[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2644":3,"related-2644":81},{"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":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":61,"slug":64,"view_count":22,"doi":65,"paper":66,"created_at":80},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",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-15T00:00:00Z","论文",25,false,59,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,14,10,9,1,"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。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22762487",[29,30,31,32,33],"智慧农业","数字素养","农业无人机","农业教育","尼日利亚",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":48},[47,56],{"code":35,"score":20,"supported":48,"title":49,"abstract":50,"tags":55},true,[],[51,52,53,54],"精准农业","数字农业","precision agriculture","digital agriculture",[29],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"I6",[],[],[30],[62,63],"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":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":71,"oa_url":6,"card":72,"direction":78,"ingested_from":79},"W7213296064",[69],{"name":70,"orcid":9},"Chijioke-Eke Joy Ndidi",0,{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"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。","数字乡村与农业信息化","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。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:11.968810Z",{"total":82,"page":22,"page_size":82,"items":83},6,[84,130,202,244,290,344],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":9,"content":9,"source_name":89,"source_url":9,"published_at":90,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":91,"score_detail":92,"sources":96,"tags":98,"primary_topic":102,"secondary_topics":103,"classification":104,"search_phrases":117,"slug":120,"view_count":22,"doi":9,"paper":121,"created_at":129},5298,"姚霞等《江苏高教》:新质生产力背景下新农科建设的实践探索——以智慧农业交叉学科为例","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F172563.html","《江苏高教》2026年第08期刊载南京农业大学姚霞、曹卫星、刘小军、刘蕾蕾、朱艳等论文,阐述新质生产力背景下建设智慧农业交叉学科的必要性,解析其组建模式及\"四大融合\"(学科、专业、课程、平台)的实践路径,剖析我国智慧农业交叉学科发展现状,并提出未来发展对策。","江苏高教(2026,08)","2026-10-13T00:00:00Z",75,{"impact":18,"substance":93,"depth":94,"authority":19,"freshness":82,"relevant":22,"comment":95},20,17,"核心期刊论文，系统阐述智慧农业交叉学科建设路径，对农业信息化人才培养有参考价值，但属学术探讨、时效一般，可入精选备选。",[97],{"name":89,"url":87},[29,99,32,100,101],"新质生产力","新农科","交叉学科",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":105},{"candidates":106,"agricultural_evidence":48},[107,112],{"code":35,"score":108,"supported":48,"title":109,"abstract":110,"tags":111},12,[29],[29],[29],{"code":113,"score":22,"supported":14,"title":114,"abstract":115,"tags":116},"J2",[],[],[99],[118,119],"南京农业大学 智慧农业 交叉学科","姚霞 新农科 江苏高教","南京农业大学智慧农业交叉学科-5298",{"doi":9,"openalex_id":9,"authors":122,"venue":9,"cited_by_count":71,"oa_url":9,"card":123,"direction":76,"ingested_from":128},[],{"tldr":124,"method":125,"finding":126,"direction":76,"opportunity":127},"以智慧农业交叉学科为例，探讨新质生产力背景下新农科建设的实践路径。","案例分析与现状剖析，提出学科、专业、课程、平台“四大融合”路径。","智慧农业交叉学科需通过“四大融合”组建，并针对现状提出发展对策。","可延伸研究智慧农业交叉学科融合成效的量化评价与人才培养质量追踪机制。","agent","2026-10-19T00:07:48.366123Z",{"id":131,"title":132,"url":133,"summary":134,"summary_zh":135,"content":9,"source_name":136,"source_url":133,"published_at":137,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":138,"score_detail":139,"sources":141,"tags":143,"primary_topic":147,"secondary_topics":148,"classification":149,"search_phrases":168,"slug":171,"view_count":71,"doi":172,"paper":173,"created_at":201},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","Frontiers in Education","2026-10-16T00:00:00Z",61,{"impact":17,"substance":18,"depth":19,"authority":108,"freshness":21,"relevant":22,"comment":140},"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。",[142],{"name":136,"url":133},[29,30,144,145,146],"农业慕课","巴勒斯坦","高等教育",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":150},{"candidates":151,"agricultural_evidence":48},[152,157,164],{"code":35,"score":153,"supported":48,"title":154,"abstract":155,"tags":156},15,[53],[51,53],[29],{"code":158,"score":82,"supported":48,"title":159,"abstract":160,"tags":163},"I7",[],[161,162],"因果推断","causal inference",[],{"code":57,"score":22,"supported":14,"title":165,"abstract":166,"tags":167},[],[],[30],[169,170],"BENEFIT Erasmus 精准农业 MOOC","巴勒斯坦 精准农业 慕课","BENEFITErasmus精准农业MOOC-5160","10.3389\u002Ffeduc.2026.1954745",{"doi":172,"openalex_id":174,"authors":175,"venue":136,"cited_by_count":71,"oa_url":195,"card":196,"direction":78,"ingested_from":79},"W7222418765",[176,178,180,183,186,189,192],{"name":177,"orcid":9},"Walid Khalilia",{"name":179,"orcid":9},"Hazem Horoub",{"name":181,"orcid":182},"Hani Iwidat","https:\u002F\u002Forcid.org\u002F0009-0007-4824-6711",{"name":184,"orcid":185},"Faiz Abdelhafid","https:\u002F\u002Forcid.org\u002F0000-0003-4758-9142",{"name":187,"orcid":188},"Radoslav Židek","https:\u002F\u002Forcid.org\u002F0000-0003-4751-1257",{"name":190,"orcid":191},"Ľubomí­r Belej","https:\u002F\u002Forcid.org\u002F0000-0001-8523-6650",{"name":193,"orcid":194},"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":197,"method":198,"finding":199,"direction":76,"opportunity":200},"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。","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。","2026-10-17T23:30:16.666582Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":9,"source_name":10,"source_url":205,"published_at":90,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":138,"score_detail":208,"sources":210,"tags":212,"primary_topic":215,"secondary_topics":216,"classification":217,"search_phrases":224,"slug":227,"view_count":71,"doi":228,"paper":229,"created_at":243},4905,"Impact of Digital Technologies on Quality Learning Engagement in Agricultural Science Among Students in Secondary Schools in Rivers State","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23342866","This study examined the impact of digital technologies on quality learning engagement in agricultural science among students in secondary schools in Rivers State, Nigeria. A descriptive survey design was used. Through multi-stage sampling, 400 respondents (112 teachers and 288 students) were drawn from 24 public secondary schools in six Local Government Areas across the three senatorial zones. Data were collected with a self-constructed four-point questionnaire, validated by three experts, with an overall Cronbach’s alpha of 0.77, and analyzed using means, standard deviations and the z-test at the 0.05 level of significance. The study revealed that digital technologies had a positive impact on students’ learning engagement. Instructional technologies such as virtual simulation software, precision agriculture tools and virtual laboratory technology are perceived to enhance quality learning engagement of students in agricultural science. The z-tests showed no significant difference between the groups for any technology. The results show that teachers and students hold similarly favourable views of these technologies, which supports their planned integration into Agricultural Science teaching, particularly where physical laboratory and farm facilities are limited. The study concludes that the adoption of digital technologies into the teaching and learning process of agricultural science has considerable potential to improving the quality of learning engagement and can bridge the gap between theoretical instruction and practical agricultural experiences. the study thus recommends the integration of digital technologies into agricultural science instruction and that schools should organize professional development on its effective use, to enable teachers provide realistic practical experiences.","本研究考察了数字技术对尼日利亚河流州中学生农业科学优质学习投入的影响。研究采用描述性调查设计。通过多阶段抽样，从三个参议员选区六个地方政府区域的24所公立中学中抽取了400名受访者（112名教师和288名学生）。数据采用自行编制的四点式问卷收集，该问卷经三位专家验证，总体Cronbach's alpha系数为0.77，并使用均值、标准差和0.05显著性水平下的z检验进行分析。研究发现，数字技术对学生的学习投入产生了积极影响。虚拟仿真软件、精准农业工具和虚拟实验室技术等教学技术被认为能够提升农业科学学生的优质学习投入。z检验表明，各组在任何技术方面均无显著差异。结果表明，教师和学生对上述技术持相似的良好看法，这支持将其有计划地整合到农业科学教学中，尤其是在实体实验室和农场设施有限的情况下。研究得出结论，将数字技术纳入农业科学的教学过程具有相当大的潜力，能够提高学习投入的质量，并弥合理论教学与实践农业经验之间的差距。因此，研究建议将数字技术整合到农业科学教学中，学校应组织关于其有效使用的专业发展培训，以使教师能够提供真实的实践体验。",{"impact":17,"substance":18,"depth":19,"authority":108,"freshness":21,"relevant":22,"comment":209},"尼日利亚地区性实证研究，样本与方法规范，结论支持数字技术融入农业科学教学，对农业信息化教育应用有参考价值，但影响范围限于地方。",[211],{"name":10,"url":205},[29,213,51,32,214],"数字技术","虚拟仿真",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":218},{"candidates":219,"agricultural_evidence":48},[220],{"code":35,"score":21,"supported":48,"title":221,"abstract":222,"tags":223},[],[51,53],[213,29,51],[225,226],"Rivers State 农业科学 数字技术","尼日利亚 中学 农业科学 学习投入","RiversState农业科学数字技术-4905","10.5281\u002Fzenodo.23342866",{"doi":228,"openalex_id":230,"authors":231,"venue":10,"cited_by_count":71,"oa_url":205,"card":238,"direction":76,"ingested_from":79},"W7221595725",[232,234,236],{"name":233,"orcid":9},"P.O. Chukwuocha",{"name":235,"orcid":9},"A.U. Nnodim",{"name":237,"orcid":9},"N.S. Amadi",{"tldr":239,"method":240,"finding":241,"direction":76,"opportunity":242},"研究数字技术对尼日利亚河流州中学农业科学学习投入的影响。","对400名师生问卷调查，用均值、标准差和z检验分析。","数字技术正向提升学习投入，虚拟仿真、精准农业工具等受认可，师生观点无显著差异。","可探究数字技术在农业实践教学中的长期效果及资源匮乏地区的适配模式。","2026-10-14T23:30:34.199346Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":9,"source_name":10,"source_url":247,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":254,"tags":258,"primary_topic":260,"secondary_topics":261,"classification":262,"search_phrases":271,"slug":274,"view_count":275,"doi":276,"paper":277,"created_at":289},3272,"Smart Agriculture in Northern Nigeria: Prospects and Challenges for Graduate Farmers","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22891268","Smart Agriculture in Northern Nigeria: Prospects and Challenges for Graduate Farmers。Zenodo (CERN European Organization for Nuclear Research)","尼日利亚北部智慧农业：研究生农民的前景与挑战。Zenodo（CERN欧洲核子研究组织）","2026-09-22T00:00:00Z",45,{"impact":17,"substance":82,"depth":20,"authority":108,"freshness":21,"relevant":22,"comment":253},"主题相关但为尼日利亚区域研究，缺乏新数据与实质结论，仅具参考价值，不宜进入每日精选。",[255,256],{"name":10,"url":247},{"name":10,"url":257},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22891267",[52,29,33,259],"农业人才",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":263},{"candidates":264,"agricultural_evidence":48},[265],{"code":35,"score":266,"supported":48,"title":267,"abstract":269,"tags":270},16,[268],"smart agriculture",[29,268],[52,29],[272,273],"尼日利亚 智慧农业 毕业生","农业人才 尼日利亚 数字农业 智慧农业","尼日利亚智慧农业毕业生-3272",3,"10.5281\u002Fzenodo.22891268",{"doi":276,"openalex_id":278,"authors":279,"venue":10,"cited_by_count":71,"oa_url":247,"card":284,"direction":78,"ingested_from":79},"W7214014972",[280,282],{"name":281,"orcid":9},"Baman Abubakar",{"name":283,"orcid":9},"A.A Akinsoru",{"tldr":285,"method":286,"finding":287,"direction":76,"opportunity":288},"调查尼日利亚北部毕业农户对智慧农业的前景认知与实际挑战。","针对毕业农户的问卷调查与定性分析。","毕业农户认可智慧农业潜力，但受基础设施、资金与技能制约。","可延伸至小农户数字素养培训与低成本智慧农业适配方案研究。","2026-09-23T23:30:09.435022Z",{"id":291,"title":292,"url":293,"summary":294,"summary_zh":9,"content":9,"source_name":295,"source_url":293,"published_at":296,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":297,"score_detail":298,"sources":300,"tags":302,"primary_topic":306,"secondary_topics":307,"classification":308,"search_phrases":325,"slug":328,"view_count":329,"doi":330,"paper":331,"created_at":343},2037,"Spatial Assessment of Biophysical Suitability for Climate-Smart Agriculture across Nigeria","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10618150\u002Fv1","Spatial Assessment of Biophysical Suitability for Climate-Smart Agriculture across Nigeria。Research Square","Research Square","2026-09-09T00:00:00Z",65,{"impact":108,"substance":18,"depth":153,"authority":108,"freshness":17,"relevant":22,"comment":299},"基于空间评估方法识别尼日利亚气候智慧型农业的生物物理适宜性，属农业遥感与气候适应交叉研究，方法有参考价值但区域性强、时效略滞后。",[301],{"name":295,"url":293},[29,303,304,33,305],"遥感","气候智慧型农业","空间评估",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":309},{"candidates":310,"agricultural_evidence":48},[311,315,320],{"code":35,"score":108,"supported":48,"title":312,"abstract":313,"tags":314},[268],[268],[29],{"code":316,"score":22,"supported":14,"title":317,"abstract":318,"tags":319},"H7",[],[],[304],{"code":321,"score":22,"supported":14,"title":322,"abstract":323,"tags":324},"I3",[],[],[303],[326,327],"气候智慧型农业 尼日利亚 智慧农业 空间评估","气候智慧型农业 尼日利亚","气候智慧型农业尼日利亚智慧农业空间评估-2037",2,"10.21203\u002Frs.3.rs-10618150\u002Fv1",{"doi":330,"openalex_id":332,"authors":333,"venue":295,"cited_by_count":71,"oa_url":293,"card":9,"direction":78,"ingested_from":79},"W7211997027",[334,336,339,341],{"name":335,"orcid":9},"Bimbola Rasheed Bashorun",{"name":337,"orcid":338},"Lanre Tajudeen Ajibade","https:\u002F\u002Forcid.org\u002F0009-0001-1182-8140",{"name":340,"orcid":9},"Toluwalope Mubo Agaja",{"name":342,"orcid":9},"Davies Ojo Ajewole","2026-09-10T23:30:09.109080Z",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":9,"content":9,"source_name":349,"source_url":347,"published_at":350,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":351,"score_detail":352,"sources":355,"tags":357,"primary_topic":361,"secondary_topics":362,"classification":363,"search_phrases":376,"slug":379,"view_count":71,"doi":380,"paper":381,"created_at":396},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":266,"substance":353,"depth":18,"authority":19,"freshness":20,"relevant":22,"comment":354},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。",[356],{"name":349,"url":347},[29,358,51,359,360],"农业人工智能","机器视觉","作物行检测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":364},{"candidates":365,"agricultural_evidence":48},[366,371],{"code":35,"score":367,"supported":48,"title":368,"abstract":369,"tags":370},5,[],[53],[29,51],{"code":372,"score":329,"supported":14,"title":373,"abstract":374,"tags":375},"I2",[],[],[358,359],[377,378],"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":380,"openalex_id":382,"authors":383,"venue":349,"cited_by_count":71,"oa_url":9,"card":9,"direction":9,"ingested_from":79},"W7223117828",[384,387,390,393],{"name":385,"orcid":386},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":388,"orcid":389},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":391,"orcid":392},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":394,"orcid":395},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z"]