[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4648":3,"related-4648":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"primary_topic":29,"secondary_topics":35,"classification":41,"search_phrases":70,"slug":73,"view_count":74,"doi":8,"paper":75,"created_at":85},4648,"[预印本]面向农业机器人在多变天气与地形下基于视觉-语言模型的上下文自适应农药喷洒(Context-Aware Adaptive Pesticide Spraying for Agricultural Robots under Changing Weather and Terrain Using Vision-Language Models)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.08807","arXiv论文(2610.08807,2026年10月6日提交)提出基于视觉-语言模型(VLM)的上下文自适应喷洒框架,通过空间推理整合作物、农药、气象数据驱动喷洒决策,并结合MPPI轨迹跟踪控制。作物行检测精度至少提升30%,机器人可在两种环境中灵活调节喷洒量与行驶速度并减少农药漂移。[预印本未经同行评议,尚未实地部署验证]。",null,"arXiv 2026-10-06 [预印本]","2026-10-06T00:00:00Z","论文",10,false,72,{"impact":16,"substance":17,"depth":16,"authority":12,"freshness":18,"relevant":19,"comment":20},18,20,6,1,"预印本提出VLM驱动的自适应喷洒框架，方法新颖且减药减排指向明确，但未经同行评议与实地验证，宜作前沿动态收录而非重点精选。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","农业机器人","精准施药","视觉语言模型","农药减量",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[36],{"code":37,"slug":38,"name":39,"path":40,"parent_code":34},"F6","agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots",{"status":42,"locked":13,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,55,60,65],{"code":30,"score":48,"supported":49,"title":50,"abstract":53,"tags":54},21,true,[51,52],"农药","pesticide",[51],[28,26],{"code":37,"score":56,"supported":49,"title":57,"abstract":58,"tags":59},9,[25],[],[25],{"code":61,"score":19,"supported":13,"title":62,"abstract":63,"tags":64},"I1",[],[],[24],{"code":66,"score":19,"supported":13,"title":67,"abstract":68,"tags":69},"I2",[],[],[27],[71,72],"农业机器人 视觉语言模型 农药喷洒","MPPI 轨迹跟踪 作物行检测","农业机器人视觉语言模型农药喷洒-4648",2,{"doi":8,"openalex_id":8,"authors":76,"venue":8,"cited_by_count":77,"oa_url":8,"card":78,"direction":82,"ingested_from":84},[],0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"提出基于视觉-语言模型的上下文自适应农药喷洒框架，结合MPPI控制实现精准喷洒。","视觉-语言模型空间推理，融合作物、农药与气象数据，MPPI轨迹跟踪控制。","作物行检测精度至少提升30%，可灵活调节喷洒量与速度并减少农药漂移。","智慧农业 \u002F 农业物联网","预印本未实地验证，可探索真实多变天气地形下的VLM鲁棒性与多模态数据融合优化。","agent","2026-10-11T00:09:47.810289Z",{"total":18,"page":19,"page_size":18,"items":87},[88,184,243,293,355,432],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":8,"source_name":94,"source_url":91,"published_at":95,"category":11,"cover_url":8,"hotness":96,"is_selected":13,"score":97,"score_detail":98,"sources":103,"tags":107,"primary_topic":110,"secondary_topics":116,"classification":118,"search_phrases":158,"slug":161,"view_count":19,"doi":162,"paper":163,"created_at":183},4323,"Intelligent Sprinkler: Design and Evaluation of a Low-Cost Mobile Agricultural Robot for Sensor-Driven Irrigation and Vision-Guided Targeted Spraying","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23151414","Uniform irrigation and blanket spraying can apply water or treatment liquid without accounting for local field conditions. This work presents the design of the Intelligent Sprinkler, a low-cost tracked mobile agricultural platform that combines soil and climate sensing, sensor-driven irrigation, camera-based target detection, targeted liquid delivery, obstacle-aware navigation, and feedback logging within a SENSE–PERCEIVE–DECIDE–NAVIGATE–ACTUATE–MEASURE–FEEDBACK loop. The platform uses a Raspberry Pi for high-level processing and a microcontroller for real-time sensing and actuation. Irrigation is implemented as a threshold-based closed-loop controller rather than as a machine-learning irrigation predictor, while YOLOv8n is specified for target detection and edge deployment. The evaluation protocol defines controlled irrigation, vision, spraying, navigation, and complete-cycle experiments with predefined metrics and statistical tests. This manuscript includes an explicitly labeled synthetic dataset to demonstrate the intended reporting structure. The numerical values are not experimental findings and are placeholders for verified measurements from the physical prototype.","均匀灌溉和全面喷施可以在不考虑田间局部条件的情况下施加水或处理液。本研究介绍了智能喷灌机（Intelligent Sprinkler）的设计，这是一种低成本履带式移动农业平台，在“感知—认知—决策—导航—执行—测量—反馈”闭环中集成了土壤与气候传感、传感器驱动灌溉、基于摄像头的目标检测、定向液体输送、障碍物感知导航以及反馈记录。该平台使用树莓派（Raspberry Pi）进行高层处理，并使用微控制器进行实时传感与执行。灌溉以基于阈值的闭环控制器实现，而非机器学习灌溉预测器，同时指定YOLOv8n用于目标检测和边缘部署。评估方案定义了受控灌溉、视觉、喷施、导航和完整周期实验，并预设了指标和统计检验。本手稿包含一个明确标注的合成数据集，以展示预期的报告结构。其中的数值并非实验发现，而是用于物理样机验证测量的占位符。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-05T00:00:00Z",25,52,{"impact":99,"substance":100,"depth":101,"authority":56,"freshness":56,"relevant":19,"comment":102},8,12,14,"低成本履带式农业机器人整合传感灌溉与视觉靶向施药，方法框架清晰，但数值均为合成占位数据，尚缺实测验证，暂不宜进入每日精选。",[104,105],{"name":94,"url":91},{"name":94,"url":106},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23151415",[24,25,108,26,109],"边缘计算","精准灌溉",{"code":111,"slug":112,"name":113,"path":114,"parent_code":115},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[117],{"code":37,"slug":38,"name":39,"path":40,"parent_code":34},{"status":42,"locked":13,"version":43,"method":44,"evidence":119},{"candidates":120,"agricultural_evidence":49},[121,127,132,139,145,149,153],{"code":111,"score":122,"supported":49,"title":123,"abstract":124,"tags":126},15,[112],[125,112],"灌溉",[109],{"code":37,"score":56,"supported":49,"title":128,"abstract":130,"tags":131},[129],"agricultural robot",[],[25],{"code":133,"score":18,"supported":49,"title":134,"abstract":135,"tags":138},"H2",[],[136,137],"土壤","soil",[],{"code":66,"score":18,"supported":49,"title":140,"abstract":141,"tags":144},[],[142,143],"目标检测","机器学习",[],{"code":30,"score":19,"supported":13,"title":146,"abstract":147,"tags":148},[],[],[26],{"code":61,"score":19,"supported":13,"title":150,"abstract":151,"tags":152},[],[],[24],{"code":154,"score":19,"supported":13,"title":155,"abstract":156,"tags":157},"I5",[],[],[108],[159,160],"Intelligent Sprinkler 农业机器人","YOLOv8n 靶向施药","IntelligentSprinkler农业机器人-4323","10.5281\u002Fzenodo.23151414",{"doi":162,"openalex_id":164,"authors":165,"venue":94,"cited_by_count":77,"oa_url":91,"card":176,"direction":181,"ingested_from":182},"W7220424553",[166,168,170,172,174],{"name":167,"orcid":8},"Sampathkumar Nithyanantham",{"name":169,"orcid":8},"Doddi Sricharan Rao",{"name":171,"orcid":8},"Venkatesh Gurram",{"name":173,"orcid":8},"Satyanarayana Reddy Karri L O V E",{"name":175,"orcid":8},"Balaji Muram Reddy",{"tldr":177,"method":178,"finding":179,"direction":82,"opportunity":180},"设计并评估一款低成本履带式农业机器人，实现传感驱动灌溉与视觉引导靶向喷洒。","基于树莓派与微控制器，采用阈值闭环灌溉和YOLOv8n目标检测，含合成数据演示。","提出完整感知-决策-执行闭环架构，但数值为占位符，尚未有真实实验结果。","可开展真实田间实验验证性能，并探索机器学习灌溉预测替代阈值控制。","农业人工智能与决策模型","openalex","2026-10-06T23:30:38.487282Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":189,"content":8,"source_name":190,"source_url":187,"published_at":191,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":192,"score_detail":193,"sources":196,"tags":198,"primary_topic":200,"secondary_topics":201,"classification":202,"search_phrases":218,"slug":221,"view_count":206,"doi":222,"paper":223,"created_at":242},1279,"Scene-graph-grounded vision–language reasoning to action for autonomous multipurpose agricultural robots","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112347","Scene-graph-grounded vision–language reasoning to action for autonomous multipurpose agricultural robots。Computers and Electronics in Agriculture","面向自主多用途农业机器人的场景图接地视觉-语言推理到行动。计算机与电子在农业中的应用","Computers and Electronics in Agriculture","2026-08-31T00:00:00Z",76,{"impact":17,"substance":194,"depth":16,"authority":101,"freshness":74,"relevant":19,"comment":195},22,"论文提出基于场景图的视觉-语言推理方法，提升农业机器人自主作业能力，方法新颖，数据详实，但发表于2026年8月，时效性较低。",[197],{"name":190,"url":187},[24,199,25,27],"农业人工智能",{"code":37,"slug":38,"name":39,"path":40,"parent_code":34},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":203},{"candidates":204,"agricultural_evidence":49},[205,210,214],{"code":37,"score":206,"supported":49,"title":207,"abstract":208,"tags":209},4,[],[25],[25],{"code":66,"score":74,"supported":13,"title":211,"abstract":212,"tags":213},[],[],[199,27],{"code":61,"score":19,"supported":13,"title":215,"abstract":216,"tags":217},[],[],[24],[219,220],"农业人工智能 视觉语言模型 农业机器人 智慧农业","农业人工智能 视觉语言模型","农业人工智能视觉语言模型农业机器人智慧农业-1279","10.1016\u002Fj.compag.2026.112347",{"doi":222,"openalex_id":224,"authors":225,"venue":190,"cited_by_count":77,"oa_url":8,"card":237,"direction":181,"ingested_from":182},"W7204843057",[226,229,231,234],{"name":227,"orcid":228},"Yonghyun Park","https:\u002F\u002Forcid.org\u002F0000-0001-6085-5957",{"name":230,"orcid":8},"Changjo Kim",{"name":232,"orcid":233},"Gangmin Kim","https:\u002F\u002Forcid.org\u002F0009-0007-3565-4900",{"name":235,"orcid":236},"Hyoung Il Son","https:\u002F\u002Forcid.org\u002F0000-0002-7249-907X",{"tldr":238,"method":239,"finding":240,"direction":181,"opportunity":241},"提出场景图增强的视觉-语言-行动模型，用于自主多功能农业机器人。","利用场景图、视觉-语言模型和推理到行动机制。","场景图提升农业机器人对复杂场景的理解和任务执行能力。","可探索场景图在农业机器人多任务协同、动态环境适应及人机交互中的进一步应用。","2026-09-01T23:30:01.788088Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":8,"content":248,"source_name":249,"source_url":8,"published_at":250,"category":251,"cover_url":8,"hotness":12,"is_selected":13,"score":252,"score_detail":253,"sources":257,"tags":259,"primary_topic":263,"secondary_topics":264,"classification":265,"search_phrases":288,"slug":291,"view_count":77,"doi":8,"paper":8,"created_at":292},5420,"广西印发具身智能产业创新发展五年行动计划 布局具身智能加现代农业场景","https:\u002F\u002Fwww.nanning.gov.cn\u002Fzt\u002Frdzt\u002Fyhyshj\u002Fyszc\u002Fbrphcx\u002Ft6740281.html","广西壮族自治区党委科技委员会办公室印发《具身智能产业创新发展五年行动计划（2026—2030年）》（桂科委办发〔2026〕12号），在农业领域明确要求凝练具身智能在农产品采摘、优势作物种植、养殖巡检等真实场景的技术需求，面向矿山开采、农产品采摘、海洋渔业、物流运输等场景开展特种机器人技术攻关。行动提出到2030年组织实施30项以上重点科技攻关项目、培育2家以上具身智能龙头企业、10家以上科技型中小企业、打造20个以上应用场景标杆，其中包括「具身智能+现代农业」场景。农业机器人与采摘装备由此获得明确的地方产业政策支持路径。","（十）挖掘特色应用场景需求。建立健全具身智能应用场景挖掘机制，面向企业、高校、科研院所、医疗机构等，在工业制造、教育培训、科研应用、民生服务等领域常态化征集具身智能应用场景需求，挖掘一批技术适配性强、应用价值高、示范效应好、可复制推广的具身智能应用场景。（责任单位：自治区数据局、发展改革委、教育厅、科技厅、工业和信息化厅、卫生健康委等）\n\n（十一）大力培育标杆应用场景。聚焦广西有色金属及关键金属材料、先进钢铁材料、现代绿色化工、新一代信息技术、机械及高端装备制造、新能源汽车及电池、林产加工及纸业、食品加工、生物医药及健康、绿色建材等十大支柱产业发展需求，围绕矿山无人化、高危环境巡检、材料分拣、精密装配、物料搬运、智能检测、远程运维、智能仓储等重要环节，打造“具身智能+制造业”应用场景；在优势作物种植、采摘、养殖巡检等现代农业领域开展创新应用与示范推广，打造“具身智能+现代农业”应用场景；在港口物流、能源、文旅等领域拓展具身智能应用，打造一批融合应用场景。到2030年，打造20个以上应用场景标杆。（责任单位：自治区科技厅、发展改革委、工业和信息化厅、交通运输厅、农业农村厅、文化和旅游厅、数据局等）","南宁市人民政府门户网站 2026-10-15","2026-10-15T00:00:00Z","政策",71,{"impact":16,"substance":17,"depth":254,"authority":255,"freshness":18,"relevant":19,"comment":256},16,11,"省级具身智能五年行动计划明确布局“具身智能+现代农业”场景，含2030年20个标杆等量化目标，政策条款具体，对农业机器人方向有实质参考价值。",[258],{"name":249,"url":246},[24,25,260,261,262],"具身智能","现代农业","产业规划",{"code":37,"slug":38,"name":39,"path":40,"parent_code":34},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":266},{"candidates":267,"agricultural_evidence":49},[268,272,279,283],{"code":37,"score":206,"supported":49,"title":269,"abstract":270,"tags":271},[],[25],[25],{"code":273,"score":274,"supported":49,"title":275,"abstract":276,"tags":278},"D",3,[],[277],"渔业",[],{"code":61,"score":19,"supported":13,"title":280,"abstract":281,"tags":282},[],[],[24],{"code":284,"score":19,"supported":13,"title":285,"abstract":286,"tags":287},"J2",[],[],[261],[289,290],"广西 具身智能 现代农业","具身智能 农业机器人 应用场景","广西具身智能现代农业-5420","2026-10-21T00:09:54.769055Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":8,"source_name":299,"source_url":296,"published_at":300,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":301,"score_detail":302,"sources":305,"tags":307,"primary_topic":311,"secondary_topics":312,"classification":313,"search_phrases":330,"slug":333,"view_count":19,"doi":334,"paper":335,"created_at":354},5240,"Low-Cost Car-Like Autonomous Mobile Robot Under-Canopy Row Following, and Detection of Patterns Associated with Diseases in Plantain Crops","https:\u002F\u002Fdoi.org\u002F10.3991\u002Fijoe.v22i10.61223","Labor constraints and the need for frequent visual inspection of lower plantain (Musa paradisiaca) leaves in Colombia’s Orinoquia region motivate the development of low-cost robotic tools capable of autonomous under-canopy navigation and systematic image capture. This study reports the current stage of an ongoing research effort on a low-cost, small-scale, car-like agricultural robot for autonomous under-canopy row-following and systematic image capture in plantain fields. The platform uses a modular ROS 2 workflow, a low-cost 2D LiDAR, an inertial measurement unit, and an ESP32-based actuation layer. LiDAR data were processed through an adaptive measurement cone guided by the vehicle heading error to estimate the lateral distance to the crop row during forward row-following motion, and a fuzzy controller generated steering references. Initial field tests across five trials provided preliminary evidence of consistent under-canopy navigation under the tested conditions. In the first rowfollowing segment, the median MAE and RMSE of the lateral error were 0.0258 m and 0.0342 m, respectively; after the row-to-row maneuver, they were 0.0356 m and 0.0602 m, respectively. Additionally, an upward-facing RGB camera captured under-canopy images that were processed offline to create probability-based visual summaries of leaf-condition patterns. The results show that low-cost robotic navigation can help structure under-canopy data collection for scalable crop health monitoring in plantain plantations.","哥伦比亚奥里诺基亚地区劳动力受限，且需要对下部车前草（Musa paradisiaca）叶片进行频繁目视检查，这促使人们开发能够自主冠层下导航并系统采集图像的低成本机器人工具。本研究报道了针对一种低成本、小型、类车式农业机器人开展的在研工作的当前阶段，该机器人用于车前草田间的自主冠层下行跟随与系统图像采集。该平台采用模块化ROS 2工作流、低成本二维激光雷达、惯性测量单元以及基于ESP32的驱动层。激光雷达数据通过由车辆航向误差引导的自适应测量锥进行处理，以在前进行跟随运动中估计与作物行的横向距离，并由模糊控制器生成转向参考。在五种试验条件下的初步田间测试提供了在所测条件下冠层下导航具有一致性的初步证据。在第一个行跟随段中，横向误差的中位MAE和RMSE分别为0.0258 m和0.0342 m；在行间机动之后，分别为0.0356 m和0.0602 m。此外，一台朝上的RGB相机采集了冠层下图像，并对其进行离线处理，以生成基于概率的叶片状况模式视觉摘要。结果表明，低成本机器人导航有助于构建冠层下数据采集流程，从而支持车前草种植园中可扩展的作物健康监测。","International Journal of Online and Biomedical Engineering (iJOE)","2026-10-16T00:00:00Z",70,{"impact":100,"substance":17,"depth":303,"authority":100,"freshness":56,"relevant":19,"comment":304},17,"低成本ROS 2农业机器人实现冠层下自主行跟随与叶片病害图像采集，方法新颖、田间数据初步可靠，但属细分领域阶段性成果，公共影响有限。",[306],{"name":299,"url":296},[24,25,308,309,310],"自主导航","作物病害检测","香蕉种植",{"code":37,"slug":38,"name":39,"path":40,"parent_code":34},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":314},{"candidates":315,"agricultural_evidence":49},[316,321,326],{"code":37,"score":317,"supported":49,"title":318,"abstract":319,"tags":320},7,[],[25,129],[25],{"code":322,"score":19,"supported":13,"title":323,"abstract":324,"tags":325},"F5",[],[],[308],{"code":61,"score":19,"supported":13,"title":327,"abstract":328,"tags":329},[],[],[24],[331,332],"低成本农业机器人 大蕉 冠层导航","plantain 病害 图像检测","低成本农业机器人大蕉冠层导航-5240","10.3991\u002Fijoe.v22i10.61223",{"doi":334,"openalex_id":336,"authors":337,"venue":299,"cited_by_count":77,"oa_url":348,"card":349,"direction":82,"ingested_from":182},"W7222629670",[338,341,343,345],{"name":339,"orcid":340},"Danna Celena Montenegro","https:\u002F\u002Forcid.org\u002F0009-0003-0909-7806",{"name":342,"orcid":8},"Iván D. Duarte",{"name":344,"orcid":8},"Andres Leonardo Jutinico",{"name":346,"orcid":347},"Henry Borrero Guerrero","https:\u002F\u002Forcid.org\u002F0000-0003-4243-4205","https:\u002F\u002Fonline-journals.org\u002Findex.php\u002Fi-joe\u002Farticle\u002Fdownload\u002F61223\u002F17460",{"tldr":350,"method":351,"finding":352,"direction":82,"opportunity":353},"开发低成本类车机器人实现车前草冠层下自主行跟随与图像采集。","ROS 2、2D LiDAR、IMU、ESP32及模糊控制器，离线处理RGB图像","横向误差中位数MAE约0.026-0.036米，验证了低成本导航可行性。","可研究多行自主切换、病害图像在线识别及复杂冠层下鲁棒导航。","2026-10-18T23:30:09.565705Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":8,"source_name":361,"source_url":358,"published_at":250,"category":11,"cover_url":8,"hotness":96,"is_selected":13,"score":362,"score_detail":363,"sources":367,"tags":372,"primary_topic":376,"secondary_topics":377,"classification":378,"search_phrases":407,"slug":410,"view_count":77,"doi":411,"paper":412,"created_at":431},5069,"The impact of digital technology on green economies","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1787960","Introduction Digital agriculture is regarded as an important instrument for achieving green economies, with the potential to raise productivity while reducing environmental impacts. This study explores how digitalization in agriculture affects greenhouse gas emissions, pesticide use and productivity. Methods The research uses a mixed-methods design. Quantitative analysis draws on international environmental and economic indicators for approximately 180 countries between 2000 and 2023 (415 country-year observations), combining Spearman correlations, regression models with GDP interaction terms, and K-means cluster analysis. A qualitative case study of digital agriculture in Latin America complements the statistical findings. Results Digital infrastructure is strongly associated with the adoption of agricultural digital technologies, and smart farming investment is associated with better economic performance of the agricultural sector. Investment in digital agriculture shows a positive association with both emissions and pesticide use, while the interaction with GDP is negative and significant. Cluster analysis confirms that countries with higher GDP and stronger digital infrastructure achieve more balanced environmental outcomes. Discussion The environmental benefits of digital agriculture are conditional on economic capacity. In economically advanced settings, digital technologies reduce resource use, waste and emissions; in economically constrained regions, higher output can offset the environmental gains. Digital agricultural transformation therefore offers opportunities for sustainable development, but its success depends on equitable access to digital technologies and on policy support that mitigates the trade-off between productivity and environmental protection.","引言 数字农业被视为实现绿色经济的重要工具，具有提高生产力同时减少环境影响的潜力。本研究探讨农业数字化如何影响温室气体排放、农药使用和生产力。方法 研究采用混合方法设计。定量分析基于约180个国家2000年至2023年的国际环境和经济指标（415个国家-年份观测值），结合斯皮尔曼相关性分析、包含GDP交互项的回归模型以及K均值聚类分析。拉丁美洲数字农业的定性案例研究补充了统计发现。结果 数字基础设施与农业数字技术的采用密切相关，智慧农业投资与农业部门更好的经济表现相关。数字农业投资与排放量和农药使用量均呈正相关，而与GDP的交互项为负且显著。聚类分析证实，GDP较高且数字基础设施较强的国家实现了更平衡的环境结果。讨论 数字农业的环境效益以经济能力为条件。在经济发达环境中，数字技术减少了资源使用、浪费和排放；在经济受限地区，更高的产出可能抵消环境收益。因此，数字农业转型为可持续发展提供了机遇，但其成功取决于数字技术的公平获取以及能够缓解生产力与环境保护之间权衡的政策支持。","Frontiers in Sustainable Food Systems",86,{"impact":194,"substance":364,"depth":365,"authority":101,"freshness":99,"relevant":19,"comment":366},23,19,"基于180国2000—2023年面板数据与拉美案例，揭示数字农业环境效益受经济水平制约，方法扎实、结论有政策参考价值。",[368,369],{"name":361,"url":358},{"name":370,"url":371},"Frontiers in Sustainable Food Systems 2026-10-15","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1787960\u002Ffull",[373,24,374,375,28],"数字农业","农业数字化","农业碳排放",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":379},{"candidates":380,"agricultural_evidence":49},[381,386,390,397,402],{"code":61,"score":100,"supported":49,"title":382,"abstract":383,"tags":385},[],[24,373,384],"digital agriculture",[374,373,24],{"code":30,"score":317,"supported":49,"title":387,"abstract":388,"tags":389},[],[51,52],[28],{"code":391,"score":18,"supported":49,"title":392,"abstract":393,"tags":396},"B7",[],[394,395],"温室","greenhouse",[],{"code":398,"score":19,"supported":13,"title":399,"abstract":400,"tags":401},"H4",[],[],[375],{"code":403,"score":19,"supported":13,"title":404,"abstract":405,"tags":406},"I",[],[],[374],[408,409],"数字农业 温室气体排放","拉丁美洲 数字农业 案例","数字农业温室气体排放-5069","10.3389\u002Ffsufs.2026.1787960",{"doi":411,"openalex_id":413,"authors":414,"venue":361,"cited_by_count":77,"oa_url":424,"card":425,"direction":82,"ingested_from":182},"W7221916400",[415,418,421],{"name":416,"orcid":417},"Botond Géza Kálmán","https:\u002F\u002Forcid.org\u002F0000-0001-8031-8016",{"name":419,"orcid":420},"Zsuzsanna Szeiner","https:\u002F\u002Forcid.org\u002F0000-0003-3051-9674",{"name":422,"orcid":423},"Szilvia Valéria Módosné Szalai","https:\u002F\u002Forcid.org\u002F0009-0005-2164-9014","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1787960\u002Fpdf",{"tldr":426,"method":427,"finding":428,"direction":429,"opportunity":430},"研究约180国2000-2023年数据，分析农业数字化对温室气体排放、农药使用与生产率的影响。","混合方法：Spearman相关、含GDP交互项的回归、K-means聚类，辅以拉","数字农业环境效益取决于经济能力，高GDP国家更均衡，低GDP国家增产抵消环境收益。","农业绿色发展与碳","可探究不同经济水平下数字农业减排阈值与政策干预机制，关注数字鸿沟对绿色转型的调节作用。","2026-10-16T23:30:06.211945Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":8,"source_name":438,"source_url":435,"published_at":439,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":440,"score_detail":441,"sources":443,"tags":445,"primary_topic":449,"secondary_topics":450,"classification":451,"search_phrases":462,"slug":465,"view_count":19,"doi":466,"paper":467,"created_at":500},4991,"Direct‐Printed Textile Circuits for Smart Gloves With Enhanced Integration","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fadem.71290","Integrating deformable sensors, conductive routing, embedded electronics, and local data processing on a textile carrier remains challenging for wearable gesture interfaces. Here, a direct‐printed textile circuit smart glove was constructed using a hybrid textile–electronic architecture. Low‐resistance carbon paste and Ag paste were sequentially screen‐printed to form the strain‐sensitive layer, electrodes, interconnects, and component pads. Packaged modules were attached with silver‐filled epoxy electrically conductive adhesive and protected by localized silicone‐rubber encapsulation. The sensor produced a linear response over 2.5%–15% strain ( R 2 = 0.9995), with response and recovery times of approximately 200 and 100 ms, and stable electrical output during a 10 000s test corresponding to about 4000 cycles at 0.4 Hz. An ESP32‐based threshold classifier processed five finger channels locally and recognized eight static gestures with an average accuracy of 95.2% over 480 trials. The encoded gestures were then used to command a uHand 2.0 bionic hand and an agricultural robot. These results demonstrate the system‐level feasibility of the hybrid textile–electronic architecture for integrating textile sensing, on‐device gesture recognition, wireless communication, and robotic command execution.","将可变形传感器、导电布线、嵌入式电子器件和本地数据处理集成到纺织品载体上，对于可穿戴手势接口而言仍具挑战性。本文采用混合纺织-电子架构构建了一种直写印刷纺织电路智能手套。通过依次丝网印刷低阻碳浆和银浆，形成应变敏感层、电极、互连线和元件焊盘。封装模块采用银填充环氧导电胶粘接，并通过局部硅橡胶封装进行保护。该传感器在2.5%–15%应变范围内呈线性响应（R² = 0.9995），响应时间和恢复时间分别约为200 ms和100 ms，在10 000 s测试（对应0.4 Hz下约4000次循环）中电输出保持稳定。基于ESP32的阈值分类器在本地处理五个手指通道，在480次试验中以平均95.2%的准确率识别八种静态手势。随后，编码手势被用于控制uHand 2.0仿生手和一台农业机器人。这些结果证明了混合纺织-电子架构在集成纺织传感、设备端手势识别、无线通信和机器人指令执行方面的系统级可行性。","Advanced Engineering Materials","2026-10-14T00:00:00Z",81,{"impact":16,"substance":194,"depth":16,"authority":101,"freshness":56,"relevant":19,"comment":442},"直印纺织电路智能手套实现本地手势识别并驱动农业机器人，系统级验证扎实，对农业人机交互有参考价值。",[444],{"name":438,"url":435},[24,25,446,447,448],"可穿戴设备","人机交互","柔性传感",{"code":37,"slug":38,"name":39,"path":40,"parent_code":34},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":452},{"candidates":453,"agricultural_evidence":49},[454,458],{"code":37,"score":317,"supported":49,"title":455,"abstract":456,"tags":457},[],[25,129],[25],{"code":61,"score":19,"supported":13,"title":459,"abstract":460,"tags":461},[],[],[24],[463,464],"直印纺织电路 智能手套","ESP32 手势识别 农业机器人","直印纺织电路智能手套-4991","10.1002\u002Fadem.71290",{"doi":466,"openalex_id":468,"authors":469,"venue":438,"cited_by_count":77,"oa_url":8,"card":495,"direction":82,"ingested_from":182},"W7221824460",[470,472,474,476,478,480,482,484,487,489,492],{"name":471,"orcid":8},"Hao Wu",{"name":473,"orcid":8},"Huimin Ren",{"name":475,"orcid":8},"Shubei Zhi",{"name":477,"orcid":8},"Xutao Huang",{"name":479,"orcid":8},"Junxian Guo",{"name":481,"orcid":8},"Siyu Li",{"name":483,"orcid":8},"Lingxiao Cao",{"name":485,"orcid":486},"Zhonghao Wang","https:\u002F\u002Forcid.org\u002F0009-0004-7227-3929",{"name":488,"orcid":8},"Yunlong Ni",{"name":490,"orcid":491},"Zhi‐Zhu He","https:\u002F\u002Forcid.org\u002F0000-0001-8861-9964",{"name":493,"orcid":494},"Zhen Li","https:\u002F\u002Forcid.org\u002F0000-0001-6010-0298",{"tldr":496,"method":497,"finding":498,"direction":82,"opportunity":499},"开发直接印刷纺织电路智能手套，集成传感、本地手势识别与机器人控制。","丝网印刷碳\u002F银浆料制备柔性电路，ESP32本地阈值分类，控制仿生手与农业机器人。","传感器线性度R²=0.9995，8种手势识别平均准确率95.2%，可稳定控制机器人。","可探索将智能手套用于农业机器人遥操作、采摘辅助或农户动作数据采集与技能数字化。","2026-10-15T23:30:09.135126Z"]