[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1265":3,"related-1265":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":29,"secondary_topics":35,"classification":36,"search_phrases":54,"slug":57,"view_count":58,"doi":59,"paper":60,"created_at":80},1265,"Applying task division techniques on wireless multimedia sensor networks to improve lifetime","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10617-026-09300-5","Abstract This paper introduces two novel techniques, TDMEN-CACHE and TDMEN-INTERSEC, designed to enhance load balancing and energy efficiency in Wireless Multimedia Sensor Networks (WMSNs). These techniques leverage temporal and spatial redundancies to optimize task division among sensor nodes, reducing energy consumption and extending network lifetime. TDMEN-CACHE minimizes reprocessing by utilizing cached data from previous multimedia events, while TDMEN-INTERSEC capitalizes on overlapping sensing areas to distribute processing tasks among neighboring nodes. The proposed methods were evaluated using real-world case studies in precision agriculture and bird monitoring, demonstrating significant improvements in energy efficiency, event detection rates, and network longevity compared to conventional solutions. A packet loss model was also integrated to simulate real-world communication scenarios, further validating the robustness of the techniques under varying conditions.",null,"Design Automation for Embedded Systems","2026-08-29T00:00:00Z","论文",10,false,60,{"impact":16,"substance":17,"depth":18,"authority":16,"freshness":19,"relevant":20,"comment":21},12,18,16,2,1,"论文提出WMSN任务划分新技术，提升能效与网络寿命，对农业物联网有参考价值，但时效性较低。",[23],{"name":9,"url":6},[25,26,27,28],"智慧农业","精准农业","无线传感器网络","节能技术",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":48},5,true,[],[47],"precision agriculture",[25,26],{"code":50,"score":20,"supported":13,"title":51,"abstract":52,"tags":53},"I5",[],[],[27],[55,56],"无线传感器网络 智慧农业 精准农业 节能技术","无线传感器网络 智慧农业","无线传感器网络智慧农业精准农业节能技术-1265",0,"10.1007\u002Fs10617-026-09300-5",{"doi":59,"openalex_id":61,"authors":62,"venue":9,"cited_by_count":58,"oa_url":71,"card":72,"direction":78,"ingested_from":79},"W7204637025",[63,65,68],{"name":64,"orcid":8},"Weslen Schiavon de Souza",{"name":66,"orcid":67},"Lisane Brisolara","https:\u002F\u002Forcid.org\u002F0000-0003-3552-4456",{"name":69,"orcid":70},"Paulo R. Ferreira","https:\u002F\u002Forcid.org\u002F0000-0001-5631-6766","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10617-026-09300-5.pdf",{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"提出两种任务分配技术，利用时空冗余优化无线多媒体传感器网络，提升能效和寿命。","利用缓存和感知区域重叠，设计TDMEN-CACHE和TDMEN-INTERSEC","在精准农业和鸟类监测中，显著提高能效、事件检测率和网络寿命。","其他","可探索将任务分配与边缘计算结合，或针对动态场景优化缓存策略，以进一步提升网络适应性。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:11.812178Z",{"total":82,"page":20,"page_size":82,"items":83},6,[84,137,179,227,280,326],{"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":96,"tags":98,"primary_topic":102,"secondary_topics":103,"classification":104,"search_phrases":116,"slug":119,"view_count":58,"doi":120,"paper":121,"created_at":136},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":18,"substance":93,"depth":17,"authority":94,"freshness":12,"relevant":20,"comment":95},21,14,"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。",[97],{"name":89,"url":87},[25,99,26,100,101],"农业人工智能","机器视觉","作物行检测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":105},{"candidates":106,"agricultural_evidence":44},[107,111],{"code":30,"score":43,"supported":44,"title":108,"abstract":109,"tags":110},[],[47],[25,26],{"code":112,"score":19,"supported":13,"title":113,"abstract":114,"tags":115},"I2",[],[],[99,100],[117,118],"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":120,"openalex_id":122,"authors":123,"venue":89,"cited_by_count":58,"oa_url":8,"card":8,"direction":8,"ingested_from":79},"W7223117828",[124,127,130,133],{"name":125,"orcid":126},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":128,"orcid":129},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":131,"orcid":132},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":134,"orcid":135},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":8,"content":8,"source_name":142,"source_url":140,"published_at":143,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":144,"score_detail":145,"sources":148,"tags":150,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":168,"slug":171,"view_count":58,"doi":172,"paper":173,"created_at":178},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","OpenAlex","2026-10-19T00:00:00Z",56,{"impact":17,"substance":146,"depth":12,"authority":16,"freshness":146,"relevant":20,"comment":147},8,"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。",[149],{"name":142,"url":140},[25,151,26,152],"农业机器人","智能采收",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":156},{"candidates":157,"agricultural_evidence":44},[158,163],{"code":30,"score":159,"supported":44,"title":160,"abstract":161,"tags":162},13,[47],[47],[25,26],{"code":164,"score":20,"supported":13,"title":165,"abstract":166,"tags":167},"F6",[],[],[151],[169,170],"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":172,"openalex_id":174,"authors":175,"venue":8,"cited_by_count":58,"oa_url":140,"card":8,"direction":78,"ingested_from":79},"W7222971426",[176],{"name":177,"orcid":8},"Joydeep Dey","2026-10-20T23:30:08.600540Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":8,"source_name":185,"source_url":182,"published_at":143,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":91,"score_detail":186,"sources":190,"tags":192,"primary_topic":195,"secondary_topics":196,"classification":197,"search_phrases":210,"slug":213,"view_count":58,"doi":214,"paper":215,"created_at":226},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。","Zenodo (CERN European Organization for Nuclear Research)",{"impact":17,"substance":187,"depth":17,"authority":16,"freshness":188,"relevant":20,"comment":189},22,9,"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[191],{"name":185,"url":182},[25,99,193,26,194],"数字孪生","植物表型",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":198},{"candidates":199,"agricultural_evidence":44},[200,206],{"code":30,"score":201,"supported":44,"title":202,"abstract":204,"tags":205},20,[203],"digital twin",[26,193,47],[193,25,26],{"code":112,"score":20,"supported":13,"title":207,"abstract":208,"tags":209},[],[],[99],[211,212],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":214,"openalex_id":216,"authors":217,"venue":185,"cited_by_count":58,"oa_url":182,"card":220,"direction":224,"ingested_from":79},"W7222885652",[218],{"name":219,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":221,"method":222,"finding":223,"direction":224,"opportunity":225},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","农业遥感与作物表型","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":8,"content":8,"source_name":232,"source_url":230,"published_at":233,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":234,"score_detail":235,"sources":238,"tags":240,"primary_topic":242,"secondary_topics":243,"classification":244,"search_phrases":260,"slug":263,"view_count":19,"doi":264,"paper":265,"created_at":279},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology","2026-10-14T00:00:00Z",67,{"impact":16,"substance":18,"depth":236,"authority":94,"freshness":146,"relevant":20,"comment":237},17,"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[239],{"name":232,"url":230},[25,99,26,241],"生物防治",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":245},{"candidates":246,"agricultural_evidence":44},[247,251,256],{"code":30,"score":159,"supported":44,"title":248,"abstract":249,"tags":250},[47],[47],[25,26],{"code":252,"score":20,"supported":13,"title":253,"abstract":254,"tags":255},"F4",[],[],[241],{"code":112,"score":20,"supported":13,"title":257,"abstract":258,"tags":259},[],[],[99],[261,262],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":264,"openalex_id":266,"authors":267,"venue":232,"cited_by_count":58,"oa_url":8,"card":8,"direction":78,"ingested_from":79},"W7221746877",[268,271,274,276],{"name":269,"orcid":270},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":272,"orcid":273},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":275,"orcid":8},"İsmail Alper Susurluk",{"name":277,"orcid":278},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":8,"source_name":185,"source_url":283,"published_at":286,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":287,"score_detail":288,"sources":290,"tags":292,"primary_topic":296,"secondary_topics":297,"classification":298,"search_phrases":305,"slug":308,"view_count":58,"doi":309,"paper":310,"created_at":325},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。","2026-10-13T00:00:00Z",61,{"impact":146,"substance":17,"depth":94,"authority":16,"freshness":188,"relevant":20,"comment":289},"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。",[291],{"name":185,"url":283},[25,293,26,294,295],"数字技术","农业教育","虚拟仿真",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":299},{"candidates":300,"agricultural_evidence":44},[301],{"code":30,"score":188,"supported":44,"title":302,"abstract":303,"tags":304},[],[26,47],[293,25,26],[306,307],"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":309,"openalex_id":311,"authors":312,"venue":185,"cited_by_count":58,"oa_url":283,"card":319,"direction":323,"ingested_from":79},"W7221595725",[313,315,317],{"name":314,"orcid":8},"P.O. Chukwuocha",{"name":316,"orcid":8},"A.U. Nnodim",{"name":318,"orcid":8},"N.S. Amadi",{"tldr":320,"method":321,"finding":322,"direction":323,"opportunity":324},"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。","数字乡村与农业信息化","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":327,"title":328,"url":329,"summary":330,"summary_zh":8,"content":8,"source_name":89,"source_url":329,"published_at":331,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":332,"score_detail":333,"sources":335,"tags":337,"primary_topic":341,"secondary_topics":342,"classification":343,"search_phrases":355,"slug":358,"view_count":20,"doi":359,"paper":360,"created_at":377},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":201,"depth":17,"authority":94,"freshness":146,"relevant":20,"comment":334},"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。",[336],{"name":89,"url":329},[25,26,338,339,340],"遥感","作物表型","三维建模",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":344},{"candidates":345,"agricultural_evidence":44},[346,350],{"code":30,"score":159,"supported":44,"title":347,"abstract":348,"tags":349},[47],[47],[25,26],{"code":351,"score":19,"supported":13,"title":352,"abstract":353,"tags":354},"I3",[],[],[339,338],[356,357],"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":359,"openalex_id":361,"authors":362,"venue":89,"cited_by_count":58,"oa_url":329,"card":8,"direction":78,"ingested_from":79},"W7221570971",[363,366,369,371,374],{"name":364,"orcid":365},"Valeria Maritano","https:\u002F\u002Forcid.org\u002F0000-0002-5844-3184",{"name":367,"orcid":368},"Orou Berme Herve Gonroudobou","https:\u002F\u002Forcid.org\u002F0000-0003-0614-2731",{"name":370,"orcid":8},"Offer Rozenstein",{"name":372,"orcid":373},"Paolo Gay","https:\u002F\u002Forcid.org\u002F0000-0002-8342-425X",{"name":375,"orcid":376},"Lorenzo Comba","https:\u002F\u002Forcid.org\u002F0000-0001-5466-0918","2026-10-13T23:30:02.534333Z"]