[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2273":3,"related-2273":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":58,"slug":61,"view_count":21,"doi":8,"paper":62,"created_at":72},2273,"Scientific Reports 基于区块链的可信食品供应链透明度框架研究","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-63360-y","提出基于区块链的食品供应链可追溯框架,集成IPFS、IoT传感器和混合GM-PBFT共识机制,采用Hyperledger Fabric和以太坊测试网络实验评估,实现99.3%的可追溯精度,召回响应速度提升37%,数据篡改风险降低。",null,"Nature Scientific Reports","2026-09-06T16:00:00Z","论文",10,false,84,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,23,19,14,6,1,"该研究提出集成IPFS、IoT与混合共识机制的可信食品供应链框架，实验数据扎实、方法新颖，对农业信息化与食品安全溯源具有较高参考价值，值得进入每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","物联网","区块链","食品安全","供应链溯源",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":13,"reason":57},[38,46,52],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"I5",4,true,[],[44],"IoT",[27],{"code":47,"score":48,"supported":13,"title":49,"abstract":50,"tags":51},"E4",2,[],[],[30,29],{"code":53,"score":21,"supported":13,"title":54,"abstract":55,"tags":56},"I1",[],[],[26],"农业相关性需要复核",[59,60],"供应链溯源 智慧农业 食品安全 区块链","供应链溯源 智慧农业","供应链溯源智慧农业食品安全区块链-2273",{"doi":8,"openalex_id":8,"authors":63,"venue":8,"cited_by_count":64,"oa_url":8,"card":65,"direction":69,"ingested_from":71},[],0,{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"提出基于区块链的食品供应链可追溯框架，实现高精度追溯与快速召回。","集成IPFS、IoT传感器与混合GM-PBFT共识，基于Hyperledger ","可追溯精度达99.3%，召回响应速度提升37%，数据篡改风险降低。","智慧农业 \u002F 农业物联网","可探索轻量级共识与跨链互操作，降低中小农户上链成本与能耗。","agent","2026-09-13T00:04:05.211463Z",{"total":20,"page":21,"page_size":20,"items":74},[75,151,205,302,402,482],{"id":76,"title":77,"url":78,"summary":79,"summary_zh":80,"content":8,"source_name":81,"source_url":78,"published_at":82,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":83,"score_detail":84,"sources":90,"tags":92,"primary_topic":95,"secondary_topics":100,"classification":101,"search_phrases":128,"slug":131,"view_count":48,"doi":132,"paper":133,"created_at":150},1723,"Bridging challenges with technology: A scientometric and systematic study on sustainability in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.59953\u002Fpaperasia.v42i4b.1489","The rapid evolution of agricultural practices driven by population growth, climate change, and technological advancements has necessitated the integration of smart technologies to enhance productivity and sustainability. This study aims to achieve two research objectives: (1) to provide a comprehensive overview of research patterns and key contributors in the domain of technology integration in smart agriculture, and (2) to evaluate the advantages of integrating technology in smart agriculture to address existing challenges. A scientometric analysis and a systematic literature review (SLR) were employed to examine the research landscape and synthesize current evidence. Following the PRISMA screening process, 18 articles were retained for the final systematic review. The findings reveal the transformative role of the Internet of Things (IoT), artificial intelligence (AI), blockchain, and unmanned aerial vehicles (UAVs) in improving resource management, reducing labour costs, enhancing real-time monitoring, and strengthening farm safety. The scientometric analysis further highlights global research collaborations, influential contributors, and emerging technological trends shaping the field of smart agriculture. These findings demonstrate the significant potential of advanced technologies to address contemporary agricultural challenges while promoting sustainable and resilient farming practices worldwide.","人口增长、气候变化和技术进步推动农业实践快速演变，这要求整合智能技术以提升生产力和可持续性。本研究旨在实现两个研究目标：（1）全面概述智能农业技术整合领域的研究模式及主要贡献者；（2）评估智能农业中技术整合的优势，以应对现有挑战。采用科学计量分析和系统文献综述（SLR）来考察研究格局并综合现有证据。经过PRISMA筛选流程，最终纳入18篇文章进行系统综述。研究结果揭示了物联网（IoT）、人工智能（AI）、区块链和无人机（UAVs）在改善资源管理、降低劳动力成本、增强实时监测及强化农场安全方面的变革性作用。科学计量分析进一步凸显了全球研究合作、有影响力的贡献者以及塑造智能农业领域的新兴技术趋势。这些发现表明，先进技术在应对当代农业挑战、同时促进全球可持续和韧性农业实践方面具有巨大潜力。","PaperAsia","2026-09-04T00:00:00Z",56,{"impact":85,"substance":86,"depth":87,"authority":88,"freshness":48,"relevant":21,"comment":89},12,18,16,8,"系统综述与科学计量分析，梳理智慧农业技术应用与挑战，但时效性较低。",[91],{"name":81,"url":78},[26,93,94,27,28],"无人机","农业人工智能",{"code":39,"slug":96,"name":97,"path":98,"parent_code":99},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":102,"locked":13,"version":34,"method":35,"evidence":103},"classified",{"candidates":104,"agricultural_evidence":41},[105,110,115,122],{"code":53,"score":85,"supported":41,"title":106,"abstract":108,"tags":109},[107],"smart agriculture",[107],[26],{"code":39,"score":12,"supported":41,"title":111,"abstract":112,"tags":114},[],[27,113,44],"internet of things",[27],{"code":116,"score":20,"supported":41,"title":117,"abstract":118,"tags":121},"H7",[],[119,120],"气候变化","climate change",[],{"code":123,"score":40,"supported":41,"title":124,"abstract":125,"tags":127},"I2",[],[126],"artificial intelligence",[94],[129,130],"农业人工智能 智慧农业 区块链 无人机","农业人工智能 智慧农业","农业人工智能智慧农业区块链无人机-1723","10.59953\u002Fpaperasia.v42i4b.1489",{"doi":132,"openalex_id":134,"authors":135,"venue":81,"cited_by_count":64,"oa_url":78,"card":144,"direction":69,"ingested_from":149},"W7208757733",[136,138,140,142],{"name":137,"orcid":8},"Nor Suzylah Sohaimi",{"name":139,"orcid":8},"Nor Syahidah Ishak",{"name":141,"orcid":8},"Rozaimi Majid",{"name":143,"orcid":8},"Siti Noor Zilawati Mingat@Minhad",{"tldr":145,"method":146,"finding":147,"direction":69,"opportunity":148},"通过科学计量和系统综述，评估智能农业中技术整合的研究格局与优势。","科学计量分析结合PRISMA系统文献综述，筛选18篇文章。","IoT、AI、区块链和无人机提升资源管理、降低成本、增强监测与安全。","可深入探究区块链与AI在农业中的协同应用，或针对特定作物\u002F区域的实证研究。","openalex","2026-09-05T23:30:09.377361Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":8,"source_name":157,"source_url":154,"published_at":158,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":159,"score_detail":160,"sources":163,"tags":165,"primary_topic":166,"secondary_topics":167,"classification":168,"search_phrases":188,"slug":190,"view_count":191,"doi":192,"paper":193,"created_at":204},1419,"Smart Agriculture with Quantum Intelligence and Blockchain Security","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84736","Selecting energy-efficient, trustworthy cluster heads (CHs) remains a key bottleneck in agricultural wireless sensor and Internet of Things (IoT) networks, where heuristic protocols such as LEACH often converge to unstable configurations and cannot verify whether a selected node is trustworthy. This paper proposes a framework that combines a Quantum-Inspired Genetic Algorithm (QIGA) for cluster head selection with a permissioned blockchain trust-verification layer. Unlike prior work that applies quantum classifiers to sensor-data classification, the proposed method encodes candidate cluster head assignments as qubit chromosomes and evolves them through quantum rotation-gate updates, treating cluster formation as a combinatorial optimization problem rather than a classification task. Verified assignments and node reputation scores are recorded as immutable blockchain transactions validated through a lightweight Byzantine fault-tolerant consensus, enabling automatic exclusion of low-trust nodes without a central authority. A discrete-event simulation comparing the proposed framework against LEACH and a classical genetic algorithm baseline shows comparable or improved energy retention and reliable detection of malicious nodes across a range of attack ratios. The results indicate that combining quantum-inspired combinatorial optimization with blockchain-verified trust offers a practical, hardware-independent pathway toward energy-aware, tamperresistant clustering for precision agriculture","在农业无线传感器与物联网（IoT）网络中，选择节能且可信的簇头（CH）仍是关键瓶颈，诸如LEACH等启发式协议往往收敛于不稳定配置，且无法验证所选节点是否可信。本文提出一个框架，将量子启发遗传算法（QIGA）用于簇头选择，并融合许可型区块链信任验证层。与以往将量子分类器应用于传感器数据分类的研究不同，本方法将候选簇头分配编码为量子比特染色体，并通过量子旋转门更新进行演化，将簇形成视为组合优化问题而非分类任务。验证后的分配结果与节点信誉评分被记录为不可篡改的区块链交易，并通过轻量级拜占庭容错共识机制加以验证，从而在无中心权威的情况下自动排除低信任节点。将所提框架与LEACH及经典遗传算法基线进行离散事件仿真对比，结果表明，在不同攻击比率下，该框架在能量保持方面表现相当或更优，并能可靠检测恶意节点。研究结果表明，将量子启发组合优化与区块链验证信任相结合，为实现精准农业中能量感知、防篡改的分簇提供了一条实用且不依赖硬件的路径。","International Journal for Research in Applied Science and Engineering Technology","2026-08-31T00:00:00Z",59,{"impact":85,"substance":86,"depth":87,"authority":12,"freshness":161,"relevant":21,"comment":162},3,"提出量子启发遗传算法与区块链结合的簇头选择框架，方法新颖，但尚处仿真阶段，影响有限。",[164],{"name":157,"url":154},[26,94,27,28],{"code":39,"slug":96,"name":97,"path":98,"parent_code":99},[],{"status":102,"locked":13,"version":34,"method":35,"evidence":169},{"candidates":170,"agricultural_evidence":41},[171,178,184],{"code":53,"score":172,"supported":41,"title":173,"abstract":174,"tags":177},15,[107],[175,176],"精准农业","precision agriculture",[26],{"code":39,"score":12,"supported":41,"title":179,"abstract":180,"tags":183},[],[27,181,113,44,182],"无线传感","wireless sensor",[27],{"code":123,"score":21,"supported":13,"title":185,"abstract":186,"tags":187},[],[],[94],[189,130],"农业人工智能 智慧农业 区块链 物联网","农业人工智能智慧农业区块链物联网-1419",5,"10.22214\u002Fijraset.2026.84736",{"doi":192,"openalex_id":194,"authors":195,"venue":157,"cited_by_count":64,"oa_url":154,"card":198,"direction":69,"ingested_from":149},"W7204875790",[196],{"name":197,"orcid":8},"Munta Padmavathi",{"tldr":199,"method":200,"finding":201,"direction":202,"opportunity":203},"提出量子启发遗传算法与区块链结合，用于农业物联网中可信且节能的簇头选择。","量子启发遗传算法（QIGA）优化簇头选择，结合许可区块链和拜占庭容错共识验证信任","相比LEACH和经典遗传算法，QIGA+区块链在能量保持和恶意节点检测上相当或更优。","其他","可探索将量子启发优化与区块链用于动态作物监测场景，或扩展到边缘计算与真实硬件部署验证。","2026-09-02T23:30:17.512521Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":210,"content":8,"source_name":211,"source_url":208,"published_at":212,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":213,"score_detail":214,"sources":217,"tags":219,"primary_topic":222,"secondary_topics":226,"classification":227,"search_phrases":288,"slug":291,"view_count":64,"doi":292,"paper":293,"created_at":301},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":86,"substance":16,"depth":215,"authority":88,"freshness":12,"relevant":21,"comment":216},17,"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。",[218],{"name":211,"url":208},[26,94,27,220,221],"杂草识别","病虫害监测",{"code":99,"slug":223,"name":224,"path":225,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":228,"locked":13,"version":34,"method":35,"evidence":229},"partial",{"candidates":230,"agricultural_evidence":41,"reason":287},[231,240,245,254,259,266,273,280],{"code":123,"score":86,"supported":41,"title":232,"abstract":234,"tags":239},[233],"deep learning",[235,236,237,233,126,238],"深度学习","计算机视觉","病虫害识别","computer vision",[94],{"code":39,"score":86,"supported":41,"title":241,"abstract":242,"tags":244},[44],[27,113,44,243],"edge computing",[27],{"code":246,"score":12,"supported":41,"title":247,"abstract":248,"tags":253},"F4",[],[249,250,251,252],"农药","除草","pesticide","crop protection",[220],{"code":53,"score":255,"supported":41,"title":256,"abstract":257,"tags":258},7,[],[175,176],[26],{"code":260,"score":20,"supported":41,"title":261,"abstract":262,"tags":265},"A2",[],[263,264],"小麦","wheat",[],{"code":267,"score":20,"supported":41,"title":268,"abstract":269,"tags":272},"A3",[],[270,271],"玉米","maize",[],{"code":274,"score":20,"supported":41,"title":275,"abstract":276,"tags":279},"A4",[],[277,278],"大豆","soybean",[],{"code":281,"score":20,"supported":41,"title":282,"abstract":283,"tags":286},"B1",[],[284,285],"蔬菜","vegetable",[],"一级领域明确，二级方向需要细分",[289,290],"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":292,"openalex_id":294,"authors":295,"venue":211,"cited_by_count":64,"oa_url":208,"card":296,"direction":69,"ingested_from":149},"W7223145687",[],{"tldr":297,"method":298,"finding":299,"direction":69,"opportunity":300},"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":303,"title":304,"url":305,"summary":306,"summary_zh":307,"content":8,"source_name":308,"source_url":305,"published_at":212,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":309,"score_detail":310,"sources":314,"tags":316,"primary_topic":318,"secondary_topics":322,"classification":330,"search_phrases":377,"slug":380,"view_count":64,"doi":381,"paper":382,"created_at":401},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":86,"substance":16,"depth":86,"authority":311,"freshness":312,"relevant":21,"comment":313},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。",[315],{"name":308,"url":305},[26,94,27,175,317],"气候韧性",{"code":123,"slug":319,"name":320,"path":321,"parent_code":99},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[323,324],{"code":39,"slug":96,"name":97,"path":98,"parent_code":99},{"code":325,"slug":326,"name":327,"path":328,"parent_code":329},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":102,"locked":13,"version":34,"method":35,"evidence":331},{"candidates":332,"agricultural_evidence":41},[333,339,343,348,354,358,365,371],{"code":123,"score":86,"supported":41,"title":334,"abstract":335,"tags":338},[126],[235,336,236,337,233,238],"机器学习","machine learning",[94],{"code":53,"score":87,"supported":41,"title":340,"abstract":341,"tags":342},[176],[175,176],[26,175],{"code":39,"score":12,"supported":41,"title":344,"abstract":345,"tags":347},[],[27,346,44,243],"边缘计算",[27],{"code":325,"score":312,"supported":41,"title":349,"abstract":350,"tags":353},[],[351,352,326],"灌溉","水资源",[],{"code":116,"score":255,"supported":41,"title":355,"abstract":356,"tags":357},[],[119,120],[317],{"code":359,"score":20,"supported":41,"title":360,"abstract":361,"tags":364},"H2",[],[362,363],"土壤","soil",[],{"code":366,"score":161,"supported":41,"title":367,"abstract":368,"tags":370},"A7",[],[369],"间作",[],{"code":372,"score":161,"supported":41,"title":373,"abstract":374,"tags":376},"F3",[],[375],"养分管理",[],[378,379],"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":381,"openalex_id":383,"authors":384,"venue":308,"cited_by_count":64,"oa_url":305,"card":395,"direction":69,"ingested_from":149},"W7223121456",[385,388,390,392],{"name":386,"orcid":387},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":389,"orcid":8},"Oluwadare OYEBODE",{"name":391,"orcid":8},"Iretiayo Adelaiye",{"name":393,"orcid":394},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":396,"method":397,"finding":398,"direction":399,"opportunity":400},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":8,"source_name":408,"source_url":405,"published_at":409,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":309,"score_detail":410,"sources":412,"tags":414,"primary_topic":416,"secondary_topics":417,"classification":423,"search_phrases":455,"slug":458,"view_count":64,"doi":459,"paper":460,"created_at":481},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",{"impact":86,"substance":16,"depth":86,"authority":19,"freshness":88,"relevant":21,"comment":411},"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[413],{"name":408,"url":405},[26,94,336,29,415],"文献计量",{"code":123,"slug":319,"name":320,"path":321,"parent_code":99},[418],{"code":47,"slug":419,"name":420,"path":421,"parent_code":422},"food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":102,"locked":13,"version":34,"method":35,"evidence":424},{"candidates":425,"agricultural_evidence":41},[426,430,435,441,445,451],{"code":123,"score":18,"supported":41,"title":427,"abstract":428,"tags":429},[126],[235,336,236,337,233,126,238],[94,336],{"code":47,"score":172,"supported":41,"title":431,"abstract":433,"tags":434},[432],"food safety",[29,432],[29],{"code":436,"score":172,"supported":41,"title":437,"abstract":439,"tags":440},"I7",[438],"bibliometric",[415,438],[415],{"code":116,"score":20,"supported":41,"title":442,"abstract":443,"tags":444},[],[119,120],[],{"code":446,"score":161,"supported":41,"title":447,"abstract":448,"tags":450},"A8",[],[449],"food security",[],{"code":53,"score":21,"supported":13,"title":452,"abstract":453,"tags":454},[],[],[26],[456,457],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":459,"openalex_id":461,"authors":462,"venue":408,"cited_by_count":64,"oa_url":8,"card":476,"direction":399,"ingested_from":149},"W7222920742",[463,465,467,470,473],{"name":464,"orcid":8},"Harang Lee",{"name":466,"orcid":8},"지은서",{"name":468,"orcid":469},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":471,"orcid":472},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":474,"orcid":475},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":477,"method":478,"finding":479,"direction":399,"opportunity":480},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z",{"id":483,"title":484,"url":485,"summary":486,"summary_zh":487,"content":8,"source_name":488,"source_url":485,"published_at":409,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":489,"score_detail":490,"sources":493,"tags":495,"primary_topic":499,"secondary_topics":502,"classification":503,"search_phrases":526,"slug":529,"view_count":64,"doi":530,"paper":531,"created_at":548},5389,"A Wideband Flexible Waterproof Antenna for Internet of Underground Things: Design, Characterization, and Field Validation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204815","The Internet of Underground Things (IoUT) is seen as a new technology that could revolutionize soil management, improving irrigation efficiency, soil and plant health, and food safety using reliable and resilient wireless connectivity. Rapid deployment of IoUT in precision agriculture requires flexible, waterproof antennas specifically designed for operation in soil. This study introduces a methodology for the design and characterization of waterproof, flexible antennas that maintain robust performance in soil across varying gravimetric water contents (GWC). Applying this methodology, a wideband, waterproof, and flexible antenna was designed and fabricated. Its parameters and characteristics were evaluated numerically and experimentally in soils with different GWCs to determine its suitability for IoUT applications. Within the GWC range of 10% to 20%, which is optimal for supporting plant health and water use, the antenna demonstrates a wide bandwidth of 0.6 to 1.8 GHz (|S11| \u003C −10 dB) and radiation efficiency ranging from 3.56% to 0.15% at 868 MHz. Experimental results obtained in free space, dry sand, and garden soil agree reasonably well with simulation predictions, showing that the proposed antenna is suitable for IoUT applications. Field validation demonstrated that the antenna prototype maintained its impedance response after two months of in-soil operation, indicating stable performance over the evaluated period.","地下物联网（Internet of Underground Things, IoUT）被视为一项可能彻底改变土壤管理的新技术，通过可靠且具有韧性的无线连接提升灌溉效率、土壤与植物健康以及食品安全。在精准农业中快速部署IoUT需要专为土壤环境运行而设计的柔性防水天线。本研究提出了一种防水柔性天线的设计与表征方法，使天线在不同重量含水率（gravimetric water content, GWC）的土壤中保持稳健性能。应用该方法，设计并制作了一款宽带防水柔性天线。在不同GWC的土壤中对其参数与特性进行了数值仿真与实验评估，以确定其用于IoUT应用的适用性。在10%至20%的GWC范围内（该范围最有利于维持植物健康与水分利用），天线在868 MHz处展现出0.6至1.8 GHz的宽带宽（|S11| \u003C −10 dB）以及3.56%至0.15%的辐射效率。在自由空间、干沙和花园土壤中获得的实验结果与仿真预测吻合良好，表明所提出的天线适用于IoUT应用。现场验证表明，天线原型在土壤中运行两个月后仍保持其阻抗响应，表明在所评估时段内性能稳定。","Electronics",70,{"impact":85,"substance":491,"depth":215,"authority":311,"freshness":88,"relevant":21,"comment":492},20,"面向地下物联网的柔性防水天线设计与田间验证，方法新颖、数据扎实，对精准农业无线感知有参考价值，但属细分技术进展，公共影响有限。",[494],{"name":488,"url":485},[26,27,496,497,498],"土壤水分","地下物联网","柔性天线",{"code":359,"slug":363,"name":500,"path":501,"parent_code":329},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":102,"locked":13,"version":34,"method":35,"evidence":504},{"candidates":505,"agricultural_evidence":41},[506,510,514,518,522],{"code":359,"score":255,"supported":41,"title":507,"abstract":508,"tags":509},[],[362,363],[496],{"code":53,"score":255,"supported":41,"title":511,"abstract":512,"tags":513},[],[175,176],[26],{"code":47,"score":20,"supported":41,"title":515,"abstract":516,"tags":517},[],[29,432],[],{"code":325,"score":20,"supported":41,"title":519,"abstract":520,"tags":521},[],[351,326],[],{"code":39,"score":40,"supported":41,"title":523,"abstract":524,"tags":525},[],[27],[27],[527,528],"IoUT 柔性防水天线","地下物联网 土壤水分","IoUT柔性防水天线-5389","10.3390\u002Felectronics15204815",{"doi":530,"openalex_id":532,"authors":533,"venue":488,"cited_by_count":64,"oa_url":485,"card":543,"direction":69,"ingested_from":149},"W7222978032",[534,537,540],{"name":535,"orcid":536},"Nikolay Todorov Atanasov","https:\u002F\u002Forcid.org\u002F0000-0002-7707-0133",{"name":538,"orcid":539},"Blagovest Nikolaev Atanasov","https:\u002F\u002Forcid.org\u002F0009-0001-0145-5619",{"name":541,"orcid":542},"Gabriela Lachezarova Atanasova","https:\u002F\u002Forcid.org\u002F0000-0002-7177-8317",{"tldr":544,"method":545,"finding":546,"direction":69,"opportunity":547},"设计并验证了一款用于地下物联网的宽带柔性防水天线。","采用数值仿真与实验测试，在不同含水率土壤中评估天线性能。","在10%-20%含水率下带宽0.6-1.8GHz，埋土两个月阻抗响应稳定。","可探索天线辐射效率随土壤含水率下降的补偿机制，或结合土壤传感节点做长期田间组网验证。","2026-10-20T23:30:13.823825Z"]