[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3128":3,"related-3128":63},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":62},3128,"Spatiotemporal synchronization-based seed position estimation for multi-row precision planting","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112396","Spatiotemporal synchronization-based seed position estimation for multi-row precision planting。Computers and Electronics in Agriculture","基于时空同步的多行精量播种种子位置估计。《农业计算机与电子》",null,"Computers and Electronics in Agriculture","2026-09-22T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,20,14,1,"核心期刊论文，提出基于时空同步的多行精量播种种子位置估计方法，对智能播种机具研发有参考价值，但属细分技术进展，公共影响有限。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","智能农机","玉米","精准播种",[31,32],"多行精准播种 种子位置估计","时空同步 播种监测","多行精准播种种子位置估计-3128",0,"10.1016\u002Fj.compag.2026.112396",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":9,"card":55,"direction":59,"ingested_from":61},"W7213976849",[39,42,44,47,49,51,53],{"name":40,"orcid":41},"Lin Jia","https:\u002F\u002Forcid.org\u002F0009-0002-2422-7974",{"name":43,"orcid":9},"Qingjie Wang",{"name":45,"orcid":46},"Hongwen Li","https:\u002F\u002Forcid.org\u002F0000-0002-5536-2346",{"name":48,"orcid":9},"Chao Wang",{"name":50,"orcid":9},"Jin He",{"name":52,"orcid":9},"Caiyun Lu",{"name":54,"orcid":9},"Xinyue Zhang",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"提出基于时空同步的多行精量播种种子位置估计方法。","利用播种机多行作业的时空同步信号估计种子落点位置。","该方法能实现多行精量播种的种子位置估计，提升播种质量监测精度。","智慧农业 \u002F 农业物联网","可结合机器视觉与GNSS实时校正，发展播种质量在线评估与变量播种闭环控制。","openalex","2026-09-22T23:30:01.680027Z",{"total":64,"page":20,"page_size":64,"items":65},6,[66,90,115,178,211,238],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":9,"content":71,"source_name":72,"source_url":9,"published_at":11,"category":73,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":80,"tags":82,"search_phrases":85,"slug":88,"view_count":34,"doi":9,"paper":9,"created_at":89},3215,"秋分逢丰收节 机器人成主角！浙江田野正被AI\"接管\"——浙江农科院数字农业研究所研发AI眼镜+害虫识别小程序","https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714","9-22 新蓝网专题报道：在湖州德清县农博家庭农场，种植大户王菊仙戴上一副AI眼镜，对着诱杀害虫的黄板轻轻一扫，\"镜片上、手机端，种类、数量、位置等数据瞬间显现\"。这套由浙江省农科院数字农业研究所研发的设备，正将虫害防控从\"事后补救\"推向\"提前预警\"。在湖州吴兴丰盛湾水产种业，\"云眸\"沼虾养殖AI系统能在3-5秒内捕捉沼虾触须末端细微影像，自动生成比对图谱，一旦发现活动异常即刻标记预警，自动投料机器人与水下传感器联动精准计算投喂量，饲料利用率提升12%-15%、巡塘人力节省六成、养殖效益整体提高10%以上。在杭州余杭区径山镇，无人驾驶拖拉机搭载北斗导航系统自主作业。在杭州临平区田立方未来农场，450亩无人智慧农场示范区配套200余个田间传感器和4个物联网微基站，可根据土壤饱和度和实时水位自动确定浇灌量，一亩地一季油菜花可节水约1000吨。浙江省农业农村厅数据显示，截至目前浙江已累计建成数字农业工厂729家、未来农场63家。","![Image 2](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FloginBg-OXMHhVd9.png)\n\n验证码登录\n\n获取验证码\n\n 一键登录 \n\n- [x]  \n\n登录代表同意 《用户协议》及 《隐私政策》\n\n扫码登录\n\n![Image 3](https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714)\n\n鼠标悬浮刷新二维码\n\n打开Z视介扫码登录\n\n![Image 4](blob:http:\u002F\u002Flocalhost\u002F08f361b6a75bd12fbead3ead9d0ae42d)\n\n[![Image 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网络举报APP下载 \n\n 违法和不良信息公开举报电话：12377、0571-81089789 有害信息举报邮箱：jubao@12377.cn 涉未成年人有害信息举报电话: 0571-81089789 \n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fvideo)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveTV)\n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fradio)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveRadio)","新蓝网","报道",60,{"impact":76,"substance":19,"depth":77,"authority":78,"freshness":78,"relevant":20,"comment":79},16,12,9,"省级官媒报道浙江农科院数字农业研究所AI眼镜与害虫识别小程序落地，属智慧农业细分进展，时效性强但正文信息量有限。",[81],{"name":72,"url":69},[83,25,26,27,84],"数字农业","害虫识别",[86,87],"浙江农科院 数字农业研究所 AI眼镜","浙江 害虫识别 小程序","浙江农科院数字农业研究所AI眼镜-3215","2026-09-23T00:04:29.218317Z",{"id":91,"title":92,"url":93,"summary":94,"summary_zh":9,"content":95,"source_name":96,"source_url":9,"published_at":97,"category":73,"cover_url":9,"hotness":13,"is_selected":98,"score":99,"score_detail":100,"sources":104,"tags":106,"search_phrases":110,"slug":113,"view_count":34,"doi":9,"paper":9,"created_at":114},3210,"【光明论坛】藏粮于地、藏粮于技 扎实推进乡村全面振兴——农业新质生产力引领\"十五五\"种植业高质量发展","https:\u002F\u002Fepaper.gmw.cn\u002Fgmrb\u002Fhtml\u002Fcontent\u002F202609\u002F23\u002Fcontent_25817.html","9-23 光明日报\"光明论坛\"刊文解读农业农村部近日印发的《全国种植业发展\"十五五\"规划》：规划在深入实施藏粮于地、藏粮于技战略的同时，专门就\"发挥农业新质生产力引领作用\"作出部署。种植业科技战略正由以良田、良种、良机、良法集成为主，向数据、算法、智能装备与生物技术深度融合拓展。2025年全国粮食总产量达14298亿斤、增长1.2%，单位面积产量增长1.1%，单产提升是增产的主要来源；农作物耕种收综合机械化率达76.7%，农业科技进步贡献率超过64%；农用无人机保有量超过30万架，年作业面积突破4.6亿亩。规划首次提出以精准监测、AI决策、自主作业为重点发展智慧种植，推动无人机、具身机器人等先进装备与传统植保深度融合。","**【光明论坛】**\n\n习近平总书记指出，建设农业强国，利器在科技，关键靠改革。近日，农业农村部印发《全国种植业发展“十五五”规划》，在深入实施藏粮于地、藏粮于技战略的同时，专门就“发挥农业新质生产力引领作用”作出部署。这表明，种植业科技战略正由以良田、良种、良机、良法集成为主，向数据、算法、智能装备与生物技术深度融合拓展。以新质生产力引领科技战略升级是未来五年种植业高质量发展的重要路径。\n\n实施藏粮于地、藏粮于技战略并非抽象概念，它实实在在体现在产量、装备和技术水平的变化里。相关数据显示，2025年全国粮食总产量达14298亿斤，比上年增长1.2%，其中单位面积产量增长1.1%，单产提升是增产的主要来源；农作物耕种收综合机械化率达76.7%，农业科技进步贡献率超过64%；农用无人机保有量超过30万架，年作业面积突破4.6亿亩，农机北斗终端设备应用超过220万台（套），智慧农场、智能育秧工厂等智能化场景不断涌现。《“十四五”全国种植业发展规划》把“坚持创新驱动，转型升级”作为基本原则，把“增强科技支撑”列入保障措施，智能化内容散见于相关章节，提出“探索应用智慧农业技术”。新规划则将发挥农业新质生产力引领作用单列为一个部分，与增强供给保障能力、推进绿色转型、增强防灾减灾救灾能力并列，科技由“支撑”走向“引领”，这是这份规划最鲜明的亮点。\n\n科技引领具体体现在五个方面的部署上。在品种上，把抗赤霉病小麦、耐密宜机收玉米、高油高产大豆等列为选育重点，向突破性品种要产量；在技术上，强调集成推广引领性技术，并把提高技术到位率和覆盖率作为推广要求；在装备上，一手抓大型高端智能农机，一手抓丘陵山区轻简高效农机，让不同地形都有合用的机具；在投入品上，在种植业五年规划中首次提出运用基因编辑、核酸干扰等前沿手段创制核糖核酸农药等新型产品；在场景上，首次提出以精准监测、AI决策、自主作业为重点发展智慧种植，推动无人机、具身机器人等先进装备与传统植保深度融合，并提出培育无人农机操作员等专业技术人员。这些部署与2026年中央一号文件促进人工智能与农业发展相结合的要求相衔接，也服务于国务院《加快农业农村现代化“十五五”规划》提出的“到2030年，粮食等重要农产品供给保障能力稳步提升，粮食安全根基持续夯实，农业质量效益和竞争力不断提高”的目标。其实质，是生产力三要素的同步跃升，劳动对象从良田良种扩展到数据资源，劳动资料从机械装备升级为智能装备和模型算法，劳动者从传统农机手拓展到新型专业人才。“藏粮于地”稳的是根基，“藏粮于技”强的是手段，粮食产能不仅沉淀在耕地和良种里，也生成于以数据集成与智能作业为代表的农业新质生产力之中。\n\n目前，把产能真正蓄积到数据和智能作业之中，还要经历一段爬坡过坎的过程，应清晰地看到，这场生产方式变革还处在“起步期”。因此，未来需要给农业新质生产力发展加点“催化剂”，推动其由示范应用走向大面积普及。\n\n应加快关键核心技术攻关，使其成为农业新质生产力的“发动机”。以国家重大科技任务为牵引，设立智慧农业科技创新专项，聚焦高精度低成本传感器、农业专用芯片、作物生长模型与核心算法、智能决策系统、重型智能农机装备等领域，实行“揭榜挂帅”机制，推动产学研深度联动，明确企业创新主体地位，引导创新资源向农业科技领军企业集聚。同步夯实数据要素基础，加快构建“天空地”一体化农情灾情监测网络，健全数据标准和管理体系，面向育种、栽培、植保、农机作业等场景建设高质量数据集，形成“场景牵引数据、数据驱动模型、模型赋能应用、应用创造价值”的良性循环，并探索建立数据权益确认和收益分享机制，促进数据安全有序流通。\n\n应发展农业社会化服务，使其成为智能技术进村入田的“摆渡船”。通过发挥专业化服务组织和农机社会化服务中心的作用，通过托管、代耕代种等方式，将智能农机、植保无人机和智能决策服务导入小农户生产，使农户不必购置装备也能用上智能技术。通过鼓励服务主体提供贯穿全生产周期的农事指导、灾害预警等信息服务，把技术优势转化为农户的经营收益。通过加快智慧农场等应用场景示范推广，以技术到位率覆盖率和增产增效实绩检验成效，推动智能技术由点上示范向面上应用拓展。\n\n应完善配套制度体系，使其成为推动农业新质生产力发展的“稳压器”。通过建立适应智能农机、算法模型、数据产品等新型成果的分类评价机制，让科研力量向大田需求集聚。通过推动农机购置与应用补贴向智能装备和智能作业服务延伸，引导政策资源向智能化方向倾斜。通过加快前沿产品的登记资料要求、命名规则和评价标准制定，同步完善农业数据安全管理，使新技术新产品有标可依、有规可循。通过推进基层农技推广体系改革与建设，培育大田无人农机操作员、现代设施农业技术员等专业技术人才，为智能技术落地提供人才支撑。\n\n从“藏粮于地”到“藏粮于技”，体现了种植业科技战略在新一轮科技革命和产业变革中的深化拓展。“十五五”时期是基本实现农业农村现代化的关键时期，面向未来，当数据跃升为产能提升的核心要素，算法沉淀为生产决策的智能引擎，智能装备演化为田间作业的新型农具，农业新质生产力便会从战略蓝图照进田野现实。我们期待中国人的饭碗端得更稳、成色更足。\n\n**（作者：王洋，系东北农业大学经济管理学院副院长、教授）**","光明日报","2026-09-22T23:00:00Z",true,93,{"impact":101,"substance":102,"depth":17,"authority":19,"freshness":78,"relevant":20,"comment":103},28,24,"央媒权威解读种植业“十五五”规划，首次将农业新质生产力单列部署，政策条款与数据详实，值得进入每日精选。",[105],{"name":96,"url":93},[25,26,27,107,108,109],"农业新质生产力","藏粮于技","种植业规划",[111,112],"农业新质生产力 智慧种植","农业新质生产力 农业人工智能 种植业规划 智慧农业","农业新质生产力智慧种植-3210","2026-09-23T00:04:27.059734Z",{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":129,"tags":131,"search_phrases":134,"slug":137,"view_count":34,"doi":138,"paper":139,"created_at":177},3199,"Recommendation of Suitable Planting Locations for Hybrid Maize Varieties Based on Graph Collaborative Filtering","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finsilicoplants\u002Fdiag026","Abstract Enhancing the precision of crop variety adaptability evaluation is critical for maximizing yields and ensuring food security under climate change. Traditional methods relying on expert knowledge or process-based models face scalability challenges and difficulties in capturing complex variety–environment interactions. This study proposes a scalable graph-based collaborative filtering framework for assessing suitable planting locations for hybrid maize varieties, reformulating adaptability evaluation as a ranking task. The framework integrates two key innovations: a multi-layer perceptron (MLP) encoding module that leverages 15 variety trait features and 24 environmental variables to address the cold-start problem, enabling recommendations for previously unseen varieties; and a BPR+ loss function that distinguishes unsuitable environments from merely unplanted ones to accommodate the unique sparsity structure of agricultural trial data. Evaluated against six state-of-the-art graph-based recommendation models on a dataset of 189 locations and 1,102 hybrid maize varieties across China's major agro-ecological zones, our method consistently outperforms existing approaches, achieving approximately 5% improvement over the strongest baseline. The MLP module attains cold-start NDCG above 0.8, and BPR+ yields significant gains in Recall@1–4 and NDCG@1–5. A case study of the widely cultivated variety ZD958, validated against both national promotion data (2019–2024) and multi-year field-trial yields from 40 locations (2017–2024), confirms that the model's rank-ordered recommendations align closely with independent yield evidence: top-ranked locations achieve both the highest productivity and the greatest yield stability. This framework provides an AI-based decision support tool for variety placement optimization and climate-resilient agriculture.","摘要 提高作物品种适应性评价的精度，对于在气候变化背景下最大化产量和保障粮食安全至关重要。依赖专家知识或过程模型的传统方法面临可扩展性挑战，且难以捕捉复杂的品种–环境互作。本研究提出了一种可扩展的基于图协同过滤框架，用于评估杂交玉米品种的适宜种植地点，将适应性评价重新表述为排序任务。该框架融合了两项关键创新：一是多层感知机（MLP）编码模块，利用15个品种性状特征和24个环境变量来缓解冷启动问题，从而能够对未见过的品种进行推荐；二是BPR+损失函数，将不适宜环境与仅未种植环境区分开来，以适应农业试验数据特有的稀疏结构。在中国主要农业生态区189个地点和1，102个杂交玉米品种的数据集上，与六种最先进的基于图的推荐模型进行对比评估，我们的方法始终优于现有方法，相较最强基线提升约5%。MLP模块的冷启动NDCG达到0.8以上，BPR+在Recall@1–4和NDCG@1–5上均带来显著提升。对广泛种植品种ZD958的案例研究，结合国家推广数据（2019–2024年）和40个地点多年田间试验产量数据（2017–2024年）进行验证，证实模型的排序推荐与独立产量证据高度一致：排名靠前的地点既具有最高生产力，也具有最大产量稳定性。该框架为品种布局优化和气候韧性农业提供了基于人工智能的决策支持工具。","in silico Plants","2026-09-20T00:00:00Z",79,{"impact":17,"substance":125,"depth":17,"authority":126,"freshness":127,"relevant":20,"comment":128},22,13,8,"提出基于图协同过滤的杂交玉米品种适宜种植区推荐框架，覆盖全国189个地点、1102个品种，冷启动与排序指标均有提升，对品种布局优化有实用价值。",[130],{"name":121,"url":118},[25,26,132,28,133],"种业振兴","品种布局",[135,136],"杂交玉米 品种 适宜种植区","图协同过滤 玉米 推荐","杂交玉米品种适宜种植区-3199","10.1093\u002Finsilicoplants\u002Fdiag026",{"doi":138,"openalex_id":140,"authors":141,"venue":121,"cited_by_count":34,"oa_url":118,"card":171,"direction":175,"ingested_from":61},"W7213886755",[142,145,148,151,153,156,159,162,165,168],{"name":143,"orcid":144},"Yanyun Han","https:\u002F\u002Forcid.org\u002F0000-0003-4333-8565",{"name":146,"orcid":147},"Zhongqiang Liu","https:\u002F\u002Forcid.org\u002F0009-0008-4290-8718",{"name":149,"orcid":150},"Aiwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0031-5857",{"name":152,"orcid":9},"Yong Zhang",{"name":154,"orcid":155},"Shouhui Pan","https:\u002F\u002Forcid.org\u002F0000-0003-4917-9921",{"name":157,"orcid":158},"Xiangyu Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7668-1509",{"name":160,"orcid":161},"Qiusi Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5469-1802",{"name":163,"orcid":164},"Qi Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-5061-5422",{"name":166,"orcid":167},"Xinglin Piao","https:\u002F\u002Forcid.org\u002F0000-0003-3774-5789",{"name":169,"orcid":170},"Kaiyi Wang","https:\u002F\u002Forcid.org\u002F0009-0000-5365-7178",{"tldr":172,"method":173,"finding":174,"direction":175,"opportunity":176},"提出图协同过滤框架，将玉米品种适种区评估转化为排序任务，推荐适宜种植地点。","图协同过滤+MLP编码15个品种性状与24个环境变量，BPR+损失，189地点1","方法优于六个基线约5%，冷启动NDCG超0.8，推荐排名与独立产量证据高度一致。","农业人工智能与决策模型","可拓展至多作物多品种跨区域迁移推荐，并融合气候情景预测未来适种区变化。","2026-09-22T23:30:49.262877Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":189,"tags":191,"search_phrases":194,"slug":197,"view_count":34,"doi":198,"paper":199,"created_at":210},3194,"Optimization and Analysis of Agricultural Robot Drive Systems","https:\u002F\u002Fdoi.org\u002F10.54254\u002F2753-8818\u002F2026.37178","This study takes the agricultural robot drive system as the research object and carries out optimization research from two dimensions: mechanical structure and intelligent control. At the mechanical level, redundant degrees of freedom are eliminated through topological analysis of mechanism freedom, a 'short-chain direct-drive' transmission scheme is introduced to reduce mechanical losses, and finite element topology optimization is combined to achieve lightweight design of key components. At the control level, artificial intelligence is introduced to establish a perception system based on convolutional neural networks (CNN) and multi-source information fusion, enabling accurate recognition and prediction of complex terrain. A Back Propagation (BP) neural network Proportion Integration Differentiation (PID) control strategy based on the Dung beetle Optimization (DBO)algorithm is proposed, which effectively addresses the shortcomings of traditional algorithms, such as slow convergence and large overshoot. Meanwhile, model predictive control and an improved soft actor-critic reinforcement learning algorithm are integrated to achieve online adaptive regulation of drive parameters and global optimal control to a considerable extent. The optimized drive system effectively improves the working performance and environmental adaptability of agricultural robots in complex terrain and provides new theoretical and technical ideas for the development of intelligent agricultural machinery.","本研究以农业机器人驱动系统为研究对象，从机械结构与智能控制两个维度开展优化研究。在机械层面，通过机构自由度拓扑分析去除冗余自由度，引入“短链直驱”传动方案以降低机械损耗，并结合有限元拓扑优化实现关键零部件的轻量化设计。在控制层面，引入人工智能，建立基于卷积神经网络（CNN）与多源信息融合的感知系统，实现对复杂地形的准确识别与预测；提出基于蜣螂优化（DBO）算法的BP神经网络PID控制策略，有效改善传统算法收敛慢、超调大等不足；同时融合模型预测控制与改进的柔性演员-评论家强化学习算法，实现驱动参数的在线自适应调节及较大程度上的全局最优控制。优化后的驱动系统有效提升了农业机器人在复杂地形下的作业性能与环境适应性，为智能农业机械的发展提供了新的理论与技术思路。","Theoretical and Natural Science",69,{"impact":77,"substance":18,"depth":187,"authority":13,"freshness":13,"relevant":20,"comment":188},17,"论文提出短链直驱与DBO-BP-PID等控制优化方案，方法新颖但偏理论，产业影响有限。",[190],{"name":184,"url":181},[25,26,192,27,193],"农业机器人","驱动系统",[195,196],"农业机器人 驱动系统 优化","DBO BP神经网络 PID控制","农业机器人驱动系统优化-3194","10.54254\u002F2753-8818\u002F2026.37178",{"doi":198,"openalex_id":200,"authors":201,"venue":184,"cited_by_count":34,"oa_url":181,"card":205,"direction":175,"ingested_from":61},"W7213976318",[202],{"name":203,"orcid":204},"Ziyuan Ma","https:\u002F\u002Forcid.org\u002F0000-0002-7931-3258",{"tldr":206,"method":207,"finding":208,"direction":175,"opportunity":209},"从机械结构与智能控制两方面优化农业机器人驱动系统，提升复杂地形适应性与作业性能。","机构自由度拓扑分析、有限元拓扑优化、CNN多源融合感知、DBO-BP-PID与强","优化驱动系统有效提升农业机器人在复杂地形下的作业性能与环境适应性。","可探索轻量化驱动与在线强化学习控制在真实农田多机协同中的泛化性与能耗权衡。","2026-09-22T23:30:39.723894Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":9,"content":9,"source_name":216,"source_url":214,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":217,"sources":219,"tags":221,"search_phrases":224,"slug":227,"view_count":34,"doi":228,"paper":229,"created_at":237},3152,"An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2725400","An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania。Cogent Food & Agriculture","Cogent Food & Agriculture",{"impact":77,"substance":17,"depth":76,"authority":126,"freshness":13,"relevant":20,"comment":218},"论文提出物联网与机器学习融合的玉米产量预测框架，方法有新意但属区域性应用研究，影响力有限，时效性强。",[220],{"name":216,"url":214},[25,26,222,223,28],"产量预测","物联网",[225,226],"坦桑尼亚 玉米 产量预测","物联网 机器学习 智慧农业","坦桑尼亚玉米产量预测-3152","10.1080\u002F23311932.2026.2725400",{"doi":228,"openalex_id":230,"authors":231,"venue":216,"cited_by_count":34,"oa_url":214,"card":9,"direction":59,"ingested_from":61},"W7213978365",[232,235],{"name":233,"orcid":234},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":236,"orcid":9},"Yasinta Nzogera","2026-09-22T23:30:10.178097Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":10,"source_url":241,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":247,"tags":249,"search_phrases":252,"slug":255,"view_count":34,"doi":256,"paper":257,"created_at":280},3127,"Robust slip-ratio regulation for agricultural tractors with transformer-enhanced speed estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112436","Robust slip-ratio regulation for agricultural tractors with transformer-enhanced speed estimation。Computers and Electronics in Agriculture","农业拖拉机滑转率的鲁棒调节：基于变压器增强的速度估计。《计算机与电子农业》",74,{"impact":77,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":246},"核心期刊最新论文，将Transformer用于拖拉机速度估计与滑转率鲁棒控制，方法新颖但属细分技术进展，产业影响有限。",[248],{"name":10,"url":241},[25,26,27,250,251],"拖拉机","滑转率控制",[253,254],"农业拖拉机 滑转率 控制","Transformer 车速估计 拖拉机","农业拖拉机滑转率控制-3127","10.1016\u002Fj.compag.2026.112436",{"doi":256,"openalex_id":258,"authors":259,"venue":10,"cited_by_count":34,"oa_url":9,"card":275,"direction":175,"ingested_from":61},"W7213970956",[260,262,264,267,269,272],{"name":261,"orcid":9},"Xianghai Yan",{"name":263,"orcid":9},"Zijian Tong",{"name":265,"orcid":266},"Hang Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0881-0553",{"name":268,"orcid":9},"Liyou Xu",{"name":270,"orcid":271},"Yiwei Wu","https:\u002F\u002Forcid.org\u002F0000-0003-0866-1228",{"name":273,"orcid":274},"Di Ao","https:\u002F\u002Forcid.org\u002F0000-0002-9455-7111",{"tldr":276,"method":277,"finding":278,"direction":175,"opportunity":279},"提出拖拉机滑转率鲁棒调节方法，用Transformer增强速度估计。","Transformer增强的速度估计与鲁棒滑转率控制。","Transformer提升速度估计精度，实现滑转率鲁棒调节。","可探索Transformer在农机复杂工况下多传感器融合与实时控制中的泛化能力。","2026-09-22T23:30:01.599253Z"]