[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2037":3,"related-2037":81},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":80},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",null,"Research Square","2026-09-09T00:00:00Z","论文",10,false,65,{"impact":16,"substance":17,"depth":18,"authority":16,"freshness":19,"relevant":20,"comment":21},12,18,15,8,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","遥感","气候智慧型农业","尼日利亚","空间评估",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,49,54],{"code":31,"score":16,"supported":44,"title":45,"abstract":47,"tags":48},true,[46],"smart agriculture",[46],[25],{"code":50,"score":20,"supported":13,"title":51,"abstract":52,"tags":53},"H7",[],[],[27],{"code":55,"score":20,"supported":13,"title":56,"abstract":57,"tags":58},"I3",[],[],[26],[60,61],"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":64,"openalex_id":66,"authors":67,"venue":9,"cited_by_count":77,"oa_url":6,"card":8,"direction":78,"ingested_from":79},"W7211997027",[68,70,73,75],{"name":69,"orcid":8},"Bimbola Rasheed Bashorun",{"name":71,"orcid":72},"Lanre Tajudeen Ajibade","https:\u002F\u002Forcid.org\u002F0009-0001-1182-8140",{"name":74,"orcid":8},"Toluwalope Mubo Agaja",{"name":76,"orcid":8},"Davies Ojo Ajewole",0,"智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.109080Z",{"total":82,"page":20,"page_size":82,"items":83},6,[84,147,199,243,292,341],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":8,"content":8,"source_name":89,"source_url":87,"published_at":90,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":91,"score_detail":92,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":105,"classification":106,"search_phrases":129,"slug":132,"view_count":20,"doi":133,"paper":134,"created_at":146},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture","Precision Agriculture","2026-10-14T00:00:00Z",75,{"impact":93,"substance":94,"depth":95,"authority":96,"freshness":19,"relevant":20,"comment":97},16,20,17,14,"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[99],{"name":89,"url":87},[25,101,102,26,103],"无人机","农业人工智能","大麦",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":107},{"candidates":108,"agricultural_evidence":44},[109,115,120,125],{"code":31,"score":110,"supported":44,"title":111,"abstract":112,"tags":114},4,[],[113],"precision agriculture",[25],{"code":116,"score":20,"supported":13,"title":117,"abstract":118,"tags":119},"A5",[],[],[103],{"code":121,"score":20,"supported":13,"title":122,"abstract":123,"tags":124},"I2",[],[],[102],{"code":55,"score":20,"supported":13,"title":126,"abstract":127,"tags":128},[],[],[26],[130,131],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":133,"openalex_id":135,"authors":136,"venue":89,"cited_by_count":77,"oa_url":8,"card":8,"direction":8,"ingested_from":79},"W7221758985",[137,140,143],{"name":138,"orcid":139},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":141,"orcid":142},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":144,"orcid":145},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":8,"content":8,"source_name":89,"source_url":150,"published_at":152,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":153,"score_detail":154,"sources":156,"tags":158,"primary_topic":162,"secondary_topics":163,"classification":164,"search_phrases":176,"slug":179,"view_count":20,"doi":180,"paper":181,"created_at":198},4787,"3D modeling for crop applications in precision agriculture: a review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10466-y","3D modeling for crop applications in precision agriculture: a review。Precision Agriculture","2026-10-12T00:00:00Z",78,{"impact":17,"substance":94,"depth":17,"authority":96,"freshness":19,"relevant":20,"comment":155},"核心期刊发表的作物三维建模综述，方法学价值高，对精准农业与表型研究有参考意义。",[157],{"name":89,"url":150},[25,159,26,160,161],"精准农业","作物表型","三维建模",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":165},{"candidates":166,"agricultural_evidence":44},[167,172],{"code":31,"score":168,"supported":44,"title":169,"abstract":170,"tags":171},13,[113],[113],[25,159],{"code":55,"score":63,"supported":13,"title":173,"abstract":174,"tags":175},[],[],[160,26],[177,178],"三维建模 作物表型 智慧农业 精准农业","三维建模 作物表型","三维建模作物表型智慧农业精准农业-4787","10.1007\u002Fs11119-026-10466-y",{"doi":180,"openalex_id":182,"authors":183,"venue":89,"cited_by_count":77,"oa_url":150,"card":8,"direction":78,"ingested_from":79},"W7221570971",[184,187,190,192,195],{"name":185,"orcid":186},"Valeria Maritano","https:\u002F\u002Forcid.org\u002F0000-0002-5844-3184",{"name":188,"orcid":189},"Orou Berme Herve Gonroudobou","https:\u002F\u002Forcid.org\u002F0000-0003-0614-2731",{"name":191,"orcid":8},"Offer Rozenstein",{"name":193,"orcid":194},"Paolo Gay","https:\u002F\u002Forcid.org\u002F0000-0002-8342-425X",{"name":196,"orcid":197},"Lorenzo Comba","https:\u002F\u002Forcid.org\u002F0000-0001-5466-0918","2026-10-13T23:30:02.534333Z",{"id":200,"title":201,"url":202,"summary":203,"summary_zh":8,"content":8,"source_name":204,"source_url":202,"published_at":205,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":206,"score_detail":207,"sources":209,"tags":211,"primary_topic":213,"secondary_topics":214,"classification":215,"search_phrases":230,"slug":233,"view_count":77,"doi":234,"paper":235,"created_at":242},4499,"Digital Technology for Climate Smart Agriculture: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.28386.90569","Digital Technology for Climate Smart Agriculture: A Comprehensive Review。OpenAlex","OpenAlex","2026-10-07T00:00:00Z",69,{"impact":17,"substance":93,"depth":18,"authority":16,"freshness":19,"relevant":20,"comment":208},"OpenAlex收录的气候智慧型农业数字技术综述，主题契合农业信息化，但仅见标题与来源、缺乏方法与数据细节，信息增量有限。",[210],{"name":204,"url":202},[25,102,212,27],"数字技术",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":216},{"candidates":217,"agricultural_evidence":44},[218,222,226],{"code":31,"score":168,"supported":44,"title":219,"abstract":220,"tags":221},[46],[46],[212,25],{"code":50,"score":20,"supported":13,"title":223,"abstract":224,"tags":225},[],[],[27],{"code":121,"score":20,"supported":13,"title":227,"abstract":228,"tags":229},[],[],[102],[231,232],"气候智慧型农业 农业人工智能 数字技术 智慧农业","气候智慧型农业 农业人工智能","气候智慧型农业农业人工智能数字技术智慧农业-4499","10.13140\u002Frg.2.2.28386.90569",{"doi":234,"openalex_id":236,"authors":237,"venue":8,"cited_by_count":77,"oa_url":202,"card":8,"direction":78,"ingested_from":79},"W7221016064",[238,240],{"name":239,"orcid":8},"Vaibhav Singh",{"name":241,"orcid":8},"Dipak Kumar Bose","2026-10-09T23:30:12.341364Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":8,"source_name":249,"source_url":246,"published_at":250,"category":11,"cover_url":8,"hotness":251,"is_selected":13,"score":252,"score_detail":253,"sources":256,"tags":260,"primary_topic":263,"secondary_topics":264,"classification":265,"search_phrases":272,"slug":275,"view_count":276,"doi":277,"paper":278,"created_at":291},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）","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,45,{"impact":19,"substance":82,"depth":12,"authority":16,"freshness":254,"relevant":20,"comment":255},9,"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。",[257,258],{"name":249,"url":246},{"name":249,"url":259},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22891267",[261,25,28,262],"数字农业","农业人才",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":266},{"candidates":267,"agricultural_evidence":44},[268],{"code":31,"score":93,"supported":44,"title":269,"abstract":270,"tags":271},[46],[25,46],[261,25],[273,274],"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":277,"openalex_id":279,"authors":280,"venue":249,"cited_by_count":77,"oa_url":246,"card":285,"direction":78,"ingested_from":79},"W7214014972",[281,283],{"name":282,"orcid":8},"Baman Abubakar",{"name":284,"orcid":8},"A.A Akinsoru",{"tldr":286,"method":287,"finding":288,"direction":289,"opportunity":290},"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。","数字乡村与农业信息化","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":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":8,"source_name":249,"source_url":295,"published_at":298,"category":11,"cover_url":8,"hotness":251,"is_selected":13,"score":299,"score_detail":300,"sources":302,"tags":306,"primary_topic":310,"secondary_topics":311,"classification":312,"search_phrases":325,"slug":328,"view_count":20,"doi":329,"paper":330,"created_at":340},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","2026-09-15T00:00:00Z",59,{"impact":19,"substance":17,"depth":96,"authority":12,"freshness":254,"relevant":20,"comment":301},"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。",[303,304],{"name":249,"url":295},{"name":249,"url":305},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22762487",[25,307,308,309,28],"数字素养","农业无人机","农业教育",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":313},{"candidates":314,"agricultural_evidence":44},[315,320],{"code":31,"score":12,"supported":44,"title":316,"abstract":317,"tags":319},[],[159,261,113,318],"digital agriculture",[25],{"code":321,"score":20,"supported":13,"title":322,"abstract":323,"tags":324},"I6",[],[],[307],[326,327],"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":329,"openalex_id":331,"authors":332,"venue":249,"cited_by_count":77,"oa_url":295,"card":335,"direction":78,"ingested_from":79},"W7213296064",[333],{"name":334,"orcid":8},"Chijioke-Eke Joy Ndidi",{"tldr":336,"method":337,"finding":338,"direction":289,"opportunity":339},"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":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":8,"source_name":347,"source_url":344,"published_at":348,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":153,"score_detail":349,"sources":353,"tags":355,"primary_topic":357,"secondary_topics":358,"classification":359,"search_phrases":374,"slug":377,"view_count":20,"doi":378,"paper":379,"created_at":395},1403,"CFAEVM-UNet: cross-fusion attention enhanced VM-UNet for UAV image segmentation of small and dense field crop pests","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1896392","Precise segmentation of small and dense field crop pests in UAV images remains challenging due to limited long-range dependency modeling in CNNs and quadratic complexity in ViTs. Although VM-UNet captures global context efficiently, it lacks local texture detail and multi-scale feature integration for small targets. To address these limitations, this paper proposes CFAEVM-UNet (CrossFusion Attention Enhanced VM-UNet), which incorporates three core modules: a Visual State Space (VSS) block for efficient long-range dependency modeling, a Local-Global Spatial-Channel Gated Attention (LG-SCGA) module for multi-scale gated attention fusion, and a Multi-Stage Channel-Wise Attention (MSCWA) module for cross-stage channel reweighting. Extensive experiments on an integrated dataset (IP102 subset combined with UAV-captured field images) demonstrate that CFAEVM-UNet achieves state-of-the-art performance, attaining an mIoU of 81.23% and a DSC of 83.67%, outperforming U-Net by 8.89% in mIoU. This work provides an effective and practical solution for automated pest monitoring in precision agriculture.","无人机图像中密集小尺度农作物害虫的精确分割仍面临挑战，原因在于卷积神经网络（CNN）中长距离依赖建模能力有限，以及视觉Transformer（ViT）中的二次复杂度问题。尽管VM-UNet能够高效捕获全局上下文，但其在局部纹理细节和小目标多尺度特征整合方面存在不足。为解决上述局限，本文提出CFAEVM-UNet（CrossFusion Attention Enhanced VM-UNet），该模型融合了三个核心模块：用于高效长距离依赖建模的视觉状态空间（VSS）模块、用于多尺度门控注意力融合的局部-全局空间-通道门控注意力（LG-SCGA）模块，以及用于跨阶段通道重加权的多阶段通道注意力（MSCWA）模块。在综合数据集（IP102子集结合无人机拍摄的田间图像）上进行的大量实验表明，CFAEVM-UNet达到了最先进的性能，其平均交并比（mIoU）为81.23%，Dice相似系数（DSC）为83.67%，相较于U-Net在mIoU上提升了8.89%。本研究为精准农业中的自动化害虫监测提供了一种有效且实用的解决方案。","Frontiers in Plant Science","2026-09-01T00:00:00Z",{"impact":17,"substance":350,"depth":17,"authority":168,"freshness":351,"relevant":20,"comment":352},22,7,"提出针对无人机图像小目标害虫分割的新模型，性能显著提升，对精准农业有实用价值。",[354],{"name":347,"url":344},[25,102,26,356],"病虫害防治",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":360},{"candidates":361,"agricultural_evidence":44},[362,366,370],{"code":31,"score":351,"supported":44,"title":363,"abstract":364,"tags":365},[],[159,113],[25],{"code":121,"score":20,"supported":13,"title":367,"abstract":368,"tags":369},[],[],[102],{"code":55,"score":20,"supported":13,"title":371,"abstract":372,"tags":373},[],[],[26],[375,376],"农业人工智能 病虫害防治 智慧农业 遥感","农业人工智能 病虫害防治","农业人工智能病虫害防治智慧农业遥感-1403","10.3389\u002Ffpls.2026.1896392",{"doi":378,"openalex_id":380,"authors":381,"venue":347,"cited_by_count":77,"oa_url":388,"card":389,"direction":78,"ingested_from":79},"W7204879529",[382,384,386],{"name":383,"orcid":8},"Guohong Qi",{"name":385,"orcid":8},"Jing Zhang",{"name":387,"orcid":8},"Shanwen Zhang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1896392\u002Fpdf",{"tldr":390,"method":391,"finding":392,"direction":393,"opportunity":394},"提出CFAEVM-UNet模型，用于无人机图像中密集小害虫的精确分割。","结合VSS块、LG-SCGA模块和MSCWA模块，增强多尺度特征融合与通道注意力","在集成数据集上mIoU达81.23%，DSC达83.67%，比U-Net高8.89%。","农业人工智能与决策模型","可探索模型在更多作物害虫种类和不同无人机高度下的泛化性，以及轻量化设计以适配边缘设备。","2026-09-02T23:30:10.832021Z"]