potential negative environmental impacts, increased production costs and reduced yields.The application of Artificial Intelligence (AI), Internet of Things (IoT), Deep Learning, robotics, and laser technologies are a promising way of developing sustainable and autonomous agricultural systems.This study introduces an intelligent agricultural system based on Artificial Intelligence (AI) and Internet of Things (IoT) for weed identification, harmful insect identification, crop condition monitoring and automatic weed removal using a laser beam.The system will be based on Internet of Things (IoT) sensors, Computer vision modules, Convolutional neural networks (CNN), Unmanned robotic platforms, Environmental sensing systems, and Electronic Swarm Suppression (ESS) mechanisms to enhance agricultural productivity and reduce crop losses.The system uses deep learning algorithms for real time weed, crop, beneficial and harmful insect classification.High-resolution cameras and sensing devices collect environmental and crop data continuously and feed the data into an IoT system for processing in a cloud-edge computing system.High precision weed elimination with a robotic platform containing laser emitters which do not harm adjacent crops.Further, computer vision and environmental monitoring are employed to identify aerial insect swarms and to enable automatic attacks of insect swarms by means of a laser beam and ESS technology.Multiple agricultural blocks were used for experimental evaluation of the maize, wheat, soybean and vegetable crops.The results show substantial increases in the accuracy of weed detection, pest identification, crop protection and cost savings for operations over current practices.The proposed system achieved a 97.6% accuracy in the weed classification, 96.8% accuracy in the detection of the insect and a 42.3% reduction in pesticide use.In addition, the crop yield rose by 18.7% and the operational costs dropped by 28.4%.Overall, the results suggest that AI-powered IoT can significantly enhance precision agriculture and promote sustainable food production.","在杂草侵扰日益加剧的背景下，杂草、病虫害侵袭、劳动力短缺以及过度依赖化学农药是农业部门面临的严峻挑战。人工除草和病虫害防治具有潜在的环境负面影响，会增加生产成本并降低产量。人工智能（AI）、物联网（IoT）、深度学习、机器人技术及激光技术的应用，是发展可持续自主农业系统的有前景的途径。本研究提出一种基于人工智能（AI）和物联网（IoT）的智能农业系统，用于杂草识别、有害昆虫识别、作物状况监测以及利用激光束自动除草。该系统将基于物联网（IoT）传感器、计算机视觉模块、卷积神经网络（CNN）、无人机器人平台、环境感知系统及电子蜂群抑制（ESS）机制，以提高农业生产力并减少作物损失。该系统采用深度学习算法对杂草、作物、有益昆虫和有害昆虫进行实时分类。高分辨率摄像头和传感设备持续采集环境与作物数据，并将数据输入物联网系统，在云边计算系统中进行处理。通过配备激光发射器的机器人平台实现高精度除草，且不伤害邻近作物。此外，利用计算机视觉和环境监测来识别空中昆虫蜂群，并通过激光束和ESS技术实现对昆虫蜂群的自动攻击。研究采用多个农业区块对玉米、小麦、大豆和蔬菜作物进行了实验评估。结果表明，与当前做法相比，杂草检测、病虫害识别、作物保护的准确性显著提高，作业成本得以节省。所提出的系统在杂草分类中达到97.6%的准确率，昆虫检测准确率为96.8%，农药使用量减少42.3%。此外，作物产量提高18.7%，作业成本下降28.4%。总体而言，结果表明人工智能驱动的物联网能够显著增强精准农业并促进可持续粮食生产。","International Journal of Drug Delivery Technology","2026-10-21T00:00:00Z",75,{"impact":33,"substance":97,"depth":98,"authority":99,"freshness":13,"relevant":85,"comment":100},22,17,8,"论文给出97.6%杂草识别率、42.3%农药减量等量化结论，方法新颖且数据完整，但期刊权威性一般，属细分领域技术进展。",[102],{"name":93,"url":90},[104,21,22,105,106],"智慧农业","杂草识别","病虫害监测",{"code":108,"slug":109,"name":110,"path":111,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":114,"locked":14,"version":27,"method":28,"evidence":115},"partial",{"candidates":116,"agricultural_evidence":34,"reason":174},[117,126,131,140,146,153,160,167],{"code":41,"score":33,"supported":34,"title":118,"abstract":120,"tags":125},[119],"deep learning",[121,122,123,119,45,124],"深度学习","计算机视觉","病虫害识别","computer vision",[21],{"code":32,"score":33,"supported":34,"title":127,"abstract":128,"tags":130},[38],[22,36,38,129],"edge computing",[22],{"code":132,"score":13,"supported":34,"title":133,"abstract":134,"tags":139},"F4",[],[135,136,137,138],"农药","除草","pesticide","crop protection",[105],{"code":49,"score":42,"supported":34,"title":141,"abstract":142,"tags":145},[],[143,144],"精准农业","precision agriculture",[104],{"code":147,"score":84,"supported":34,"title":148,"abstract":149,"tags":152},"A2",[],[150,151],"小麦","wheat",[],{"code":154,"score":84,"supported":34,"title":155,"abstract":156,"tags":159},"A3",[],[157,158],"玉米","maize",[],{"code":161,"score":84,"supported":34,"title":162,"abstract":163,"tags":166},"A4",[],[164,165],"大豆","soybean",[],{"code":168,"score":84,"supported":34,"title":169,"abstract":170,"tags":173},"B1",[],[171,172],"蔬菜","vegetable",[],"一级领域明确，二级方向需要细分",[176,177],"AI IoT 激光除草 病虫害识别","深度学习 精准农业 农药减量","AIIoT激光除草病虫害识别-5556","10.25258\u002Fijddt.16.76s.32",{"doi":179,"openalex_id":181,"authors":182,"venue":93,"cited_by_count":15,"oa_url":90,"card":183,"direction":80,"ingested_from":81},"W7223145687",[],{"tldr":184,"method":185,"finding":186,"direction":80,"opportunity":187},"提出AI+IoT系统，用深度学习和激光机器人实现杂草与害虫识别及自动清除。","IoT传感器、CNN视觉分类、无人机\u002F机器人平台与激光除草，云边协同处理。","杂草分类准确率97.6%、害虫识别96.8%，农药减42.3%，增产18.7%。","可探索激光除草对非靶标生物与土壤的长期生态影响，及多作物跨域模型泛化。","2026-10-22T11:20:18.540359Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":201,"tags":203,"primary_topic":205,"secondary_topics":209,"classification":220,"search_phrases":272,"slug":275,"view_count":15,"doi":276,"paper":277,"created_at":295},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)",80,{"impact":33,"substance":97,"depth":33,"authority":198,"freshness":199,"relevant":85,"comment":200},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。",[202],{"name":195,"url":192},[104,21,22,143,204],"气候韧性",{"code":41,"slug":206,"name":207,"path":208,"parent_code":108},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[210,214],{"code":32,"slug":211,"name":212,"path":213,"parent_code":108},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":215,"slug":216,"name":217,"path":218,"parent_code":219},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":221,"locked":14,"version":27,"method":28,"evidence":222},"classified",{"candidates":223,"agricultural_evidence":34},[224,230,235,240,246,253,260,266],{"code":41,"score":33,"supported":34,"title":225,"abstract":226,"tags":229},[45],[121,227,122,228,119,124],"机器学习","machine learning",[21],{"code":49,"score":231,"supported":34,"title":232,"abstract":233,"tags":234},16,[144],[143,144],[104,143],{"code":32,"score":13,"supported":34,"title":236,"abstract":237,"tags":239},[],[22,238,38,129],"边缘计算",[22],{"code":215,"score":199,"supported":34,"title":241,"abstract":242,"tags":245},[],[243,244,216],"灌溉","水资源",[],{"code":247,"score":42,"supported":34,"title":248,"abstract":249,"tags":252},"H7",[],[250,251],"气候变化","climate change",[204],{"code":254,"score":84,"supported":34,"title":255,"abstract":256,"tags":259},"H2",[],[257,258],"土壤","soil",[],{"code":261,"score":50,"supported":34,"title":262,"abstract":263,"tags":265},"A7",[],[264],"间作",[],{"code":267,"score":50,"supported":34,"title":268,"abstract":269,"tags":271},"F3",[],[270],"养分管理",[],[273,274],"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":276,"openalex_id":278,"authors":279,"venue":195,"cited_by_count":15,"oa_url":192,"card":290,"direction":80,"ingested_from":81},"W7223121456",[280,283,285,287],{"name":281,"orcid":282},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":284,"orcid":9},"Oluwadare OYEBODE",{"name":286,"orcid":9},"Iretiayo Adelaiye",{"name":288,"orcid":289},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":291,"method":292,"finding":293,"direction":78,"opportunity":294},"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":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":9,"source_name":302,"source_url":299,"published_at":303,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":95,"score_detail":304,"sources":308,"tags":310,"primary_topic":313,"secondary_topics":314,"classification":315,"search_phrases":345,"slug":348,"view_count":15,"doi":349,"paper":350,"created_at":360},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering","2026-10-16T00:00:00Z",{"impact":231,"substance":305,"depth":98,"authority":306,"freshness":199,"relevant":85,"comment":307},21,12,"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[309],{"name":302,"url":299},[104,21,22,311,312],"作物胁迫","遥感监测",{"code":32,"slug":211,"name":212,"path":213,"parent_code":108},[],{"status":221,"locked":14,"version":27,"method":28,"evidence":316},{"candidates":317,"agricultural_evidence":34},[318,322,329,333,337,341],{"code":32,"score":33,"supported":34,"title":319,"abstract":320,"tags":321},[38],[22,36,38],[22],{"code":323,"score":42,"supported":34,"title":324,"abstract":325,"tags":328},"I3",[],[326,327],"遥感","remote sensing",[312],{"code":254,"score":84,"supported":34,"title":330,"abstract":331,"tags":332},[],[257,258],[],{"code":215,"score":84,"supported":34,"title":334,"abstract":335,"tags":336},[],[243,216],[],{"code":49,"score":85,"supported":14,"title":338,"abstract":339,"tags":340},[],[],[104],{"code":41,"score":85,"supported":14,"title":342,"abstract":343,"tags":344},[],[],[21],[346,347],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":349,"openalex_id":351,"authors":352,"venue":302,"cited_by_count":15,"oa_url":299,"card":355,"direction":80,"ingested_from":81},"W7222730658",[353],{"name":354,"orcid":9},"Sunitha Thankappan",{"tldr":356,"method":357,"finding":358,"direction":80,"opportunity":359},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":367,"source_url":364,"published_at":368,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":369,"score_detail":370,"sources":373,"tags":375,"primary_topic":377,"secondary_topics":378,"classification":379,"search_phrases":412,"slug":415,"view_count":85,"doi":416,"paper":417,"created_at":430},5074,"QISHLOQ XO‘JALIGI OLIY TA’LIM MUASSASALARIDA MAXSUS FANLARNI O‘QITISHDA “SMART AGRICULTURE” TEXNOLOGIYALARIDAN FOYDALANISH METODIKASI","https:\u002F\u002Fdoi.org\u002F10.67547\u002Ffanistiqbollari.2026.876","The article examines a methodology for integrating Smart Agriculture technologies into the teaching of specialized subjects at agricultural higher education institutions. The study analyzes the didactic potential of the Internet of Things, sensor monitoring, GIS, remote sensing, artificial intelligence, smart irrigation and digital data analytics.","本文探讨了将智慧农业技术融入农业高等院校专业课程教学的方法论。研究分析了物联网、传感器监测、地理信息系统、遥感、人工智能、智能灌溉和数字数据分析在教学中的潜力。","Ta limda Istiqbolli Izlanishlar xalqaro ilmiy metodik jurnal","2026-10-15T00:00:00Z",49,{"impact":99,"substance":371,"depth":198,"authority":84,"freshness":99,"relevant":85,"comment":372},14,"乌兹别克语论文探讨智慧农业技术融入农业高校专业教学的路径，属农业信息化人才培养方向，但为境外一般期刊、影响面有限，暂不宜进入每日精选。",[374],{"name":367,"url":364},[104,21,22,326,376],"农业教育",{"code":108,"slug":109,"name":110,"path":111,"parent_code":9},[],{"status":114,"locked":14,"version":27,"method":28,"evidence":380},{"candidates":381,"agricultural_evidence":34,"reason":174},[382,388,392,396,400,407],{"code":49,"score":383,"supported":34,"title":384,"abstract":386,"tags":387},15,[385],"smart agriculture",[104,385],[104],{"code":323,"score":42,"supported":34,"title":389,"abstract":390,"tags":391},[],[326,327],[326],{"code":32,"score":42,"supported":34,"title":393,"abstract":394,"tags":395},[],[22,36],[22],{"code":215,"score":84,"supported":34,"title":397,"abstract":398,"tags":399},[],[243,216],[],{"code":401,"score":84,"supported":34,"title":402,"abstract":403,"tags":406},"I4",[],[404,405],"地理信息","GIS",[],{"code":41,"score":408,"supported":34,"title":409,"abstract":410,"tags":411},4,[],[45],[21],[413,414],"农业人工智能 农业教育 智慧农业 物联网","农业人工智能 农业教育","农业人工智能农业教育智慧农业物联网-5074","10.67547\u002Ffanistiqbollari.2026.876",{"doi":416,"openalex_id":418,"authors":419,"venue":367,"cited_by_count":15,"oa_url":423,"card":424,"direction":80,"ingested_from":81},"W7221997102",[420],{"name":421,"orcid":422},"O'G'ILOY SADULLO QIZI SHAMSIYEV","https:\u002F\u002Forcid.org\u002F0009-0003-4706-6488","https:\u002F\u002Ffanistiqbollari.uz\u002Fstorage\u002Farticle876.pdf",{"tldr":425,"method":426,"finding":427,"direction":428,"opportunity":429},"研究将智慧农业技术融入农业高校专业课程教学的方法论。","分析物联网、传感器、GIS、遥感、AI等技术的教学潜力。","智慧农业技术可有效支撑农业专业课程的教学方法设计。","数字乡村与农业信息化","可实证检验该教学法对学生技能与就业的影响，并开发配套课程资源。","2026-10-16T23:30:07.890026Z",{"id":432,"title":433,"url":434,"summary":435,"summary_zh":436,"content":9,"source_name":437,"source_url":434,"published_at":438,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":439,"score_detail":440,"sources":442,"tags":444,"primary_topic":446,"secondary_topics":447,"classification":448,"search_phrases":477,"slug":480,"view_count":50,"doi":481,"paper":482,"created_at":500},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":99,"substance":371,"depth":231,"authority":84,"freshness":99,"relevant":85,"comment":441},"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。",[443],{"name":437,"url":434},[104,21,445,22,143],"农业机器人",{"code":32,"slug":211,"name":212,"path":213,"parent_code":108},[],{"status":221,"locked":14,"version":27,"method":28,"evidence":449},{"candidates":450,"agricultural_evidence":34},[451,455,460,464,468,472],{"code":32,"score":33,"supported":34,"title":452,"abstract":453,"tags":454},[38],[22,36,38],[22],{"code":49,"score":456,"supported":34,"title":457,"abstract":458,"tags":459},11,[],[104,143,144],[104,143],{"code":41,"score":42,"supported":34,"title":461,"abstract":462,"tags":463},[],[45,46],[21],{"code":254,"score":84,"supported":34,"title":465,"abstract":466,"tags":467},[],[257,258],[],{"code":215,"score":84,"supported":34,"title":469,"abstract":470,"tags":471},[],[243,216],[],{"code":473,"score":85,"supported":14,"title":474,"abstract":475,"tags":476},"F6",[],[],[445],[478,479],"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":481,"openalex_id":483,"authors":484,"venue":437,"cited_by_count":15,"oa_url":434,"card":495,"direction":80,"ingested_from":81},"W7221682750",[485,487,489,491,493],{"name":486,"orcid":9},"Chaitra A S",{"name":488,"orcid":9},"Venugopal Gowda V",{"name":490,"orcid":9},"Vinay H",{"name":492,"orcid":9},"Jeevan T",{"name":494,"orcid":9},"Puneeth M",{"tldr":496,"method":497,"finding":498,"direction":80,"opportunity":499},"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":502,"title":503,"url":504,"summary":505,"summary_zh":506,"content":9,"source_name":507,"source_url":504,"published_at":508,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":509,"score_detail":510,"sources":513,"tags":515,"primary_topic":517,"secondary_topics":523,"classification":526,"search_phrases":555,"slug":558,"view_count":85,"doi":559,"paper":560,"created_at":573},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":231,"substance":511,"depth":98,"authority":306,"freshness":199,"relevant":85,"comment":512},20,"基于树莓派与轻量深度学习的棉花叶病检测与物联网监测方案，方法具体、指标明确，对智慧植保有参考价值。",[514],{"name":507,"url":504},[104,21,22,143,516],"棉花病害",{"code":518,"slug":519,"name":520,"path":521,"parent_code":522},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[524,525],{"code":32,"slug":211,"name":212,"path":213,"parent_code":108},{"code":41,"slug":206,"name":207,"path":208,"parent_code":108},{"status":221,"locked":14,"version":27,"method":28,"evidence":527},{"candidates":528,"agricultural_evidence":34},[529,533,539,543,547,551],{"code":32,"score":383,"supported":34,"title":530,"abstract":531,"tags":532},[38],[22,38],[22],{"code":518,"score":371,"supported":34,"title":534,"abstract":536,"tags":538},[535],"cotton",[537,535],"棉花",[],{"code":49,"score":456,"supported":34,"title":540,"abstract":541,"tags":542},[],[104,143,144],[104,143],{"code":41,"score":13,"supported":34,"title":544,"abstract":545,"tags":546},[],[121,122,119,45,124],[21],{"code":132,"score":84,"supported":34,"title":548,"abstract":549,"tags":550},[],[135,137],[],{"code":254,"score":84,"supported":34,"title":552,"abstract":553,"tags":554},[],[257,258],[],[556,557],"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":559,"openalex_id":561,"authors":562,"venue":507,"cited_by_count":15,"oa_url":504,"card":568,"direction":80,"ingested_from":81},"W7221340722",[563,565],{"name":564,"orcid":9},"Deepak R Khadse",{"name":566,"orcid":567},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":569,"method":570,"finding":571,"direction":80,"opportunity":572},"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"]