[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4447":3,"related-4447":133},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":100,"slug":103,"view_count":22,"doi":104,"paper":105,"created_at":132},4447,"Microbiomes of soils and leafy vegetables in urban agriculture systems reflect crop management and local environmental conditions","https:\u002F\u002Fdoi.org\u002F10.1128\u002Faem.01551-26","ABSTRACT Soil and plant microbiomes influence crop quality and preharvest food safety, yet these processes have been largely unexplored in urban agriculture environments. Here, we investigated how the microbiota of soil and leafy greens (e.g., kale, lettuce, chard, and cabbage) interact with site-specific management practices and environmental conditions across seven urban agriculture sites in the greater Washington, DC, area. Samples of leaf tissue ( n = 92), rhizosphere soil ( n = 92), and “native” or bulk soil ( n = 39) were collected and analyzed with 16S rRNA gene and ITS2 genomic region amplicon sequencing. Microbial community composition varied significantly across sites for all sample types, as influenced by irrigation water source (i.e., municipal vs natural water), crop type, soil pH, soil moisture content, and climatic conditions (i.e., rainfall, temperature). Rhizosphere soils harbored the most bacterial taxa known to respond to organic inputs (e.g., Massilia and various members of Firmicutes and Verrucomicrobiota ), whereas fungal networks dominated by saprotrophic members of Ascomycota were more stable in the bulk soils. Source-tracking analysis indicated that the phyllosphere microbiota, although variable and transient in composition, were largely derived from the rhizosphere soil. Notably, leafy greens at some sites were enriched with taxa associated with biocontrol potential (e.g., Lactobacillaceae ). Moreover, machine learning models yielded strong performance in predicting specific production sites based on the bacterial communities in the rhizosphere and phyllosphere, suggesting possible microbiome-based applications for food traceability. Collectively, these findings demonstrate how local environments and crop management practices shape preharvest leafy green microbiomes in urban production. IMPORTANCE Urban farming and gardening are rapidly expanding to support local communities and enhance food security, yet these small-scale food systems are often established in uniquely developed landscapes and with resource-dependent operations. This study shows that complex site-specific factors influence the soil and leafy green microbiomes in urban agriculture systems. Our findings have important implications for future efforts in targeted farm management to enhance sustainable production and preharvest food safety.","摘要 土壤和植物微生物组影响作物品质和采前食品安全，然而这些过程在城市农业环境中很大程度上尚未被探索。在此，我们研究了华盛顿特区大都会地区七个城市农业站点中，土壤和叶菜类蔬菜（如羽衣甘蓝、生菜、甜菜和卷心菜）的微生物群如何与站点特定的管理措施和环境条件相互作用。我们采集了叶片组织（n = 92）、根际土壤（n = 92）以及“原生”或 bulk 土壤（n = 39）样本，并通过16S rRNA基因和ITS2基因组区域扩增子测序进行分析。所有样本类型的微生物群落组成在各站点间均存在显著差异，受到灌溉水源（即市政用水与自然水源）、作物类型、土壤pH、土壤含水量和气候条件（即降雨量、温度）的影响。根际土壤中富含已知响应有机投入的细菌类群（如Massilia以及Firmicutes和Verrucomicrobiota的多个成员），而以子囊菌门（Ascomycota）腐生成员为主的真菌网络在bulk土壤中更为稳定。来源追踪分析表明，叶际微生物群虽然组成多变且短暂，但主要来源于根际土壤。值得注意的是，某些站点的叶菜类蔬菜中富集了与生物防治潜力相关的类群（如乳杆菌科Lactobacillaceae）。此外，机器学习模型在基于根际和叶际细菌群落预测特定生产站点方面表现出强劲性能，这提示了基于微生物组的食品溯源应用的可能性。总体而言，这些发现表明局部环境和作物管理措施如何塑造城市生产中采前叶菜类蔬菜微生物组。重要性 城市农业和园艺正在迅速扩展，以支持当地社区和增强粮食安全，然而这些小型食品系统往往建立在独特开发景观中，并依赖资源型运营。本研究表明，复杂的站点特定因素影响城市农业系统中的土壤和叶菜类蔬菜微生物组。我们的发现对未来开展针对性农场管理以增强可持续生产和采前食品安全具有重要意义。",null,"Applied and Environmental Microbiology","2026-10-07T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,8,1,"研究揭示都市农业中土壤与叶菜微生物组受管理方式与本地环境影响，并展示基于微生物组的食品溯源潜力，方法扎实、结论有新意，对都市农业与食品安全有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","都市农业","食品安全","土壤微生物","叶菜",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":41,"reason":99},[39,47,56,65,72,80,87,94],{"code":40,"score":13,"supported":41,"title":42,"abstract":43,"tags":46},"E4",true,[],[29,44,45],"food safety","food traceability",[29],{"code":48,"score":13,"supported":41,"title":49,"abstract":50,"tags":55},"H2",[],[51,52,53,54],"土壤","根际","soil","rhizosphere",[30],{"code":57,"score":58,"supported":41,"title":59,"abstract":60,"tags":64},"B1",9,[],[61,62,63],"蔬菜","生菜","甘蓝",[],{"code":66,"score":67,"supported":41,"title":68,"abstract":69,"tags":71},"I2",7,[],[27,70],"machine learning",[27],{"code":73,"score":74,"supported":41,"title":75,"abstract":76,"tags":79},"A8",6,[],[77,78],"粮食安全","food security",[],{"code":81,"score":74,"supported":41,"title":82,"abstract":83,"tags":86},"H3",[],[84,85],"灌溉","irrigation",[],{"code":88,"score":89,"supported":41,"title":90,"abstract":91,"tags":93},"F4",3,[],[92],"生物防治",[],{"code":95,"score":22,"supported":14,"title":96,"abstract":97,"tags":98},"B7",[],[],[28],"多个产业证据相当，需要确认主归属",[101,102],"华盛顿都市农业 叶菜 微生物","根际土壤 叶际微生物 溯源","华盛顿都市农业叶菜微生物-4447","10.1128\u002Faem.01551-26",{"doi":104,"openalex_id":106,"authors":107,"venue":10,"cited_by_count":124,"oa_url":6,"card":125,"direction":129,"ingested_from":131},"W7220832236",[108,111,113,115,118,121],{"name":109,"orcid":110},"Mai‐Rui Gao","https:\u002F\u002Forcid.org\u002F0000-0001-9331-7389",{"name":112,"orcid":9},"Qingyue Zeng",{"name":114,"orcid":9},"Kevin Lam",{"name":116,"orcid":117},"Magaly Toro","https:\u002F\u002Forcid.org\u002F0000-0002-6280-2215",{"name":119,"orcid":120},"Shirley Ann Micallef","https:\u002F\u002Forcid.org\u002F0000-0003-0041-2139",{"name":122,"orcid":123},"Ryan A. Blaustein","https:\u002F\u002Forcid.org\u002F0000-0001-8879-7453",0,{"tldr":126,"method":127,"finding":128,"direction":129,"opportunity":130},"研究城市农业中土壤与叶菜微生物组如何受管理措施和局地环境影响。","对7个城市农场土壤和叶菜样本进行16S rRNA和ITS2扩增子测序及机器学习分","微生物组成随地点、灌溉水源、作物和气候显著变化，叶际菌群主要源自根际，可用于溯源。","农业人工智能与决策模型","可探索基于微生物组的城市农产品溯源与精准管理模型，结合多组学提升预测鲁棒性。","openalex","2026-10-08T23:30:50.350801Z",{"total":74,"page":22,"page_size":74,"items":134},[135,231,291,381,412,480],{"id":136,"title":137,"url":138,"summary":139,"summary_zh":140,"content":9,"source_name":141,"source_url":138,"published_at":142,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":143,"score_detail":144,"sources":146,"tags":148,"primary_topic":152,"secondary_topics":157,"classification":163,"search_phrases":204,"slug":207,"view_count":124,"doi":208,"paper":209,"created_at":230},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":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":145},"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。",[147],{"name":141,"url":138},[149,150,27,29,151],"智慧农业","农业人工智能","文献计量",{"code":66,"slug":153,"name":154,"path":155,"parent_code":156},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[158],{"code":40,"slug":159,"name":160,"path":161,"parent_code":162},"food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":164,"locked":14,"version":35,"method":36,"evidence":165},"classified",{"candidates":166,"agricultural_evidence":41},[167,177,182,188,195,199],{"code":66,"score":168,"supported":41,"title":169,"abstract":171,"tags":176},19,[170],"artificial intelligence",[172,27,173,70,174,170,175],"深度学习","计算机视觉","deep learning","computer vision",[150,27],{"code":40,"score":178,"supported":41,"title":179,"abstract":180,"tags":181},15,[44],[29,44],[29],{"code":183,"score":178,"supported":41,"title":184,"abstract":186,"tags":187},"I7",[185],"bibliometric",[151,185],[151],{"code":189,"score":74,"supported":41,"title":190,"abstract":191,"tags":194},"H7",[],[192,193],"气候变化","climate change",[],{"code":73,"score":89,"supported":41,"title":196,"abstract":197,"tags":198},[],[78],[],{"code":200,"score":22,"supported":14,"title":201,"abstract":202,"tags":203},"I1",[],[],[149],[205,206],"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":208,"openalex_id":210,"authors":211,"venue":141,"cited_by_count":124,"oa_url":9,"card":225,"direction":129,"ingested_from":131},"W7222920742",[212,214,216,219,222],{"name":213,"orcid":9},"Harang Lee",{"name":215,"orcid":9},"지은서",{"name":217,"orcid":218},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":220,"orcid":221},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":223,"orcid":224},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":226,"method":227,"finding":228,"direction":129,"opportunity":229},"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":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":239,"sources":242,"tags":244,"primary_topic":9,"secondary_topics":247,"classification":248,"search_phrases":272,"slug":275,"view_count":89,"doi":276,"paper":277,"created_at":290},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。","Next Materials","2026-09-09T00:00:00Z",{"impact":19,"substance":240,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":241},20,"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。",[243],{"name":237,"url":234},[149,27,29,245,246],"纳米材料","农业传感器",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":249},{"candidates":250,"agricultural_evidence":14,"reason":271},[251,256,263,267],{"code":66,"score":178,"supported":41,"title":252,"abstract":253,"tags":255},[70],[27,254],"decision support",[27],{"code":257,"score":67,"supported":41,"title":258,"abstract":259,"tags":262},"I5",[],[260,261],"物联网","IoT",[246],{"code":40,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},[],[],[29],{"code":200,"score":22,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[149],"农业相关性需要复核",[273,274],"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","10.1016\u002Fj.nxmate.2026.103476",{"doi":276,"openalex_id":278,"authors":279,"venue":237,"cited_by_count":124,"oa_url":283,"card":284,"direction":289,"ingested_from":131},"W7212013524",[280],{"name":281,"orcid":282},"Kushagra Sharma","https:\u002F\u002Forcid.org\u002F0000-0003-1126-8697","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2949822826018939\u002Fpdf",{"tldr":285,"method":286,"finding":287,"direction":129,"opportunity":288},"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。","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","2026-09-11T23:30:12.070495Z",{"id":292,"title":293,"url":294,"summary":295,"summary_zh":296,"content":9,"source_name":297,"source_url":294,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":299,"score_detail":300,"sources":305,"tags":307,"primary_topic":311,"secondary_topics":317,"classification":323,"search_phrases":357,"slug":360,"view_count":124,"doi":361,"paper":362,"created_at":380},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":17,"substance":301,"depth":302,"authority":303,"freshness":13,"relevant":22,"comment":304},21,17,13,"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。",[306],{"name":297,"url":294},[149,27,308,309,310],"遥感监测","土壤墒情","冬小麦",{"code":312,"slug":313,"name":314,"path":315,"parent_code":316},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[318,322],{"code":48,"slug":53,"name":319,"path":320,"parent_code":321},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":66,"slug":153,"name":154,"path":155,"parent_code":156},{"status":164,"locked":14,"version":35,"method":36,"evidence":324},{"candidates":325,"agricultural_evidence":41},[326,332,336,340,348,352],{"code":312,"score":327,"supported":41,"title":328,"abstract":330,"tags":331},26,[329,313],"winter wheat",[314,329,313],[310],{"code":48,"score":178,"supported":41,"title":333,"abstract":334,"tags":335},[53],[51,53],[309],{"code":66,"score":178,"supported":41,"title":337,"abstract":338,"tags":339},[70],[27,70],[27],{"code":341,"score":13,"supported":41,"title":342,"abstract":343,"tags":347},"I3",[],[344,345,346],"遥感","remote sensing","multispectral",[308],{"code":81,"score":74,"supported":41,"title":349,"abstract":350,"tags":351},[],[84,85],[],{"code":200,"score":353,"supported":41,"title":354,"abstract":355,"tags":356},4,[],[149],[149],[358,359],"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":361,"openalex_id":363,"authors":364,"venue":297,"cited_by_count":124,"oa_url":294,"card":374,"direction":289,"ingested_from":131},"W7223156204",[365,368,370,372],{"name":366,"orcid":367},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":369,"orcid":9},"Yi Zhang",{"name":371,"orcid":9},"WEI Lei",{"name":373,"orcid":9},"Chengcai Zhang",{"tldr":375,"method":376,"finding":377,"direction":378,"opportunity":379},"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":382,"title":383,"url":384,"summary":385,"summary_zh":9,"content":386,"source_name":387,"source_url":9,"published_at":142,"category":388,"cover_url":9,"hotness":13,"is_selected":14,"score":389,"score_detail":390,"sources":394,"tags":396,"primary_topic":9,"secondary_topics":401,"classification":402,"search_phrases":407,"slug":410,"view_count":124,"doi":9,"paper":9,"created_at":411},5524,"广东农科院质标所牵头地方标准获广东标准化创新发展大比武银奖","https:\u002F\u002Fwww.gdaas.cn\u002Fysdtn\u002Fkyjz\u002Fcontent\u002Fpost_1340862.html","质标所牵头制定的广东省地方标准《粤菜预制菜包装标识通用要求》（DB44\u002FT 2602—2025）获2026广东省标准化创新发展「大比武」银奖。该标准是全国首个粤菜预制菜包装标识省级地方标准，联合26家预制菜代表企事业单位制定，规定包装选材、安全设计与减量化管控，细化标签标识规则并鼓励增设粤菜文化二维码与追溯码。","近日，2026广东省标准化创新发展“大比武”获奖名单正式公布，我院质标所牵头制定的广东省地方标准《粤菜预制菜包装标识通用要求》（DB44\u002FT 2602—2025）获银奖，并在“广东省2026年世界标准日宣传活动”中进行获奖展示。该标准是全国首个粤菜预制菜包装标识省级地方标准，是我院质标所预制菜标准化研究的重要成果，标志我省预制菜标准体系建设迈上新台阶。\n\n本次“大比武”由广东省市场监督管理局主办，旨在深入贯彻《国家标准化发展纲要》及省委、省政府关于发挥标准引领作用促进高质量发展的部署要求，以“标准领航赋能高质量发展”为主题，面向全省有关单位主导制定的国家标准和广东省级地方标准，聚焦智能家电家居、现代农业两大领域开展比拼。经报名征集、初赛评审、专业核验、决赛答辩等环节角逐，每个领域仅设金奖2项、银奖4项、优秀奖6项，竞争激烈、荣誉分量十足。\n\n该标准由我院质标所牵头，联合26家预制菜代表企事业单位、汇集产学研多方力量制定，于2025年1月发布、4月实施。标准立足广府菜、客家菜、潮菜等粤菜产业实际，对预制菜包装选材、安全设计、减量化管控作出系统性规定；同时细化标签标识规则，明确产品需标注“预制菜”或“预制菜肴”，标明主要原料投料量、烹饪食用方法、贮存条件等关键信息，鼓励增设粤菜文化二维码、食品安全追溯码、反对浪费提示等内容，为企业生产规范、市场监督执法提供清晰技术支撑，兼顾食品安全、产业发展与粤菜文化传播，有效带动了粤菜预制菜产业的标准化、规范化发展。","广东省农业科学院 2026-10-19","报道",70,{"impact":19,"substance":240,"depth":178,"authority":391,"freshness":392,"relevant":22,"comment":393},12,5,"全国首个粤菜预制菜包装标识省级地方标准获省级标准化奖项，属细分行业标准化实质进展，具备产业参考价值。",[395],{"name":387,"url":384},[29,397,398,399,400],"地方标准","预制菜","粤菜","包装标识",[],{"status":403,"locked":14,"version":35,"method":36,"evidence":404},"unclassified",{"candidates":405,"agricultural_evidence":41,"reason":406},[],"缺少足够的主题证据",[408,409],"粤菜预制菜 包装标识 地方标准","广东农科院 质标所 预制菜","粤菜预制菜包装标识地方标准-5524","2026-10-22T00:07:04.669455Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":418,"source_url":415,"published_at":419,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":124,"score_detail":420,"sources":422,"tags":424,"primary_topic":427,"secondary_topics":428,"classification":434,"search_phrases":457,"slug":460,"view_count":124,"doi":461,"paper":462,"created_at":479},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":124,"substance":124,"depth":124,"authority":124,"freshness":124,"relevant":124,"comment":421},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[423],{"name":418,"url":415},[27,308,425,426],"土地利用","城市扩张",{"code":66,"slug":153,"name":154,"path":155,"parent_code":156},[429],{"code":430,"slug":431,"name":432,"path":433,"parent_code":156},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":164,"locked":14,"version":35,"method":36,"evidence":435},{"candidates":436,"agricultural_evidence":41},[437,441,448,452],{"code":66,"score":178,"supported":41,"title":438,"abstract":439,"tags":440},[70],[27,70],[27],{"code":430,"score":58,"supported":41,"title":442,"abstract":443,"tags":447},[],[444,445,446],"空间分析","GIS","spatial analysis",[],{"code":341,"score":67,"supported":41,"title":449,"abstract":450,"tags":451},[],[344,345],[308],{"code":453,"score":22,"supported":14,"title":454,"abstract":455,"tags":456},"H6",[],[],[425],[458,459],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":461,"openalex_id":463,"authors":464,"venue":418,"cited_by_count":124,"oa_url":415,"card":474,"direction":378,"ingested_from":131},"W7223022155",[465,468,470,472],{"name":466,"orcid":467},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":469,"orcid":9},"Goanar Jal Dup",{"name":471,"orcid":9},"Weyesa Merga Chemeda",{"name":473,"orcid":9},"Shankar Karuppannan",{"tldr":475,"method":476,"finding":477,"direction":378,"opportunity":478},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":481,"title":482,"url":483,"summary":484,"summary_zh":485,"content":9,"source_name":486,"source_url":483,"published_at":419,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":487,"score_detail":488,"sources":490,"tags":492,"primary_topic":497,"secondary_topics":498,"classification":499,"search_phrases":541,"slug":544,"view_count":124,"doi":545,"paper":546,"created_at":570},5466,"Microbial and enzymatic response of soil to organic and natural management practices in chickpea-based cropping systems at the Satpura foothills","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1814274","Inroduction Natural and organic farming systems are increasingly recognized as sustainable agroecological approaches that promote regenerative practices, ecological balance, and reduced reliance on external inputs. This study aimed to assess the effects of different nutrient management strategies on the soil enzymatic and biological properties of chickpea-based cropping systems. Methods A field experiment was conducted during the rabi seasons of 2022–23 and 2023–24 at the Centre for Organic Agriculture Research and Training, Department of Agronomy, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, Maharashtra, India. The experiment followed a strip-plot design with four replications. Four cropping systems were evaluated in a 2:2 row proportion: sole chickpea, chickpea intercropped with coriander, chickpea intercropped with linseed, and chickpea intercropped with sorghum. Three nutrient management strategies were assessed: organic farming, natural farming, and an integrated approach combining organic and natural farming inputs. Results Across both years, sole chickpea consistently supported greater soil biological activity than the intercropping systems. Compared with chickpea–sorghum intercropping under natural farming, sole chickpea managed using integrated organic and natural farming practices significantly increased soil nitrogen, phosphorus, and potassium by 60%, 100%, and 192%, respectively. Fungal, bacterial, and actinomycete populations increased by 78.0%, 85.4%, and 109.8%, respectively. Soil microbial biomass carbon and nitrogen increased by 42.4% and 64.6%, respectively. Urease, dehydrogenase, acid phosphatase, and alkaline phosphatase activities increased by 75.6%, 100%, 116.8%, and 42.4%, respectively. Among the nutrient management strategies, the integrated application of organic and natural farming inputs showed superior performance in improving soil biological properties compared with either approach alone. Discussion The findings indicate that sole chickpea combined with integrated organic and natural nutrient management can improve soil microbial populations, enzymatic activities, nutrient availability, and overall soil biological health. This management strategy may contribute to long-term soil fertility and sustainable agricultural production in Inceptisols of the Satpura foothill region of India under semi-arid conditions.","引言 自然农业与有机农业体系日益被视为可持续的农业生态方法，能够促进再生性实践、生态平衡并减少对外部投入的依赖。本研究旨在评估不同养分管理策略对鹰嘴豆（chickpea）种植体系土壤酶学及生物学特性的影响。方法 于2022—23年度和2023—24年度rabi季在印度马哈拉施特拉邦阿科拉市Dr. Panjabrao Deshmukh Krishi Vidyapeeth农学系有机农业研究与培训中心开展田间试验。试验采用条区设计（strip-plot design），设4次重复。以2：2行比评估4种种植体系：鹰嘴豆单作、鹰嘴豆与芫荽间作、鹰嘴豆与亚麻间作、鹰嘴豆与高粱间作。评估3种养分管理策略：有机农业、自然农业以及有机与自然农业投入相结合的集成方式。结果 两年间，鹰嘴豆单作始终较间作体系支持更高的土壤生物学活性。与自然农业下的鹰嘴豆—高粱间作相比，采用有机与自然农业集成实践的鹰嘴豆单作使土壤氮、磷、钾分别显著提高60%、100%和192%；真菌、细菌和放线菌数量分别增加78.0%、85.4%和109.8%；土壤微生物生物量碳和氮分别增加42.4%和64.6%；脲酶、脱氢酶、酸性磷酸酶和碱性磷酸酶活性分别提高75.6%、100%、116.8%和42.4%。在养分管理策略中，有机与自然农业投入的集成施用较任一单一方式在改善土壤生物学特性方面表现更优。讨论 研究结果表明，鹰嘴豆单作结合有机与自然养分集成管理可改善土壤微生物种群、酶活性、养分有效性及整体土壤生物学健康。该管理策略可能有助于印度萨特普拉山麓地区半干旱条件下始成土（Inceptisols）的长期土壤肥力和可持续农业生产。","Frontiers in Sustainable Food Systems",69,{"impact":13,"substance":240,"depth":302,"authority":303,"freshness":58,"relevant":22,"comment":489},"田间试验数据扎实、结论明确，但属印度区域尺度的农学论文，对国内智慧农业主题页聚合价值有限，可作国际有机农业参考。",[491],{"name":486,"url":483},[493,494,495,30,496],"间作系统","有机农业","养分管理","鹰嘴豆",{"code":48,"slug":53,"name":319,"path":320,"parent_code":321},[],{"status":164,"locked":14,"version":35,"method":36,"evidence":500},{"candidates":501,"agricultural_evidence":41},[502,506,513,520,525,531,536],{"code":48,"score":178,"supported":41,"title":503,"abstract":504,"tags":505},[53],[51,53],[30],{"code":507,"score":74,"supported":41,"title":508,"abstract":509,"tags":512},"A5",[],[510,511],"高粱","sorghum",[],{"code":514,"score":74,"supported":41,"title":515,"abstract":516,"tags":519},"A7",[],[517,518],"间作","intercropping",[],{"code":521,"score":353,"supported":41,"title":522,"abstract":523,"tags":524},"F3",[],[495],[495],{"code":526,"score":353,"supported":41,"title":527,"abstract":528,"tags":530},"H8",[],[529],"可持续农业",[494],{"code":453,"score":89,"supported":41,"title":532,"abstract":533,"tags":535},[],[534],"农业生态",[],{"code":189,"score":89,"supported":41,"title":537,"abstract":538,"tags":540},[],[539],"干旱",[],[542,543],"鹰嘴豆 有机农业 土壤微生物","Satpura 鹰嘴豆 间作","鹰嘴豆有机农业土壤微生物-5466","10.3389\u002Ffsufs.2026.1814274",{"doi":545,"openalex_id":547,"authors":548,"venue":486,"cited_by_count":124,"oa_url":563,"card":564,"direction":568,"ingested_from":131},"W7223012759",[549,551,553,555,557,559,561],{"name":550,"orcid":9},"Nalla Charitha",{"name":552,"orcid":9},"Adinath N. Paslawar",{"name":554,"orcid":9},"Vilas Kashinath Kharche",{"name":556,"orcid":9},"Madastu Saikrishna",{"name":558,"orcid":9},"Yogesh V. Ingle",{"name":560,"orcid":9},"Akanksha Singh",{"name":562,"orcid":9},"Anita B. Chorey","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1814274\u002Fpdf",{"tldr":565,"method":566,"finding":567,"direction":568,"opportunity":569},"比较有机与自然农法下鹰嘴豆种植体系对土壤微生物和酶活性的影响。","两年条区试验，4种种植体系×3种养分管理，测土壤微生物数量、生物量碳氮及酶活性。","单作鹰嘴豆配合有机+自然综合管理显著提升土壤养分、微生物数量与酶活性。","农业绿色发展与碳","可探究综合有机-自然管理下微生物群落结构与碳固持的长期耦合机制。","2026-10-21T23:30:06.876518Z"]