[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4266":3,"related-4266":63},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":57,"slug":60,"view_count":61,"doi":8,"paper":8,"created_at":62},4266,"上海金山区举办果树产业绿色智慧发展技术研讨会","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7691262770355995199\u002F","9月29日上海金山区委统战部和区农业农村委联合举办果树产业绿色智慧发展技术研讨会。市农科院研究员骆军作《智慧化果园管理技术进展》报告、奚晓军作《葡萄智慧果园建设与数字化管控技术》报告、市农技中心博士陶海玉作《现代果园智慧提升前沿技术》报告，共同探讨果园绿色化智慧化建设路径。",null,"## 构建生态种植体系，赋能果园智管升级——金山区举办果树产业绿色智慧发展技术研讨会\n\n2026-09-30 17:53·[田舍风物](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAA2JO9o5WFfAutKbO3-HIWZ7VRDW8sfqqvQFLJ_XKCq-tgQYyWVwPY2Pmd1TlFr130\u002F?source=tuwen_detail)\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F5a049e06f15d4724a4f40d553927667b~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849966&x-signature=S1ZwuoY8OJsqX8BdUzqLvZP7ez4%3D)![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F7e8e0f97855b48938a4b78ad5549953c~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849966&x-signature=JSMuvqYsvDzw5GzxvftSCHvcL%2Fk%3D)\n为深入贯彻落实现代农业绿色高质量发展相关工作要求，充分发挥统一战线“同心聚力”优势，汇聚农业专家、农技人员、农业主体合力，以科技为抓手，加快推进金山区经济果林产业标准化、品牌化、数字化转型升级，9月29日，区委统战部和区农业农村委联合举办金山区果树产业绿色智慧发展技术研讨会。区委统战部副部长苏振洪出席会议，区农技中心党支部书记卢春光主持会议。会议邀请市农科院、市农技中心相关专家作专题报告，区、镇两级果树产业技术人员、果树种植户代表参加会议。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F582bf87a812c46d8a7473c0cfb723396~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849966&x-signature=kdjgepwFVB8237AGJxHgP%2BVVJkA%3D)\n会上，苏振洪指出，要持续深耕品牌化建设，共同推进我区果业高质量发展，对标果树产业国际化发展标准，抓实示范点位建设；要充分吸纳专家意见，用好专家工作站平台，走绿色、高效、智慧的现代化果业发展路径；要坚守产业发展底层逻辑，积极应对多维市场挑战，持续做强产业链，并落实好从业人员保险等基础保障工作，筑牢产业发展根基。\n\n本次研讨会聚焦果树产业绿色转型、智慧升级，邀请三位行业专家带来前沿专题报告，助力金山果树产业提质增效。\n\n市农科院研究员骆军以《智慧化果园管理技术进展》为题作专题报告，围绕产业发展概况、上海果园机械化、智慧化管理适配技术探索、果园智慧机械研发以及智慧果园建设的条件与问题四个方面展开深度分享。\n\n市农科院研究员奚晓军围绕《葡萄智慧果园建设与数字化管控技术》开展专题授课，从葡萄产业现状、智慧果园建设、数字化管控技术、应用效果及产业未来展望五个方面进行系统讲解。\n\n市农技中心博士陶海玉作《现代果园智慧提升前沿技术》专题报告，深入剖析果园智慧化升级的必要性、智慧果园技术体系、技术产业化路径，并结合上海果树产业发展实际，分享智慧化转型的思考。\n\n座谈交流环节，与会专家、农技人员及种植户代表聚焦金山区果树产业发展，围绕智慧农业应用场景落地、农业劳动力短缺、果园设施升级改造及设施农业长效管护等问题展开深入交流。\n\n此次研讨会精准对接金山果树产业发展实际需求，进一步明晰了果树产业绿色化、智慧化的发展路径，助力金山果树产业高质量、可持续发展。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002Ff51bd117bf844340830720ab9a3eb5da~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849966&x-signature=LdhsBrQh6ZmpcnQCplhIcDOwT4s%3D)\n**供稿：区农技中心**\n\n![Image 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F944dd73a7f5c4683af74dffd55d22317~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849966&x-signature=R5i3byEmfYM1mBCVaMcZfDTaDCM%3D)\n三农资讯 ｜ 农业旅游 ｜ 三农政策\n\n**激情 | 创新 | 务实 | 高效**\n\n**投稿邮箱 jinshansannong@sina.com**","今日头条 田舍风物 2026-09-30","2026-09-30T09:53:00Z","报道",10,false,40,{"impact":17,"substance":18,"depth":13,"authority":19,"freshness":20,"relevant":21,"comment":22},8,12,6,4,1,"地市级技术研讨会，聚焦果园智慧化与数字化管控，有专家报告实质内容，但层级与时效性一般。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","智慧农业","智慧果园","果树产业","上海金山",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},14,true,[34],[34,49],"葡萄",[],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":56},"I1",2,[],[],[26,27],[58,59],"上海金山区 果树产业 智慧发展","骆军 智慧化果园管理技术","上海金山区果树产业智慧发展-4266",0,"2026-10-06T00:05:02.406626Z",{"total":19,"page":21,"page_size":19,"items":64},[65,134,187,228,302,345],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":70,"content":8,"source_name":71,"source_url":68,"published_at":72,"category":73,"cover_url":8,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":79,"tags":81,"primary_topic":85,"secondary_topics":86,"classification":87,"search_phrases":110,"slug":113,"view_count":20,"doi":114,"paper":115,"created_at":133},3780,"Epistemological Friction: Embodied Expertise and the ‘Last Mile’ of Agricultural Automation in the US Tree‐Fruit Industry","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fntwe.70047","ABSTRACT Autonomous and AI‐driven agricultural technology is often framed as a panacea for labour dependency. While proponents envision autonomous technologies seamlessly optimising farming, this rationalised model misunderstands the reality of on‐farm integration. Based on an ethnographic and comparative study of 16 operations in the US West Coast Tree Fruit Industry (California and Washington), this article examines the ‘last mile’ of agricultural automation. The study reveals that integrating AI and robotics is a contested negotiation shaped by epistemological friction—the tension between the algorithmic logic of developers and the embodied, site‐specific expertise of farmers and farm workers. Rather than resulting in seamless labour replacement, we find that agricultural automation reconfigures work, creating an invisible reliance on ‘human middleware’ to navigate the biological recalcitrance of the orchard. Demonstrating how regional contexts and participatory implementation shape technological trajectories, this study extends Labour Process Theory. We argue for the need for a relational approach recognising the essential cognitive labour of farmers and farm workers that is demanded by digital agriculture. Ultimately, this study shows the ‘farm of the future’ depends on reconciling rigid systems with the situated expertise of the farm workforce.","自主与人工智能驱动的农业技术常被描绘为解决劳动力依赖问题的万能药。支持者设想自主技术能够无缝优化农业生产，然而这种理性化模型误解了农场整合的现实。本文基于对美国西海岸果树产业（加利福尼亚州和华盛顿州）16家经营主体的民族志比较研究，考察农业自动化的“最后一英里”。研究发现，人工智能与机器人技术的整合是一场充满争议的协商过程，其受到认识论摩擦的塑造——即开发者的算法逻辑与农民及农场工人具身的、场所特定的专业知识之间的张力。我们发现，农业自动化并未实现无缝的劳动力替代，而是重构了工作形态，制造了对“人类中间件”的隐性依赖，以应对果园的生物性阻力。本研究通过展示区域情境与参与式实施如何塑造技术轨迹，拓展了劳动过程理论。我们主张需要一种关系性方法，承认数字农业所要求的农民和农场工人不可或缺的认知劳动。最终，本研究表明“未来农场”取决于刚性系统与农场劳动力情境化专业知识之间的调和。","New Technology Work and Employment","2026-09-27T00:00:00Z","论文",80,{"impact":76,"substance":77,"depth":76,"authority":45,"freshness":17,"relevant":21,"comment":78},18,22,"基于美西16家果园民族志研究，揭示AI与机器人落地的“最后一公里”认识论摩擦与“人类中间件”隐性依赖，对智慧农业人机协同具参考价值。",[80],{"name":71,"url":68},[26,27,82,83,84],"农业人工智能","农业机器人","果园自动化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":88},{"candidates":89,"agricultural_evidence":46},[90,95,100,105],{"code":52,"score":17,"supported":46,"title":91,"abstract":92,"tags":94},[],[26,93],"digital agriculture",[26,27],{"code":32,"score":19,"supported":46,"title":96,"abstract":97,"tags":99},[],[34,98],"orchard",[],{"code":101,"score":21,"supported":14,"title":102,"abstract":103,"tags":104},"F6",[],[],[83],{"code":106,"score":21,"supported":14,"title":107,"abstract":108,"tags":109},"I2",[],[],[82],[111,112],"美国 果树 自动化","果园 机器人 劳动力","美国果树自动化-3780","10.1111\u002Fntwe.70047",{"doi":114,"openalex_id":116,"authors":117,"venue":71,"cited_by_count":61,"oa_url":124,"card":125,"direction":131,"ingested_from":132},"W7214618881",[118,121],{"name":119,"orcid":120},"M. Anne Visser","https:\u002F\u002Forcid.org\u002F0000-0001-5047-7702",{"name":122,"orcid":123},"Alex Bladimir Hernandez","https:\u002F\u002Forcid.org\u002F0009-0001-5320-7203","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1111\u002Fntwe.70047",{"tldr":126,"method":127,"finding":128,"direction":129,"opportunity":130},"通过美西果树业民族志研究，揭示农业自动化落地中算法逻辑与农户经验知识的冲突。","对加州和华盛顿16个果园开展民族志与比较研究。","自动化未无缝替代劳动，反而依赖‘人类中间件’协调生物不确定性。","数字乡村与农业信息化","可探索参与式设计如何将农户隐性知识嵌入AI系统，弥合‘最后一英里’鸿沟。","智慧农业 \u002F 农业物联网","openalex","2026-09-29T23:30:12.281947Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":8,"content":139,"source_name":140,"source_url":8,"published_at":141,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":149,"tags":151,"primary_topic":155,"secondary_topics":156,"classification":157,"search_phrases":181,"slug":184,"view_count":185,"doi":8,"paper":8,"created_at":186},131,"\"四良\"合力强根基——广西推动热区葡萄质量安全与品质提升,推广耘眼+托普云农智能监测系统覆盖100万亩","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_5376a6aa8d655552","人民日报报道:广西针对南方果园人工成本高、机械化程度低、病虫害预警滞后等痛点,以\"良机\"(强化数字赋能)实现果园降本提质增效。搭建智慧果园监测平台,推广\"耘眼\"和\"托普云农\"智能监测系统,搭配巡园机器人、田间传感器,自动采集作物长势、温湿度、病虫害图像,AI识别准确率达90%以上,可提前预警并推送科学用药方案,移动端面向果农开放用户超50万,推广面积突破100万亩;应用开沟、割草、打药、旋耕、轨道运输等全环节机械化装备,作业效率提升50%以上,农药化肥减量30%以上,亩产提升10%以上,综合生产成本下降20%以上。","为解决南方高温高湿、病虫害多发等问题，广西普及避雨栽培设施。棚膜隔绝雨水，阻断霜霉病、灰霉病传播路径，病虫害发生率下降60%以上，每年减少农药、人工成本千元以上，商品果率提升至85%，亩均节本增效超2500元，现已成为南方葡萄园标配设施。创新热区葡萄一年两收栽培技术。配套破眠催芽、采后修剪、产期调控成套方案，实现同园一年两茬结果采收。夏果于每年6—7月抢占早市，冬果于每年12月至次年1月错峰上市。广西秋冬光照充足、气候干燥、温差大，利于糖分、香气物质积累，巨峰葡萄冬果可溶性固形物含量较夏果提升约30%，夏黑葡萄冬果香气物质总量较夏果提升约60%。优质精品果亩产值突破1万元。该技术已辐射我国滇、粤、闽、琼等地及越南等东南亚国家，累计推广130万亩。推广标准化绿色高效栽培技术。发布《广西葡萄植物生长调节剂规范使用明白纸》，严格按照标签说明使用，落实安全间隔期。融合诱虫灯、色板等理化诱控技术，运用微生物菌剂、植物源农药生物防治手段，配套地布覆盖、防虫网、“果实打伞、套袋”管理，农药使用量较常规种植减少30%以上，筑牢果品安全底线。","人民日报 2026-07-30 14版","2026-07-30T00:00:00Z",83,{"impact":144,"substance":77,"depth":145,"authority":146,"freshness":147,"relevant":21,"comment":148},24,17,15,5,"央媒报道广西葡萄产业技术集成应用，数据详实，具有省级产业推广价值，但时效性稍弱。",[150],{"name":140,"url":137},[26,28,152,153,154],"广西","热区葡萄","数字赋能",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":158},{"candidates":159,"agricultural_evidence":46},[160,164,171,177],{"code":32,"score":17,"supported":46,"title":161,"abstract":162,"tags":163},[49],[],[],{"code":165,"score":166,"supported":46,"title":167,"abstract":168,"tags":170},"F4",3,[],[169],"农药",[],{"code":172,"score":166,"supported":46,"title":173,"abstract":174,"tags":176},"F5",[],[175],"机械化",[],{"code":52,"score":53,"supported":14,"title":178,"abstract":179,"tags":180},[],[],[26,154],[182,183],"数字农业 数字赋能 智慧果园 热区葡萄","数字农业 数字赋能","数字农业数字赋能智慧果园热区葡萄-131",9,"2026-08-01T06:01:52.467937Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":8,"content":192,"source_name":193,"source_url":8,"published_at":194,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":200,"tags":202,"primary_topic":206,"secondary_topics":207,"classification":208,"search_phrases":223,"slug":226,"view_count":61,"doi":8,"paper":8,"created_at":227},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z",65,{"impact":197,"substance":145,"depth":198,"authority":198,"freshness":19,"relevant":21,"comment":199},16,13,"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[201],{"name":193,"url":190},[27,203,49,204,205],"智能农机","栽培技术","国际交流",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":209},{"candidates":210,"agricultural_evidence":46},[211,215,219],{"code":32,"score":18,"supported":46,"title":212,"abstract":213,"tags":214},[49],[49],[49],{"code":52,"score":20,"supported":46,"title":216,"abstract":217,"tags":218},[],[27],[27],{"code":172,"score":21,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[203],[224,225],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516","2026-10-22T00:07:04.061911Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":234,"source_url":231,"published_at":235,"category":73,"cover_url":8,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":240,"tags":242,"primary_topic":246,"secondary_topics":247,"classification":248,"search_phrases":275,"slug":278,"view_count":61,"doi":279,"paper":280,"created_at":301},5401,"A HIERARCHICAL DISTRIBUTED EDGE AI FRAMEWORK FOR REAL-TIME APPLE LEAF DISEASE CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr260506002g","The application of artificial intelligence (AI) in agriculture has transformed crop management, particularly in the detection of plant diseases. Apple trees, a key agricultural commodity, are vulnerable to various diseases, which can severely impact crop yields. Traditional disease detection methods, often reliant on manual inspections, are time-consuming and prone to error. In response, this study proposes a novel two-stage edge AI framework for apple leaf disease classification that integrates RGB imagery with distributed edge computing. The first stage performs binary classification to distinguish between healthy and diseased leaves, achieving an F1 score of 95.50%. This optimization is achieved by utilizing lightweight models on edge devices, thereby enhancing computational efficiency. The second stage employs a consensus-based multiclass classification approach, achieving a 96.44% F1 score in identifying specific diseases. This dual-stage system, supported by an edge AI architecture, ensures real-time disease detection while minimizing resource consumption and enhancing scalability in large-scale agricultural environments. The full system achieved a 95.50% accuracy and F1 score on the special dataset. The results demonstrate the system’s efficacy in controlled laboratory conditions, offering a promising solution for precise, real-time agricultural monitoring. This study underscores the potential of distributed edge AI systems for scalable and efficient disease detection in precision agriculture.","人工智能（AI）在农业中的应用已深刻改变了作物管理方式，尤其是在植物病害检测方面。苹果树作为重要的农业经济作物，易受多种病害侵袭，严重影响作物产量。传统的病害检测方法通常依赖人工检查，耗时且易出错。为此，本研究提出了一种新颖的两阶段边缘AI框架，用于苹果叶片病害分类，该框架将RGB图像与分布式边缘计算相结合。第一阶段执行二分类以区分健康叶片与患病叶片，F1分数达到95.50%。该优化通过在边缘设备上采用轻量级模型实现，从而提升了计算效率。第二阶段采用基于共识的多类分类方法，在识别具体病害方面F1分数达到96.44%。该双阶段系统在边缘AI架构的支持下，实现了实时病害检测，同时最大限度地降低了资源消耗，并增强了在大规模农业环境中的可扩展性。完整系统在专用数据集上达到了95.50%的准确率和F1分数。结果表明，该系统在受控实验室条件下具有有效性，为精准、实时的农业监测提供了一种有前景的解决方案。本研究凸显了分布式边缘AI系统在精准农业中实现可扩展且高效病害检测的潜力。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",76,{"impact":76,"substance":238,"depth":145,"authority":18,"freshness":17,"relevant":21,"comment":239},21,"该研究提出两级边缘AI框架，在苹果叶片病害识别上取得95%以上F1值，方法新颖、数据可靠，对精准农业实时监测有参考价值，但尚处实验室验证阶段，产业影响有限。",[241],{"name":234,"url":231},[27,82,243,244,245],"边缘计算","作物病害识别","苹果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":249},{"candidates":250,"agricultural_evidence":46},[251,257,264,270],{"code":32,"score":76,"supported":46,"title":252,"abstract":254,"tags":256},[253],"apple",[34,255,253],"苹果",[245],{"code":52,"score":258,"supported":46,"title":259,"abstract":260,"tags":263},7,[],[261,262],"精准农业","precision agriculture",[27],{"code":265,"score":258,"supported":46,"title":266,"abstract":267,"tags":269},"I5",[],[243,268],"edge computing",[243],{"code":106,"score":147,"supported":46,"title":271,"abstract":272,"tags":274},[],[273],"artificial intelligence",[244,82],[276,277],"苹果叶片病害 边缘AI","分布式边缘计算 病害分类","苹果叶片病害边缘AI-5401","10.22190\u002Ffuacr260506002g",{"doi":279,"openalex_id":281,"authors":282,"venue":234,"cited_by_count":61,"oa_url":231,"card":295,"direction":300,"ingested_from":132},"W7222964752",[283,286,289,292],{"name":284,"orcid":285},"Milan Grujev","https:\u002F\u002Forcid.org\u002F0009-0002-3211-6844",{"name":287,"orcid":288},"Dragan H. Stojanovic","https:\u002F\u002Forcid.org\u002F0000-0003-4893-3814",{"name":290,"orcid":291},"Miloš Ilić","https:\u002F\u002Forcid.org\u002F0000-0002-9696-2874",{"name":293,"orcid":294},"Aleksandar Milosavljević","https:\u002F\u002Forcid.org\u002F0000-0003-3057-7727",{"tldr":296,"method":297,"finding":298,"direction":131,"opportunity":299},"提出两级边缘AI框架，用RGB图像实时分类苹果叶片病害。","分布式边缘计算+轻量模型，先二分类再共识多分类。","两级系统准确率与F1均达95.50%，多分类F1为96.44%。","仅在受控实验室验证，可延伸至田间复杂光照与多作物泛化及边缘能耗优化。","农业人工智能与决策模型","2026-10-20T23:30:33.830748Z",{"id":303,"title":304,"url":305,"summary":306,"summary_zh":8,"content":8,"source_name":307,"source_url":8,"published_at":308,"category":73,"cover_url":8,"hotness":13,"is_selected":14,"score":309,"score_detail":310,"sources":312,"tags":314,"primary_topic":319,"secondary_topics":320,"classification":321,"search_phrases":332,"slug":335,"view_count":61,"doi":8,"paper":336,"created_at":344},5297,"高志永团队《基于特征优选与优化算法的干热河谷区柑橘蒸腾模拟》","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1087305323_122014422","《智慧农业(中英文)》2026年第8卷第4期刊载云南农业大学吴明清、高志永等团队论文,以10年生柑橘树为研究对象收集2020—2022年树干液流与环境因子数据;针对干热河谷加快建设智慧灌区背景下传统机理模型参数需求高、对部分参数高度敏感且误差来源复杂等难题,提出基于特征优选与优化算法的蒸腾模拟方法,可为优化灌区水分调控与配水策略提供决策支撑。","智慧农业(中英文)(2026,8(4))","2026-10-13T00:00:00Z",72,{"impact":197,"substance":238,"depth":145,"authority":198,"freshness":147,"relevant":21,"comment":311},"核心期刊论文，方法有创新且数据规模扎实，对干热河谷智慧灌区水分调控有决策参考价值，但属细分领域研究，时效性一般。",[313],{"name":307,"url":305},[27,315,316,317,318],"云南","柑橘","智慧灌区","蒸腾模拟",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":322},{"candidates":323,"agricultural_evidence":46},[324,328],{"code":32,"score":18,"supported":46,"title":325,"abstract":326,"tags":327},[316],[316],[316],{"code":52,"score":20,"supported":46,"title":329,"abstract":330,"tags":331},[],[27],[27],[333,334],"云南农业大学 柑橘 蒸腾模拟","干热河谷 智慧灌区","云南农业大学柑橘蒸腾模拟-5297",{"doi":8,"openalex_id":8,"authors":337,"venue":8,"cited_by_count":61,"oa_url":8,"card":338,"direction":131,"ingested_from":343},[],{"tldr":339,"method":340,"finding":341,"direction":131,"opportunity":342},"用特征优选与优化算法模拟干热河谷区柑橘蒸腾。","基于2020—2022年树干液流与环境因子数据构建特征优选与优化算法模型。","该方法可降低机理模型参数依赖与敏感性，提升蒸腾模拟精度。","可探索将蒸腾模拟嵌入智慧灌区实时配水决策，研究多树种迁移与边缘部署。","agent","2026-10-19T00:07:48.135945Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":8,"content":8,"source_name":350,"source_url":8,"published_at":351,"category":73,"cover_url":8,"hotness":13,"is_selected":14,"score":352,"score_detail":353,"sources":356,"tags":358,"primary_topic":361,"secondary_topics":362,"classification":363,"search_phrases":382,"slug":385,"view_count":61,"doi":8,"paper":386,"created_at":393},5130,"DefoliationCAM:面向葡萄藤食叶严重度实时评估的边缘AI系统 [预印本]","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.20879","Fuchen Li等提出DefoliationCAM(D-CAM),一种太阳能供电的边缘AI系统,用于估算葡萄藤树冠食叶程度并通过LoRa将结果传输至Web仪表盘。流水线使用YOLOX-Nano在4K图像中定位葡萄藤主干,并使用基于MobileNet的轻量级通道注意力分类器对320×320像素的斑块进行分类。29,674个田间采集斑块被标注为背景或四种损伤等级之一;合并中度和重度类别将类别不平衡比从约40:1降至8.8:1。合成增强使macro-F1从0.768提升至0.838。分类器以更少的参数量(1.08M vs 23.52M)在macro-F1上优于ResNet-50。利用TensorRT FP16在NVIDIA Jetson Orin Nano上每张4K图像处理时间约1.5秒,完整运行周期57-60秒。两台设备为期五周的田间部署验证了自主太阳能运行、LoRa通信和远程食叶监测的可行性。","arXiv 2610.20879 2026 年 10 月 14 日","2026-10-14T00:00:00Z",75,{"impact":197,"substance":77,"depth":76,"authority":354,"freshness":17,"relevant":21,"comment":355},11,"预印本提出太阳能边缘AI系统，实现葡萄藤食叶严重度实时评估与LoRa远程监测，方法新颖、数据规模大、田间验证充分，具智慧农业应用价值。",[357],{"name":350,"url":348},[27,82,243,359,360],"葡萄藤","病害评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":364},{"candidates":365,"agricultural_evidence":46},[366,370,374,378],{"code":32,"score":354,"supported":46,"title":367,"abstract":368,"tags":369},[49],[49],[],{"code":52,"score":21,"supported":14,"title":371,"abstract":372,"tags":373},[],[],[27],{"code":106,"score":21,"supported":14,"title":375,"abstract":376,"tags":377},[],[],[82],{"code":265,"score":21,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[243],[383,384],"DefoliationCAM 葡萄藤 食叶","Jetson Orin Nano 边缘AI 葡萄","DefoliationCAM葡萄藤食叶-5130",{"doi":8,"openalex_id":8,"authors":387,"venue":8,"cited_by_count":61,"oa_url":8,"card":388,"direction":131,"ingested_from":343},[],{"tldr":389,"method":390,"finding":391,"direction":131,"opportunity":392},"提出太阳能边缘AI系统DefoliationCAM，实时评估葡萄藤食叶严重度并远程传输。","YOLOX-Nano定位主干，MobileNet通道注意力分类，TensorRT","轻量分类器macro-F1达0.838，优于ResNet-50，田间部署验证可行。","可探索多作物迁移、损伤等级细化及边缘模型自适应更新以提升泛化性。","2026-10-17T00:13:44.932340Z"]