[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2217":3,"related-2217":81},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":65,"slug":68,"view_count":69,"doi":8,"paper":70,"created_at":80},2217,"《数字经济参与与农场扩张:来自中国主要粮食产区农地租赁市场进入和经营耕地面积的证据》","https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1677","Caihua Xu等(长安大学、西北农林科技大学)基于安徽、河南、河北、山东四省1025户农户调查数据,考察农民在数字化农业活动中的参与是否与中国主要粮食产区的农场扩张相关。数字经济参与通过采购、生产、供销、金融四个农业价值链阶段衡量,农场扩张通过进入农地租赁市场和经营耕地面积对数两个结果衡量。采用双重\u002F去偏机器学习框架灵活调整观察到的家庭、地方和县级协变量及潜在非线性关系。数字经济参与与两个结果均呈正相关。",null,"Land 2026年9月10日","2026-09-10T00:00:00Z","论文",10,false,76,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,18,13,8,1,"基于四省1025户调查与双重机器学习方法，为数字农业参与促进农地租赁与规模经营提供了实证证据，对数字乡村与适度规模经营政策有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"机器学习","数字经济","粮食主产区","土地流转","农场扩张",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,55,60],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},4,true,[],[26],[26],{"code":51,"score":21,"supported":13,"title":52,"abstract":53,"tags":54},"A8",[],[],[28],{"code":56,"score":21,"supported":13,"title":57,"abstract":58,"tags":59},"G2",[],[],[29],{"code":61,"score":21,"supported":13,"title":62,"abstract":63,"tags":64},"I6",[],[],[27],[66,67],"粮食主产区 农场扩张 土地流转 数字经济","粮食主产区 农场扩张","粮食主产区农场扩张土地流转数字经济-2217",5,{"doi":8,"openalex_id":8,"authors":71,"venue":8,"cited_by_count":72,"oa_url":8,"card":73,"direction":77,"ingested_from":79},[],0,{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"基于四省1025户调查，研究农户数字经济参与是否促进农场扩张。","安徽等四省1025户调查数据，双重\u002F去偏机器学习框架。","数字经济参与与农地租赁市场进入和经营耕地面积均呈正相关。","数字乡村与农业信息化","可探究数字经济参与促进农场扩张的因果机制及不同价值链阶段的异质性效应。","agent","2026-09-12T00:06:40.676162Z",{"total":82,"page":21,"page_size":82,"items":83},6,[84,183,253,349,417,495],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":93,"sources":96,"tags":98,"primary_topic":103,"secondary_topics":109,"classification":117,"search_phrases":157,"slug":160,"view_count":72,"doi":161,"paper":162,"created_at":182},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":16,"substance":17,"depth":94,"authority":19,"freshness":12,"relevant":21,"comment":95},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。",[97],{"name":90,"url":87},[99,26,100,101,102],"智慧农业","遥感监测","土壤墒情","冬小麦",{"code":104,"slug":105,"name":106,"path":107,"parent_code":108},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[110,116],{"code":111,"slug":112,"name":113,"path":114,"parent_code":115},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":118},{"candidates":119,"agricultural_evidence":46},[120,126,132,137,145,152],{"code":104,"score":121,"supported":46,"title":122,"abstract":124,"tags":125},26,[123,105],"winter wheat",[106,123,105],[102],{"code":111,"score":127,"supported":46,"title":128,"abstract":129,"tags":131},15,[112],[130,112],"土壤",[101],{"code":32,"score":127,"supported":46,"title":133,"abstract":135,"tags":136},[134],"machine learning",[26,134],[26],{"code":138,"score":12,"supported":46,"title":139,"abstract":140,"tags":144},"I3",[],[141,142,143],"遥感","remote sensing","multispectral",[100],{"code":146,"score":82,"supported":46,"title":147,"abstract":148,"tags":151},"H3",[],[149,150],"灌溉","irrigation",[],{"code":153,"score":45,"supported":46,"title":154,"abstract":155,"tags":156},"I1",[],[99],[99],[158,159],"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":161,"openalex_id":163,"authors":164,"venue":90,"cited_by_count":72,"oa_url":87,"card":174,"direction":180,"ingested_from":181},"W7223156204",[165,168,170,172],{"name":166,"orcid":167},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":169,"orcid":8},"Yi Zhang",{"name":171,"orcid":8},"WEI Lei",{"name":173,"orcid":8},"Chengcai Zhang",{"tldr":175,"method":176,"finding":177,"direction":178,"opportunity":179},"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。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:18.887301Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":8,"source_name":189,"source_url":186,"published_at":190,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":72,"score_detail":191,"sources":193,"tags":195,"primary_topic":198,"secondary_topics":199,"classification":205,"search_phrases":230,"slug":233,"view_count":72,"doi":234,"paper":235,"created_at":252},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":72,"substance":72,"depth":72,"authority":72,"freshness":72,"relevant":72,"comment":192},"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。",[194],{"name":189,"url":186},[26,100,196,197],"土地利用","城市扩张",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[200],{"code":201,"slug":202,"name":203,"path":204,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":39,"locked":13,"version":40,"method":41,"evidence":206},{"candidates":207,"agricultural_evidence":46},[208,212,220,225],{"code":32,"score":127,"supported":46,"title":209,"abstract":210,"tags":211},[134],[26,134],[26],{"code":201,"score":213,"supported":46,"title":214,"abstract":215,"tags":219},9,[],[216,217,218],"空间分析","GIS","spatial analysis",[],{"code":138,"score":221,"supported":46,"title":222,"abstract":223,"tags":224},7,[],[141,142],[100],{"code":226,"score":21,"supported":13,"title":227,"abstract":228,"tags":229},"H6",[],[],[196],[231,232],"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":234,"openalex_id":236,"authors":237,"venue":189,"cited_by_count":72,"oa_url":186,"card":247,"direction":178,"ingested_from":181},"W7223022155",[238,241,243,245],{"name":239,"orcid":240},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":242,"orcid":8},"Goanar Jal Dup",{"name":244,"orcid":8},"Weyesa Merga Chemeda",{"name":246,"orcid":8},"Shankar Karuppannan",{"tldr":248,"method":249,"finding":250,"direction":178,"opportunity":251},"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":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":8,"source_name":259,"source_url":256,"published_at":260,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":261,"score_detail":262,"sources":266,"tags":268,"primary_topic":272,"secondary_topics":273,"classification":280,"search_phrases":321,"slug":324,"view_count":72,"doi":325,"paper":326,"created_at":348},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":18,"substance":263,"depth":18,"authority":264,"freshness":20,"relevant":21,"comment":265},22,14,"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。",[267],{"name":259,"url":256},[99,269,26,270,271],"农业人工智能","食品安全","文献计量",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[274],{"code":275,"slug":276,"name":277,"path":278,"parent_code":279},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":39,"locked":13,"version":40,"method":41,"evidence":281},{"candidates":282,"agricultural_evidence":46},[283,293,298,304,311,317],{"code":32,"score":284,"supported":46,"title":285,"abstract":287,"tags":292},19,[286],"artificial intelligence",[288,26,289,134,290,286,291],"深度学习","计算机视觉","deep learning","computer vision",[269,26],{"code":275,"score":127,"supported":46,"title":294,"abstract":296,"tags":297},[295],"food safety",[270,295],[270],{"code":299,"score":127,"supported":46,"title":300,"abstract":302,"tags":303},"I7",[301],"bibliometric",[271,301],[271],{"code":305,"score":82,"supported":46,"title":306,"abstract":307,"tags":310},"H7",[],[308,309],"气候变化","climate change",[],{"code":51,"score":312,"supported":46,"title":313,"abstract":314,"tags":316},3,[],[315],"food security",[],{"code":153,"score":21,"supported":13,"title":318,"abstract":319,"tags":320},[],[],[99],[322,323],"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":325,"openalex_id":327,"authors":328,"venue":259,"cited_by_count":72,"oa_url":8,"card":342,"direction":346,"ingested_from":181},"W7222920742",[329,331,333,336,339],{"name":330,"orcid":8},"Harang Lee",{"name":332,"orcid":8},"지은서",{"name":334,"orcid":335},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":337,"orcid":338},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":340,"orcid":341},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":343,"method":344,"finding":345,"direction":346,"opportunity":347},"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":350,"title":351,"url":352,"summary":353,"summary_zh":354,"content":8,"source_name":355,"source_url":356,"published_at":357,"category":11,"cover_url":8,"hotness":358,"is_selected":13,"score":359,"score_detail":360,"sources":363,"tags":367,"primary_topic":370,"secondary_topics":371,"classification":372,"search_phrases":401,"slug":404,"view_count":72,"doi":405,"paper":406,"created_at":416},5322,"MACHINE LEARNING BASED PREDICTION OF HYDROGEOLOGICAL PARAMETERS AND GROUNDWATER POTENTIAL INDEX USING GEOSPATIAL AND BOREHOLE DATA","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23434956","Abstract: Groundwater is a critical natural resource for domestic consumption, agricultural production, industrial development, and ecological sustainability. Accurate prediction of hydrogeological parameters and groundwater potential is essential for sustainable groundwater management, particularly in regions experiencing irregular rainfall, increasing water demand, and declining groundwater levels. This research paper presents a machine learning-based framework for predicting hydrogeological parameters and developing a Groundwater Potential Index (GWPI) by integrating geospatial datasets with borehole records. The proposed approach combines terrain characteristics, geological formations, soil properties, land use and land cover, drainage density, rainfall, slope, and remotely sensed indices with borehole-derived information, including water-table depth, aquifer thickness, lithology, and well yield. Machine learning algorithms such as Random Forest, Support Vector Regression, Gradient Boosting, and Artificial Neural Networks can be employed to estimate hydrogeological variables and identify areas with different groundwater potential levels. The predicted parameters are integrated within a geospatial framework to generate a spatially distributed groundwater potential index. Model performance can be assessed using the coefficient of determination (\\(R^2\\)), root mean square error (RMSE), and mean absolute error (MAE). The proposed framework offers a reproducible and scalable approach for groundwater exploration, borehole siting, and regional water-resource planning. Since no study-area dataset or model outputs have been provided, the paper presents a methodological framework rather than claiming experimentally validated results. Keywords: Machine Learning, Groundwater Potential Index, Hydrogeological Parameters, Geospatial Analysis, Borehole Data, Random Forest, Remote Sensing, Groundwater Management.","摘要：地下水是生活用水、农业生产、工业发展和生态可持续性的关键自然资源。准确预测水文地质参数和地下水潜力对于可持续地下水管理至关重要，尤其是在降雨不规律、用水需求不断增加和地下水位持续下降的地区。本文提出了一种基于机器学习的框架，通过将地理空间数据集与钻孔记录相结合，预测水文地质参数并构建地下水潜力指数（GWPI）。所提方法综合了地形特征、地质构造、土壤性质、土地利用与土地覆盖、河网密度、降雨、坡度以及遥感指数，并结合钻孔衍生信息，包括水位埋深、含水层厚度、岩性和井出水量。可采用随机森林、支持向量回归、梯度提升和人工神经网络等机器学习算法估算水文地质变量，并识别具有不同地下水潜力水平的区域。预测参数在地理空间框架内进行集成，以生成空间分布的地下水潜力指数。模型性能可通过决定系数（\\(R^2\\)）、均方根误差（RMSE）和平均绝对误差（MAE）进行评估。所提框架为地下水勘探、钻孔选址和区域水资源规划提供了一种可重复且可扩展的方法。由于未提供研究区数据集或模型输出，本文呈现的是方法学框架，而非声称经过实验验证的结果。关键词：机器学习，地下水潜力指数，水文地质参数，地理空间分析，钻孔数据，随机森林，遥感，地下水管理。","ACCENT JOURNAL OF  ECONOMICS ECOLOGY  & ENGINEERING","https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fview\u002F6754","2026-10-17T00:00:00Z",25,47,{"impact":20,"substance":361,"depth":264,"authority":45,"freshness":213,"relevant":21,"comment":362},12,"方法框架类论文，无实测数据与验证结果，主题与农业水资源相关但公共价值有限，不宜进入每日精选。",[364,365],{"name":355,"url":356},{"name":355,"url":366},"https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fdownload\u002F6754\u002F5489",[26,141,368,369],"水资源管理","地下水监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":373},{"candidates":374,"agricultural_evidence":46},[375,379,383,387,392,396],{"code":32,"score":127,"supported":46,"title":376,"abstract":377,"tags":378},[134],[26,134],[26],{"code":138,"score":221,"supported":46,"title":380,"abstract":381,"tags":382},[],[141,142],[141],{"code":111,"score":82,"supported":46,"title":384,"abstract":385,"tags":386},[],[130,112],[],{"code":146,"score":45,"supported":46,"title":388,"abstract":389,"tags":391},[],[390],"水资源",[368],{"code":201,"score":312,"supported":46,"title":393,"abstract":394,"tags":395},[],[216],[],{"code":299,"score":312,"supported":46,"title":397,"abstract":398,"tags":400},[],[399],"methodological framework",[],[402,403],"机器学习 地下水潜力指数","钻孔数据 水文地质参数","机器学习地下水潜力指数-5322","10.5281\u002Fzenodo.23434956",{"doi":405,"openalex_id":407,"authors":408,"venue":355,"cited_by_count":72,"oa_url":356,"card":411,"direction":178,"ingested_from":181},"W7222805722",[409],{"name":410,"orcid":8},"Prof. I Pandu Ranga Reddy Dr. Hari Ramavath",{"tldr":412,"method":413,"finding":414,"direction":178,"opportunity":415},"提出融合地理空间与钻孔数据的机器学习框架，预测水文地质参数与地下水潜力指数。","随机森林、SVR、梯度提升、神经网络结合遥感与钻孔数据建模。","构建了可复现的地下水潜力评估方法框架，尚未经实测数据验证。","可将该框架落地到具体农业区，用实测钻孔数据验证并耦合灌溉需水预测。","2026-10-19T23:30:22.978385Z",{"id":418,"title":419,"url":420,"summary":421,"summary_zh":422,"content":8,"source_name":423,"source_url":420,"published_at":424,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":425,"sources":428,"tags":430,"primary_topic":433,"secondary_topics":434,"classification":435,"search_phrases":452,"slug":455,"view_count":72,"doi":456,"paper":457,"created_at":494},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":18,"substance":426,"depth":94,"authority":264,"freshness":20,"relevant":21,"comment":427},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。",[429],{"name":423,"url":420},[99,269,26,431,432],"精准农业","农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":436},{"candidates":437,"agricultural_evidence":46},[438,443,447],{"code":32,"score":439,"supported":46,"title":440,"abstract":441,"tags":442},11,[],[26,134,286],[269,26],{"code":111,"score":82,"supported":46,"title":444,"abstract":445,"tags":446},[],[130,112],[],{"code":153,"score":448,"supported":13,"title":449,"abstract":450,"tags":451},2,[],[],[99,431],[453,454],"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":456,"openalex_id":458,"authors":459,"venue":423,"cited_by_count":72,"oa_url":420,"card":489,"direction":346,"ingested_from":181},"W7222866367",[460,463,466,469,472,475,478,481,483,486],{"name":461,"orcid":462},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":464,"orcid":465},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":467,"orcid":468},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":470,"orcid":471},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":473,"orcid":474},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":476,"orcid":477},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":479,"orcid":480},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":482,"orcid":8},"M. Z. Walkup",{"name":484,"orcid":485},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":487,"orcid":488},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":490,"method":491,"finding":492,"direction":346,"opportunity":493},"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":496,"title":497,"url":498,"summary":499,"summary_zh":500,"content":8,"source_name":501,"source_url":498,"published_at":502,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":503,"score_detail":504,"sources":506,"tags":508,"primary_topic":512,"secondary_topics":513,"classification":519,"search_phrases":534,"slug":537,"view_count":72,"doi":538,"paper":539,"created_at":579},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science","2026-10-15T00:00:00Z",69,{"impact":361,"substance":426,"depth":16,"authority":19,"freshness":20,"relevant":21,"comment":505},"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[507],{"name":501,"url":498},[26,141,509,510,511],"土壤碳","Landsat","数字土壤制图",{"code":111,"slug":112,"name":113,"path":114,"parent_code":115},[514,515],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":138,"slug":516,"name":517,"path":518,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":13,"version":40,"method":41,"evidence":520},{"candidates":521,"agricultural_evidence":46},[522,526,530],{"code":111,"score":264,"supported":46,"title":523,"abstract":524,"tags":525},[112],[130,112],[],{"code":32,"score":213,"supported":46,"title":527,"abstract":528,"tags":529},[134],[],[26],{"code":138,"score":20,"supported":46,"title":531,"abstract":532,"tags":533},[],[141,142],[510,141],[535,536],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":538,"openalex_id":540,"authors":541,"venue":501,"cited_by_count":72,"oa_url":8,"card":574,"direction":178,"ingested_from":181},"W7222107492",[542,545,548,551,554,557,560,563,565,568,571],{"name":543,"orcid":544},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":546,"orcid":547},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":549,"orcid":550},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":552,"orcid":553},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":555,"orcid":556},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":558,"orcid":559},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":561,"orcid":562},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":564,"orcid":8},"A. E. Shatunov",{"name":566,"orcid":567},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":569,"orcid":570},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":572,"orcid":573},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":575,"method":576,"finding":577,"direction":178,"opportunity":578},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z"]