[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-597":3,"related-597":65},{"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":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":60,"slug":63,"view_count":19,"doi":8,"paper":8,"created_at":64},597,"广西田东县推动芒果产业全链条智慧化转型:智慧农运系统覆盖率 71%","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fgx\u002F202608\u002Ft20260812_8863374.htm","广西田东县林逢镇东养芒果基地建成智慧果园,集成物联网、AI 识别、区块链溯源等技术,无人机单台日均运力 12-15 吨,运输效率提升 3 倍、成本降低 40%。截至目前,全县 63 个千亩以上基地接入智慧农运系统,配备作业无人机超 280 架,覆盖率达 71%。首批 60 名学员通过粤桂协作促就业培训获 CAAC 无人机执照。",null,"盛夏八月，右江河谷暑气蒸腾。田东县林逢镇东养芒果核心智慧示范基地里，种植大户陆秀缎没往山上走，却往屋里钻。她站在果园中控大屏前，指尖轻滑，200亩果园的土壤湿度、虫情预警、长势热力图等信息呈现在屏幕上。\n\n“以前施肥浇水全凭感觉，现在土壤‘喊渴’、虫子‘报警’，手机上都看得清清楚楚。”这位与芒果树打了多年交道的老把式，如今成了“数据控”。\n\n从凭经验种果到靠数据管园，陆秀缎的“屏幕种田”，是田东芒果产业数字化转型的缩影。当物联网、AI识别、区块链溯源等新技术嵌入种植、采收、加工、销售全链条，一颗芒果从枝头到市场，每一步都留下了“数据脚印”。\n\n1智管入园，老把式遇上新算法\n\n上午10时，艳阳高照。陆秀缎坐在自家院子的竹椅上，笑着查看手机里的果园实况，“往年要去果地观察有没有虫咬、长没长病，全靠这双眼睛。现在不用了，手机一目了然”。\n\n笑容背后，是一张在果园里织就的“科技网”：微型气象站、土壤墒情监测点、虫情AI识别设备全域布点；高清摄像头实时回传画面，水肥一体化系统精准补给；空天地一体化监测叠加AI分析，覆盖芒果全生长周期。\n\n技术落地，果农感受最直观。班彩帽边演示边说：“只需选定果树片区，设置水肥配比，山下泵房便会自动输送营养液直达树根。”\n\n数字化管护实惠算得明白：人工减少约一成，农资降了三成，亩均省三成水肥，优质果率攀升，今年产量又比去年高出20%。传统粗放种植每亩纯收入约3800元，智慧果园亩均收益稳定在5000元以上。\n\n种得好，还要运得出。祥周镇百银村芒果种植基地山高坡陡，往年采摘后全靠人工一趟趟往下扛。如今启用无人机点对点直运，单台无人机日均运力可达12—15吨，运输效率提升3倍，成本降低四成，商品果损耗率下降18%。\n\n运力上去了，飞手队伍也在壮大。田东县首批参加粤桂协作促就业培训的60名学员，日前通过考核，拿到了民航局CAAC无人机执照和植保专项技能证书。\n\n截至目前，田东县已有63个千亩以上基地接入智慧农运系统，配备作业无人机超280架，覆盖率达71%。\n\n2码上溯源，好果子卖出好身价\n\n好芒果下山，第一件事是“验明正身”。\n\n在平马镇小龙村芒果标准园打包车间，工人们正给一箱箱芒果贴上二维码标签。“以前最怕假货砸招牌。”田东县小龙村臻芒合作社理事长黄忠伟拿起一箱芒果说：“现在一扫码，承诺达标合格证、产品产地、质量认证、检测报告、主体信息、农事记录全跳出来，从枝头到舌尖，透明得跟玻璃似的。”\n\n这张“数字身份证”背后，是田东县大力推行的产地承诺达标合格证制度。全县35家芒果绿色食品认证经营主体、17.7万亩认证面积，年产绿色认证芒果20万吨，筑牢“百色芒果”地理标志品质防线。\n\n但好果子不该只卖原料价。鲜友食品智能化车间里，芒果排着队“体检”——AI分拣线机器一扫，果型、着色、果径即刻分档，速度是人工的5倍。分级后的果子随即送入智能恒温冷链仓储，保鲜期延长12天，赶早市变成了错峰卖，好果子再也不怕卖不上价。\n\n车间另一头，自动化生产线正把芒果“吃干榨尽”：果肉变果干、果浆、芒果冰棒，一颗芒果衍生出十多种形态。田东县已引进培育精深加工企业5家，建成20多条自动化生产线，年加工鲜果能力超过8万吨。\n\n3云上出圈，新农人闯出新市场\n\n田间车间忙完了，直播间里又热闹起来。\n\n晚8时，平马镇百林村，新农人黄尚善支起三脚架，补光灯把满桌芒果照得透亮。“家人们，这是咱们百色田东的桂七芒，标准的S型身材还带个小翘嘴，核薄肉厚……”他对着镜头侃侃而谈。而谁能想到，这个如今张口就来产品卖点的年轻人，2021年第一次直播时，对着镜头半晌憋不出一句整话。\n\n“近两个月，直播芒果带货营业额近80万元。”黄尚善说，“手机成了新农具，直播成了新农活。我自己就是果农，对着镜头讲得出门道，观众看得明白，买得放心。”\n\n一个人走得快，一群人走得远。为壮大新农人队伍，6月25日，田东县组织50名芒果种植户、电商从业者、合作社负责人及乡村创业者，参加了2026年粤桂协作百色芒果“新农人”传播人才培育活动。系统教学后，学员们又在芒果标准园开展实地直播带货练习，现学现卖。\n\n“2025年，芒果线上销售额突破7.6亿元。”田东县电子商务协会会长蒙天庚说，“今年我们提前对接各大电商平台、直播团队、外地采购商，持续优化销售渠道，完善分级筛选、冷链保鲜、物流配送体系，同时规范果品分级标准和包装标准，销售额预计会有新的突破”。\n\n从枝头到舌尖，从果园到云端，数字技术正让右江河谷的芒果产业焕发出新的生机。","中国农业信息网 2026-08-12 田东数字农业","2026-08-12T00:00:00Z","报道",10,false,74,{"impact":17,"substance":18,"depth":17,"authority":13,"freshness":19,"relevant":20,"comment":21},18,22,6,1,"报道田东县芒果产业全链条智慧化转型，含具体数据与案例，具行业参考价值，但属地方性进展，时效性稍弱。",[23],{"name":10,"url":6},[25,26,27,28],"数字农业","无人机","芒果","区块链溯源",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,48,55],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":47},12,true,[27],[27],[27],{"code":49,"score":50,"supported":44,"title":51,"abstract":52,"tags":54},"I5",3,[],[53],"物联网",[],{"code":56,"score":20,"supported":14,"title":57,"abstract":58,"tags":59},"I1",[],[],[25],[61,62],"区块链溯源 数字农业 无人机 芒果","区块链溯源 数字农业","区块链溯源数字农业无人机芒果-597","2026-08-16T23:55:38.819914Z",{"total":19,"page":20,"page_size":19,"items":66},[67,163,221,258,318,395],{"id":68,"title":69,"url":70,"summary":71,"summary_zh":72,"content":8,"source_name":73,"source_url":70,"published_at":74,"category":75,"cover_url":8,"hotness":13,"is_selected":14,"score":76,"score_detail":77,"sources":83,"tags":85,"primary_topic":90,"secondary_topics":91,"classification":103,"search_phrases":133,"slug":136,"view_count":20,"doi":137,"paper":138,"created_at":162},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z","论文",77,{"impact":78,"substance":79,"depth":17,"authority":80,"freshness":81,"relevant":20,"comment":82},16,21,13,9,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[84],{"name":73,"url":70},[86,26,87,88,89],"智慧农业","农业人工智能","遥感监测","病害预警",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[92,98],{"code":93,"slug":94,"name":95,"path":96,"parent_code":97},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":99,"slug":100,"name":101,"path":102,"parent_code":97},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":37,"locked":14,"version":38,"method":39,"evidence":104},{"candidates":105,"agricultural_evidence":44},[106,114,121,126],{"code":30,"score":107,"supported":44,"title":108,"abstract":110,"tags":113},17,[109],"apple",[111,112,109],"苹果","葡萄",[],{"code":93,"score":115,"supported":44,"title":116,"abstract":118,"tags":120},15,[117],"deep learning",[119,117],"深度学习",[87],{"code":99,"score":43,"supported":44,"title":122,"abstract":124,"tags":125},[123],"multispectral",[123],[88],{"code":56,"score":127,"supported":44,"title":128,"abstract":129,"tags":132},7,[],[130,131],"精准农业","precision agriculture",[86],[134,135],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":137,"openalex_id":139,"authors":140,"venue":73,"cited_by_count":152,"oa_url":153,"card":154,"direction":160,"ingested_from":161},"W7221558726",[141,144,147,149],{"name":142,"orcid":143},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":145,"orcid":146},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":148,"orcid":8},"Fatih Sinan Bir",{"name":150,"orcid":151},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428",0,"https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":155,"method":156,"finding":157,"direction":158,"opportunity":159},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","农业遥感与作物表型","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:10.242467Z",{"id":164,"title":165,"url":166,"summary":167,"summary_zh":8,"content":8,"source_name":168,"source_url":8,"published_at":169,"category":75,"cover_url":8,"hotness":170,"is_selected":14,"score":171,"score_detail":172,"sources":175,"tags":180,"primary_topic":185,"secondary_topics":186,"classification":187,"search_phrases":204,"slug":207,"view_count":20,"doi":208,"paper":209,"created_at":220},4407,"From Downstream Demand to Upstream Innovation: How a Digital Platform Coordinates Agri-Food Value Chain Upgrading—The Case of Taotian Digital Agriculture","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1972526\u002Ffull","曹帆基于对中国淘天集团即食猕猴桃项目的深度案例研究,通过对访谈、田野观察、企业内部文件、政府材料与公开来源的分析,揭示数字平台通过将消费者需求转化为清晰产品目标,并在生产、加工、储存、物流、销售各环节进行技术与组织协调的「需求—创新」机制;案例显示平台主导升级的持久性与包容性可能取决于稳定经济回报、可触达本地服务、可管理合规成本、风险分担安排与平台治理。","Frontiers in Sustainable Food Systems 2026-10-02 接受","2026-10-02T00:00:00Z",25,78,{"impact":17,"substance":18,"depth":17,"authority":173,"freshness":19,"relevant":20,"comment":174},14,"核心期刊案例研究，揭示数字平台驱动农产品价值链升级的机制，对农业数字化转型有参考价值。",[176,177],{"name":168,"url":166},{"name":178,"url":179},"Frontiers in Sustainable Food Systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1972526",[25,181,182,183,184],"猕猴桃","农业数字化","数字平台","农产品价值链",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":188},{"candidates":189,"agricultural_evidence":44},[190,195,200],{"code":56,"score":13,"supported":44,"title":191,"abstract":193,"tags":194},[192],"digital agriculture",[],[182,25],{"code":30,"score":196,"supported":44,"title":197,"abstract":198,"tags":199},4,[],[181],[181],{"code":97,"score":20,"supported":14,"title":201,"abstract":202,"tags":203},[],[],[182],[205,206],"淘天集团 猕猴桃 数字平台","数字农业 价值链 案例研究","淘天集团猕猴桃数字平台-4407","10.3389\u002Ffsufs.2026.1972526\u002Ffull",{"doi":208,"openalex_id":210,"authors":211,"venue":178,"cited_by_count":152,"oa_url":179,"card":214,"direction":218,"ingested_from":161},"W7221535736",[212],{"name":213,"orcid":8},"Fan Cao",{"tldr":215,"method":216,"finding":217,"direction":218,"opportunity":219},"基于淘天即食猕猴桃案例，揭示数字平台以需求驱动农业食品价值链升级的机制。","深度案例研究，分析访谈、田野观察、企业文件与政府材料。","平台将消费需求转为产品目标并协调全链，升级持续性取决于回报、服务、合规、风险分担与治理。","数字乡村与农业信息化","可量化平台治理与风险分担机制对农户包容性升级的影响，并比较不同品类与平台的差异。","2026-10-08T00:07:47.273966Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":8,"content":226,"source_name":227,"source_url":8,"published_at":228,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":233,"tags":235,"primary_topic":239,"secondary_topics":240,"classification":241,"search_phrases":253,"slug":256,"view_count":152,"doi":8,"paper":8,"created_at":257},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。","## 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",40,{"impact":231,"substance":43,"depth":13,"authority":19,"freshness":196,"relevant":20,"comment":232},8,"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。",[234],{"name":227,"url":224},[25,86,236,237,238],"智慧果园","果树产业","上海金山",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":242},{"candidates":243,"agricultural_evidence":44},[244,248],{"code":30,"score":173,"supported":44,"title":245,"abstract":246,"tags":247},[32],[32,112],[],{"code":56,"score":249,"supported":14,"title":250,"abstract":251,"tags":252},2,[],[],[25,86],[254,255],"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","2026-10-06T00:05:02.406626Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":8,"source_name":264,"source_url":261,"published_at":265,"category":75,"cover_url":8,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":269,"tags":271,"primary_topic":274,"secondary_topics":275,"classification":276,"search_phrases":297,"slug":300,"view_count":196,"doi":301,"paper":302,"created_at":317},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",80,{"impact":17,"substance":18,"depth":17,"authority":173,"freshness":231,"relevant":20,"comment":268},"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。",[270],{"name":264,"url":261},[25,86,87,272,273],"农业机器人","果园自动化",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":277},{"candidates":278,"agricultural_evidence":44},[279,283,288,293],{"code":56,"score":231,"supported":44,"title":280,"abstract":281,"tags":282},[],[25,192],[25,86],{"code":30,"score":19,"supported":44,"title":284,"abstract":285,"tags":287},[],[32,286],"orchard",[],{"code":289,"score":20,"supported":14,"title":290,"abstract":291,"tags":292},"F6",[],[],[272],{"code":93,"score":20,"supported":14,"title":294,"abstract":295,"tags":296},[],[],[87],[298,299],"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":301,"openalex_id":303,"authors":304,"venue":264,"cited_by_count":152,"oa_url":311,"card":312,"direction":160,"ingested_from":161},"W7214618881",[305,308],{"name":306,"orcid":307},"M. Anne Visser","https:\u002F\u002Forcid.org\u002F0000-0001-5047-7702",{"name":309,"orcid":310},"Alex Bladimir Hernandez","https:\u002F\u002Forcid.org\u002F0009-0001-5320-7203","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1111\u002Fntwe.70047",{"tldr":313,"method":314,"finding":315,"direction":218,"opportunity":316},"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。","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。","2026-09-29T23:30:12.281947Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":323,"content":8,"source_name":324,"source_url":321,"published_at":325,"category":75,"cover_url":8,"hotness":13,"is_selected":14,"score":171,"score_detail":326,"sources":328,"tags":330,"primary_topic":333,"secondary_topics":334,"classification":337,"search_phrases":365,"slug":368,"view_count":50,"doi":369,"paper":370,"created_at":394},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports","2026-09-22T00:00:00Z",{"impact":173,"substance":18,"depth":17,"authority":173,"freshness":13,"relevant":20,"comment":327},"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[329],{"name":324,"url":321},[86,331,27,88,332],"机器学习","作物估产",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[335,336],{"code":93,"slug":94,"name":95,"path":96,"parent_code":97},{"code":99,"slug":100,"name":101,"path":102,"parent_code":97},{"status":37,"locked":14,"version":38,"method":39,"evidence":338},{"candidates":339,"agricultural_evidence":44},[340,344,349,355,359],{"code":30,"score":115,"supported":44,"title":341,"abstract":342,"tags":343},[286],[27,286],[27],{"code":93,"score":115,"supported":44,"title":345,"abstract":347,"tags":348},[346],"machine learning",[331,346],[331],{"code":99,"score":231,"supported":44,"title":350,"abstract":351,"tags":354},[],[352,353],"遥感","remote sensing",[332,88],{"code":56,"score":196,"supported":44,"title":356,"abstract":357,"tags":358},[],[130],[86],{"code":360,"score":50,"supported":44,"title":361,"abstract":362,"tags":364},"J1",[],[363],"农业规划",[],[366,367],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":369,"openalex_id":371,"authors":372,"venue":324,"cited_by_count":152,"oa_url":321,"card":389,"direction":158,"ingested_from":161},"W7213920056",[373,375,378,380,382,385,387],{"name":374,"orcid":8},"V. Raju",{"name":376,"orcid":377},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":379,"orcid":8},"Dr. V. S. Thorat",{"name":381,"orcid":8},"V. T. Shinde",{"name":383,"orcid":384},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":386,"orcid":8},"Alok Shrivastava",{"name":388,"orcid":8},"A. P. Chaudhary",{"tldr":390,"method":391,"finding":392,"direction":158,"opportunity":393},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z",{"id":396,"title":397,"url":398,"summary":399,"summary_zh":400,"content":8,"source_name":401,"source_url":398,"published_at":402,"category":75,"cover_url":8,"hotness":13,"is_selected":14,"score":403,"score_detail":404,"sources":406,"tags":408,"primary_topic":411,"secondary_topics":412,"classification":414,"search_phrases":432,"slug":435,"view_count":196,"doi":436,"paper":437,"created_at":467},2617,"Dynamic ROI-constrained UAV multispectral estimation of single-plant LAI in trellis-trained kiwifruit","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112437","Leaf area index (LAI) is a key biophysical parameter for characterizing orchard canopy structure and growth status, and it is closely associated with crop productivity. In trellis-trained orchards, severe branch overlap and persistent background interference make it difficult to delineate single-plant observation boundaries, while radiometric drift across multi-temporal observations further reduces feature comparability. To address these issues, this study proposes a dynamic ROI-constrained framework for single-plant LAI estimation from UAV multispectral imagery. First, relative radiometric calibration based on pseudo-invariant features was applied to reduce inter-date spectral bias and establish a unified radiometric baseline for multi-temporal analysis. Second, a dynamic ROI extraction algorithm was developed using dual constraints from the spatial distance percentile (P) and the local spectral purity threshold (Q). Parameter ablation results showed that the P90_Q70 combination achieved the best balance between retaining core canopy information and suppressing background noise. Compared with the best fixed-radius strategy (3.00 m), this scheme improved R 2 from 0.700 to 0.766 and reduced RMSE to 0.122. On this basis, spectral vegetation indices and near-infrared texture features were integrated to construct a multidimensional feature set. The gradient boosting decision tree (GBDT) model achieved the best predictive performance, with R 2 = 0.772, RMSE = 0.120, and RPD = 2.094, and was further used to generate high-resolution LAI maps of the orchard. These results indicate that the combination of dynamic boundary constraints and spectral purity screening can improve the robustness of single-plant feature extraction in trellis-trained kiwifruit orchards and provide a practical reference for quantitative remote sensing of other densely closed orchard systems.","叶面积指数（LAI）是表征果园冠层结构与生长状态的关键生物物理参数，与作物生产力密切相关。在棚架式栽培果园中，严重的枝条重叠和持续的背景干扰使得单株观测边界难以界定，而多时相观测间的辐射漂移进一步降低了特征可比性。为解决上述问题，本研究提出了一种基于无人机多光谱影像的单株LAI估算动态ROI约束框架。首先，采用基于伪不变特征的相对辐射校正以减小日期间的光谱偏差，为多时相分析建立统一的辐射基准。其次，利用空间距离百分位数（P）和局部光谱纯度阈值（Q）的双重约束，开发了动态ROI提取算法。参数消融结果表明，P90_Q70组合在保留核心冠层信息与抑制背景噪声之间取得了最佳平衡。与最优固定半径策略（3.00 m）相比，该方案将R²从0.700提升至0.766，并将RMSE降至0.122。在此基础上，融合光谱植被指数与近红外纹理特征构建多维特征集。梯度提升决策树（GBDT）模型取得了最佳预测性能，R²=0.772，RMSE=0.120，RPD=2.094，并进一步用于生成果园高分辨率LAI分布图。上述结果表明，动态边界约束与光谱纯度筛选相结合可提高棚架式猕猴桃果园单株特征提取的鲁棒性，并为其他密集郁闭果园系统的定量遥感提供实践参考。","Computers and Electronics in Agriculture","2026-09-15T00:00:00Z",79,{"impact":78,"substance":18,"depth":17,"authority":173,"freshness":81,"relevant":20,"comment":405},"提出动态ROI约束与辐射校正结合的无人机多光谱单株LAI估测框架，方法新颖、指标提升明确，对密植果园定量遥感有实用参考价值。",[407],{"name":401,"url":398},[86,26,409,181,410],"农业遥感","叶面积指数",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[413],{"code":99,"slug":100,"name":101,"path":102,"parent_code":97},{"status":37,"locked":14,"version":38,"method":39,"evidence":415},{"candidates":416,"agricultural_evidence":44},[417,423,428],{"code":99,"score":418,"supported":44,"title":419,"abstract":420,"tags":422},19,[123],[352,421,353,123],"无人机多光谱",[409,410],{"code":30,"score":17,"supported":44,"title":424,"abstract":426,"tags":427},[425],"kiwifruit",[181,286,425],[181],{"code":56,"score":20,"supported":14,"title":429,"abstract":430,"tags":431},[],[],[86],[433,434],"叶面积指数 农业遥感 智慧农业 无人机","叶面积指数 农业遥感","叶面积指数农业遥感智慧农业无人机-2617","10.1016\u002Fj.compag.2026.112437",{"doi":436,"openalex_id":438,"authors":439,"venue":401,"cited_by_count":152,"oa_url":398,"card":462,"direction":158,"ingested_from":161},"W7213243341",[440,442,444,446,449,451,453,455,457,460],{"name":441,"orcid":8},"Wenjie Li",{"name":443,"orcid":8},"Hongen Liu",{"name":445,"orcid":8},"Linghuan Ouyang",{"name":447,"orcid":448},"Qian Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7916-1370",{"name":450,"orcid":8},"Tianqi Lv",{"name":452,"orcid":8},"Jiali Li",{"name":454,"orcid":8},"Xintao Lin",{"name":456,"orcid":8},"Chen Yao",{"name":458,"orcid":459},"Yongqiang Zheng","https:\u002F\u002Forcid.org\u002F0000-0003-3246-2800",{"name":461,"orcid":8},"Jianping Qian",{"tldr":463,"method":464,"finding":465,"direction":158,"opportunity":466},"提出动态ROI约束框架，用无人机多光谱影像估算棚架猕猴桃单株LAI。","伪不变特征辐射校正、动态ROI提取（P90_Q70）、植被指数与纹理特征、GBD","动态ROI优于固定半径，GBDT最优（R²=0.772，RMSE=0.120，RPD=2.094）。","可迁移至其他密植果园，探索多时相辐射校正与动态边界约束的普适性及轻量化模型。","2026-09-16T23:30:02.136941Z"]