[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3212":3,"related-3212":117},{"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":24,"tags":26,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":112,"slug":115,"view_count":55,"doi":8,"paper":8,"created_at":116},3212,"数智赋能粤桂协作 深圳\"AI+\"激活乡村发展新动能——百色田林阳光玫瑰葡萄一级果率从45%提升至85% 西林沙糖桔\"无人机+AI\"全域巡飞","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7688280919010165271\u002F","9-22 深圳晚报报道粤桂协作数字赋能乡村振兴：百色市田林县爱善庄园上线\"5G+AI智慧农业\"云平台，手机远程管控大棚通风、灌溉、光照，AI自动识别病虫害、输出水肥管护方案；葡萄一级果率从45%大幅提升至85%，节水、节肥、农药减量成效显著，绿色优质果品依托粤桂协作渠道直供深圳高端商超；项目创新\"一园生六金\"联农带农模式（土地租金\u002F务工薪金\u002F集体股金\u002F产业产金\u002F文旅旅金\u002F节本增收节金），覆盖周边1.6万名群众。西林县打造沙糖桔全链条数字化升级体系，配齐气象站、土壤墒情检测仪、AI智能虫情测报仪等全套物联网硬件；无人机全域巡飞+AI算法识别黄化、病虫害、长势异常；专属AI溯源系统将果树施肥、灌溉、用药、采收全流程记录留痕，每颗沙糖桔都有专属\"身份码\"。",null,"“5G+AI智慧农业”云平台全天候监测作物长势，AI数智人精准匹配岗位、解答求职咨询，AI虫情测报设备自动识别田间虫害并推送管护方案……跨越千里山海，一套套贴合实际的数字化解决方案，正把深圳的科创优势转化为乡村振兴实实在在的动能，在粤桂协作的乡土大地上落地生根。\n\n**田间管理有了“数字大脑”**\n\n走进百色市田林县爱善庄园，智慧大棚内阳光玫瑰葡萄长势喜人。村民姚志华从传统种地农户成长为庄园技术管护人员。在光明区推动下，庄园上线 “5G+AI 智慧农业” 云平台，手机就能远程管控大棚通风、灌溉、光照，AI 自动识别病虫害、输出水肥管护方案。“现在AI系统全程精准把控，种植省心、品质稳定，好果子不愁卖。”姚志华坦言，数字技术让自己实现了家门口稳定就业、技能增收的双重突破。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F1c3f3679ab3e4f118c392ab2c0d1a2f5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790726783&x-signature=Aq2WdcgoBulFAxCurvXft417neo%3D)\n\n爱善庄园建设的AI智慧大棚\n\nAI标准化、数字化管控带来实实在在的效益，葡萄一级果率从45%大幅提升至85%，节水、节肥、农药减量成效显著，绿色优质的果品依托粤桂协作渠道直供深圳高端商超。项目创新“一园生六金”联农带农模式，通过土地租金、务工薪金、集体股金、产业产金、文旅旅金、节本增收节金六大渠道拓宽群众收入，覆盖周边1.6万名群众，让农户共享发展红利。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F5a48a5c55668a815241debdab5f9e0a8~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790726783&x-signature=myIK1VfJU2hnI7na%2BI%2B2XB3wkps%3D)\n姚志华在庄园采摘葡萄\n\n素有“中国沙糖桔之乡”美誉的西林县，漫山桔园装上了“数字大脑”。罗湖区助力西林县打造沙糖桔全链条数字化升级体系，在核心试点基地配齐气象站、土壤墒情检测仪、AI智能虫情测报仪等全套物联网硬件。AI图像识别自动抓拍、识别虫害种类，结合田间气象、土壤养分数据，智能生成水肥管理方案与病虫害防控举措，在移动端实时推送虫情预警和管护建议。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fe538e4825a6bcecfddc253e00facc94f~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790726783&x-signature=TSTKRY6OFzHhnQpDBIYctS8wjvs%3D)\n西林县沙糖桔“人工智能+可视化”标准管护显示屏\n\n同时，当地创新运用“无人机+AI”技术，搭建全域沙糖桔产业普查监测系统。无人机全域巡飞覆盖整片桔园，精准摸清种植底数，搭配AI算法快速识别果树黄化、病虫害、长势异常等问题，搭建起动态更新的产业数据库。田林县搭建的专属AI溯源系统，将果树施肥、灌溉、用药、采收全流程记录留痕，每一颗沙糖桔都有专属“身份码”，来源可查、品质可溯，牢牢守住区域特色农产品的口碑与信誉。依托AI技术开展包装设计与智能化内容营销，借助新媒体矩阵开展年轻化传播，西林县带动全县8万果农搭上数字农业的快车，推动产业从“种得出、种得好”向“卖上好价、长效增收”跨越。\n\n**AI“职介人”打通就业堵点**\n\n在南宁隆安县震东易地搬迁集中安置区，“小梁送工”就业服务站内，AI就业互动一体机格外醒目。以本土就业服务者为原型打造的AI数智人，24小时开展带岗服务。系统智能解析劳动力画像与企业岗位需求，实现全天候、精准化人岗匹配。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fa0994ed736af08759aaf3c377f72c751~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790726783&x-signature=Mjk65fKd%2BH04HXAPkKV52KrItNM%3D)\n“小梁送工”就业服务站内的AI就业互动一体机\n\n当地创新“企业派单、居民点单、社区接单”三张单闭环机制，搭配AI智能匹配，开发分层分类岗位，配套订单式技能培训。宝妈梁秋丽通过平台向AI数智人提出求职需求，系统快速匹配到安得塑业圆织工岗位，企业为她安排时间灵活的“宝妈流水线”，方便接送孩子，如今她每月稳定收入3500元以上。\n\n截至2025年底，项目累计带动就业超70万人次，务工人员月均收入较项目实施前提升20%以上。\n\n**智慧课堂点亮乡村教育**\n\n人才是乡村长远发展的根基。在乐业县，依托粤桂协作资金建成330间AI高效课堂。课前AI协助教师备课生成习题课件，课中采集课堂互动状态，课后自动批改作业、生成学情报告，帮助山区教师从“凭经验教学”转向“靠数据教学”，精准定位学生知识薄弱点。\n\n以科创架起协作桥梁，以数字激活乡土活力。立足乡土需求，协作之路方能行稳致远，持续浇灌出乡村振兴的累累硕果。\n\n读特&晶报记者 陈淑莹\n\n（作者：读特&晶报记者 陈淑莹）","深圳晚报","2026-09-22T08:56:00Z","报道",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,21,17,8,9,1,"粤桂协作下AI赋能种植、就业、教育的多场景落地报道，数据具体、案例扎实，具备精选价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"数字乡村","智慧农业","农业人工智能","农产品溯源","粤桂协作","无人机巡飞",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,53,59,66,72,78,84,90,96,102,107],{"code":34,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},14,true,[50],"葡萄",[36,50],[],{"code":54,"score":55,"supported":48,"title":56,"abstract":57,"tags":58},"I1",4,[],[28],[28],{"code":60,"score":61,"supported":48,"title":62,"abstract":63,"tags":65},"F3",3,[],[64],"施肥",[],{"code":67,"score":61,"supported":48,"title":68,"abstract":69,"tags":71},"F4",[],[70],"农药",[],{"code":73,"score":61,"supported":48,"title":74,"abstract":75,"tags":77},"G",[],[76],"乡村振兴",[],{"code":79,"score":61,"supported":48,"title":80,"abstract":81,"tags":83},"G4",[],[82],"联农带农",[],{"code":85,"score":61,"supported":48,"title":86,"abstract":87,"tags":89},"H2",[],[88],"土壤",[],{"code":91,"score":61,"supported":48,"title":92,"abstract":93,"tags":95},"H3",[],[94],"灌溉",[],{"code":97,"score":61,"supported":48,"title":98,"abstract":99,"tags":101},"I5",[],[100],"物联网",[],{"code":103,"score":22,"supported":14,"title":104,"abstract":105,"tags":106},"I2",[],[],[29],{"code":108,"score":22,"supported":14,"title":109,"abstract":110,"tags":111},"I6",[],[],[27],[113,114],"田林 阳光玫瑰葡萄 AI","西林 沙糖桔 无人机 AI","田林阳光玫瑰葡萄AI-3212","2026-09-23T00:04:27.284431Z",{"total":118,"page":22,"page_size":118,"items":119},6,[120,169,210,286,335,439],{"id":121,"title":122,"url":123,"summary":124,"summary_zh":8,"content":125,"source_name":126,"source_url":8,"published_at":127,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":128,"sources":131,"tags":133,"primary_topic":136,"secondary_topics":137,"classification":138,"search_phrases":164,"slug":167,"view_count":22,"doi":8,"paper":8,"created_at":168},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":129,"substance":17,"depth":19,"authority":21,"freshness":21,"relevant":22,"comment":130},20,"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。",[132],{"name":126,"url":123},[27,28,134,29,135],"低空经济","荔枝产业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":139},{"candidates":140,"agricultural_evidence":48},[141,147,151,156,160],{"code":34,"score":142,"supported":48,"title":143,"abstract":145,"tags":146},11,[144],"荔枝",[144],[],{"code":54,"score":55,"supported":48,"title":148,"abstract":149,"tags":150},[],[28],[28],{"code":67,"score":61,"supported":48,"title":152,"abstract":153,"tags":155},[],[154],"植保",[],{"code":103,"score":22,"supported":14,"title":157,"abstract":158,"tags":159},[],[],[29],{"code":108,"score":22,"supported":14,"title":161,"abstract":162,"tags":163},[],[],[27],[165,166],"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":170,"title":171,"url":172,"summary":173,"summary_zh":8,"content":174,"source_name":175,"source_url":8,"published_at":176,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":181,"tags":183,"primary_topic":186,"secondary_topics":187,"classification":188,"search_phrases":205,"slug":208,"view_count":61,"doi":8,"paper":8,"created_at":209},2454,"中国农科院资划所构建可解释 AI 模型实现猕猴桃产地精准溯源(AI in Agriculture)","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571168.shtm","中国农科院农业资源与农业区划研究所智慧农业创新团队利用光谱成像技术与多模型组合策略,以中国 6 个地区猕猴桃为对象构建产地溯源模型,并运用可解释性分析方法对最优组合模型进行分层和整体解读。在提升溯源准确率的同时,揭示了光谱数据背后判断产地的内在逻辑。该研究为破解光谱分析中数据分析模型难以解释的难题提供了系统解决思路,在关键波长选择、模型稳定性提升及预测结果可信度增强等方面具有参考价值。成果发表在《Artificial Intelligence in Agriculture》。","[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨,姬悦 来源：中国科学报 发布时间：2026\u002F9\u002F9 21:39:55 选择字号：小 中 大 新模型实现猕猴桃产地精准溯源 近日，中国农业科学院农业资源与农业区划研究所智慧农业创新团队根据可解释人工智能构建农产品产地溯源模型，在提升溯源准确率的同时揭示了光谱数据判断产地的内在逻辑，相关研究成果发表在《农业人工智能》（_**Artificial Intelligence in Agriculture**_）上。 利用光谱数据判断农产品产地时，现有方法多依赖单一算法，往往只追求预测准确度，却忽略了模型决策逻辑的解释，限制了可靠溯源模型的开发和对判断过程的理解。 科研团队以中国六个地区的猕猴桃为对象，融合光谱成像技术与多模型组合策略构建产地溯源模型，并运用可解释性分析方法对最优组合模型进行分层和整体解读，在提升溯源准确率的同时，揭示了光谱数据背后判断产地的内在逻辑。 该研究为破解光谱分析中数据分析模型难以解释的难题提供了系统解决思路，在关键波长选择、模型稳定性提升及预测结果可信度增强等方面具有参考价值，有助于推动光谱技术在农业生产与品质监管中的实际应用。研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。 相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.05.010 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=571168)\n\n相关新闻 相关论文\n*   1\n*   [基金委发布2个2026年度项目指南](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571167.shtm)\n\n*   2\n*   [2026年度 “澳门青年学者计划”获选结果公布](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571166.shtm)\n\n*   3\n*   [不收论文，一份陶哲轩参与的新期刊诞生了](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571162.shtm)\n\n*   4\n*   [浙江多所高校人事调整](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571160.shtm)\n\n*   5\n*   [我国天文学界倡议建设“新型智能社区”](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571156.shtm)\n\n*   6\n*   [OpenAI称破解数学“千禧年难题”](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571153.shtm)\n\n*   7\n*   [基金委发布3个国家自然科学基金专项项目申请指南](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571152.shtm)\n\n*   8\n*   [我国建成全球首套年产500吨碳酸铷铯示范线](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571151.shtm)\n\n*   1\n*   [Energies 期刊青年编委招募——邀您共同促进学术期刊发展！](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F202699142522865155325.shtm)\n\n*   2\n*   [文献清单：肝脏病学2024–2025热门高访问论文| MDPI Livers](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F20269915317833155324.shtm)\n\n*   3\n*   [从土地覆被变化看彩虹鱼保护：西新几内亚鸟头半岛的遥感证据 | MDPI Land](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F20269915133880155323.shtm)\n\n*   4\n*   [JLPEA期刊客座编辑招募 | 邀您共同促进学术期刊发展](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F202699145758302155322.shtm)\n\n*   5\n*   [攻克国家二级保护动物繁育瓶颈：斑鳠性别分化机制研究取得重要进展 | MDPI Fishes](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F2026991345299155321.shtm)\n\n*   6\n*   [从防栓到精准抗凝：抗凝治疗的临床平衡与未来趋势](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F202699145724255155320.shtm)\n\n*   7\n*   [文献清单：“提升老年人护理质量”方向 | MDPI Healthcare](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F20269914565411155319.shtm)\n\n*   8\n*   [Foods“食品包装与保鲜”栏目——金征宇院士领衔，聚焦食品保藏与包装前沿研究](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F202699145459758155318.shtm)\n\n[图片新闻](https:\u002F\u002Fnews.sciencenet.cn\u002Fimgnews.aspx)\n\n[![Image 3: 改造人体细胞能否重建骨骼](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fupload\u002Fnews\u002Fimages\u002F2026\u002F9\u002F2026913172457210.png)](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571360.shtm)[![Image 4: 我国科学家实现量子安全定位](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fupload\u002Fnews\u002Fimages\u002F2026\u002F9\u002F20269112252315680.png)](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571313.shtm)\n[![Image 5: 科学网2026年8月十佳博文榜单公布](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fupload\u002Fnews\u002Fimages\u002F2026\u002F9\u002F20269101430125990.png)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhttps:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1551789.html)[![Image 6: 新技术可为千年木构古建“无痛”加固](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fupload\u002Fnews\u002Fimages\u002F2026\u002F9\u002F2026952235515210.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F570943.shtm)\n[>>更多](https:\u002F\u002Fnews.sciencenet.cn\u002Fimgnews.aspx)\n\n[一周新闻排行](https:\u002F\u002Fnews.sciencenet.cn\u002Fzphnews.aspx)\n*   1\n*   [到2050年，全球癌症病例可能激增67%](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571066.shtm)\n\n*   2\n*   [2025年度世界一流科技期刊目录发布](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571326.shtm)\n\n*   3\n*   [直播回放|清华团队“种”出光通信新材料](https:\u002F\u002Fnews.sciencenet.cn\u002Fhttps:\u002F\u002Fweibo.com\u002Fl\u002Fwblive\u002Fp\u002Fshow\u002F1022:2321325338760983478439)\n\n*   4\n*   [科学家首次捕捉到中子星从星团走向银河的踪迹](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571018.shtm)\n\n*   5\n*   [OpenAI称破解数学“千禧年难题”](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571153.shtm)\n\n*   6\n*   [AGI时代到来与否仍待定 教育变革已应开始](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571038.shtm)\n\n*   7\n*   [广东省二医成立顽固性痛经多学科诊疗中心](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571287.shtm)\n\n*   8\n*   [日本诺奖得主遭质疑多篇论文存在图片造假](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571033.shtm)\n\n*   9\n*   [校企联合完成三维粘性挂谷猜想形式化验证](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571198.shtm)\n\n*   10\n*   [“诺奖风向标”：2026年拉斯克奖揭晓](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571247.shtm)\n\n[编辑部推荐博文](http:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)\n*   ![Image 7](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [科学网2026年8月十佳博文榜单公布！](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1551789.html)\n\n*   ![Image 8](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [封面文章 | 《盐湖研究》：锂镁分离性能研究](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1551710.html)\n\n*   ![Image 9](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [博士生论坛开放申请！“光化学研究：面向多学科交叉的前沿探索” | MDP ...](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-3516770-1552450.html)\n\n*   ![Image 10](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [永久收藏！IEEE Transactions 全刊盘点，选刊精准不盲投！工科投稿再也 ...](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-3462448-1552447.html)\n\n*   ![Image 11](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [为什么年轻女性多数都有纤细的腰身？](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-374215-1552436.html)\n\n*   ![Image 12](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [科学家正尝试把药厂搬到太空](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-41174-1552162.html)\n\n*   [更多>>](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)","中国科学报 2026-09-09","2026-09-08T16:00:00Z",75,{"impact":179,"substance":129,"depth":19,"authority":47,"freshness":118,"relevant":22,"comment":180},18,"国家级科研团队用可解释AI提升猕猴桃产地溯源准确率并揭示光谱判别逻辑，方法新颖、结论可靠，属智慧农业细分领域实质进展，值得进入每日精选。",[182],{"name":175,"url":172},[28,29,30,184,185],"猕猴桃","光谱成像",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":189},{"candidates":190,"agricultural_evidence":48},[191,196,200],{"code":34,"score":192,"supported":48,"title":193,"abstract":194,"tags":195},12,[184],[184],[184],{"code":54,"score":55,"supported":48,"title":197,"abstract":198,"tags":199},[],[28],[28],{"code":103,"score":55,"supported":48,"title":201,"abstract":202,"tags":204},[],[203],"artificial intelligence",[29],[206,207],"农业人工智能 农产品溯源 光谱成像 智慧农业","农业人工智能 农产品溯源","农业人工智能农产品溯源光谱成像智慧农业-2454","2026-09-15T00:04:22.091088Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":8,"source_name":216,"source_url":213,"published_at":217,"category":218,"cover_url":8,"hotness":13,"is_selected":14,"score":219,"score_detail":220,"sources":222,"tags":224,"primary_topic":228,"secondary_topics":229,"classification":230,"search_phrases":256,"slug":259,"view_count":260,"doi":261,"paper":262,"created_at":285},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","论文",76,{"impact":179,"substance":18,"depth":19,"authority":192,"freshness":20,"relevant":22,"comment":221},"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。",[223],{"name":216,"url":213},[28,29,225,226,227],"边缘计算","作物病害识别","苹果病害",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":231},{"candidates":232,"agricultural_evidence":48},[233,239,246,251],{"code":34,"score":179,"supported":48,"title":234,"abstract":236,"tags":238},[235],"apple",[36,237,235],"苹果",[227],{"code":54,"score":240,"supported":48,"title":241,"abstract":242,"tags":245},7,[],[243,244],"精准农业","precision agriculture",[28],{"code":97,"score":240,"supported":48,"title":247,"abstract":248,"tags":250},[],[225,249],"edge computing",[225],{"code":103,"score":252,"supported":48,"title":253,"abstract":254,"tags":255},5,[],[203],[226,29],[257,258],"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",0,"10.22190\u002Ffuacr260506002g",{"doi":261,"openalex_id":263,"authors":264,"venue":216,"cited_by_count":260,"oa_url":213,"card":277,"direction":283,"ingested_from":284},"W7222964752",[265,268,271,274],{"name":266,"orcid":267},"Milan Grujev","https:\u002F\u002Forcid.org\u002F0009-0002-3211-6844",{"name":269,"orcid":270},"Dragan H. Stojanovic","https:\u002F\u002Forcid.org\u002F0000-0003-4893-3814",{"name":272,"orcid":273},"Miloš Ilić","https:\u002F\u002Forcid.org\u002F0000-0002-9696-2874",{"name":275,"orcid":276},"Aleksandar Milosavljević","https:\u002F\u002Forcid.org\u002F0000-0003-3057-7727",{"tldr":278,"method":279,"finding":280,"direction":281,"opportunity":282},"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%。","智慧农业 \u002F 农业物联网","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","openalex","2026-10-20T23:30:33.830748Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":8,"content":8,"source_name":291,"source_url":8,"published_at":292,"category":218,"cover_url":8,"hotness":13,"is_selected":14,"score":177,"score_detail":293,"sources":296,"tags":298,"primary_topic":301,"secondary_topics":302,"classification":303,"search_phrases":322,"slug":325,"view_count":260,"doi":8,"paper":326,"created_at":334},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",{"impact":294,"substance":17,"depth":179,"authority":142,"freshness":20,"relevant":22,"comment":295},16,"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。",[297],{"name":291,"url":289},[28,29,225,299,300],"葡萄藤","病害评估",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":304},{"candidates":305,"agricultural_evidence":48},[306,310,314,318],{"code":34,"score":142,"supported":48,"title":307,"abstract":308,"tags":309},[50],[50],[],{"code":54,"score":22,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[28],{"code":103,"score":22,"supported":14,"title":315,"abstract":316,"tags":317},[],[],[29],{"code":97,"score":22,"supported":14,"title":319,"abstract":320,"tags":321},[],[],[225],[323,324],"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":327,"venue":8,"cited_by_count":260,"oa_url":8,"card":328,"direction":281,"ingested_from":333},[],{"tldr":329,"method":330,"finding":331,"direction":281,"opportunity":332},"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。","agent","2026-10-17T00:13:44.932340Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":8,"source_name":341,"source_url":338,"published_at":292,"category":218,"cover_url":8,"hotness":13,"is_selected":14,"score":342,"score_detail":343,"sources":345,"tags":347,"primary_topic":351,"secondary_topics":356,"classification":363,"search_phrases":412,"slug":415,"view_count":22,"doi":416,"paper":417,"created_at":438},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science",81,{"impact":179,"substance":17,"depth":179,"authority":47,"freshness":21,"relevant":22,"comment":344},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[346],{"name":341,"url":338},[28,29,348,349,350],"产量预测","多模态融合","遥感",{"code":103,"slug":352,"name":353,"path":354,"parent_code":355},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[357,358],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":359,"slug":360,"name":361,"path":362,"parent_code":355},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":41,"locked":14,"version":42,"method":43,"evidence":364},{"candidates":365,"agricultural_evidence":48},[366,374,380,386,390,395,400,406],{"code":103,"score":142,"supported":48,"title":367,"abstract":368,"tags":373},[],[369,370,371,372],"深度学习","机器学习","machine learning","deep learning",[29,349],{"code":34,"score":21,"supported":48,"title":375,"abstract":376,"tags":379},[],[237,377,378,235],"芒果","orchard",[],{"code":359,"score":20,"supported":48,"title":381,"abstract":382,"tags":385},[],[383,384],"无人机多光谱","multispectral",[348,350],{"code":54,"score":240,"supported":48,"title":387,"abstract":388,"tags":389},[],[243,244],[28],{"code":85,"score":118,"supported":48,"title":391,"abstract":392,"tags":394},[],[88,393],"soil",[],{"code":97,"score":118,"supported":48,"title":396,"abstract":397,"tags":399},[],[100,398],"IoT",[],{"code":401,"score":61,"supported":48,"title":402,"abstract":403,"tags":405},"H6",[],[404],"农业生态",[],{"code":407,"score":61,"supported":48,"title":408,"abstract":409,"tags":411},"H7",[],[410],"气候适应",[],[413,414],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":416,"openalex_id":418,"authors":419,"venue":341,"cited_by_count":260,"oa_url":338,"card":433,"direction":281,"ingested_from":284},"W7221795850",[420,423,425,427,429,431],{"name":421,"orcid":422},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":424,"orcid":8},"Umarfarooque Momin",{"name":426,"orcid":8},"Sudeep Kumar E",{"name":428,"orcid":8},"Itfaq Ahmad Mir",{"name":430,"orcid":8},"Ab Rouf Khan",{"name":432,"orcid":8},"Ghulam Mohammad Mir",{"tldr":434,"method":435,"finding":436,"direction":283,"opportunity":437},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","2026-10-15T23:30:08.750140Z",{"id":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":8,"source_name":445,"source_url":442,"published_at":446,"category":218,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":447,"sources":450,"tags":452,"primary_topic":456,"secondary_topics":457,"classification":460,"search_phrases":480,"slug":483,"view_count":22,"doi":484,"paper":485,"created_at":506},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",{"impact":294,"substance":18,"depth":179,"authority":448,"freshness":21,"relevant":22,"comment":449},13,"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。",[451],{"name":445,"url":442},[28,453,29,454,455],"无人机","遥感监测","病害预警",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[458,459],{"code":103,"slug":352,"name":353,"path":354,"parent_code":355},{"code":359,"slug":360,"name":361,"path":362,"parent_code":355},{"status":41,"locked":14,"version":42,"method":43,"evidence":461},{"candidates":462,"agricultural_evidence":48},[463,467,472,476],{"code":34,"score":19,"supported":48,"title":464,"abstract":465,"tags":466},[235],[237,50,235],[],{"code":103,"score":468,"supported":48,"title":469,"abstract":470,"tags":471},15,[372],[369,372],[29],{"code":359,"score":192,"supported":48,"title":473,"abstract":474,"tags":475},[384],[384],[454],{"code":54,"score":240,"supported":48,"title":477,"abstract":478,"tags":479},[],[243,244],[28],[481,482],"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":484,"openalex_id":486,"authors":487,"venue":445,"cited_by_count":260,"oa_url":499,"card":500,"direction":281,"ingested_from":284},"W7221558726",[488,491,494,496],{"name":489,"orcid":490},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":492,"orcid":493},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":495,"orcid":8},"Fatih Sinan Bir",{"name":497,"orcid":498},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":501,"method":502,"finding":503,"direction":504,"opportunity":505},"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。","2026-10-13T23:30:10.242467Z"]