[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2149":3,"related-2149":89},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":88},2149,"Smart biosensing ecosystems: Integrating nanotechnology, chemometrics, and machine learning for precision analytical applications","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxmate.2026.103476","The growing complexity of food, environmental, and biological matrices has intensified demand for analytical technologies that are rapid, selective, and deployable outside centralized laboratories, exposing the limitations of conventional chromatographic and single-analyte biosensing approaches. This review introduces the concept of a Smart Biosensing Ecosystem, a framework in which biorecognition elements (enzymes, antibodies, nucleic acids, aptamers, whole cells), functional nanomaterials, and electrochemical or optical transducers are integrated with chemometric preprocessing, machine-learning-based interpretation, IoT connectivity, and cloud-enabled decision support into a single analytical pipeline. Within this framework, the review examines smart biosensor architecture, biorecognition mechanisms, enzyme-based catalytic sensing, electrochemical and optical transduction physics, and a comparative evaluation of graphene, MXenes, carbon nanotubes, quantum dots, and metal-based nanomaterials for signal amplification and multiplexing. It further surveys chemometric tools (PCA, PLS, LDA) and machine-learning algorithms (SVM, Random Forest, ANN, CNN, gradient boosting, ensemble learning) for model development and validation, alongside wearable, multiplexed, and hybrid electrochemical–optical platforms applied to food-quality, environmental, and biomedical analysis. Persistent challenges, including biofouling, batch-to-batch reproducibility, scalable manufacturing, regulatory translation, explainable AI, and data governance, are critically discussed as barriers limiting laboratory-to-market translation. The review concludes that continued convergence of nanotechnology, intelligent analytics, and connected infrastructure is poised to transform biosensors from isolated transduction devices into autonomous, self-calibrating, and predictive analytical ecosystems with substantial translational potential across precision food, environmental, and biomedical monitoring.","食品、环境和生物基质的日益复杂，加剧了对快速、选择性且可在中心实验室之外部署的分析技术的需求，也暴露了传统色谱方法和单分析物生物传感方法的局限性。本综述引入了“智能生物传感生态系统”的概念，该框架将生物识别元件（酶、抗体、核酸、适配体、全细胞）、功能纳米材料以及电化学或光学换能器，与化学计量学预处理、基于机器学习的解析、物联网连接和云端决策支持整合为单一分析流程。在该框架内，本综述考察了智能生物传感器架构、生物识别机制、基于酶的催化传感、电化学与光学换能物理，并对石墨烯、MXenes、碳纳米管、量子点和金属基纳米材料在信号放大与多重检测方面进行了比较评估。文章进一步综述了用于模型开发与验证的化学计量学工具（PCA、PLS、LDA）和机器学习算法（SVM、随机森林、ANN、CNN、梯度提升、集成学习），以及应用于食品质量、环境和生物医学分析的可穿戴、多重和混合电化学–光学平台。文章批判性地讨论了持续存在的挑战，包括生物污损、批次间重现性、可规模化制造、法规转化、可解释人工智能和数据治理，这些是限制实验室到市场转化的障碍。综述得出结论：纳米技术、智能分析和互联基础设施的持续融合，有望将生物传感器从孤立的换能设备转变为自主、自校准和预测性分析生态系统，在精准食品、环境和生物医学监测方面具有巨大的转化潜力。",null,"Next Materials","2026-09-09T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,14,8,1,"综述提出智能生物传感生态系统框架，整合纳米材料、化学计量学与机器学习，对农产品质量与食品安全精准检测有较强参考价值，但属实验室前沿综述，产业落地尚远。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","机器学习","食品安全","纳米材料","农业传感器",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":65},[38,47,55,60],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":46},"I2",15,true,[43],"machine learning",[27,45],"decision support",[27],{"code":48,"score":49,"supported":41,"title":50,"abstract":51,"tags":54},"I5",7,[],[52,53],"物联网","IoT",[30],{"code":56,"score":21,"supported":14,"title":57,"abstract":58,"tags":59},"E4",[],[],[28],{"code":61,"score":21,"supported":14,"title":62,"abstract":63,"tags":64},"I1",[],[],[26],"农业相关性需要复核",[67,68],"农业传感器 智慧农业 机器学习 纳米材料","农业传感器 智慧农业","农业传感器智慧农业机器学习纳米材料-2149",3,"10.1016\u002Fj.nxmate.2026.103476",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":78,"oa_url":79,"card":80,"direction":86,"ingested_from":87},"W7212013524",[75],{"name":76,"orcid":77},"Kushagra Sharma","https:\u002F\u002Forcid.org\u002F0000-0003-1126-8697",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2949822826018939\u002Fpdf",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"综述提出智能生物传感生态系统，融合纳米材料、化学计量学、机器学习与物联网，实现精准分析。","综述生物识别、纳米材料、电化学\u002F光学传感、PCA\u002FPLS及SVM\u002FCNN等机器学","纳米技术、智能分析与互联基础设施融合，可将生物传感器变为自主预测性分析生态系统。","农业人工智能与决策模型","可探索面向农业食品链的智能生物传感生态系统，解决生物污损、可解释AI与数据治理等落地瓶颈。","智慧农业 \u002F 农业物联网","openalex","2026-09-11T23:30:12.070495Z",{"total":90,"page":21,"page_size":90,"items":91},6,[92,189,294,357,418,501],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":104,"tags":106,"primary_topic":109,"secondary_topics":114,"classification":120,"search_phrases":162,"slug":165,"view_count":78,"doi":166,"paper":167,"created_at":188},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":102,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":103},22,"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。",[105],{"name":98,"url":95},[26,107,27,28,108],"农业人工智能","文献计量",{"code":39,"slug":110,"name":111,"path":112,"parent_code":113},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[115],{"code":56,"slug":116,"name":117,"path":118,"parent_code":119},"food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":121,"locked":14,"version":34,"method":35,"evidence":122},"classified",{"candidates":123,"agricultural_evidence":41},[124,134,139,145,152,158],{"code":39,"score":125,"supported":41,"title":126,"abstract":128,"tags":133},19,[127],"artificial intelligence",[129,27,130,43,131,127,132],"深度学习","计算机视觉","deep learning","computer vision",[107,27],{"code":56,"score":40,"supported":41,"title":135,"abstract":137,"tags":138},[136],"food safety",[28,136],[28],{"code":140,"score":40,"supported":41,"title":141,"abstract":143,"tags":144},"I7",[142],"bibliometric",[108,142],[108],{"code":146,"score":90,"supported":41,"title":147,"abstract":148,"tags":151},"H7",[],[149,150],"气候变化","climate change",[],{"code":153,"score":70,"supported":41,"title":154,"abstract":155,"tags":157},"A8",[],[156],"food security",[],{"code":61,"score":21,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[26],[163,164],"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":166,"openalex_id":168,"authors":169,"venue":98,"cited_by_count":78,"oa_url":9,"card":183,"direction":84,"ingested_from":87},"W7222920742",[170,172,174,177,180],{"name":171,"orcid":9},"Harang Lee",{"name":173,"orcid":9},"지은서",{"name":175,"orcid":176},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":178,"orcid":179},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":181,"orcid":182},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":184,"method":185,"finding":186,"direction":84,"opportunity":187},"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":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":202,"tags":204,"primary_topic":207,"secondary_topics":211,"classification":213,"search_phrases":275,"slug":278,"view_count":20,"doi":279,"paper":280,"created_at":293},3536,"Functional photonic and optoelectronic materials and devices for climate-resilient smart agriculture: a review","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmats.2026.1943334","The variation in the temperature, precipitation, and extreme events has rendered climate change very threatening to crop production and the overall world food security. The prospects of sustainable and climate resilient agricultural production is brought up with the use of photonics and optoelectronics coupled with smart farming technology. The paper offers an overall foundation of smart agricultural systems that are assisted by photonics and optoelectronics that combine the state-of-the-art optical sensors, Internet of Things (IoT) communications, and machine learning algorithms in the crop and soil monitoring. Due to optical sensing technologies including fiber optic sensors, LiDAR, fluorescence spectroscopy, and hyperspectral imaging, optical sensors can also be used in high resolution and non-destructive measurement of physiological properties of plants, nutrient status, water stress and disease incidence. With the help of optoelectronic devices, it is possible to carry out the accurate signal processing, data collection, automated control of irrigation, fertigation and microclimate regulating systems. When sensor-based information is incorporated with any decision support systems the quality of early stress detection is improved, wastage of resources is minimized and the effect on the environment is minimized. Multispectral and temporal data based on machine learning models enhance forecasting of climate risk, diseases, and pest outbreaks forecasting, and crop yield forecasting. The system will aid in making the contemporary crop production systems more sustainable and resilient in the long-term due to the fact that it will provide scalable and flexible solutions across the agroecological regions.","温度、降水及极端事件的变化使气候变化对作物生产和全球粮食安全构成严重威胁。光子学与光电子学结合智慧农业技术，为实现可持续且气候韧性的农业生产带来了前景。本文提供了由光子学和光电子学辅助的智慧农业系统的总体基础，该系统将先进的光学传感器、物联网（IoT）通信和机器学习算法结合应用于作物与土壤监测。借助光纤传感器、激光雷达（LiDAR）、荧光光谱和高光谱成像等光学传感技术，光学传感器还可用于高分辨率、非破坏性地测量植物生理特性、养分状况、水分胁迫和病害发生情况。借助光电子器件，可以实现精确的信号处理、数据采集以及灌溉、施肥和微气候调节系统的自动控制。当基于传感器的信息与决策支持系统相结合时，早期胁迫检测的质量得以提高，资源浪费降至最低，对环境的影响也降至最小。基于机器学习模型的多光谱和时间序列数据增强了气候风险、病虫害暴发预测以及作物产量预测的能力。该系统将有助于使当代作物生产系统在长期内更具可持续性和韧性，因为它将为各农业生态区域提供可扩展且灵活的解决方案。","Frontiers in Materials","2026-09-25T00:00:00Z",76,{"impact":17,"substance":18,"depth":199,"authority":200,"freshness":20,"relevant":21,"comment":201},17,13,"系统综述光子与光电子技术在气候韧性智慧农业中的应用，方法覆盖全面、结论可靠，对农业信息化领域有较高参考价值。",[203],{"name":195,"url":192},[26,205,27,30,206],"农业物联网","光谱遥感",{"code":48,"slug":208,"name":209,"path":210,"parent_code":113},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[212],{"code":39,"slug":110,"name":111,"path":112,"parent_code":113},{"status":121,"locked":14,"version":34,"method":35,"evidence":214},{"candidates":215,"agricultural_evidence":41},[216,222,228,232,237,244,251,258,262,269],{"code":61,"score":217,"supported":41,"title":218,"abstract":220,"tags":221},12,[219],"smart agriculture",[26],[26],{"code":48,"score":223,"supported":41,"title":224,"abstract":225,"tags":227},11,[],[52,226,53],"internet of things",[30,205],{"code":39,"score":13,"supported":41,"title":229,"abstract":230,"tags":231},[],[27,43,45],[27],{"code":153,"score":90,"supported":41,"title":233,"abstract":234,"tags":236},[],[235,156],"粮食安全",[],{"code":238,"score":90,"supported":41,"title":239,"abstract":240,"tags":243},"F3",[],[241,242],"施肥","fertigation",[],{"code":245,"score":90,"supported":41,"title":246,"abstract":247,"tags":250},"H2",[],[248,249],"土壤","soil",[],{"code":252,"score":90,"supported":41,"title":253,"abstract":254,"tags":257},"H3",[],[255,256],"灌溉","irrigation",[],{"code":146,"score":90,"supported":41,"title":259,"abstract":260,"tags":261},[],[149,150],[],{"code":263,"score":90,"supported":41,"title":264,"abstract":265,"tags":268},"I3",[],[266,267],"multispectral","hyperspectral",[],{"code":270,"score":70,"supported":41,"title":271,"abstract":272,"tags":274},"H6",[],[273],"农业生态",[],[276,277],"光子学 光电材料 智慧农业","光纤传感 高光谱成像 作物监测","光子学光电材料智慧农业-3536","10.3389\u002Ffmats.2026.1943334",{"doi":279,"openalex_id":281,"authors":282,"venue":195,"cited_by_count":78,"oa_url":192,"card":288,"direction":86,"ingested_from":87},"W7214293156",[283,285],{"name":284,"orcid":9},"Karthika Vishnu Priya Kathula",{"name":286,"orcid":287},"Murugesan Mohana Keerthi","https:\u002F\u002Forcid.org\u002F0000-0002-2018-0200",{"tldr":289,"method":290,"finding":291,"direction":86,"opportunity":292},"综述光子与光电子材料器件结合物联网和机器学习，支撑气候韧性智慧农业的作物与土壤监测。","综述光纤传感、LiDAR、荧光光谱、高光谱成像与IoT、机器学习融合方案。","光学传感与光电器件可实现无损高分辨监测，提升早期胁迫检测并减少资源浪费。","可探索低成本光子传感器与轻量ML模型在田间边缘端的集成及跨生态区泛化验证。","2026-09-26T23:30:09.644423Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":300,"source_url":297,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":302,"sources":304,"tags":306,"primary_topic":309,"secondary_topics":310,"classification":311,"search_phrases":329,"slug":332,"view_count":21,"doi":333,"paper":334,"created_at":356},2803,"Volatile Fingerprinting Empowers Salinity Monitoring in Peppermint Using MOS Sensors and Feature-Optimized Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15184233","Early detection of salinity stress is essential for precision agriculture, particularly in scalable, resource-constrained monitoring systems. This study presents a portable sensing module integrating a low-cost metal oxide semiconductor (MOS) sensor array with potential application for edge deployment to detect salinity stress in peppermint. Salinity significantly reduced plant biomass, confirming physiological stress induction. Volatile organic compound (VOC) fingerprints were collected over eleven consecutive days in a controlled enclosure. Sensor signals underwent outlier filtering, normalization, and smoothing, while treatment discrimination was verified using the Kruskal–Wallis test. Thirty-three machine learning models were evaluated using a 75:25 train–test split with five-fold cross-validation. Wide neural network models achieved the highest predictive performance, exceeding 98% test accuracy and a 97% macro F1 score. Feature adequacy analysis showed that six sensors captured the dominant variance required for reliable classification. Considering computational constraints, a bilayered neural network using only six features maintained over 97% accuracy with a memory footprint of 0.008 MB while remaining Pareto optimal. These findings support the feasibility of a compact, computationally efficient, and edge-compatible VOC sensing framework for salinity stress detection in precision agriculture and intelligent crop monitoring.","盐胁迫的早期检测对精准农业至关重要，尤其是在可扩展、资源受限的监测系统中。本研究提出了一种便携式传感模块，将低成本金属氧化物半导体（MOS）传感器阵列集成其中，具备边缘部署的应用潜力，用于检测薄荷中的盐胁迫。盐胁迫显著降低了植物生物量，证实了生理胁迫的诱导作用。在受控密闭环境中连续十一天采集了挥发性有机化合物（VOC）指纹图谱。对传感器信号进行了异常值过滤、归一化和平滑处理，并使用Kruskal–Wallis检验验证了处理组间的区分度。采用75:25的训练-测试划分和五折交叉验证评估了三十三种机器学习模型。宽神经网络模型取得了最高的预测性能，测试准确率超过98%，宏F1分数达到97%。特征充分性分析表明，六个传感器即可捕获可靠分类所需的主要方差。考虑到计算约束，仅使用六个特征的双层神经网络在保持超过97%准确率的同时，内存占用仅为0.008 MB，且仍处于帕累托最优。这些发现支持了一种紧凑、计算高效且兼容边缘计算的VOC传感框架用于精准农业和智能作物监测中盐胁迫检测的可行性。","Electronics","2026-09-17T00:00:00Z",{"impact":40,"substance":102,"depth":17,"authority":200,"freshness":13,"relevant":21,"comment":303},"低成本MOS传感器阵列结合特征优化机器学习实现薄荷盐胁迫早期无损检测，方法新颖、数据扎实，对边缘部署式作物监测有参考价值。",[305],{"name":300,"url":297},[26,27,307,30,308],"精准农业","盐胁迫监测",{"code":39,"slug":110,"name":111,"path":112,"parent_code":113},[],{"status":121,"locked":14,"version":34,"method":35,"evidence":312},{"candidates":313,"agricultural_evidence":41},[314,318,323],{"code":39,"score":40,"supported":41,"title":315,"abstract":316,"tags":317},[43],[27,43],[27],{"code":61,"score":20,"supported":41,"title":319,"abstract":320,"tags":322},[],[307,321],"precision agriculture",[26,307],{"code":48,"score":324,"supported":41,"title":325,"abstract":326,"tags":328},4,[],[327],"边缘计算",[30],[330,331],"农业传感器 盐胁迫监测 智慧农业 机器学习","农业传感器 盐胁迫监测","农业传感器盐胁迫监测智慧农业机器学习-2803","10.3390\u002Felectronics15184233",{"doi":333,"openalex_id":335,"authors":336,"venue":300,"cited_by_count":78,"oa_url":297,"card":351,"direction":84,"ingested_from":87},"W7213452763",[337,340,343,345,348],{"name":338,"orcid":339},"Ahmad Ali","https:\u002F\u002Forcid.org\u002F0000-0001-5530-7374",{"name":341,"orcid":342},"Vinie Lee Silva Alvarado","https:\u002F\u002Forcid.org\u002F0009-0000-5857-3248",{"name":344,"orcid":9},"Arman Heydari",{"name":346,"orcid":347},"Sandra Sendra","https:\u002F\u002Forcid.org\u002F0000-0001-9556-9088",{"name":349,"orcid":350},"Jaime Lloret","https:\u002F\u002Forcid.org\u002F0000-0002-0862-0533",{"tldr":352,"method":353,"finding":354,"direction":86,"opportunity":355},"用低成本MOS传感器阵列采集薄荷VOC指纹，结合特征优化机器学习实现盐胁迫检测。","11天VOC指纹采集，33种机器学习模型，五折交叉验证，特征充分性分析。","宽神经网络准确率超98%，仅用6个特征的双层网络保持97%以上且内存仅0.008MB。","可探索多作物VOC指纹迁移学习与田间边缘设备长期稳定性验证。","2026-09-17T23:30:59.249847Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":9,"source_name":363,"source_url":360,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":364,"score_detail":365,"sources":367,"tags":369,"primary_topic":372,"secondary_topics":376,"classification":377,"search_phrases":399,"slug":402,"view_count":403,"doi":404,"paper":405,"created_at":417},2069,"Nanomaterial-enabled Sensors for Soil and Crop Analysis in Agriculture: A Focused Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Facri\u002F2026\u002Fv26i92158","Rapid, sensitive and portable measurements are important for precision agriculture, particularly when soil and crop conditions vary spatially and temporally. This focused narrative review evaluates nanomaterial-enabled sensors for soil nutrient assessment, crop physiological monitoring and plant-disease detection. Four representative studies were examined across contrasting sensing architectures involving graphene derivatives, conductive nanofibres, gold nanoparticles and carbon nanotubes. The selected platforms targeted soil nitrate, indole-3-acetic acid, pathogen-associated DNA and a citrus Huanglongbing protein biomarker. Reported studies demonstrated low analytical detection limits and successful measurements in soil extracts, plant tissues or other agriculturally relevant samples. However, their evidence primarily supports analytical feasibility rather than routine field deployment or direct agronomic benefit. Important constraints include matrix interference, calibration stability, durability of biological recognition elements, electrode reproducibility, sample-preparation requirements, environmental safety and fabrication cost. Recent developments in wearable, wireless and portable sensing further indicate a shift towards integrated monitoring systems, but connectivity alone does not establish field readiness. Future work should emphasise multilocation validation, comparison with accepted reference methods, standardised calibration, long-term stability, batch reproducibility, safe device management and integration of sensor outputs with agricultural decision-support systems. Nanomaterial-enabled sensors therefore show promise as complementary analytical tools, while routine adoption requires reliable performance under real production conditions and evidence that measurements support better management decisions.","快速、灵敏且便携的测量对精准农业十分重要，尤其是当土壤和作物状况在空间和时间上存在变化时。本聚焦式叙述综述评估了用于土壤养分评估、作物生理监测和植物病害检测的纳米材料传感器。本文考察了四项代表性研究，涉及石墨烯衍生物、导电纳米纤维、金纳米颗粒和碳纳米管等不同传感架构。所选平台分别针对土壤硝酸盐、吲哚-3-乙酸、病原体相关DNA和柑橘黄龙病蛋白生物标志物。所报道的研究展示了较低的分析检测限，并成功实现了在土壤提取物、植物组织或其他农业相关样品中的测量。然而，其证据主要支持分析可行性，而非常规田间部署或直接农艺效益。重要限制包括基质干扰、校准稳定性、生物识别元件的耐久性、电极重现性、样品制备要求、环境安全性和制造成本。可穿戴、无线和便携式传感的最新进展进一步表明，传感正转向集成监测系统，但仅凭连接性并不能确立田间就绪性。未来工作应强调多地点验证、与公认参考方法的比较、标准化校准、长期稳定性、批次重现性、安全器件管理，以及将传感器输出与农业决策支持系统相整合。因此，纳米材料传感器作为补充性分析工具显示出前景，而常规采用则需要在真实生产条件下具备可靠性能，并提供测量能够支持更好管理决策的证据。","Archives of Current Research International",67,{"impact":217,"substance":17,"depth":199,"authority":217,"freshness":20,"relevant":21,"comment":366},"综述系统梳理纳米材料传感器在土壤养分、作物生理与病害检测中的分析可行性，但作者明确指出其证据尚不足以支撑田间常规应用，属细分领域学术进展，适合主题聚合而非每日精选头条。",[368],{"name":363,"url":360},[26,29,30,370,371],"土壤养分检测","作物病害监测",{"code":245,"slug":249,"name":373,"path":374,"parent_code":375},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":121,"locked":14,"version":34,"method":35,"evidence":378},{"candidates":379,"agricultural_evidence":41},[380,384,388,395],{"code":245,"score":19,"supported":41,"title":381,"abstract":382,"tags":383},[249],[248,249],[],{"code":61,"score":49,"supported":41,"title":385,"abstract":386,"tags":387},[],[307,321],[26],{"code":389,"score":90,"supported":41,"title":390,"abstract":391,"tags":394},"B2",[],[392,393],"柑橘","citrus",[],{"code":48,"score":21,"supported":14,"title":396,"abstract":397,"tags":398},[],[],[30],[400,401],"作物病害监测 土壤养分检测 农业传感器 智慧农业","作物病害监测 土壤养分检测","作物病害监测土壤养分检测农业传感器智慧农业-2069",2,"10.9734\u002Facri\u002F2026\u002Fv26i92158",{"doi":404,"openalex_id":406,"authors":407,"venue":363,"cited_by_count":78,"oa_url":360,"card":412,"direction":84,"ingested_from":87},"W7212060484",[408,410],{"name":409,"orcid":9},"N. Pruthviraj",{"name":411,"orcid":9},"V. Lakshmi",{"tldr":413,"method":414,"finding":415,"direction":86,"opportunity":416},"综述纳米材料传感器在土壤养分、作物生理和病害检测中的分析可行性及田间应用瓶颈。","聚焦综述4项研究，涵盖石墨烯、导电纳米纤维、金纳米颗粒和碳纳米管传感平台。","传感器检测限低但仅证明分析可行性，田间部署受基质干扰、校准稳定性等限制。","需开展多地点验证、标准化校准及与决策支持系统集成，以证明田间实用性和农艺效益。","2026-09-10T23:30:53.443004Z",{"id":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":9,"source_name":424,"source_url":421,"published_at":425,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":426,"score_detail":427,"sources":431,"tags":433,"primary_topic":437,"secondary_topics":443,"classification":446,"search_phrases":477,"slug":480,"view_count":78,"doi":481,"paper":482,"created_at":500},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":428,"substance":429,"depth":199,"authority":200,"freshness":13,"relevant":21,"comment":430},16,21,"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。",[432],{"name":424,"url":421},[26,27,434,435,436],"遥感监测","土壤墒情","冬小麦",{"code":438,"slug":439,"name":440,"path":441,"parent_code":442},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[444,445],{"code":245,"slug":249,"name":373,"path":374,"parent_code":375},{"code":39,"slug":110,"name":111,"path":112,"parent_code":113},{"status":121,"locked":14,"version":34,"method":35,"evidence":447},{"candidates":448,"agricultural_evidence":41},[449,455,459,463,469,473],{"code":438,"score":450,"supported":41,"title":451,"abstract":453,"tags":454},26,[452,439],"winter wheat",[440,452,439],[436],{"code":245,"score":40,"supported":41,"title":456,"abstract":457,"tags":458},[249],[248,249],[435],{"code":39,"score":40,"supported":41,"title":460,"abstract":461,"tags":462},[43],[27,43],[27],{"code":263,"score":13,"supported":41,"title":464,"abstract":465,"tags":468},[],[466,467,266],"遥感","remote sensing",[434],{"code":252,"score":90,"supported":41,"title":470,"abstract":471,"tags":472},[],[255,256],[],{"code":61,"score":324,"supported":41,"title":474,"abstract":475,"tags":476},[],[26],[26],[478,479],"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":481,"openalex_id":483,"authors":484,"venue":424,"cited_by_count":78,"oa_url":421,"card":494,"direction":86,"ingested_from":87},"W7223156204",[485,488,490,492],{"name":486,"orcid":487},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":489,"orcid":9},"Yi Zhang",{"name":491,"orcid":9},"WEI Lei",{"name":493,"orcid":9},"Chengcai Zhang",{"tldr":495,"method":496,"finding":497,"direction":498,"opportunity":499},"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":502,"title":503,"url":504,"summary":505,"summary_zh":506,"content":9,"source_name":507,"source_url":504,"published_at":508,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":426,"score_detail":509,"sources":511,"tags":513,"primary_topic":515,"secondary_topics":516,"classification":517,"search_phrases":532,"slug":535,"view_count":78,"doi":536,"paper":537,"created_at":574},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":18,"depth":199,"authority":19,"freshness":20,"relevant":21,"comment":510},"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。",[512],{"name":507,"url":504},[26,107,27,307,514],"农户认知",{"code":39,"slug":110,"name":111,"path":112,"parent_code":113},[],{"status":121,"locked":14,"version":34,"method":35,"evidence":518},{"candidates":519,"agricultural_evidence":41},[520,524,528],{"code":39,"score":223,"supported":41,"title":521,"abstract":522,"tags":523},[],[27,43,127],[107,27],{"code":245,"score":90,"supported":41,"title":525,"abstract":526,"tags":527},[],[248,249],[],{"code":61,"score":403,"supported":14,"title":529,"abstract":530,"tags":531},[],[],[26,307],[533,534],"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":536,"openalex_id":538,"authors":539,"venue":507,"cited_by_count":78,"oa_url":504,"card":569,"direction":84,"ingested_from":87},"W7222866367",[540,543,546,549,552,555,558,561,563,566],{"name":541,"orcid":542},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":544,"orcid":545},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":547,"orcid":548},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":550,"orcid":551},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":553,"orcid":554},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":556,"orcid":557},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":559,"orcid":560},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":562,"orcid":9},"M. Z. Walkup",{"name":564,"orcid":565},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":567,"orcid":568},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":570,"method":571,"finding":572,"direction":84,"opportunity":573},"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"]