[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2147":3,"related-2147":100},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":99},2147,"From Farm to Fork: Integrating Smart Farming Data with Isotopic and Spectroscopic Analysis for Food Authentication and Traceability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fpr14182884","Ensuring food authenticity, traceability, and quality has become a critical challenge in increasingly complex and globalized food supply chains. Conventional post-harvest analytical approaches, while powerful, often operate in isolation and fail to fully capture the influence of pre-harvest conditions on food composition. In parallel, the emergence of smart farming technologies has enabled the collection of high-resolution environmental and agronomic data, offering new opportunities to establish baseline signatures linked to geographical origin and production practices. This review explores the integration of pre-harvest data from precision agriculture with advanced post-harvest analytical techniques, focusing on spectroscopic and isotopic methods for food authentication. Recent advances in vibrational spectroscopy, including near- and mid-infrared, Fourier-transform infrared, and Raman techniques, alongside complementary methods such as nuclear magnetic resonance and fluorescence spectroscopy, have enabled rapid and non-destructive food fingerprinting. In parallel, isotope ratio mass spectrometry and compound-specific isotope analysis provide robust markers of origin, climate conditions, and agricultural inputs through the analysis of stable isotopes of carbon, hydrogen, oxygen, nitrogen, and sulfur. The combination of these analytical approaches with chemometric and machine learning tools facilitates the extraction of meaningful patterns from complex datasets. A central focus of this review is the development of integrated farm-to-fork frameworks that use multi-source data, including field sensor technologies, spectral fingerprints, and isotopic signatures, to enhance traceability and authentication. Applications across a wide range of food systems, including edible oils, beverages, plant-based products, and animal-derived foods, are critically evaluated to highlight the strengths and limitations of current methodologies. Key challenges related to data standardization, system interoperability, cost, portability, miniaturization and regulatory acceptance are discussed, alongside emerging solutions such as artificial intelligence-driven models, digital twins, and blockchain-enabled traceability systems. The review underscores a paradigm shift from reactive testing toward predictive and real-time food authentication systems, driven by the convergence of smart agriculture and advanced analytical chemistry. This integrated approach has the potential to significantly enhance transparency, trust, and sustainability in the global food system.","在日益复杂和全球化的食品供应链中，确保食品真实性、可追溯性和质量已成为一项关键挑战。传统的采后分析方法虽然功能强大，但往往独立运作，无法充分捕捉采前条件对食品成分的影响。与此同时，智慧农业技术的出现使得高分辨率环境和农艺数据的采集成为可能，为建立与地理来源和生产实践相关的基线特征谱提供了新机遇。本综述探讨了精准农业采前数据与先进采后分析技术的整合，重点关注用于食品鉴伪的光谱和同位素方法。振动光谱学的最新进展，包括近红外和中红外、傅里叶变换红外及拉曼技术，以及核磁共振和荧光光谱等互补方法，已使快速、无损的食品指纹分析成为可能。与此同时，同位素比值质谱（IRMS）和化合物特异性同位素分析（CSIA）通过分析碳、氢、氧、氮和硫的稳定同位素，提供了可靠的产地、气候条件和农业投入品标记。将这些分析方法与化学计量学和机器学习工具相结合，有助于从复杂数据集中提取有意义的模式。本综述的核心焦点是开发从农场到餐桌的集成框架，利用多源数据（包括田间传感器技术、光谱指纹和同位素特征）来增强可追溯性和鉴伪能力。本文对食用油、饮料、植物基产品和动物源性食品等广泛食品系统中的应用进行了批判性评估，以突出当前方法的优势和局限性。讨论了与数据标准化、系统互操作性、成本、便携性、小型化和法规接受度相关的关键挑战，以及人工智能驱动模型、数字孪生和区块链赋能可追溯系统等新兴解决方案。本综述强调了由智慧农业与先进分析化学融合所驱动的、从被动检测向预测性和实时食品鉴伪系统的范式转变。这种集成方法有望显著",null,"Processes","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"综述性论文，系统梳理精准农业数据与同位素\u002F光谱分析融合的农场到餐桌溯源框架，方法学视角有参考价值，但属综述而非原创突破，产业落地仍受成本与标准制约。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"智慧农业","机器学习","农产品质量安全","区块链溯源","光谱检测","食品溯源",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,55,61],{"code":46,"score":13,"supported":47,"title":48,"abstract":49,"tags":54},"I1",true,[],[26,50,51,52,53],"精准农业","数字孪生","smart agriculture","precision agriculture",[26],{"code":33,"score":13,"supported":47,"title":56,"abstract":57,"tags":60},[],[27,58,59],"machine learning","artificial intelligence",[27],{"code":62,"score":63,"supported":14,"title":64,"abstract":65,"tags":66},"E4",3,[],[],[30,28,29],[68,69],"农产品质量安全 区块链溯源 光谱检测 智慧农业","农产品质量安全 区块链溯源","农产品质量安全区块链溯源光谱检测智慧农业-2147",4,"10.3390\u002Fpr14182884",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7212176948",[76,79,82,85,88],{"name":77,"orcid":78},"Maria Tarapoulouzi","https:\u002F\u002Forcid.org\u002F0000-0003-0206-4860",{"name":80,"orcid":81},"Jordi Cruz","https:\u002F\u002Forcid.org\u002F0000-0001-8191-8689",{"name":83,"orcid":84},"Yakdiel Rodríguez-Gallo","https:\u002F\u002Forcid.org\u002F0000-0002-5737-6442",{"name":86,"orcid":87},"Guillermo Medina-González","https:\u002F\u002Forcid.org\u002F0000-0002-2630-3400",{"name":89,"orcid":90},"Ioannis Pashalidis","https:\u002F\u002Forcid.org\u002F0000-0002-7587-6395",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"综述将精准农业采前数据与光谱、同位素分析结合，构建从农场到餐桌的食品溯源与真伪鉴别框架。","整合田间传感器数据、振动光谱、同位素比质谱及化学计量学与机器学习。","多源数据融合可推动食品鉴别从被动检测转向预测性、实时化体系。","智慧农业 \u002F 农业物联网","可研究田间传感器数据与光谱\u002F同位素指纹的标准化融合模型，并探索数字孪生与区块链在实时溯源中的落地。","openalex","2026-09-11T23:30:10.732050Z",{"total":101,"page":21,"page_size":101,"items":102},6,[103,199,292,369,430,521],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":117,"tags":119,"primary_topic":123,"secondary_topics":129,"classification":137,"search_phrases":175,"slug":178,"view_count":91,"doi":179,"paper":180,"created_at":198},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":113,"substance":114,"depth":115,"authority":19,"freshness":13,"relevant":21,"comment":116},16,21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[118],{"name":109,"url":106},[26,27,120,121,122],"遥感监测","土壤墒情","冬小麦",{"code":124,"slug":125,"name":126,"path":127,"parent_code":128},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[130,136],{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":138},{"candidates":139,"agricultural_evidence":47},[140,146,152,156,164,171],{"code":124,"score":141,"supported":47,"title":142,"abstract":144,"tags":145},26,[143,125],"winter wheat",[126,143,125],[122],{"code":131,"score":147,"supported":47,"title":148,"abstract":149,"tags":151},15,[132],[150,132],"土壤",[121],{"code":33,"score":147,"supported":47,"title":153,"abstract":154,"tags":155},[58],[27,58],[27],{"code":157,"score":13,"supported":47,"title":158,"abstract":159,"tags":163},"I3",[],[160,161,162],"遥感","remote sensing","multispectral",[120],{"code":165,"score":101,"supported":47,"title":166,"abstract":167,"tags":170},"H3",[],[168,169],"灌溉","irrigation",[],{"code":46,"score":71,"supported":47,"title":172,"abstract":173,"tags":174},[],[26],[26],[176,177],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":179,"openalex_id":181,"authors":182,"venue":109,"cited_by_count":91,"oa_url":106,"card":192,"direction":96,"ingested_from":98},"W7223156204",[183,186,188,190],{"name":184,"orcid":185},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":187,"orcid":9},"Yi Zhang",{"name":189,"orcid":9},"WEI Lei",{"name":191,"orcid":9},"Chengcai Zhang",{"tldr":193,"method":194,"finding":195,"direction":196,"opportunity":197},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","农业遥感与作物表型","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":200,"title":201,"url":202,"summary":203,"summary_zh":204,"content":9,"source_name":205,"source_url":202,"published_at":206,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":207,"score_detail":208,"sources":211,"tags":213,"primary_topic":217,"secondary_topics":218,"classification":224,"search_phrases":264,"slug":267,"view_count":91,"doi":268,"paper":269,"created_at":291},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":17,"substance":18,"depth":17,"authority":209,"freshness":20,"relevant":21,"comment":210},14,"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。",[212],{"name":205,"url":202},[26,214,27,215,216],"农业人工智能","食品安全","文献计量",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[219],{"code":62,"slug":220,"name":221,"path":222,"parent_code":223},"food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":40,"locked":14,"version":41,"method":42,"evidence":225},{"candidates":226,"agricultural_evidence":47},[227,236,241,247,254,260],{"code":33,"score":228,"supported":47,"title":229,"abstract":230,"tags":235},19,[59],[231,27,232,58,233,59,234],"深度学习","计算机视觉","deep learning","computer vision",[214,27],{"code":62,"score":147,"supported":47,"title":237,"abstract":239,"tags":240},[238],"food safety",[215,238],[215],{"code":242,"score":147,"supported":47,"title":243,"abstract":245,"tags":246},"I7",[244],"bibliometric",[216,244],[216],{"code":248,"score":101,"supported":47,"title":249,"abstract":250,"tags":253},"H7",[],[251,252],"气候变化","climate change",[],{"code":255,"score":63,"supported":47,"title":256,"abstract":257,"tags":259},"A8",[],[258],"food security",[],{"code":46,"score":21,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[26],[265,266],"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":268,"openalex_id":270,"authors":271,"venue":205,"cited_by_count":91,"oa_url":9,"card":285,"direction":289,"ingested_from":98},"W7222920742",[272,274,276,279,282],{"name":273,"orcid":9},"Harang Lee",{"name":275,"orcid":9},"지은서",{"name":277,"orcid":278},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":280,"orcid":281},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":283,"orcid":284},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":286,"method":287,"finding":288,"direction":289,"opportunity":290},"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。","农业人工智能与决策模型","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":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":9,"source_name":298,"source_url":295,"published_at":299,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":300,"sources":303,"tags":305,"primary_topic":307,"secondary_topics":308,"classification":309,"search_phrases":326,"slug":329,"view_count":91,"doi":330,"paper":331,"created_at":368},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",{"impact":17,"substance":301,"depth":115,"authority":209,"freshness":20,"relevant":21,"comment":302},20,"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[304],{"name":298,"url":295},[26,214,27,50,306],"农户认知",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":310},{"candidates":311,"agricultural_evidence":47},[312,317,321],{"code":33,"score":313,"supported":47,"title":314,"abstract":315,"tags":316},11,[],[27,58,59],[214,27],{"code":131,"score":101,"supported":47,"title":318,"abstract":319,"tags":320},[],[150,132],[],{"code":46,"score":322,"supported":14,"title":323,"abstract":324,"tags":325},2,[],[],[26,50],[327,328],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":330,"openalex_id":332,"authors":333,"venue":298,"cited_by_count":91,"oa_url":295,"card":363,"direction":289,"ingested_from":98},"W7222866367",[334,337,340,343,346,349,352,355,357,360],{"name":335,"orcid":336},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":338,"orcid":339},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":341,"orcid":342},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":344,"orcid":345},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":347,"orcid":348},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":350,"orcid":351},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":353,"orcid":354},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":356,"orcid":9},"M. Z. Walkup",{"name":358,"orcid":359},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":361,"orcid":362},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":364,"method":365,"finding":366,"direction":289,"opportunity":367},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":372,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":382,"tags":384,"primary_topic":387,"secondary_topics":391,"classification":393,"search_phrases":410,"slug":413,"view_count":91,"doi":414,"paper":415,"created_at":429},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":20,"substance":17,"depth":113,"authority":379,"freshness":380,"relevant":21,"comment":381},12,9,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[383],{"name":375,"url":372},[26,214,27,385,386],"玉米","耐盐育种",{"code":388,"slug":389,"name":385,"path":390,"parent_code":128},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[392],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":394},{"candidates":395,"agricultural_evidence":47},[396,401,405],{"code":33,"score":228,"supported":47,"title":397,"abstract":398,"tags":400},[58],[27,58,399],"decision support",[214,27],{"code":388,"score":147,"supported":47,"title":402,"abstract":403,"tags":404},[389],[385,389],[385],{"code":46,"score":406,"supported":47,"title":407,"abstract":408,"tags":409},7,[],[50,53],[26],[411,412],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":414,"openalex_id":416,"authors":417,"venue":375,"cited_by_count":91,"oa_url":372,"card":424,"direction":289,"ingested_from":98},"W7221462188",[418,421],{"name":419,"orcid":420},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":422,"orcid":423},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":425,"method":426,"finding":427,"direction":289,"opportunity":428},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":437,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":438,"score_detail":439,"sources":441,"tags":443,"primary_topic":446,"secondary_topics":450,"classification":456,"search_phrases":497,"slug":500,"view_count":322,"doi":501,"paper":502,"created_at":520},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",81,{"impact":17,"substance":18,"depth":228,"authority":209,"freshness":20,"relevant":21,"comment":440},"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[442],{"name":436,"url":433},[26,444,27,120,445],"粮食安全","农业干旱",{"code":157,"slug":447,"name":448,"path":449,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[451,455],{"code":248,"slug":452,"name":453,"path":454,"parent_code":135},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":457},{"candidates":458,"agricultural_evidence":47},[459,463,469,473,477,481,487,493],{"code":157,"score":147,"supported":47,"title":460,"abstract":461,"tags":462},[161],[160,161],[120],{"code":248,"score":209,"supported":47,"title":464,"abstract":466,"tags":468},[465],"drought",[467,465],"干旱",[],{"code":33,"score":13,"supported":47,"title":470,"abstract":471,"tags":472},[],[231,27,58,233,59],[27],{"code":255,"score":406,"supported":47,"title":474,"abstract":475,"tags":476},[],[444,258],[444],{"code":131,"score":101,"supported":47,"title":478,"abstract":479,"tags":480},[],[150,132],[],{"code":482,"score":63,"supported":47,"title":483,"abstract":484,"tags":486},"D",[],[485],"水产",[],{"code":488,"score":63,"supported":47,"title":489,"abstract":490,"tags":492},"H6",[],[491],"农业生态",[],{"code":46,"score":21,"supported":14,"title":494,"abstract":495,"tags":496},[],[],[26],[498,499],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575","10.1080\u002F15481603.2026.2738981",{"doi":501,"openalex_id":503,"authors":504,"venue":436,"cited_by_count":91,"oa_url":514,"card":515,"direction":96,"ingested_from":98},"W7221081979",[505,508,511],{"name":506,"orcid":507},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":509,"orcid":510},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":512,"orcid":513},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":516,"method":517,"finding":518,"direction":196,"opportunity":519},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","2026-10-10T23:30:14.933078Z",{"id":522,"title":523,"url":524,"summary":525,"summary_zh":9,"content":9,"source_name":526,"source_url":9,"published_at":527,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":528,"score_detail":529,"sources":531,"tags":533,"primary_topic":535,"secondary_topics":536,"classification":538,"search_phrases":557,"slug":560,"view_count":71,"doi":9,"paper":561,"created_at":569},4286,"Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37386408","Navarro Rubio等系统综述2018-2026年196项AI土壤特性与农业应用研究，遵循PRISMA 2020框架。综述发现研究明显向数据驱动方法转变，机器学习和深度学习模型主导近期研究；随机森林、支持向量机、梯度提升方法、人工神经网络、卷积神经网络、长短期记忆架构最常被报告；主要数据源包括原位传感、实验室测量、遥感影像和环境协变量，常通过多源数据融合框架集成；树集成模型在不同土壤特性上提供稳健性能。","Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241","2026-10-02T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":209,"freshness":101,"relevant":21,"comment":530},"PRISMA框架下196项研究的系统综述，方法学扎实、结论可靠，对农业AI土壤应用具较高参考价值。",[532],{"name":526,"url":524},[26,214,27,160,534],"土壤监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[537],{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},{"status":40,"locked":14,"version":41,"method":42,"evidence":539},{"candidates":540,"agricultural_evidence":47},[541,545,549,553],{"code":33,"score":113,"supported":47,"title":542,"abstract":543,"tags":544},[59],[231,27],[214,27],{"code":131,"score":313,"supported":47,"title":546,"abstract":547,"tags":548},[132],[150],[],{"code":157,"score":71,"supported":47,"title":550,"abstract":551,"tags":552},[],[160],[160],{"code":46,"score":21,"supported":14,"title":554,"abstract":555,"tags":556},[],[],[26],[558,559],"AI 土壤特性 系统综述","机器学习 土壤 遥感","AI土壤特性系统综述-4286",{"doi":9,"openalex_id":9,"authors":562,"venue":9,"cited_by_count":91,"oa_url":9,"card":563,"direction":289,"ingested_from":568},[],{"tldr":564,"method":565,"finding":566,"direction":289,"opportunity":567},"系统综述2018-2026年196项AI用于土壤特性与农业的研究。","PRISMA 2020框架，分析机器学习与深度学习模型及多源数据融合。","研究转向数据驱动，树集成模型在不同土壤特性上表现稳健。","可探索可解释AI与多源数据融合在土壤特性预测中的泛化能力。","agent","2026-10-06T00:05:04.976398Z"]