[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5":3,"related-5":70},{"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":64,"slug":67,"view_count":68,"doi":8,"paper":8,"created_at":69},5,"低空\"入农\"，东莞智慧农业的\"飞驰\"新赛道｜南方观察","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7662278883894379017\u002F","东莞举办\"低空+智慧农业\"应用场景发布会，发布\"天眼\"精准巡测、\"天手\"智慧飞防、\"天翼\"高效吊运 3 大首批应用场景。植保无人机在荔枝林开展雾化喷施、测绘巡检无人机搭载多光谱镜头实时采集果树生长数据生成可视化果园数字档案；最大挂载 80 公斤的重载无人机山头到山下集中地运输仅需 2 分钟左右；机器狗可 7×24 小时自主巡检，电量低于 30% 自动回充，弥补无人机航拍盲区。东莞已构建无人机库、孢子捕捉仪、AI 虫情灯、微型气象站构成\"空天地一体化\"监测网络，孵育 3 家农服组织覆盖植保飞防、物资调运和果园巡检，农户可通过线上小程序 AI 智能体下单。东莞早在 2024 年即印发《推动低空经济高质量发展实施方案（2024—2026 年）》，将低空经济作为未来经济增长新增长极。",null,"连片起伏的莞荔园里机器嗡鸣作响，近百名基层农技干部、镇街农业负责人驻足荔枝林，沉浸式观看三类无人机实地作业，亲眼见证低空技术重塑传统荔枝种植模式。\n\n近日，东莞举办“低空+智慧农业”应用场景发布会，现场发布东莞市“低空+智慧农业”首批应用场景，分别是“天眼”精准巡测、“天手”智慧飞防、“天翼”高效吊运。\n\n**低空“入农”，一幅幅智慧农业场景正式落地，见证东莞智慧农业的“飞驰”新赛道。**\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F49ff896d347e40309344c5b4ecfcbb02~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789107769&x-signature=SROKEAwHxXzo7ySNMdsgYyPK8Sk%3D)\n一场从“经验”到“数据”的迁移\n\n东莞荔枝种植面积约16万亩，大部分位于丘陵山地。**过去几十年，果农管理果园靠的是“凭经验、靠眼看、用脚走”。效率低、精准度不足，是长期困扰产业的痛点。**\n\n**无人机改变了这个局面。**在莞荔园，植保无人机穿梭于茂密树冠之间，模拟雾化药剂均匀覆盖枝叶正反面。测绘巡检无人机搭载多光谱镜头，实时采集果树生长数据，枝叶发黄、病虫隐患点位全部精准捕捉，数据同步上传云端，自动生成可视化果园数字档案。最大挂载能力达80公斤的重载无人机，航行速度最快可达每秒20米，从山头到山下集中地仅需两分钟左右。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F003ccc0606f292f755dfddecf5f2f45d~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789107769&x-signature=L8hefWTPjKzkjUrcxghEBel%2B9gQ%3D)\n广东睿旗地理信息技术有限公司技术负责人周东动介绍，无人机可搭载高精度镜头，结合AI算法精准预测农作物长势和病虫害，同时采集田园精准三维数据底座，为植保和运载无人机提供精确地形参考。\n\n**现场还出现了一位特殊的“新成员”——机器狗。**中国电信广东公司无线网络资深工程师刘昕介绍，机器狗可7天24小时自主巡检，电量低于30%时自动回充，能够定点检测树枝健康状况及害虫危害数量，弥补了无人机在航拍时无法探查的盲区。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F6eca20c8936fe4a39354ece4b3dfac0e~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789107769&x-signature=24GemI8jXagdVjq0cONmaRGv4wA%3D)\n**这不是简单的工具替代，而是一场生产方式的底层重构。**\n\n东莞为此搭建了一整套技术底座。无人机智慧巡园平台配备无人机库、孢子捕捉仪、AI虫情灯和微型气象站，构成“空天地一体化”监测网络。\n\n东莞市农业科学研究中心高级农艺师李加强介绍，东莞已孵育3家农服组织，覆盖植保飞防、物资调运和果园巡检，农户可通过线上小程序AI智能体下单，“有效提高果园管理效率，并极大降低管理成本”。\n\n一次政策与产业的“同频共振”\n\n**东莞推动“低空+智慧农业”，不是零散的企业行为，而是一套从顶层设计到落地执行的政策体系。**\n\n早在2024年，东莞就印发《推动低空经济高质量发展实施方案（2024-2026年）》，明确将低空经济作为未来经济高质量发展新的重要增长极。2025年，东莞发布低空经济应用场景机会清单，37个具体场景涵盖农林生产等12个维度。2026年3月，东莞印发《加快场景培育和开放推动“智创优品、和美宜居”新场景应用实施方案（2026-2028年）》，进一步明确低空经济等场景培育方向。\n\n**7月9日的发布会上，全市首批“低空+智慧农业”应用场景清单正式发布，三大场景——巡检、植保、吊运——覆盖农业生产全链条。**同步落地的市级低空农业场景创新中心，则承担起技术试验和产业推广的平台功能。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F5a6f8e34c53b5e4847ea277e4c367aad~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789107769&x-signature=YH7qk7bC99RN4WDmdZ31RcH0cPg%3D)\n对于这个创新中心的定位，东莞理工学院科技创新研究院副院长魏亚东指出，它与其他低空经济平台最大的不同在于“聚焦”。“所有资源专门对准农业赛道，立足东莞本地林地、荔枝果园特色农业现状，针对性研发适配本地果林的低空解决方案，深耕特色果树细分应用。”他进一步解释，**中心不只做设备试飞展示，而是“一边开展低空农业技术试验，一边联动东莞本土无人机制造产业，打通装备生产与田间应用两端，形成完整闭环”。**\n\n![Image 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F6a00c21845dfa8664f8c404fff891c43~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789107769&x-signature=LcP8bUusPh3olfgnz56HVIRBupY%3D)\n从政策文件到场景清单，从产业基地到创新中心，东莞形成了一套完整的制度供给链条。\n\n**当无人机飞进荔枝园，改变的不仅是一颗荔枝的种植方式，更是东莞农业走向现代化的路径选择——用数据替代经验，用技术解放人力，用场景驱动产业。**这是一场静水深流的变革，值得持续观察。\n\n文字：南方+记者：施美\n\n图片：邓浩\n\n通讯员：廖凌娜\n\n【作者】 施美；邓浩\n\n【来源】 南方报业传媒集团南方+客户端","《南方+客户端》2026 年 07 月 14 日 15:15","2026-07-14T07:15:00Z","报道",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},18,20,16,2,1,"报道东莞低空+智慧农业应用场景落地，内容详实，具产业参考价值，但时效性较低。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","低空经济","无人机","广东","农业应用场景",{"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":47},[44,51,57],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},"I1",12,true,[26],[26],[26],{"code":32,"score":52,"supported":47,"title":53,"abstract":54,"tags":56},6,[],[34,55],"荔枝",[],{"code":58,"score":59,"supported":47,"title":60,"abstract":61,"tags":63},"F4",3,[],[62],"植保",[],[65,66],"农业应用场景 低空经济 智慧农业 无人机","农业应用场景 低空经济","农业应用场景低空经济智慧农业无人机-5",11,"2026-07-27T05:05:18.578394Z",{"total":52,"page":21,"page_size":52,"items":71},[72,166,214,292,354,414],{"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":87,"tags":89,"primary_topic":93,"secondary_topics":94,"classification":106,"search_phrases":136,"slug":139,"view_count":21,"doi":140,"paper":141,"created_at":165},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":19,"substance":83,"depth":17,"authority":84,"freshness":85,"relevant":21,"comment":86},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。",[88],{"name":78,"url":75},[26,28,90,91,92],"农业人工智能","遥感监测","病害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[95,101],{"code":96,"slug":97,"name":98,"path":99,"parent_code":100},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":102,"slug":103,"name":104,"path":105,"parent_code":100},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":107},{"candidates":108,"agricultural_evidence":47},[109,117,124,129],{"code":32,"score":110,"supported":47,"title":111,"abstract":113,"tags":116},17,[112],"apple",[114,115,112],"苹果","葡萄",[],{"code":96,"score":118,"supported":47,"title":119,"abstract":121,"tags":123},15,[120],"deep learning",[122,120],"深度学习",[90],{"code":102,"score":46,"supported":47,"title":125,"abstract":127,"tags":128},[126],"multispectral",[126],[91],{"code":45,"score":130,"supported":47,"title":131,"abstract":132,"tags":135},7,[],[133,134],"精准农业","precision agriculture",[26],[137,138],"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":140,"openalex_id":142,"authors":143,"venue":78,"cited_by_count":155,"oa_url":156,"card":157,"direction":163,"ingested_from":164},"W7221558726",[144,147,150,152],{"name":145,"orcid":146},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":148,"orcid":149},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":151,"orcid":8},"Fatih Sinan Bir",{"name":153,"orcid":154},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428",0,"https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"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":167,"title":168,"url":169,"summary":170,"summary_zh":8,"content":171,"source_name":172,"source_url":8,"published_at":173,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":174,"sources":177,"tags":179,"primary_topic":182,"secondary_topics":183,"classification":184,"search_phrases":209,"slug":212,"view_count":21,"doi":8,"paper":8,"created_at":213},3092,"天上无人机 地上机器狗 AI生成\"体检单\"——东莞打造\"1+3+9\"智慧果园 荔枝小年减产 65.94% 销售均价反涨 193%","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7688141623238066732\u002F","9-22 南方都市报报道东莞市农业农村局打造\"1+3+9\"智慧果园应用场景抢占荔枝产业\"智\"高地：1 个 500 亩省级智慧果园核心区（莞荔园）+ 3 大特色荔枝产区（观音绿\u002F冰荔\u002F糯米糍覆盖 5000 亩）+ 9 个市级智慧果园（樟木头\u002F厚街\u002F大岭山\u002F大朗）。今年 7 月举办的\"低空+智慧农业\"应用场景发布会展示了植保无人机喷洒、吊运无人机 1 分钟从山头到山顶、巡检无人机半小时采集百亩果园生长数据、机器狗在陡坡草丛自动巡检等多场景协同。在 2026 年荔枝\"小年\"同比减产 65.94% 的不利条件下，凭借科技赋能与品牌加持，东莞荔枝销售均价达 44 元\u002F斤，同比增加 193%。","![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F4d59596f045542389e8e5857d28dfb36~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790640358&x-signature=9XA5NxoeRN6ZAuw3YYyOaUcU%2BjI%3D)![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F5f33bad24f2f01bbd4e7a2ff663b7776~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790640358&x-signature=bPrJsnbYjihBVX658a44IiukjXo%3D)\n东莞市农业科学研究中心的莞荔园里，植保无人机开展喷洒作业。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F1f7d7ad95566e8dd4d40c1ffaf2cb9de~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790640358&x-signature=0Ko9xgpOWi0DOpi%2FxfZANKAjizA%3D)\n东莞市农业科学研究中心的莞荔园里，机器狗在斜坡灵活行动。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fb1e87a5f810ee89df8d6819202611e95~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790640358&x-signature=AUrUkHg4xmK0MQkvdICV0V2oPm8%3D)\n岭南荔园的双臂协同荔枝采摘机器人。\n\n![Image 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fe5c3a1ff6c1ef2b5dd8d87d01692f48f~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790640358&x-signature=kOu74t5Fs45cECRvNGL%2FIN0T8CE%3D)\n东莞桂味荔枝。\n\n开栏语 近年来，广东深入实施“百千万工程”，大力发展农业新质生产力，持续推进农业数字化转型与数字乡村建设，各地抢抓发展机遇，推动人工智能技术嵌入农业生产、产业发展、乡村治理等方面，积极探索人工智能赋能乡村振兴的实践路径。南都深入企业与农村，推出相关报道，介绍各地以“AI+”赋能乡村振兴的经验做法。\n\n抢抓“人工智能+农业”发展新机遇，东莞荔枝产业正在悄然发生变化。南都记者从东莞市农业农村局获悉，近年来，东莞探索打造“1+3+9”智慧果园应用场景，让荔枝生产从“看天吃饭”走向“数据决策”。\n\n据了解，2026年，在“荔枝小年”同比减产65.94%的情况下，通过科技赋能、品质提升与品牌加持，东莞荔枝销售均价达44元\u002F斤，同比增加193%。\n\n**“1+3+9”**\n\n**一张智慧果园的网络正在铺开**\n\n今年7月9日，东莞举办了“低空+智慧农业”应用场景发布会暨农技人员知识更新培训会。\n\n现场观摩环节，在东莞市农业科学研究中心的莞荔园里，随着技术人员的遥控操作，植保无人机率先向山头飞去，抵达密集林间并开展喷洒作业。另一边，吊运无人机同步开展吊运演示，肥料、农具平稳挂载起降，从山头到山顶仅花费1分钟。\n\n紧随其后，巡检无人机沿着预设航线自动飞巡，仅需半个小时，就对百亩果园的果树生长数据进行了全面采集，并依靠遥感数据精准定位病虫害隐患点位，自动生成可视化果园数字档案。\n\n现场还来了一位“客人”——机器狗，随着技术人员的遥控，它不受地形的影响，能在陡坡上、草丛间灵活行动，为果园的地面自动化巡检、无人运输等提供新的解决方案，配合无人机低空技术，还可以丰富空天地一体化协作的应用场景，解决以往大型农机设备在果园移动难、不灵活的问题。\n\n这是东莞市农业农村局抢抓“低空+智慧农业”发展机遇的一个缩影。近年来，东莞探索打造“1+3+9”智慧果园应用场景，抢占荔枝产业发展“智”高地。\n\n简单来说，“1”，是指一个500亩的省级智慧果园核心区，位于东莞市农科中心莞荔园；“3”，是指观音绿、冰荔、优质糯米糍三大特色荔枝产区，覆盖面积约5000亩；“9”，则是在樟木头、厚街、大岭山和大朗四个荔枝主产镇，挑选基础条件好的果园，建设9个市级智慧果园应用场景。\n\n从“1”到“3”再到“9”，一张智慧果园的网络正在东莞铺开。\n\n**AI算法**\n\n**精准判断每棵荔枝树的“健康状况”**\n\n无人机定时巡航，孢子捕捉仪识别病害，AI虫情灯分析虫害，微型气象站监测气象……眼下，在东莞的荔枝林里，这套全生长周期的智能监测网络，让果园环境、树体长势、病虫害情况全天候“在线”，数据识别准确率超过90%。\n\n更令人惊奇的是，东莞依托无人机系统实施智慧巡园，推动数据迭代升级，通过AI算法，系统能精准判断每一棵荔枝树的“健康状况”，自动生成并分析“体检报告”，并给予一对一指导方案，巡检效率提升超30倍。\n\n**精准用药**\n\n**从“一季鲜果”走向“全年产业”**\n\n荔枝要好吃，用药安全至关重要。东莞还构建精准用药应用场景，在荔枝花期划定“禁药期”，构建起“智能感知—智慧决策—精准作业”的应用场景，病虫害防治精准率提升到90%。推广植保无人机病虫害统防统治服务，实现作业效率比人工提升7倍，农药使用量下降20%。\n\n采摘之后，荔枝的“旅程”并未结束。东莞还开展荔枝保鲜加工研究，建设荔枝采后实验室，配置荔枝冻眠保鲜、烘干加工等设备并联接物联网，推动荔枝从“一季鲜果”走向“全年产业”。\n\n**保险+科技农服**\n\n**农户上传照片，小程序可自动诊断病虫害**\n\n技术再先进，最终还是要落到果农手里。\n\n东莞打造了“保险+科技农服”创新试点，配套线上小程序，农户上传照片就能自动诊断病虫害，遇到疑难问题还能直连农业专家。管理效率大幅提升、人工成本显著降低；政府引导、保险护航、科技支撑、农户受益的创新模式正在逐步形成。\n\n同时，东莞与省农科院对接，组建了一支涵盖栽培管理、病虫害防控、智慧农业技术、数据平台运维及农业生产资料服务等多领域的复合型服务队伍，招募17名特聘农技员，把智慧果园的经验向基层延伸。\n\n此外，建立20多个荔枝示范基地、105个(次)绿色防控技术示范点，围绕栽培管理、病虫害防治、宜机化改造等开展协同创新，帮果农解决实实在在的难题。\n\n东莞还通过承办国家荔枝龙眼产业技术体系大湾区(东莞)荔枝人工智能技术应用培训交流会、东莞市“低空+智慧农业”应用场景发布会等，邀请高等院校、科研院所、AI企业专家学者现场授课、分享前沿成果，推动人工智能从“概念”变为果农手中的“新农具”。\n\n采写:南都记者 田玲玲\n\n供图:东莞市农业农村局","南方都市报","2026-09-21T23:56:00Z",{"impact":18,"substance":175,"depth":110,"authority":85,"freshness":85,"relevant":21,"comment":176},22,"东莞“1+3+9”智慧果园落地细节与减产65.94%却均价涨193%的数据兼具产业价值与传播性，值得进入每日精选。",[178],{"name":172,"url":169},[180,26,27,90,181],"数字乡村","荔枝产业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":185},{"candidates":186,"agricultural_evidence":47},[187,191,196,200,204],{"code":32,"score":68,"supported":47,"title":188,"abstract":189,"tags":190},[55],[55],[],{"code":45,"score":192,"supported":47,"title":193,"abstract":194,"tags":195},4,[],[26],[26],{"code":58,"score":59,"supported":47,"title":197,"abstract":198,"tags":199},[],[62],[],{"code":96,"score":21,"supported":14,"title":201,"abstract":202,"tags":203},[],[],[90],{"code":205,"score":21,"supported":14,"title":206,"abstract":207,"tags":208},"I6",[],[],[180],[210,211],"东莞 智慧果园 荔枝","东莞 低空 智慧农业 无人机","东莞智慧果园荔枝-3092","2026-09-22T00:05:33.048940Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":8,"source_name":220,"source_url":217,"published_at":221,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":222,"score_detail":223,"sources":226,"tags":228,"primary_topic":232,"secondary_topics":233,"classification":235,"search_phrases":256,"slug":259,"view_count":192,"doi":260,"paper":261,"created_at":291},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":19,"substance":175,"depth":17,"authority":224,"freshness":85,"relevant":21,"comment":225},14,"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。",[227],{"name":220,"url":217},[26,28,229,230,231],"农业遥感","猕猴桃","叶面积指数",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[234],{"code":102,"slug":103,"name":104,"path":105,"parent_code":100},{"status":39,"locked":14,"version":40,"method":41,"evidence":236},{"candidates":237,"agricultural_evidence":47},[238,246,252],{"code":102,"score":239,"supported":47,"title":240,"abstract":241,"tags":245},19,[126],[242,243,244,126],"遥感","无人机多光谱","remote sensing",[229,231],{"code":32,"score":17,"supported":47,"title":247,"abstract":249,"tags":251},[248],"kiwifruit",[230,250,248],"orchard",[230],{"code":45,"score":21,"supported":14,"title":253,"abstract":254,"tags":255},[],[],[26],[257,258],"叶面积指数 农业遥感 智慧农业 无人机","叶面积指数 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Qian",{"tldr":287,"method":288,"finding":289,"direction":161,"opportunity":290},"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",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":8,"source_name":220,"source_url":295,"published_at":298,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":299,"sources":301,"tags":303,"primary_topic":305,"secondary_topics":306,"classification":307,"search_phrases":327,"slug":330,"view_count":20,"doi":331,"paper":332,"created_at":353},2284,"Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112378","Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet。Computers and Electronics in 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