[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1324":3,"related-1324":106},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":74,"slug":77,"view_count":63,"doi":78,"paper":79,"created_at":105},1324,"Artificial intelligence for crop disease detection and management: A bibliometric analysis of global research trends","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16332","Artificial intelligence (AI) has emerged as a transformative technology for crop disease detection and management by enabling rapid, accurate and automated disease diagnosis to support sustainable agricultural production. This study presents a bibliometric analysis of global research trends in AI-based crop disease detection and management from 2016 to 2026. Bibliographic data were retrieved from the Scopus database using a structured search strategy. A total of 3729 records were initially identified and 605 publications were retained after applying predefined inclusion and exclusion criteria. Bibliometric indicators and VOSviewer software were employed to analyse annual publication trends, leading contributors, collaboration networks, keyword co-occurrence patterns, bibliographic coupling relationships and co-citation structures. The results reveal a substantial increase in research output, particularly after 2021, indicating growing scientific interest in AI-enabled crop protection technologies. Keyword analysis identified deep learning, machine learning, computer vision, transfer learning and plant disease detection as the dominant research themes. In contrast, co-authorship and co-citation analyses revealed strong international collaboration and a well-established intellectual foundation.","人工智能（AI）已成为作物病害检测与管理领域的一项变革性技术，通过实现快速、准确和自动化的病害诊断，为可持续农业生产提供支撑。本研究对2016年至2026年间基于AI的作物病害检测与管理全球研究趋势进行了文献计量分析。采用结构化检索策略从Scopus数据库获取文献数据，初步识别出3729条记录，经应用预设纳入和排除标准后保留605篇出版物。运用文献计量指标和VOSviewer软件分析年度发文趋势、主要贡献者、合作网络、关键词共现模式、文献耦合关系和共被引结构。结果表明，研究产出显著增长，尤其是2021年之后，反映出科学界对AI赋能作物保护技术的兴趣日益浓厚。关键词分析识别出深度学习、机器学习、计算机视觉、迁移学习和植物病害检测为主要研究主题。相比之下，合著和共被引分析揭示了强大的国际合作和坚实的知识基础。",null,"Plant Science Today","2026-08-31T00:00:00Z","论文",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,18,16,2,1,"系统梳理AI作物病害检测研究趋势，数据详实，对智慧农业领域有参考价值。",[24],{"name":10,"url":6},[26,27,28],"农业人工智能","作物病害","文献计量",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":43},[42,54,61,68],{"code":30,"score":18,"supported":43,"title":44,"abstract":46,"tags":53},true,[45],"artificial intelligence",[47,48,49,50,51,45,52],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[26],{"code":55,"score":56,"supported":43,"title":57,"abstract":59,"tags":60},"I7",15,[58],"bibliometric",[28,58],[28],{"code":62,"score":63,"supported":43,"title":64,"abstract":65,"tags":67},"F4",3,[],[66],"crop protection",[],{"code":69,"score":63,"supported":43,"title":70,"abstract":71,"tags":73},"H8",[],[72],"可持续农业",[],[75,76],"农业人工智能 作物病害 文献计量","农业人工智能 作物病害","农业人工智能作物病害文献计量-1324","10.14719\u002Fpst.16332",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":97,"oa_url":6,"card":98,"direction":102,"ingested_from":104},"W7204773281",[82,85,88,91,94],{"name":83,"orcid":84},"D Sapthagiri","https:\u002F\u002Forcid.org\u002F0009-0005-1268-6868",{"name":86,"orcid":87},"A. Rohini","https:\u002F\u002Forcid.org\u002F0000-0002-3476-0003",{"name":89,"orcid":90},"D. Murugananthi","https:\u002F\u002Forcid.org\u002F0000-0002-0134-6631",{"name":92,"orcid":93},"M. Shanthasheela","https:\u002F\u002Forcid.org\u002F0000-0003-0155-522X",{"name":95,"orcid":96},"M Vijayabhama","https:\u002F\u002Forcid.org\u002F0000-0001-9437-9477",0,{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"通过文献计量分析，梳理了2016-2026年AI在作物病害检测与管理中的研究趋势。","使用Scopus数据库检索，结合VOSviewer进行文献计量和可视化分析。","研究产出自2021年后显著增长，深度学习、机器学习等是主要主题。","农业人工智能与决策模型","可深入探索AI模型在特定作物病害中的实际应用效果，以及跨区域合作中的技术转移与本地化适配。","openalex","2026-09-01T23:30:33.298379Z",{"total":107,"page":21,"page_size":107,"items":108},6,[109,167,251,330,421,483],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":122,"tags":124,"primary_topic":127,"secondary_topics":128,"classification":129,"search_phrases":147,"slug":150,"view_count":97,"doi":151,"paper":152,"created_at":166},5584,"Distributionally-Robust Evaluation of Computer Vision Agriculture Disease Classifiers Under Climate-Driven Pathogen Range Shifts: An Artificial Intelligence Worst-Case Generalization Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544150","Computer vision classifiers for crop disease are evaluated almost exclusively on held-out samples drawn from the same distribution as their training data, yet climate-driven pathogen range shifts mean that deployment environments are systematically out of distribution. The framework treats the deployment distribution as an adversarial perturbation of the training distribution within a Wasserstein ball whose radius is calibrated to observed climate displacement. Using NASA POWER monthly reanalysis for Harare, Zimbabwe (2000-2023, 312 monthly observations), the analysis estimates a mean air temperature of 18.914 C, a standard deviation of 3.107 C, and a warming trend of 0.2001 C per decade. These estimates anchor the radius of the ambiguity set and the resulting bound. The bound decomposes worst-case risk into an empirical risk term, a Wasserstein radius term scaled by the Lipschitz constant of the classifier, and a concentration term. Under plausible Lipschitz constants for convolutional backbones, the radius term dominates the empirical risk term once the climate displacement exceeds a threshold that the Harare record already approaches. The contribution is a formal evaluation criterion that replaces the i. i. d. assumption with a climate-calibrated ambiguity set, together with the analytical machinery needed to compute it.","作物病害的计算机视觉分类器几乎完全在与训练数据同分布的留出样本上进行评估，然而气候驱动的病原体分布范围变化意味着部署环境系统性地处于分布之外。该框架将部署分布视为训练分布在Wasserstein球内的对抗性扰动，球的半径根据观测到的气候位移进行校准。利用NASA POWER对津巴布韦哈拉雷的月度再分析数据（2000—2023年，312个月度观测值），分析估计平均气温为18.914°C，标准差为3.107°C，每十年升温趋势为0.2001°C。这些估计值锚定了模糊集的半径及由此得到的界。该界将最坏情况风险分解为经验风险项、由分类器Lipschitz常数缩放后的Wasserstein半径项以及集中项。在卷积骨干网络合理的Lipschitz常数下，一旦气候位移超过哈拉雷记录已接近的阈值，半径项便主导经验风险项。本文的贡献在于提出了一种形式化评估准则，以气候校准的模糊集替代独立同分布（i.i.d.）假设，并提供了计算该准则所需的分析工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",75,{"impact":17,"substance":119,"depth":18,"authority":120,"freshness":13,"relevant":21,"comment":121},22,13,"方法新颖、数据具体，但属方法论预印论文，产业影响有限，适合作为专业精选而非头条。",[123],{"name":115,"url":112},[125,26,49,27,126],"智慧农业","气候变化",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":130},{"candidates":131,"agricultural_evidence":43},[132,137,142],{"code":30,"score":133,"supported":43,"title":134,"abstract":135,"tags":136},24,[45,52],[49,52],[26,49],{"code":138,"score":21,"supported":14,"title":139,"abstract":140,"tags":141},"H7",[],[],[126],{"code":143,"score":21,"supported":14,"title":144,"abstract":145,"tags":146},"I1",[],[],[125],[148,149],"NASA POWER 哈拉雷 气温","Wasserstein 分布鲁棒 病害分类","NASAPOWER哈拉雷气温-5584","10.5281\u002Fzenodo.23544150",{"doi":151,"openalex_id":153,"authors":154,"venue":115,"cited_by_count":97,"oa_url":112,"card":161,"direction":102,"ingested_from":104},"W7223087892",[155,157,159],{"name":156,"orcid":9},"Kossi Afeli",{"name":158,"orcid":9},"Akouvi Amegashie",{"name":160,"orcid":9},"Komlan Sélom Dossou",{"tldr":162,"method":163,"finding":164,"direction":102,"opportunity":165},"提出气候校准的分布鲁棒评估准则，替代i.i.d.假设评价作物病害视觉分类器。","用Wasserstein球建模部署分布偏移，以NASA POWER哈拉雷气温数据","气候位移超阈值后，半径项主导经验风险，现有评估会高估模型泛化能力。","可将气候校准的鲁棒评估扩展到多作物多区域，并研究适应分布偏移的鲁棒训练方法。","2026-10-22T11:21:00.036937Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":173,"source_url":170,"published_at":174,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":175,"score_detail":176,"sources":180,"tags":182,"primary_topic":184,"secondary_topics":185,"classification":192,"search_phrases":224,"slug":227,"view_count":97,"doi":228,"paper":229,"created_at":250},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",80,{"impact":18,"substance":119,"depth":18,"authority":177,"freshness":178,"relevant":21,"comment":179},14,8,"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。",[181],{"name":173,"url":170},[125,26,48,183,28],"食品安全",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[186],{"code":187,"slug":188,"name":189,"path":190,"parent_code":191},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":37,"locked":14,"version":38,"method":39,"evidence":193},{"candidates":194,"agricultural_evidence":43},[195,200,205,209,214,220],{"code":30,"score":196,"supported":43,"title":197,"abstract":198,"tags":199},19,[45],[47,48,49,50,51,45,52],[26,48],{"code":187,"score":56,"supported":43,"title":201,"abstract":203,"tags":204},[202],"food safety",[183,202],[183],{"code":55,"score":56,"supported":43,"title":206,"abstract":207,"tags":208},[58],[28,58],[28],{"code":138,"score":107,"supported":43,"title":210,"abstract":211,"tags":213},[],[126,212],"climate change",[],{"code":215,"score":63,"supported":43,"title":216,"abstract":217,"tags":219},"A8",[],[218],"food security",[],{"code":143,"score":21,"supported":14,"title":221,"abstract":222,"tags":223},[],[],[125],[225,226],"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":228,"openalex_id":230,"authors":231,"venue":173,"cited_by_count":97,"oa_url":9,"card":245,"direction":102,"ingested_from":104},"W7222920742",[232,234,236,239,242],{"name":233,"orcid":9},"Harang Lee",{"name":235,"orcid":9},"지은서",{"name":237,"orcid":238},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":240,"orcid":241},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":243,"orcid":244},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":246,"method":247,"finding":248,"direction":102,"opportunity":249},"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":252,"title":253,"url":254,"summary":255,"summary_zh":256,"content":9,"source_name":257,"source_url":254,"published_at":258,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":260,"sources":265,"tags":267,"primary_topic":270,"secondary_topics":271,"classification":272,"search_phrases":291,"slug":294,"view_count":21,"doi":295,"paper":296,"created_at":329},5147,"FDUNet-LRSA: efficient frequency-adaptive segmentation for automated crop disease inspection","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1963106","The transition toward industrialized and automated agriculture requires reliable machine vision for high-throughput crop health monitoring and field decision support. Automated sweetpotato disease inspection, however, remains challenging because field images exhibit substantial variations in leaf scale, appearance, illumination, occlusion, and background complexity. Conventional U-shaped networks rely primarily on fixed convolutional kernels and local feature aggregation, whereas Transformer-based alternatives often introduce considerable computational overhead. This article proposes FDUNet-LRSA, a frequency-adaptive segmentation network that integrates frequency-domain dynamic convolution, low-resolution self-attention, and boundary-aware refinement. Frequency-domain dynamic convolution reconstructs input-dependent spatial kernels to improve feature adaptation under changing imaging conditions. Low-resolution self-attention captures long-range contextual dependencies in a fixed compact feature space, while boundary-aware refinement preserves irregular leaf contours during mask reconstruction. Under the reported evaluation protocol, FDUNet-LRSA achieved a mean test foreground IoU of 0.94 and a Dice coefficient of 0.97, with 3.72 GFLOPs. Additional experiments indicate that task-oriented moderate augmentation improves generalization compared with stronger augmentation. The proposed network provides an efficient visual perception approach for automated crop inspection systems.","向工业化与自动化农业转型，需要可靠的机器视觉技术，以支持高通量作物健康监测与田间决策。然而，甘薯病害自动检测仍具挑战性，因为田间图像在叶片尺度、外观、光照、遮挡和背景复杂度方面存在显著变化。传统U形网络主要依赖固定卷积核和局部特征聚合，而基于Transformer的替代方案往往带来较大的计算开销。本文提出FDUNet-LRSA，一种频率自适应分割网络，融合了频域动态卷积、低分辨率自注意力与边界感知细化。频域动态卷积重建依赖输入的空间卷积核，以提升成像条件变化下的特征适应性。低分辨率自注意力在固定的紧凑特征空间中捕获长程上下文依赖，而边界感知细化在掩码重建过程中保留不规则叶片轮廓。在所报告的评估协议下，FDUNet-LRSA取得了0.94的平均测试前景IoU和0.97的Dice系数，计算量为3.72 GFLOPs。额外实验表明，与更强的增强相比，面向任务的中等增强可提升泛化能力。所提出的网络为自动化作物检测系统提供了一种高效的视觉感知方法。","Frontiers in Plant Science","2026-10-16T00:00:00Z",76,{"impact":19,"substance":261,"depth":262,"authority":120,"freshness":263,"relevant":21,"comment":264},21,17,9,"提出频域自适应分割网络，在甘薯病害检测上取得高精度且计算量低，方法有新意、结论可靠，属细分领域实质进展，值得进入每日精选。",[266],{"name":257,"url":254},[125,26,27,268,269],"甘薯","图像分割",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":273},{"candidates":274,"agricultural_evidence":43},[275,281,287],{"code":30,"score":276,"supported":43,"title":277,"abstract":278,"tags":280},5,[],[279],"decision support",[26,269],{"code":282,"score":283,"supported":43,"title":284,"abstract":285,"tags":286},"A5",4,[],[268],[268],{"code":143,"score":21,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[125],[292,293],"FDUNet-LRSA 甘薯病害","甘薯病害 图像分割","FDUNet-LRSA甘薯病害-5147","10.3389\u002Ffpls.2026.1963106",{"doi":295,"openalex_id":297,"authors":298,"venue":257,"cited_by_count":97,"oa_url":321,"card":322,"direction":328,"ingested_from":104},"W7222502318",[299,302,305,308,310,313,315,317,319],{"name":300,"orcid":301},"Ru Han","https:\u002F\u002Forcid.org\u002F0009-0000-5128-1314",{"name":303,"orcid":304},"Lei Shu","https:\u002F\u002Forcid.org\u002F0000-0002-6700-9347",{"name":306,"orcid":307},"Grzegorz Cielniak","https:\u002F\u002Forcid.org\u002F0000-0002-6299-8465",{"name":309,"orcid":9},"Yan Zhu",{"name":311,"orcid":312},"Jucheng Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5046-2663",{"name":314,"orcid":9},"Fan Yang",{"name":316,"orcid":9},"Weihan Song",{"name":318,"orcid":9},"Qian Zhang",{"name":320,"orcid":9},"Kai Huang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1963106\u002Fpdf",{"tldr":323,"method":324,"finding":325,"direction":326,"opportunity":327},"提出频域自适应分割网络FDUNet-LRSA，用于甘薯病害自动巡检。","频域动态卷积、低分辨率自注意力与边界感知细化，结合适度数据增强。","测试前景IoU达0.94、Dice达0.97，仅3.72 GFLOPs，轻量高效。","农业遥感与作物表型","可探索频域动态卷积在多种作物病害与复杂田间光照下的跨域泛化与边缘部署。","智慧农业 \u002F 农业物联网","2026-10-17T23:30:08.487857Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":9,"source_name":336,"source_url":333,"published_at":337,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":338,"score_detail":339,"sources":341,"tags":343,"primary_topic":345,"secondary_topics":350,"classification":352,"search_phrases":400,"slug":403,"view_count":20,"doi":404,"paper":405,"created_at":420},3776,"Smart Agriculture with Machine Learning and Internet of Things Techniques: Literature Review and Bibliometric Analysis","https:\u002F\u002Fdoi.org\u002F10.7160\u002Faol.2026.180301","A booming field known as \"smart agriculture\" has emerged because of the rapid revolution and transformation of agriculture. It makes use of cutting-edge agricultural technologies, like the internet of things and machine learning (ML) algorithms, to enhance farm management tasks, boost agricultural productivity, and lessen environmental impact by empowering farmers to react swiftly and efficiently to climate change. A key component of the shift to smart farming is the Internet of Things (IoT), which makes it easier for sensors and devices to connect and share data. It also gives farmers access to actionable information and knowledge, enabling them to make well-informed decisions using machine learning algorithms to enhance crop management and boost yields. Numerous tasks, including weed identification, fertilization requirement analysis, irrigation adjustment, and pest and soil management, are made possible by the automation made possible by these technologies. They allow farmers to make targeted interventions by continuously monitoring crop health, which lowers labor costs and the environmental impact. The effects of technological advancements, such as machine learning and the Internet of Things, on the agriculture industry are thoroughly examined in this paper. It draws attention to the useful applications of these instruments, which help farmers better, more automatically, and more precisely manage their resources and output.","由于农业的快速变革与转型，一个被称为“智慧农业”的蓬勃发展的领域应运而生。它利用物联网和机器学习（ML）算法等前沿农业技术，通过赋能农民快速高效地应对气候变化，来增强农场管理任务、提高农业生产力并减少环境影响。物联网（IoT）是向智慧农业转型的关键组成部分，它使传感器和设备之间的连接与数据共享变得更加便捷。它还为农民提供可操作的信息和知识，使他们能够利用机器学习算法做出明智决策，以改善作物管理并提高产量。这些技术所实现的自动化使许多任务成为可能，包括杂草识别、施肥需求分析、灌溉调整以及病虫害和土壤管理。它们使农民能够通过持续监测作物健康来进行有针对性的干预，从而降低劳动力成本和环境影响。本文深入探讨了机器学习与物联网等技术进步对农业产业的影响。它重点关注这些工具的实际应用，这些应用帮助农民更好、更自动、更精准地管理其资源和产出。","Agris on-line Papers in Economics and Informatics","2026-09-28T00:00:00Z",65,{"impact":17,"substance":19,"depth":56,"authority":120,"freshness":263,"relevant":21,"comment":340},"核心期刊发表的智慧农业文献综述与文献计量分析，方法规范、信息量足，但属综述性研究，产业影响有限，可作为主题页聚合素材。",[342],{"name":336,"url":333},[125,26,344,28],"农业物联网",{"code":346,"slug":347,"name":348,"path":349,"parent_code":34},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[351],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":353},{"candidates":354,"agricultural_evidence":43},[355,362,367,371,375,382,389,396],{"code":346,"score":18,"supported":43,"title":356,"abstract":358,"tags":361},[357],"internet of things",[359,357,360],"物联网","IoT",[344],{"code":143,"score":56,"supported":43,"title":363,"abstract":365,"tags":366},[364],"smart agriculture",[125,364],[125],{"code":30,"score":56,"supported":43,"title":368,"abstract":369,"tags":370},[50],[48,50],[26],{"code":55,"score":263,"supported":43,"title":372,"abstract":373,"tags":374},[58],[],[28],{"code":376,"score":107,"supported":43,"title":377,"abstract":378,"tags":381},"F3",[],[379,380],"施肥","fertilization",[],{"code":383,"score":107,"supported":43,"title":384,"abstract":385,"tags":388},"H2",[],[386,387],"土壤","soil",[],{"code":390,"score":107,"supported":43,"title":391,"abstract":392,"tags":395},"H3",[],[393,394],"灌溉","irrigation",[],{"code":138,"score":107,"supported":43,"title":397,"abstract":398,"tags":399},[],[126,212],[],[401,402],"农业人工智能 农业物联网 文献计量 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网文献计量智慧农业-3776","10.7160\u002Faol.2026.180301",{"doi":404,"openalex_id":406,"authors":407,"venue":336,"cited_by_count":97,"oa_url":333,"card":415,"direction":328,"ingested_from":104},"W7214682427",[408,410,412],{"name":409,"orcid":9},"Karima El Majdouby",{"name":411,"orcid":9},"Mohamed El Mohadab",{"name":413,"orcid":414},"Karim Abouelmehdi","https:\u002F\u002Forcid.org\u002F0009-0007-8904-8762",{"tldr":416,"method":417,"finding":418,"direction":328,"opportunity":419},"综述机器学习与物联网在智慧农业中的应用并做文献计量分析。","文献综述与文献计量分析，梳理ML与IoT农业应用。","ML与IoT可实现杂草识别、灌溉施肥等自动化，提升产量并降低环境与人力成本。","可针对具体作物构建ML+IoT闭环决策系统，并量化其经济与环境效益。","2026-09-29T23:30:10.681055Z",{"id":422,"title":423,"url":424,"summary":425,"summary_zh":426,"content":9,"source_name":427,"source_url":424,"published_at":428,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":175,"score_detail":429,"sources":431,"tags":433,"primary_topic":435,"secondary_topics":436,"classification":442,"search_phrases":462,"slug":465,"view_count":276,"doi":466,"paper":467,"created_at":482},3353,"Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sftr.2026.102145","The integration of Artificial Intelligence (AI) with climate change and sustainable development (SD) studies has significant potential to enable actionable, data-driven, innovative, and long-term mitigation and adaptation strategies. However, there is a lack of a well-organized, comprehensive review of mapping that integrates AI, climate change, and the SD nexus. This study addresses this notable gap through a bibliometric review that dissects descriptive bibliometric, performance, and conceptual analyses. Utilizing the Web of Science Core Collection, 3291 publications from 2007 to 2025 were analyzed using Bibliometrix and VOSviewer software. The findings indicate a 38.29% annual publication growth rate, and original articles (80%) were the dominant publication type. The Chinese Academy of Sciences was the top contributor, and China and the USA were leaders in research output and international AI-integrated climate and SD research collaborations. Keyword analysis shows “artificial intelligence”, “climate change”, “deep learning”, “machine learning”, and “sustainability” as dominant keywords. Conceptual mapping identified four approaches: macro-level AI-driven digitalization; methodologically driven geospatial and machine-learning-based environmental monitoring; predictive modeling; and AI-driven smart agriculture applications. Thematic mapping reveals a shift from fundamental AI for climate change-focused research (2007–2013) to integrating AI with climate change and SD (2014–2019), and modeling and predicting approaches (2020–2025). Findings highlight substantial geographic asymmetries; data and research gaps persist in climate-vulnerable low-income areas. The extractivism rate for low-income countries was 41%, indicating that nearly half of their publications are led by high-income countries. Recommendations are grounded in theoretical, methodological, practical, and policy considerations, with an emphasis on SDGs 13 and 17. This study provides insights for researchers, practitioners, and policymakers to emphasize policies and technologies and implement a nexus-based framework.","人工智能（Artificial Intelligence, AI）与气候变化及可持续发展（Sustainable Development, SD）研究的融合，在推动可操作、数据驱动、创新性和长期性的减缓与适应策略方面具有巨大潜力。然而，目前缺乏对AI、气候变化与可持续发展三者交叉领域的系统化、综合性文献计量综述。本研究通过文献计量学综述填补了这一显著空白，从描述性文献计量分析、绩效分析和概念分析三个维度进行剖析。利用Web of Science核心合集，采用Bibliometrix和VOSviewer软件对2007年至2025年间的3291篇文献进行了分析。研究结果表明，年度发文增长率为38.29%，原创论文（80%）为主要文献类型。中国科学院是最大的贡献机构，中国和美国在研究产出及AI融合气候与可持续发展研究的国际合作方面处于领先地位。关键词分析显示，“人工智能”“气候变化”“深度学习”“机器学习”和“可持续性”是主导性关键词。概念图谱识别出四种研究路径：宏观层面的AI驱动数字化；方法论驱动的基于地理空间和机器学习的环境监测；预测建模；以及AI驱动的智慧农业应用。主题图谱揭示了研究重心的演变：从气候变化聚焦的基础AI研究（2007—2013年），到AI与气候变化及可持续发展的融合（2014—2019年），再到建模与预测方法（2020—2025年）。研究发现存在显著的地理不对称性；气候脆弱型低收入地区仍存在数据和研究缺口。低收入国家的提取主义率为41%，表明其近半数出版物由高收入国家主导。建议基于理论、方法论、实践和政策层面的考量，并着重关注可持续发展目标13和目标17。本研究为研究人员、实践者和政策制定者提供了洞见，以强调政策与技术的重要性，并实施基于交叉领域的框架。","Sustainable Futures","2026-09-23T00:00:00Z",{"impact":18,"substance":119,"depth":18,"authority":120,"freshness":263,"relevant":21,"comment":430},"基于3291篇文献的AI与气候变化及可持续发展文献计量综述，方法规范、数据规模大，对智慧农业与农业AI研究具有参考价值。",[432],{"name":427,"url":424},[125,26,434,126,28],"可持续发展",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[437],{"code":138,"slug":438,"name":439,"path":440,"parent_code":441},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":37,"locked":14,"version":38,"method":39,"evidence":443},{"candidates":444,"agricultural_evidence":43},[445,449,453,457],{"code":30,"score":18,"supported":43,"title":446,"abstract":447,"tags":448},[45],[47,48,50,51,45],[26],{"code":138,"score":56,"supported":43,"title":450,"abstract":451,"tags":452},[212],[126,212],[126],{"code":55,"score":56,"supported":43,"title":454,"abstract":455,"tags":456},[58],[28,58],[28],{"code":143,"score":458,"supported":43,"title":459,"abstract":460,"tags":461},7,[],[125,364],[125],[463,464],"农业人工智能 可持续发展 文献计量 智慧农业","农业人工智能 可持续发展","农业人工智能可持续发展文献计量智慧农业-3353","10.1016\u002Fj.sftr.2026.102145",{"doi":466,"openalex_id":468,"authors":469,"venue":427,"cited_by_count":97,"oa_url":424,"card":477,"direction":328,"ingested_from":104},"W7214099405",[470,473,475],{"name":471,"orcid":472},"H. B. T. P. Jayathilaka","https:\u002F\u002Forcid.org\u002F0009-0001-0589-6999",{"name":474,"orcid":9},"Shiyan Zhai",{"name":476,"orcid":9},"Yuke Feng",{"tldr":478,"method":479,"finding":480,"direction":102,"opportunity":481},"用文献计量法梳理2007-2025年AI与气候变化及可持续发展交叉研究，揭示主题演化与地域失衡。","Web of Science 3291篇文献，Bibliometrix与VOSv","年增38.29%，主题从基础AI转向建模预测，低收入国家41%论文由高收入国家主导。","可针对气候脆弱低收入地区，构建AI+智慧农业的本地化数据与决策模型，填补研究空白。","2026-09-24T23:30:10.248845Z",{"id":484,"title":485,"url":486,"summary":487,"summary_zh":9,"content":9,"source_name":488,"source_url":486,"published_at":489,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":490,"score_detail":491,"sources":493,"tags":495,"primary_topic":496,"secondary_topics":497,"classification":498,"search_phrases":509,"slug":511,"view_count":283,"doi":512,"paper":513,"created_at":522},1727,"Deep Learning-Based Crop Disease Detection Using EfficientNet-B3 for Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10104203\u002Fv1","Deep Learning-Based Crop Disease Detection Using EfficientNet-B3 for Smart Agriculture。Research Square","Research Square","2026-09-04T00:00:00Z",56,{"impact":17,"substance":56,"depth":177,"authority":178,"freshness":458,"relevant":21,"comment":492},"基于EfficientNet-B3的深度学习作物病害检测研究，方法有一定新颖性，但缺乏具体数据规模与结论细节，权威性一般。",[494],{"name":488,"url":486},[125,26,27],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":499},{"candidates":500,"agricultural_evidence":43},[501,505],{"code":143,"score":17,"supported":43,"title":502,"abstract":503,"tags":504},[364],[364],[125],{"code":30,"score":17,"supported":43,"title":506,"abstract":507,"tags":508},[51],[51],[26],[510,76],"农业人工智能 作物病害 智慧农业","农业人工智能作物病害智慧农业-1727","10.21203\u002Frs.3.rs-10104203\u002Fv1",{"doi":512,"openalex_id":514,"authors":515,"venue":488,"cited_by_count":97,"oa_url":486,"card":9,"direction":328,"ingested_from":104},"W7208777285",[516,518,520],{"name":517,"orcid":9},"Gajanan Ankatwar",{"name":519,"orcid":9},"Chitra Dhawale",{"name":521,"orcid":9},"Supriya Narad","2026-09-05T23:30:09.738110Z"]