[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5160":3,"related-5160":104},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":103},5160,"Evaluating learner satisfaction and confidence in a precision agriculture MOOC: a cross-sectional study in Palestinian higher education","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffeduc.2026.1954745","Background Precision agriculture (PA) increasingly requires graduates who can work with sensor-based, geospatial and data-driven technologies, yet access to specialised training remains uneven in many under-resourced higher-education contexts. Although massive open online courses (MOOCs) offer a potentially scalable response to this gap, empirical evaluation of precision-agriculture MOOCs and of MOOC-based learning in Palestinian higher education more broadly remains limited, so evidence on learners' satisfaction and confidence in this specific educational context is needed. Methods This study addresses that gap through a cross-sectional, questionnaire-based evaluation of learners participating in a precision agriculture MOOC developed within the BENEFIT Erasmus+ project for Palestinian higher education; the design does not support causal inference. A questionnaire administered following learners' participation in the MOOC was completed by 62 of 69 invited learners (response rate 89.9%). The course-evaluation instrument comprised eight satisfaction items and nine confidence-related items rated on a 0-4 Likert scale; five negatively worded items were reverse-scored for composite analyses. We report descriptive statistics, one-sample tests against the scale midpoint, a paired comparison of the two composites, and correlation analysis, with Spearman's ρ as a non-parametric sensitivity check. Results Learners reported positive overall satisfaction (M = 2.74, SD = 0.57) and positive overall confidence (M = 2.54, SD = 0.40), both significantly above the scale midpoint (both p \u003C 0.001, d > 1.2). Agreement was strongest for perceived MOOC design quality (82.3%), course enjoyment (75.8%), confidence in mastering content (75.8%), and confidence in passing an examination (75.8%). Satisfaction and confidence were strongly and positively correlated [ r = 0.746, 95% CI (0.610, 0.839), p \u003C 0.001; ρ = 0.757]; because both constructs were measured concurrently by self-report within the same cross-sectional survey, this association cannot establish the direction of the relationship or a causal link between the two constructs. Satisfaction was significantly higher than confidence (mean difference = 0.20, p \u003C 0.001, dz = 0.53). A single item-perceived slide information density-did not differ significantly from the neutral midpoint ( p = 0.742), and given this non-significant result and a high proportion of neutral responses (51.6%), is presented as an area that may warrant further, exploratory review rather than a confirmed design weakness. Discussion The results suggest that a structured instructional-design approach was associated with positive learner perceptions in this agriculture MOOC, while cognitive-load management in slide design may merit further, cautious attention in future course iterations.","背景：精准农业（Precision Agriculture, PA）日益需要能够运用基于传感器、地理空间和数据驱动技术的毕业生，然而在许多资源不足的高等教育环境中，获得专业培训的机会仍然不均衡。尽管大规模开放在线课程（Massive Open Online Courses, MOOCs）为弥合这一差距提供了一种潜在的可扩展方案，但针对精准农业MOOC以及更广泛的巴勒斯坦高等教育中基于MOOC的学习的实证评估仍然有限，因此需要关于学习者在这一特定教育情境中满意度和信心的证据。方法：本研究通过对参与巴勒斯坦高等教育BENEFIT Erasmus+项目所开发的精准农业MOOC的学习者进行横断面问卷评估来填补这一空白；该设计不支持因果推断。在学习者参与MOOC后发放的问卷由69名受邀学习者中的62名完成（应答率89.9%）。课程评估工具包括八个满意度条目和九个信心相关条目，采用0-4 Likert量表评分；五个负向表述条目在综合分析时进行反向计分。我们报告描述性统计、针对量表中点的单样本检验、两个综合得分的配对比较以及相关分析，并以Spearman's ρ作为非参数敏感性检验。结果：学习者报告了积极的总体满意度（M = 2.74, SD = 0.57）和积极的总体信心（M = 2.54, SD = 0.40），两者均显著高于量表中点（均p \u003C 0.001, d > 1.2）。认同度最高的方面为感知的MOOC设计质量（82.3%）、课程愉悦感（75.8%）、掌握内容的信心（75.8%）以及通过考试的信心（75.8%）。满意度与信心呈强正相关[r = 0.746, 95% CI (0.610, 0.839), p \u003C 0.001; ρ = 0.757]；由于这两个构念是在同一横断面调查中通过自我报告同时测量的，因此该关联无法确定两者关系的方向或因果关系。满意度显著高于信心（平均差 = 0.20, p \u003C 0.001, dz = 0.53）。单个条目——感知的幻灯片信息密度——与中性中点无显著差异（p = 0.742），鉴于这一非显著结果以及较高比例的中性回答（51.6%），将其作为一个可能值得进一步",null,"Frontiers in Education","2026-10-16T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,14,12,9,1,"针对巴勒斯坦精准农业慕课的小样本横断面满意度研究，方法规范、数据透明，但样本仅62人且无因果推断，属区域性教育实证，公共影响有限，可作主题聚合素材而非每日精选头条。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","数字素养","农业慕课","巴勒斯坦","高等教育",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,61],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},15,true,[49],"precision agriculture",[51,49],"精准农业",[27],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},"I7",6,[],[58,59],"因果推断","causal inference",[],{"code":62,"score":22,"supported":14,"title":63,"abstract":64,"tags":65},"I6",[],[],[28],[67,68],"BENEFIT Erasmus 精准农业 MOOC","巴勒斯坦 精准农业 慕课","BENEFITErasmus精准农业MOOC-5160",0,"10.3389\u002Ffeduc.2026.1954745",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":70,"oa_url":94,"card":95,"direction":101,"ingested_from":102},"W7222418765",[75,77,79,82,85,88,91],{"name":76,"orcid":9},"Walid Khalilia",{"name":78,"orcid":9},"Hazem Horoub",{"name":80,"orcid":81},"Hani Iwidat","https:\u002F\u002Forcid.org\u002F0009-0007-4824-6711",{"name":83,"orcid":84},"Faiz Abdelhafid","https:\u002F\u002Forcid.org\u002F0000-0003-4758-9142",{"name":86,"orcid":87},"Radoslav Židek","https:\u002F\u002Forcid.org\u002F0000-0003-4751-1257",{"name":89,"orcid":90},"Ľubomí­r Belej","https:\u002F\u002Forcid.org\u002F0000-0001-8523-6650",{"name":92,"orcid":93},"Zuzana Palková","https:\u002F\u002Forcid.org\u002F0000-0002-5782-8656","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Feducation\u002Farticles\u002F10.3389\u002Ffeduc.2026.1954745\u002Fpdf",{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"评估巴勒斯坦高校学习者对精准农业MOOC的满意度与信心。","横断面问卷，62名学习者，8项满意度+9项信心量表，描述统计与相关分析。","满意度与信心均显著高于中点且强正相关，满意度高于信心。","数字乡村与农业信息化","可研究资源匮乏地区农业MOOC的设计优化及满意度与信心的因果机制。","智慧农业 \u002F 农业物联网","openalex","2026-10-17T23:30:16.666582Z",{"total":55,"page":22,"page_size":55,"items":105},[106,157,212,267,310,349],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":114,"is_selected":14,"score":115,"score_detail":116,"sources":118,"tags":122,"primary_topic":126,"secondary_topics":127,"classification":128,"search_phrases":141,"slug":144,"view_count":22,"doi":145,"paper":146,"created_at":156},2644,"Digital Competence as a Predictor of Smart Agricultural Technology Utilization among Agricultural Education Lecturers in Colleges of Education in South-South Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22762486","Abstract This study determined digital competence as a predictor of smart agricultural technology utilization among Agricultural Education lecturers in Colleges of Education in South-South Nigeria. Specifically, the study determined the level of digital competence, identified the availability and extent of utilization of smart agricultural technologies, examined factors influencing technology utilization, and determined the relationship between digital competence and technology utilization. A descriptive survey research design was adopted for the study. The population comprised all Agricultural Education lecturers in Colleges of Education in South-South Nigeria, while an illustrative sample of 220 lecturers was used for demonstration purposes. Data were collected using the Digital Competence and Utilization of Smart Agricultural Technologies Questionnaire (DCUSATQ). The instrument was validated by experts in Agricultural Education, Educational Technology, and Measurement and Evaluation, while Cronbach's Alpha reliability coefficients of 0.87, 0.84, and 0.81 were obtained for the Digital Competence, Technology Utilization, and Influencing Factors scales, respectively, with an overall reliability coefficient of 0.86. Data were analyzed using frequency, percentage, mean, standard deviation, Pearson Product Moment Correlation, and Multiple Regression at the 0.05 level of significance. The findings revealed that Agricultural Education lecturers possessed a high level of digital competence. Smart agricultural technologies were moderately utilized, with online agricultural databases and mobile agricultural applications being the most frequently used, while drone technology and precision agriculture tools recorded relatively low utilization. Institutional support, digital infrastructure, internet connectivity, and training opportunities were identified as major factors influencing technology utilization. The study further revealed a significant positive relationship between digital competence and smart agricultural technology utilization. Multiple regression analysis showed that digital competence significantly predicted smart agricultural technology utilization, while the combined influence of digital competence, institutional support, ICT infrastructure, and training exposure explained the variance in technology utilization. Based on the findings, it was recommended that there should be continuous professional development in digital agriculture, improved institutional support, enhanced ICT infrastructure, and increased investment in smart agricultural technologies to strengthen technology integration in Agricultural Education programmes.","摘要 本研究确定了数字能力作为尼日利亚南南地区教育学院农业教育讲师使用智能农业技术的预测因素。具体而言，本研究确定了数字能力的水平，识别了智能农业技术的可用性及其使用程度，考察了影响技术使用的因素，并确定了数字能力与技术使用之间的关系。本研究采用描述性调查研究设计。研究总体包括尼日利亚南南地区教育学院的所有农业教育讲师，并使用220名讲师的示例样本进行演示。数据通过数字能力与智能农业技术使用问卷（DCUSATQ）收集。该工具由农业教育、教育技术以及测量与评价领域的专家进行了效度检验，数字能力、技术使用和影响因素量表的Cronbach's Alpha信度系数分别为0.87、0.84和0.81，总体信度系数为0.86。数据采用频数、百分比、均值、标准差、Pearson积矩相关和多元回归进行分析，显著性水平为0.05。研究结果显示，农业教育讲师具有较高水平的数字能力。智能农业技术的使用程度为中等，其中在线农业数据库和移动农业应用程序使用最为频繁，而无人机技术和精准农业工具的使用相对较低。机构支持、数字基础设施、互联网连接和培训机会被确定为影响技术使用的主要因素。研究进一步揭示，数字能力与智能农业技术使用之间存在显著正相关关系。多元回归分析表明，数字能力显著预测智能农业技术使用，而数字能力、机构支持、信息通信技术基础设施和培训经历的联合影响解释了技术使用的变异。基于研究结果，建议应持续开展数字农业专业发展，改善机构","Zenodo (CERN European Organization for Nuclear Research)","2026-09-15T00:00:00Z",25,59,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":21,"relevant":22,"comment":117},"尼日利亚农业教育讲师数字素养与智慧农业技术采纳的实证研究，样本220人、信度0.86，结论有参考价值但属区域性调查，国际借鉴意义有限。",[119,120],{"name":112,"url":109},{"name":112,"url":121},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22762487",[27,28,123,124,125],"农业无人机","农业教育","尼日利亚",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":129},{"candidates":130,"agricultural_evidence":47},[131,137],{"code":33,"score":13,"supported":47,"title":132,"abstract":133,"tags":136},[],[51,134,49,135],"数字农业","digital agriculture",[27],{"code":62,"score":22,"supported":14,"title":138,"abstract":139,"tags":140},[],[],[28],[142,143],"农业无人机 农业教育 尼日利亚 数字素养","农业无人机 农业教育","农业无人机农业教育尼日利亚数字素养-2644","10.5281\u002Fzenodo.22762486",{"doi":145,"openalex_id":147,"authors":148,"venue":112,"cited_by_count":70,"oa_url":109,"card":151,"direction":101,"ingested_from":102},"W7213296064",[149],{"name":150,"orcid":9},"Chijioke-Eke Joy Ndidi",{"tldr":152,"method":153,"finding":154,"direction":99,"opportunity":155},"研究尼日利亚农业教育讲师数字能力对智慧农业技术使用的影响。","描述性调查，220名讲师问卷，相关与多元回归分析。","数字能力高但智慧农业技术使用中等，数字能力显著正向预测技术使用。","可延伸至教师数字能力培训干预与智慧农业技术采纳的因果机制研究。","2026-09-16T23:30:11.968810Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":9,"content":9,"source_name":162,"source_url":160,"published_at":163,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":164,"score_detail":165,"sources":169,"tags":171,"primary_topic":175,"secondary_topics":176,"classification":177,"search_phrases":191,"slug":194,"view_count":70,"doi":195,"paper":196,"created_at":211},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":166,"substance":167,"depth":18,"authority":19,"freshness":13,"relevant":22,"comment":168},16,21,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[170],{"name":162,"url":160},[27,172,51,173,174],"农业人工智能","机器视觉","作物行检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":178},{"candidates":179,"agricultural_evidence":47},[180,185],{"code":33,"score":181,"supported":47,"title":182,"abstract":183,"tags":184},5,[],[49],[27,51],{"code":186,"score":187,"supported":14,"title":188,"abstract":189,"tags":190},"I2",2,[],[],[172,173],[192,193],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":195,"openalex_id":197,"authors":198,"venue":162,"cited_by_count":70,"oa_url":9,"card":9,"direction":9,"ingested_from":102},"W7223117828",[199,202,205,208],{"name":200,"orcid":201},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":203,"orcid":204},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":206,"orcid":207},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":209,"orcid":210},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":112,"source_url":215,"published_at":218,"category":12,"cover_url":9,"hotness":114,"is_selected":14,"score":219,"score_detail":220,"sources":222,"tags":226,"primary_topic":231,"secondary_topics":232,"classification":233,"search_phrases":250,"slug":253,"view_count":70,"doi":254,"paper":255,"created_at":266},5397,"The Relationship Between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23469780","Relationship between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study explores the relationship between nutrient concentrations and the optical characteristics of banana leaves using quantitative image analysis. The study focuses on measurable leaf features, including colour intensity, RGB values, and other image-derived parameters, to identify patterns associated with plant nutrient status. Digital images of banana leaves can provide valuable information about variations in leaf appearance that may reflect differences in nutrient availability. By analysing these characteristics alongside nutrient concentration measurements, the study aims to establish quantitative relationships that can support plant nutrient assessment. This approach may contribute to the development of cost-effective, non-destructive, and image-based techniques for monitoring banana plant health. The findings can be useful for precision agriculture, crop management, and future research on digital image processing for agricultural applications.","香蕉叶片养分浓度与可测量光学特征之间的关系：一项基于定量图像的研究探讨了利用定量图像分析研究香蕉叶片养分浓度与光学特征之间的关系。该研究聚焦于可测量的叶片特征，包括颜色强度、RGB值及其他图像衍生参数，以识别与植物养分状况相关的模式。香蕉叶片的数字图像可提供关于叶片外观变化的有价值信息，这些变化可能反映养分有效性的差异。通过将这些特征与养分浓度测量结果一并分析，该研究旨在建立可支持植物养分评估的定量关系。该方法可能有助于开发低成本、非破坏性且基于图像的香蕉植株健康监测技术。研究结果可用于精准农业、作物管理以及未来农业应用数字图像处理的研究。","2026-10-19T00:00:00Z",66,{"impact":20,"substance":18,"depth":46,"authority":20,"freshness":21,"relevant":22,"comment":221},"该研究基于图像定量分析香蕉叶片营养状况，方法新颖且面向无损监测，对精准农业有参考价值，但属单作物探索性研究，影响范围有限。",[223,224],{"name":112,"url":215},{"name":112,"url":225},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470020",[27,227,228,229,230],"图像分析","香蕉","无损监测","作物营养",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":234},{"candidates":235,"agricultural_evidence":47},[236,241,246],{"code":33,"score":237,"supported":47,"title":238,"abstract":239,"tags":240},7,[],[51,49],[27],{"code":242,"score":22,"supported":14,"title":243,"abstract":244,"tags":245},"B2",[],[],[228],{"code":186,"score":22,"supported":14,"title":247,"abstract":248,"tags":249},[],[],[227],[251,252],"香蕉叶片 营养浓度 图像分析","香蕉 无损监测 精准农业","香蕉叶片营养浓度图像分析-5397","10.5281\u002Fzenodo.23469780",{"doi":254,"openalex_id":256,"authors":257,"venue":112,"cited_by_count":70,"oa_url":215,"card":260,"direction":99,"ingested_from":102},"W7222941290",[258],{"name":259,"orcid":9},"Anantjit Publication",{"tldr":261,"method":262,"finding":263,"direction":264,"opportunity":265},"用定量图像分析探究香蕉叶片养分浓度与可测光学特征的关系。","采集香蕉叶片数字图像，提取颜色强度、RGB值等参数，与养分浓度做定量关联分析。","叶片图像光学特征与养分浓度存在可量化的关联，可支撑无损养分评估。","农业遥感与作物表型","可扩展到多品种、多生育期与田间光照校正，并构建图像-养分反演模型与便携诊断工具。","2026-10-20T23:30:22.619634Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":9,"content":9,"source_name":272,"source_url":270,"published_at":218,"category":12,"cover_url":9,"hotness":114,"is_selected":14,"score":273,"score_detail":274,"sources":276,"tags":280,"primary_topic":283,"secondary_topics":284,"classification":285,"search_phrases":292,"slug":295,"view_count":70,"doi":296,"paper":297,"created_at":309},5396,"Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22601.71520","Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.。OpenAlex","OpenAlex",37,{"impact":17,"substance":55,"depth":17,"authority":55,"freshness":21,"relevant":22,"comment":275},"中非数字农业系统综述，主题相关但摘要信息极少，仅题录级元数据，增量与权威性有限，不建议进入每日精选。",[277,278],{"name":272,"url":270},{"name":272,"url":279},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[134,27,281,282],"农业信息化","中非",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":286},{"candidates":287,"agricultural_evidence":47},[288],{"code":33,"score":19,"supported":47,"title":289,"abstract":290,"tags":291},[135],[135],[281,134,27],[293,294],"中非 数字农业","农业信息化 数字农业 智慧农业 中非","中非数字农业-5396","10.13140\u002Frg.2.2.22601.71520",{"doi":296,"openalex_id":298,"authors":299,"venue":9,"cited_by_count":70,"oa_url":270,"card":9,"direction":99,"ingested_from":102},"W7222930115",[300,302,304,307],{"name":301,"orcid":9},"Anani Ogou",{"name":303,"orcid":9},"Henry Chotangui",{"name":305,"orcid":306},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":308,"orcid":9},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":9,"content":9,"source_name":272,"source_url":313,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":318,"tags":320,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":338,"slug":341,"view_count":70,"doi":342,"paper":343,"created_at":348},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex",56,{"impact":18,"substance":17,"depth":13,"authority":20,"freshness":17,"relevant":22,"comment":317},"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[319],{"name":272,"url":313},[27,321,51,322],"农业机器人","智能采收",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":326},{"candidates":327,"agricultural_evidence":47},[328,333],{"code":33,"score":329,"supported":47,"title":330,"abstract":331,"tags":332},13,[49],[49],[27,51],{"code":334,"score":22,"supported":14,"title":335,"abstract":336,"tags":337},"F6",[],[],[321],[339,340],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":342,"openalex_id":344,"authors":345,"venue":9,"cited_by_count":70,"oa_url":313,"card":9,"direction":101,"ingested_from":102},"W7222971426",[346],{"name":347,"orcid":9},"Joydeep Dey","2026-10-20T23:30:08.600540Z",{"id":350,"title":351,"url":352,"summary":353,"summary_zh":354,"content":9,"source_name":355,"source_url":352,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":356,"score_detail":357,"sources":361,"tags":363,"primary_topic":368,"secondary_topics":369,"classification":370,"search_phrases":378,"slug":381,"view_count":70,"doi":382,"paper":383,"created_at":408},5375,"Triboelectric Nanogenerator Based on All‐Cellulose Triboelectric Layer for Droplet Energy Harvesting and Self‐Powered Agriculture Monitoring","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsmll.76354","Cellulose-based triboelectric nanogenerators (TENGs) attract increasing attention; however, developing high-performance all-cellulose-based triboelectric layer TENGs remains challenging. In this study, a regenerated cellulose nanofiber film (R-CNF) is used as a substrate, and an aminosilane intermediate layer is introduced. By utilizing the silanol groups on the silane chains as compatible reactive sites for fluorination grafting, and simultaneously enhancing the bonding strength between modified layers through N-H···F-C hydrogen-bond interactions between amino and fluorine groups, a fluorinated regenerated cellulose nanofiber film (F-R-CNF) is successfully prepared. The degradation rate of F-R-CNF within 13 days is approximately 0.96%. When assembled into a TENG with an ethyl cellulose nanofiber film(ECNF), the output voltage of the resulting TENG is ten times higher than that of the R-CNF-based TENGs, while maintaining 85% of its output performance at 80% relative humidity. Furthermore, a droplet energy harvesting TENG (DE-TENG) is constructed to harvest energy from falling water droplets, demonstrating the potential of the DE-TENG to power agricultural micro-sensors. This study provides a feasible strategy for designing high-performance all-cellulose-based triboelectric layer triboelectric nanogenerators and reveals the great potential of F-R-CNF for self-powered devices and smart agriculture applications.","纤维素基摩擦纳米发电机（TENGs）日益受到关注，然而开发高性能全纤维素基摩擦电层TENG仍面临挑战。本研究以再生纤维素纳米纤维膜（R-CNF）为基底，引入氨基硅烷中间层，利用硅烷链上的硅醇基团作为氟化接枝的相容反应位点，同时通过氨基与含氟基团之间的N-H···F-C氢键相互作用增强改性层间的结合强度，成功制备了氟化再生纤维素纳米纤维膜（F-R-CNF）。F-R-CNF在13天内的降解率约为0.96%。将其与乙基纤维素纳米纤维膜（ECNF）组装成TENG后，所得TENG的输出电压比基于R-CNF的TENG高十倍，同时在80%相对湿度下仍保持85%的输出性能。此外，构建了液滴能量收集TENG（DE-TENG）以收集下落水滴的能量，展示了DE-TENG为农业微传感器供电的潜力。本研究为设计高性能全纤维素基摩擦电层摩擦纳米发电机提供了可行策略，并揭示了F-R-CNF在自供电器件和智慧农业应用中的巨大潜力。","Small",81,{"impact":18,"substance":358,"depth":359,"authority":19,"freshness":17,"relevant":22,"comment":360},22,19,"全纤维素摩擦纳米发电机实现液滴能量收集并驱动农业微传感器，方法新颖、数据扎实，属智慧农业自供电传感的实质进展，值得入选。",[362],{"name":355,"url":352},[27,364,365,366,367],"摩擦纳米发电","液滴能量收集","自供电传感","纤维素材料",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":371},{"candidates":372,"agricultural_evidence":47},[373],{"code":33,"score":237,"supported":47,"title":374,"abstract":375,"tags":377},[],[27,376],"smart agriculture",[27],[379,380],"全纤维素 摩擦纳米发电机 液滴能量收集","F-R-CNF 自供电 农业监测","全纤维素摩擦纳米发电机液滴能量收集-5375","10.1002\u002Fsmll.76354",{"doi":382,"openalex_id":384,"authors":385,"venue":355,"cited_by_count":70,"oa_url":9,"card":403,"direction":101,"ingested_from":102},"W7222917387",[386,388,391,393,395,398,400],{"name":387,"orcid":9},"Hengwei Xiong",{"name":389,"orcid":390},"Qiaonan Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9365-0867",{"name":392,"orcid":9},"Weijie Li",{"name":394,"orcid":9},"Kangwen Shi",{"name":396,"orcid":397},"Xinyan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-6901-8977",{"name":399,"orcid":9},"Wenpeng Fan",{"name":401,"orcid":402},"Youqiang Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-9129-8050",{"tldr":404,"method":405,"finding":406,"direction":101,"opportunity":407},"制备全纤维素摩擦电层TENG，收集水滴能量为农业微传感器供电。","再生纤维素纳米纤维膜经氨基硅烷中间层氟化接枝，与乙基纤维素膜组装TENG。","输出达R-CNF基TENG的10倍，80%湿度下保持85%性能，13天降解约0.96%。","可探索全纤维素TENG在田间高湿环境下的长期稳定性及与无线传感节点的集成供电。","2026-10-20T23:30:08.252085Z"]