[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2107":3,"related-2107":69},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":8,"created_at":68},2107,"浙江湖州印发\"人工智能+农业\"实施方案 力争到2030年累计建设省级数字农业工厂110家以上","http:\u002F\u002Fwww.came.net.cn\u002FsecondLevelPage\u002Finfo\u002F218\u002F225631","近日湖州印发《大力发展\"人工智能+农业\"创新应用实施方案》,提出到2030年底全市累计建设省级数字农业工厂110家以上、省级未来农场18家以上,打造\"人工智能+农业\"应用场景30个以上,农业生产信息化水平位居全省前列。截至目前,湖州已累计创建省级未来农场13家、数字农业工厂88家,数量均居全省首位。南浔区与浙大团队合作构建\"天基扫描普查、空基细查建模、地基验证执行\"的三维闭环。",null,"清晨，浙江湖州长兴县和平镇的千亩稻田里，种粮大户吴传意站在田埂上，掏出手机轻点几下。无人驾驶的AI投喂车应声启动，沿田埂匀速前行，车载传感器实时回传青蛙分布密度，自动调节投喂量与行进速度。“哪里青蛙多，就多撒一些；哪里少，自动减量。”吴传意说，每跑一趟，投喂数据都会上传后台，生成一份详实的“吃饭报告”。\n\n这是湖州田野上的寻常一幕。一亩地，蛙稻共作，纯利润是普通稻田的3到4倍。\n\n近日，湖州印发《大力发展“人工智能+农业”创新应用实施方案》，提出到2030年底，全市累计建设省级数字农业工厂110家以上、省级未来农场18家以上，打造“人工智能+农业”应用场景30个以上，农业生产信息化水平位居全省前列。截至目前，湖州已累计创建省级未来农场13家、数字农业工厂88家，数量均居全省首位。\n\n![Image 1](http:\u002F\u002F202.127.42.160:3009\u002Fupload_file\u002F2026\u002F09\u002F08\u002F20260908085615487.jpg)\n\n人工智能究竟能为农业解决什么？连日来，记者走进湖州田野探寻答案。\n\n种养不再是“经验活”\n\n种地最大的困境，是“看不见”。水质好坏靠手摸鼻闻，虫害多少靠逐块清点，墒情肥力靠经验揣度。等发现问题，往往已错过最佳干预窗口。\n\n湖州早些年便开始破题——让数据替代直觉。\n\n南浔区双林镇8000亩示范园区内，技术人员在10公里外轻点鼠标，遥感无人机从机库自动升空。多光谱扫描后，土壤养分分布化作三维热力图传回大屏，种植户手机里同步弹出变量施肥处方图——哪块地缺氮、哪块地少磷，一目了然。\n\n这张“感知网”正越织越密。南浔区与浙江大学团队合作，构建起“天基扫描普查、空基细查建模、地基验证执行”的三维闭环：卫星每日提供全域作物长势初判，遥感无人机完成厘米级建模，地面126类传感器实时校准数据。去年，南浔区农用无人机作业面积达21.73万亩，占全区耕种面积的78.6%，亩均用工减少30%、增产8%。\n\n更细微的“看见”，正在发生。在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”。团队还配套研发了害虫识别小程序与小微害虫测报仪。\n\n在德清，3500个农业物联网应用示范点已铺展开来。洛舍镇先锋农事服务中心的智能育秧大棚里，“AI助手24小时在线”——棚内温度低于20℃自动关窗，高于25℃自动开窗。工作人员通过手机实时查看各项数据，育秧周期从45天缩短到25天，一亩智能化育秧中心培育的秧苗可供100亩稻田。\n\n《实施方案》明确，加速推进5G和物联网技术在现代农业园区、未来农场等核心区域全覆盖，推动视频图像、光谱、气象、生物体征等多元数据融合采集——让每一寸土地都“开口说话”。\n\n![Image 2](http:\u002F\u002F202.127.42.160:3009\u002Fupload_file\u002F2026\u002F09\u002F08\u002F20260908085634146.jpg)\n\n数据真正“用起来”\n\n数据的真正价值，在于“预见”。如果海量数据只停留在屏幕上，不过是好看的气泡图与折线图。\n\n《实施方案》提出，迭代升级安吉白茶产业大脑、智慧渔业在线、湖羊天下等特色产业大脑，推动从“数据展示平台”向“智能决策中枢”转型，让数据不仅能“看见”，更能“预见”。\n\n在吴兴丰盛湾水产种业，一套名为“云眸”的沼虾养殖AI系统，正在改写养虾规则。塘埂边，水下传感器实时捕捉水质、溶氧、温度等关键指标。浙江省淡水水产研究所副研究员徐洋介绍，“云眸”能在3到5秒内捕捉沼虾触须末端的细微影像，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，精准计算虾群每餐的“饭量”与投喂“饭点”。系统投用后，饲料利用率提升12%至15%，巡塘人力节省六成，养殖效益整体提高10%以上。\n\n安吉白茶产业大脑则是另一个样本。依托遥感技术完成全县20余万亩茶园测绘，1.7万户茶农茶园精准定位，区块链技术打通从种植指导、农资施用到茶叶交易的全链路数据。安吉龙王山茶叶开发有限公司推行“产业大脑+数字工厂”模式，新茶品质实现了从茶园到茶杯的数字化全过程管控。\n\n《实施方案》在此基础上进一步提出，研发白茶长势研判、品质预测、病虫害识别专用模型，推动品牌管理与智能化运营深度融合。\n\n市农业农村局表示，“人工智能+农业”的深层价值，是推动农业从“靠天吃饭”走向“知天而作”。\n\n在种养殖业之外，AI的“预见”能力还在向农业治理端延伸。上月，第四次全国农业普查主要畜禽养殖设施用地遥感测量全面铺开。与以往不同，此次普查综合运用卫星遥感、智能终端、AI识别等现代化技术——高分辨率卫星化身“高空天眼”全域扫描，地面普查员手持智能终端实地核验，最终形成养殖场（户）遥感测量成果地理信息数据库。\n\n![Image 3](http:\u002F\u002F202.127.42.160:3009\u002Fupload_file\u002F2026\u002F09\u002F08\u002F20260908085650935.jpg)\n\n田间地头“有帮手”\n\n看得见、想得透，最终还要有人干得了。\n\n《实施方案》明确，推广“产业大脑+未来农场+智能装备”一体化模式，推进农业全链条设施智能化改造。这一切的落脚点，是田间地头真正用得上、用得起的装备与人。\n\n在硬件层面，一批聚焦农业机器人的科技企业正在湖州生长。\n\n“95后”青年李焕运把实验室搬进了乡村。他带领团队围绕大田、果园、大棚等场景，研发出植保机器人、大马力作业机器人、旋耕机器人等近10款产品。南方大棚内北斗信号容易被遮挡，团队研发出冗余技术，将控制精度提升到1.5厘米，达到行业极限。如今，两台形似微型坦克的农业机器人已投入作业，喷雾、旋耕同步进行。\n\n浙江大学湖州研究院孵化的创毅科技，聚焦农机智能远程管理，研发出北斗定位终端、电动水闸远程遥控装置，让农机位置、作业状态实现“一键智控”。\n\n在人才层面，《实施方案》提出每年培育数字“三农”人才1000人左右，构建“高端研发人才+技术应用人才+一线操作人才+经营管理人才”梯队。吴兴区妙西镇，“90后”农创客刘文超从互联网大厂回村，主导建成9栋数字化鸡舍，搭建蛋禽养殖数字平台，全面应用自动喂料、集蛋、清粪设备。像他这样的年轻人，正成为湖州田野上活跃的“数智力量”。\n\n浙北田野上的这场变革才刚刚开始。湖州农业的“智”变，正从一亩田、一方塘、一座棚中，生长出无限可能。","中国农业机械化信息网","2026-09-08T00:00:00Z","报道",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},18,22,17,7,1,"湖州出台地市级\"人工智能+农业\"实施方案，目标数字明确，并配以蛙稻共作、AI眼镜识虫、云眸养虾、白茶产业大脑等一线案例，政策条款与落地场景兼具，信息增量扎实，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"农业人工智能","农业机器人","未来农场","数字农业工厂","产业大脑","浙江湖州",{"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},[45,52,58],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},12,true,[49],"数字农业",[49],[30],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"F6",2,[],[],[27,28],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"I2",[],[],[26],[64,65],"农业人工智能 数字农业工厂 农业机器人 产业大脑","农业人工智能 数字农业工厂","农业人工智能数字农业工厂农业机器人产业大脑-2107",3,"2026-09-11T00:04:21.740843Z",{"total":70,"page":21,"page_size":70,"items":71},6,[72,147,200,238,283,334],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":77,"content":8,"source_name":78,"source_url":75,"published_at":79,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":88,"tags":90,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":113,"slug":116,"view_count":117,"doi":118,"paper":119,"created_at":146},3908,"A Situational Awareness-Driven Human-Machine Interface Framework for Enhancing Operators’ Performance in Agricultural Robotic Teleoperation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10846-026-02469-8","Abstract Despite the increasing adoption of automated\u002Fautonomous guided vehicles (AGVs) in precision agriculture, existing human-machine interfaces (HMIs) remain function-centric, frequently neglecting the cognitive and user experience (UX) needs of non-technical operators. Consequently, current dashboards suffer from limited usability, high cognitive load, and reduced system transparency, increasing the risk of operator error. To address this gap within agricultural teleoperation interface design, this study presents a theory-driven HMI framework that systematically translates Endsley’s Situational Awareness (SA) theory (Perception, Comprehension, Projection) and ISO 9241 ergonomic principles into a structured three-panel AGV teleoperation interface. Unlike conventional function-oriented dashboards, the proposed framework organises interface components according to operator cognitive requirements to support information perception, comprehension, and projection during remote operation. A theory-driven agricultural teleoperation HMI framework was developed by translating cognitive and ergonomic principles into practical interface organization strategies. To formatively evaluate this SA-driven design architecture, we conducted a user-perception study with non-expert digitally literate participants (N = 20), evaluating three iterative HMI prototypes. The comparative results indicate that the SA-aligned layout received higher user ratings in perceived decision-support capability (SA3), cognitive workload reduction, and overall usability compared with baseline designs during formative evaluation. The study provides preliminary methodological guidance for agricultural teleoperation HMI design, while recognizing that objective operational validation with target agricultural users remains necessary.","摘要 尽管自动\u002F自主导引车（AGV）在精准农业中的应用日益广泛，现有的人机界面（HMI）仍以功能为中心，常常忽视非技术操作人员的认知与用户体验（UX）需求。因此，当前仪表盘存在可用性有限、认知负荷高、系统透明度低等问题，增加了操作人员出错的风险。为弥补农业远程操作界面设计中的这一空白，本研究提出了一种理论驱动的HMI框架，将Endsley的情境意识（SA）理论（感知、理解、预测）与ISO 9241工效学原则系统性地转化为结构化的三面板AGV远程操作界面。与传统以功能为导向的仪表盘不同，所提框架依据操作人员的认知需求组织界面组件，以支持远程操作过程中的信息感知、理解与预测。通过将认知与工效学原则转化为实际的界面组织策略，开发了理论驱动的农业远程操作HMI框架。为对该SA驱动设计架构进行形成性评估，我们开展了一项用户感知研究，招募具有数字素养的非专家参与者（N = 20），对三个迭代HMI原型进行评价。比较结果表明，在形成性评估中，与基线设计相比，符合SA的布局在感知决策支持能力（SA3）、认知负荷降低和整体可用性方面获得了更高的用户评分。本研究为农业远程操作HMI设计提供了初步的方法学指导，同时认识到仍需以目标农业用户开展客观的操作验证。","Journal of Intelligent & Robotic Systems","2026-09-30T00:00:00Z","论文",67,{"impact":83,"substance":84,"depth":19,"authority":85,"freshness":86,"relevant":21,"comment":87},8,20,13,9,"该论文提出基于态势感知理论的农机遥操作人机界面框架，方法新颖且有用户实验支撑，对智慧农业交互设计有参考价值，但属初步形成性评估，实际应用影响有限。",[89],{"name":78,"url":75},[91,26,27,92,93],"智慧农业","人机交互","远程操控",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":97},{"candidates":98,"agricultural_evidence":47},[99,105,109],{"code":33,"score":20,"supported":47,"title":100,"abstract":101,"tags":104},[],[102,103],"精准农业","precision agriculture",[91],{"code":53,"score":21,"supported":14,"title":106,"abstract":107,"tags":108},[],[],[27],{"code":59,"score":21,"supported":14,"title":110,"abstract":111,"tags":112},[],[],[26],[114,115],"农业机器人 远程操控 人机界面","态势感知 农机 界面设计","农业机器人远程操控人机界面-3908",4,"10.1007\u002Fs10846-026-02469-8",{"doi":118,"openalex_id":120,"authors":121,"venue":78,"cited_by_count":137,"oa_url":75,"card":138,"direction":144,"ingested_from":145},"W7214925819",[122,125,128,131,134],{"name":123,"orcid":124},"Khang Duong","https:\u002F\u002Forcid.org\u002F0009-0005-6817-9646",{"name":126,"orcid":127},"Thien Anh Tran","https:\u002F\u002Forcid.org\u002F0000-0001-6780-6664",{"name":129,"orcid":130},"Minh Quang Tran","https:\u002F\u002Forcid.org\u002F0000-0002-1215-3587",{"name":132,"orcid":133},"Dinh-Son Vu","https:\u002F\u002Forcid.org\u002F0000-0002-0567-6419",{"name":135,"orcid":136},"Byron Mason","https:\u002F\u002Forcid.org\u002F0000-0002-9530-5020",0,{"tldr":139,"method":140,"finding":141,"direction":142,"opportunity":143},"提出基于态势感知理论的农机遥操作人机界面框架，提升非专业操作者表现。","将Endsley态势感知理论与ISO 9241原则转化为三面板界面，20人用户感","态势感知对齐布局在决策支持、降低认知负荷和可用性上优于基线设计。","智慧农业 \u002F 农业物联网","可开展面向真实农户的客观操作绩效验证，并探索多机协同遥操作的认知适配界面。","数字乡村与农业信息化","openalex","2026-10-01T23:30:39.385589Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":8,"content":8,"source_name":152,"source_url":150,"published_at":153,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":160,"tags":162,"primary_topic":165,"secondary_topics":166,"classification":167,"search_phrases":179,"slug":182,"view_count":137,"doi":183,"paper":184,"created_at":199},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":156,"substance":157,"depth":17,"authority":158,"freshness":13,"relevant":21,"comment":159},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。",[161],{"name":152,"url":150},[91,26,102,163,164],"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":168},{"candidates":169,"agricultural_evidence":47},[170,175],{"code":33,"score":171,"supported":47,"title":172,"abstract":173,"tags":174},5,[],[103],[91,102],{"code":59,"score":54,"supported":14,"title":176,"abstract":177,"tags":178},[],[],[26,163],[180,181],"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":183,"openalex_id":185,"authors":186,"venue":152,"cited_by_count":137,"oa_url":8,"card":8,"direction":8,"ingested_from":145},"W7223117828",[187,190,193,196],{"name":188,"orcid":189},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":191,"orcid":192},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":194,"orcid":195},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":197,"orcid":198},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":8,"content":8,"source_name":205,"source_url":203,"published_at":206,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":207,"score_detail":208,"sources":210,"tags":212,"primary_topic":214,"secondary_topics":215,"classification":216,"search_phrases":227,"slug":230,"view_count":137,"doi":231,"paper":232,"created_at":237},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":83,"depth":13,"authority":46,"freshness":83,"relevant":21,"comment":209},"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。",[211],{"name":205,"url":203},[91,27,102,213],"智能采收",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":217},{"candidates":218,"agricultural_evidence":47},[219,223],{"code":33,"score":85,"supported":47,"title":220,"abstract":221,"tags":222},[103],[103],[91,102],{"code":53,"score":21,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[27],[228,229],"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":231,"openalex_id":233,"authors":234,"venue":8,"cited_by_count":137,"oa_url":203,"card":8,"direction":142,"ingested_from":145},"W7222971426",[235],{"name":236,"orcid":8},"Joydeep Dey","2026-10-20T23:30:08.600540Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":8,"source_name":244,"source_url":241,"published_at":206,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":154,"score_detail":245,"sources":247,"tags":249,"primary_topic":252,"secondary_topics":253,"classification":254,"search_phrases":266,"slug":269,"view_count":137,"doi":270,"paper":271,"created_at":282},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":18,"depth":17,"authority":46,"freshness":86,"relevant":21,"comment":246},"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。",[248],{"name":244,"url":241},[91,26,250,102,251],"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":255},{"candidates":256,"agricultural_evidence":47},[257,262],{"code":33,"score":84,"supported":47,"title":258,"abstract":260,"tags":261},[259],"digital twin",[102,250,103],[250,91,102],{"code":59,"score":21,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[26],[267,268],"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":270,"openalex_id":272,"authors":273,"venue":244,"cited_by_count":137,"oa_url":241,"card":276,"direction":280,"ingested_from":145},"W7222885652",[274],{"name":275,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":277,"method":278,"finding":279,"direction":280,"opportunity":281},"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":284,"title":285,"url":286,"summary":287,"summary_zh":8,"content":8,"source_name":288,"source_url":286,"published_at":289,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":290,"sources":292,"tags":294,"primary_topic":296,"secondary_topics":297,"classification":298,"search_phrases":314,"slug":317,"view_count":54,"doi":318,"paper":319,"created_at":333},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",{"impact":46,"substance":156,"depth":19,"authority":158,"freshness":83,"relevant":21,"comment":291},"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。",[293],{"name":288,"url":286},[91,26,102,295],"生物防治",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":299},{"candidates":300,"agricultural_evidence":47},[301,305,310],{"code":33,"score":85,"supported":47,"title":302,"abstract":303,"tags":304},[103],[103],[91,102],{"code":306,"score":21,"supported":14,"title":307,"abstract":308,"tags":309},"F4",[],[],[295],{"code":59,"score":21,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[26],[315,316],"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":318,"openalex_id":320,"authors":321,"venue":288,"cited_by_count":137,"oa_url":8,"card":8,"direction":142,"ingested_from":145},"W7221746877",[322,325,328,330],{"name":323,"orcid":324},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":326,"orcid":327},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":329,"orcid":8},"İsmail Alper Susurluk",{"name":331,"orcid":332},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":8,"content":8,"source_name":152,"source_url":337,"published_at":289,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":339,"score_detail":340,"sources":342,"tags":344,"primary_topic":348,"secondary_topics":349,"classification":350,"search_phrases":371,"slug":374,"view_count":21,"doi":375,"paper":376,"created_at":388},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",75,{"impact":156,"substance":84,"depth":19,"authority":158,"freshness":83,"relevant":21,"comment":341},"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。",[343],{"name":152,"url":337},[91,345,26,346,347],"无人机","遥感","大麦",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":351},{"candidates":352,"agricultural_evidence":47},[353,357,362,366],{"code":33,"score":117,"supported":47,"title":354,"abstract":355,"tags":356},[],[103],[91],{"code":358,"score":21,"supported":14,"title":359,"abstract":360,"tags":361},"A5",[],[],[347],{"code":59,"score":21,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[26],{"code":367,"score":21,"supported":14,"title":368,"abstract":369,"tags":370},"I3",[],[],[346],[372,373],"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":375,"openalex_id":377,"authors":378,"venue":152,"cited_by_count":137,"oa_url":8,"card":8,"direction":8,"ingested_from":145},"W7221758985",[379,382,385],{"name":380,"orcid":381},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":383,"orcid":384},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":386,"orcid":387},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z"]