[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4758":3,"related-4758":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":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":57,"slug":60,"view_count":22,"doi":61,"paper":62,"created_at":80},4758,"A Gateway-Based Approach for Integrating Digital Compass Data into Controller Area Network (CAN) in Precision Farming Systems","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2860-2869.9031","The integration of high precision orientation sensors into resilient communication infrastructures is a key requirement for next-generation autonomous systems. Digital compass sensors typically generate data in the NMEA format, whereas distributed control architectures in mobile robotics predominantly utilize the CAN 2.0B protocol. This study presents the design and implementation of a microcontroller-based hardware-software gateway that enables seamless interoperability between these heterogeneous communication standards within autonomous agricultural platforms. The proposed system operates as a protocol translation layer, acquiring orientation data (heading, pitch, and roll) from a digital compass via serial communication and converting it into CAN-compliant messages. The incoming NMEA sentences are parsed, validated, and filtered before being encapsulated into extended CAN frames. To address the payload limitations of Classical CAN and discrepancies in transmission rates, a multi-frame segmentation strategy and a synchronization mechanism based on handshaking are implemented. Additionally, buffering techniques are employed to ensure temporal consistency and prevent data loss during high-frequency data exchange. Experimental evaluations demonstrated that the proposed gateway achieved reliable real-time communication performance, with an average end to end communication latency of 19.6 ms, a 99.98% message reconstruction success rate, and 0.02% packet loss during continuous CAN Bus data transmission tests. The proposed integration framework effectively bridges the gap between legacy serial sensor protocols and modern CAN Bus-based distributed systems. Consequently, this work offers a scalable and practical solution for protocol interoperability in precision agriculture and contributes to the development of robust sensor integration strategies for autonomous field robotics.","将高精度定向传感器集成到弹性通信基础设施中，是下一代自主系统的关键需求。数字罗盘传感器通常以NMEA格式生成数据，而移动机器人中的分布式控制架构则主要采用CAN 2.0B协议。本研究介绍了一种基于微控制器的硬件-软件网关的设计与实现，该网关能够在自主农业平台中实现这些异构通信标准之间的无缝互操作。所提出的系统作为协议转换层运行，通过串行通信从数字罗盘获取定向数据（航向、俯仰和横滚），并将其转换为符合CAN标准的报文。传入的NMEA语句在封装为扩展CAN帧之前，经过解析、验证和过滤。为解决经典CAN的有效载荷限制和传输速率差异问题，实现了多帧分段策略和基于握手的同步机制。此外，采用缓冲技术以确保时间一致性，并防止高频数据交换过程中的数据丢失。实验评估表明，所提出的网关实现了可靠的实时通信性能，在连续CAN总线数据传输测试中，平均端到端通信延迟为19.6 ms，报文重构成功率为99.98%，丢包率为0.02%。所提出的集成框架有效弥合了传统串行传感器协议与现代基于CAN总线的分布式系统之间的差距。因此，这项工作为精准农业中的协议互操作性提供了一种可扩展且实用的解决方案，并为自主田间机器人稳健传感器集成策略的发展做出了贡献。",null,"Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,9,1,"该论文提出基于网关的NMEA-CAN协议转换方案，实测延迟19.6ms、重建成功率99.98%，对精准农业自主农机传感器集成有实用价值，但属细分技术进展，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业机器人","精准农业","传感器集成","CAN总线",{"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},[45,52],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},8,true,[],[29,50],"precision agriculture",[27,29],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"F6",[],[],[28],[58,59],"数字罗盘 CAN总线 精准农业","NMEA CAN网关 农业机器人","数字罗盘CAN总线精准农业-4758","10.24925\u002Fturjaf.v14i10.2860-2869.9031",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":71,"oa_url":6,"card":72,"direction":78,"ingested_from":79},"W7221447512",[65,68],{"name":66,"orcid":67},"İ̇lker Ünal","https:\u002F\u002Forcid.org\u002F0000-0002-5188-4438",{"name":69,"orcid":70},"Osman Eceoğlu","https:\u002F\u002Forcid.org\u002F0000-0001-5778-6655",0,{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"设计微控制器网关，将数字罗盘NMEA数据转换为CAN总线消息，用于精准农业自主平台。","微控制器网关、NMEA解析、CAN 2.0B、多帧分段与握手同步。","平均延迟19.6ms，消息重构成功率99.98%，丢包率0.02%，实现可靠实时通信。","智慧农业 \u002F 农业物联网","可研究多传感器异构协议统一网关及时间同步，提升农机自主系统实时性与可扩展性。","数字乡村与农业信息化","openalex","2026-10-12T23:30:30.266640Z",{"total":82,"page":22,"page_size":82,"items":83},6,[84,123,179,225,277,322],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":9,"content":9,"source_name":89,"source_url":87,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":91,"score_detail":92,"sources":95,"tags":97,"primary_topic":99,"secondary_topics":100,"classification":101,"search_phrases":112,"slug":115,"view_count":71,"doi":116,"paper":117,"created_at":122},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":93,"substance":46,"depth":13,"authority":17,"freshness":46,"relevant":22,"comment":94},18,"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。",[96],{"name":89,"url":87},[27,28,29,98],"智能采收",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":102},{"candidates":103,"agricultural_evidence":47},[104,108],{"code":33,"score":20,"supported":47,"title":105,"abstract":106,"tags":107},[50],[50],[27,29],{"code":53,"score":22,"supported":14,"title":109,"abstract":110,"tags":111},[],[],[28],[113,114],"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":116,"openalex_id":118,"authors":119,"venue":9,"cited_by_count":71,"oa_url":87,"card":9,"direction":76,"ingested_from":79},"W7222971426",[120],{"name":121,"orcid":9},"Joydeep Dey","2026-10-20T23:30:08.600540Z",{"id":124,"title":125,"url":126,"summary":127,"summary_zh":9,"content":9,"source_name":128,"source_url":126,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":136,"tags":138,"primary_topic":142,"secondary_topics":143,"classification":144,"search_phrases":158,"slug":161,"view_count":71,"doi":162,"paper":163,"created_at":178},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":132,"substance":133,"depth":93,"authority":134,"freshness":13,"relevant":22,"comment":135},16,21,14,"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。",[137],{"name":128,"url":126},[27,139,29,140,141],"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":145},{"candidates":146,"agricultural_evidence":47},[147,152],{"code":33,"score":148,"supported":47,"title":149,"abstract":150,"tags":151},5,[],[50],[27,29],{"code":153,"score":154,"supported":14,"title":155,"abstract":156,"tags":157},"I2",2,[],[],[139,140],[159,160],"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":162,"openalex_id":164,"authors":165,"venue":128,"cited_by_count":71,"oa_url":9,"card":9,"direction":9,"ingested_from":79},"W7223117828",[166,169,172,175],{"name":167,"orcid":168},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":170,"orcid":171},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":173,"orcid":174},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":176,"orcid":177},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":186,"sources":189,"tags":191,"primary_topic":194,"secondary_topics":195,"classification":196,"search_phrases":208,"slug":211,"view_count":71,"doi":212,"paper":213,"created_at":224},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":93,"substance":187,"depth":93,"authority":17,"freshness":21,"relevant":22,"comment":188},22,"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。",[190],{"name":185,"url":182},[27,139,192,29,193],"数字孪生","植物表型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":197},{"candidates":198,"agricultural_evidence":47},[199,204],{"code":33,"score":18,"supported":47,"title":200,"abstract":202,"tags":203},[201],"digital twin",[29,192,50],[192,27,29],{"code":153,"score":22,"supported":14,"title":205,"abstract":206,"tags":207},[],[],[139],[209,210],"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":212,"openalex_id":214,"authors":215,"venue":185,"cited_by_count":71,"oa_url":182,"card":218,"direction":222,"ingested_from":79},"W7222885652",[216],{"name":217,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":219,"method":220,"finding":221,"direction":222,"opportunity":223},"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":226,"title":227,"url":228,"summary":229,"summary_zh":9,"content":9,"source_name":230,"source_url":228,"published_at":231,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":232,"score_detail":233,"sources":235,"tags":237,"primary_topic":239,"secondary_topics":240,"classification":241,"search_phrases":257,"slug":260,"view_count":154,"doi":261,"paper":262,"created_at":276},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":17,"substance":132,"depth":19,"authority":134,"freshness":46,"relevant":22,"comment":234},"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。",[236],{"name":230,"url":228},[27,139,29,238],"生物防治",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":242},{"candidates":243,"agricultural_evidence":47},[244,248,253],{"code":33,"score":20,"supported":47,"title":245,"abstract":246,"tags":247},[50],[50],[27,29],{"code":249,"score":22,"supported":14,"title":250,"abstract":251,"tags":252},"F4",[],[],[238],{"code":153,"score":22,"supported":14,"title":254,"abstract":255,"tags":256},[],[],[139],[258,259],"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":261,"openalex_id":263,"authors":264,"venue":230,"cited_by_count":71,"oa_url":9,"card":9,"direction":76,"ingested_from":79},"W7221746877",[265,268,271,273],{"name":266,"orcid":267},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":269,"orcid":270},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":272,"orcid":9},"İsmail Alper Susurluk",{"name":274,"orcid":275},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":185,"source_url":280,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":287,"tags":289,"primary_topic":293,"secondary_topics":294,"classification":295,"search_phrases":302,"slug":305,"view_count":71,"doi":306,"paper":307,"created_at":321},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":46,"substance":93,"depth":134,"authority":17,"freshness":21,"relevant":22,"comment":286},"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。",[288],{"name":185,"url":280},[27,290,29,291,292],"数字技术","农业教育","虚拟仿真",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":296},{"candidates":297,"agricultural_evidence":47},[298],{"code":33,"score":21,"supported":47,"title":299,"abstract":300,"tags":301},[],[29,50],[290,27,29],[303,304],"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":306,"openalex_id":308,"authors":309,"venue":185,"cited_by_count":71,"oa_url":280,"card":316,"direction":78,"ingested_from":79},"W7221595725",[310,312,314],{"name":311,"orcid":9},"P.O. Chukwuocha",{"name":313,"orcid":9},"A.U. Nnodim",{"name":315,"orcid":9},"N.S. Amadi",{"tldr":317,"method":318,"finding":319,"direction":78,"opportunity":320},"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":323,"title":324,"url":325,"summary":326,"summary_zh":9,"content":9,"source_name":128,"source_url":325,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":328,"score_detail":329,"sources":331,"tags":333,"primary_topic":337,"secondary_topics":338,"classification":339,"search_phrases":351,"slug":354,"view_count":22,"doi":355,"paper":356,"created_at":373},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":93,"substance":18,"depth":93,"authority":134,"freshness":46,"relevant":22,"comment":330},"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。",[332],{"name":128,"url":325},[27,29,334,335,336],"遥感","作物表型","三维建模",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":340},{"candidates":341,"agricultural_evidence":47},[342,346],{"code":33,"score":20,"supported":47,"title":343,"abstract":344,"tags":345},[50],[50],[27,29],{"code":347,"score":154,"supported":14,"title":348,"abstract":349,"tags":350},"I3",[],[],[335,334],[352,353],"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":355,"openalex_id":357,"authors":358,"venue":128,"cited_by_count":71,"oa_url":325,"card":9,"direction":76,"ingested_from":79},"W7221570971",[359,362,365,367,370],{"name":360,"orcid":361},"Valeria Maritano","https:\u002F\u002Forcid.org\u002F0000-0002-5844-3184",{"name":363,"orcid":364},"Orou Berme Herve Gonroudobou","https:\u002F\u002Forcid.org\u002F0000-0003-0614-2731",{"name":366,"orcid":9},"Offer Rozenstein",{"name":368,"orcid":369},"Paolo Gay","https:\u002F\u002Forcid.org\u002F0000-0002-8342-425X",{"name":371,"orcid":372},"Lorenzo Comba","https:\u002F\u002Forcid.org\u002F0000-0001-5466-0918","2026-10-13T23:30:02.534333Z"]