[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-173":3,"related-173":77},{"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":72,"slug":75,"view_count":13,"doi":8,"paper":8,"created_at":76},173,"可解释集成学习框架助力农产品产地可信溯源——以中国六个区域猕猴桃为对象","https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fttxw\u002F6a84d96369424b73a6fc3d3846a535f8.htm","中国农业科学院农业资源与农业区划研究所智慧农业团队在光谱数据建模的可解释方法研究上取得重要进展。该研究以中国六个区域的猕猴桃为对象，结合高光谱技术和集成学习方法构建猕猴桃产地溯源模型，并利用可解释性人工智能方法对最优的溯源集成模型进行分层和整体解释，在提高产地溯源准确率的同时，深入理解光谱数据背后预测农产品产地的逻辑。博士生李佳利为第一作者，钱建平研究员和吴文斌研究员担任共同通讯作者。论文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.05.010",null,"[中国农科院](http:\u002F\u002Fwww.caas.cn\u002F)| [English](http:\u002F\u002Fiarrp.caas.cn\u002Fen)| 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Agriculture》上。\n\n当前，光谱数据预测农产品地理来源时通常侧重于单一算法，过分强调预测性能而忽略了可解释性，极大地阻碍了准确有效的溯源模型开发以及对模型内在机制的理解。研究以中国六个区域的猕猴桃为对象，结合高光谱技术和集成学习方法构建猕猴桃产地溯源模型，并利用可解释性人工智能方法对最优的溯源集成模型进行分层和整体解释，在提高产地溯源准确率的同时，深入理解光谱数据背后预测农产品产地的逻辑。\n\n该研究为破解光谱技术中机器学习模型的可解释性难题提供了系统的解决思路和技术路径，在特征波长筛选、模型泛化能力提升及预测结果可信度增强等方面具有方法学参考价值，有助于推动光谱技术在农业生产与质量监管中的实际应用。\n\n![Image 5](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-07\u002Fae272d1ce9204821b1feceb1d1e21f0b.png)\n\n中国农业科学院农业资源与农业区划研究所智慧农业团队博士生李佳利为第一作者，钱建平研究员和吴文斌研究员担任共同通讯作者。该研究得到北方干旱半干旱耕地高效利用全国重点实验室、中国农业科学院科技创新工程（CAAS-CSAL-202402）、国家重点研发计划（2022YFD1600703）以及中央级公益性科研院所基本业务费（Y2025YC82）的共同资助。\n\n论文信息：Li, J.L., Qian, J.P*., Yu, Q.Y., Chen, J.Y., Ma, W., Li, A., Chen, Q., Lin, X.T., Sun, L.M., Wu, W.B*. Explainable AI opens the black-box model of spectral technique to trace agro-products origin. Artificial Intelligence in Agriculture, 2026.\n\n论文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.05.010\n\n『[打印](https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fttxw\u002F6a84d96369424b73a6fc3d3846a535f8.htm#)』『[关闭](https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fttxw\u002F6a84d96369424b73a6fc3d3846a535f8.htm#)』\n\n[设为首页](http:\u002F\u002Fiarrp.caas.cn\u002F)∣[加入收藏](javascript:window.external.AddFavorite('http:\u002F\u002Fiarrp.caas.cn\u002F', '%E5%86%9C%E4%B8%9A%E8%B5%84%E6%BA%90%E4%B8%8E%E5%86%9C%E4%B8%9A%E5%8C%BA%E5%88%92%E7%A0%94%E7%A9%B6%E6%89%80'))∣[联系我们](https:\u002F\u002Fiarrp.caas.cn\u002Flxwm\u002Findex.htm)\n\n Copyright©2012-2021 中国农业科学院农业资源与农业区划研究所版权所有\n\n 地址：北京市海淀区中关村南大街12号 邮编：100081 电话：010-82109640\n\niarrp.caas.cn ([京ICP备14003094号-1](https:\u002F\u002Fbeian.miit.gov.cn\u002F))京公网安备 11010802028641号 技术支持：中国农业科学院农业信息研究所","中国农业科学院农业资源与农业区划研究所 \u002F Artificial Intelligence in Agriculture 2026","2026-07-30T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,15,7,1,"国家级科研机构发表的可解释集成学习框架用于农产品溯源，方法新颖且数据规模较大，具有较高专业价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"可解释AI","农产品溯源","集成学习","高光谱","猕猴桃",{"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},[44,50,57,62,67],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[30],[30],[30],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"I1",3,[],[55],"智慧农业",[],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"E4",[],[],[27],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I2",[],[],[26],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"I3",[],[],[29],[73,74],"农产品溯源 集成学习 可解释AI 猕猴桃","农产品溯源 集成学习","农产品溯源集成学习可解释AI猕猴桃-173","2026-08-01T23:53:44.495822Z",{"total":78,"page":21,"page_size":78,"items":79},6,[80,123,182,227,293,378],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":8,"content":85,"source_name":86,"source_url":8,"published_at":87,"category":88,"cover_url":8,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":94,"tags":96,"primary_topic":99,"secondary_topics":100,"classification":101,"search_phrases":118,"slug":121,"view_count":52,"doi":8,"paper":8,"created_at":122},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: 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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*   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[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","报道",75,{"impact":17,"substance":18,"depth":91,"authority":92,"freshness":78,"relevant":21,"comment":93},17,14,"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。",[95],{"name":86,"url":83},[55,97,27,30,98],"农业人工智能","光谱成像",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":102},{"candidates":103,"agricultural_evidence":46},[104,108,113],{"code":32,"score":45,"supported":46,"title":105,"abstract":106,"tags":107},[30],[30],[30],{"code":51,"score":109,"supported":46,"title":110,"abstract":111,"tags":112},4,[],[55],[55],{"code":63,"score":109,"supported":46,"title":114,"abstract":115,"tags":117},[],[116],"artificial intelligence",[97],[119,120],"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 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FoMO）\"——农户对不使用每一种可用工具以最大化产量的焦虑——是数字养分管理技术（土壤检测仪、叶绿素仪、农技咨询App等）采纳的主要障碍。研究发现，虽然感知有用性和相对优势能促进采纳，但产量安全错失恐惧对三种技术的采纳意向均呈持续负面影响。然而，参与结构化\"动手试错\"体验的农户更可能采纳土壤检测仪和叶绿素仪，且该恐惧对决策的影响更小。对农技咨询App影响较小，说明试错体验的效应可能因技术而异。该研究为农技企业和政策制定者提供启示：让农户亲手尝试新技术是弥合怀疑与采纳之间鸿沟的关键。","2026-09-19T00:00:00Z",77,{"impact":235,"substance":132,"depth":17,"authority":236,"freshness":192,"relevant":21,"comment":237},16,13,"基于675户调研揭示FoMO阻碍数字养分管理技术采纳、试错体验可缓解，对农技推广有实证参考价值。",[239],{"name":137,"url":230},[55,241,30,242,243],"农户采纳","陕西","数字农业技术",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[246],{"code":247,"slug":248,"name":249,"path":250,"parent_code":208},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers",{"status":39,"locked":14,"version":40,"method":41,"evidence":252},{"candidates":253,"agricultural_evidence":46},[254,258,264,270,275],{"code":32,"score":45,"supported":46,"title":255,"abstract":256,"tags":257},[30],[30],[30],{"code":247,"score":259,"supported":46,"title":260,"abstract":262,"tags":263},11,[261],"养分管理",[261],[],{"code":265,"score":52,"supported":46,"title":266,"abstract":267,"tags":269},"H2",[],[268],"土壤",[],{"code":271,"score":21,"supported":14,"title":272,"abstract":273,"tags":274},"G2",[],[],[241],{"code":51,"score":21,"supported":14,"title":276,"abstract":277,"tags":278},[],[],[55],[280,281],"陕西 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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\"。","“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",{"impact":132,"substance":302,"depth":91,"authority":192,"freshness":303,"relevant":21,"comment":304},21,9,"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。",[306],{"name":299,"url":296},[308,55,97,27,309,310],"数字乡村","粤桂协作","无人机巡飞",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":314},{"candidates":315,"agricultural_evidence":46},[316,321,325,330,336,342,348,352,358,364,368],{"code":32,"score":92,"supported":46,"title":317,"abstract":319,"tags":320},[318],"葡萄",[34,318],[],{"code":51,"score":109,"supported":46,"title":322,"abstract":323,"tags":324},[],[55],[55],{"code":247,"score":52,"supported":46,"title":326,"abstract":327,"tags":329},[],[328],"施肥",[],{"code":331,"score":52,"supported":46,"title":332,"abstract":333,"tags":335},"F4",[],[334],"农药",[],{"code":337,"score":52,"supported":46,"title":338,"abstract":339,"tags":341},"G",[],[340],"乡村振兴",[],{"code":343,"score":52,"supported":46,"title":344,"abstract":345,"tags":347},"G4",[],[346],"联农带农",[],{"code":265,"score":52,"supported":46,"title":349,"abstract":350,"tags":351},[],[268],[],{"code":353,"score":52,"supported":46,"title":354,"abstract":355,"tags":357},"H3",[],[356],"灌溉",[],{"code":359,"score":52,"supported":46,"title":360,"abstract":361,"tags":363},"I5",[],[362],"物联网",[],{"code":63,"score":21,"supported":14,"title":365,"abstract":366,"tags":367},[],[],[97],{"code":369,"score":21,"supported":14,"title":370,"abstract":371,"tags":372},"I6",[],[],[308],[374,375],"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",{"id":379,"title":380,"url":381,"summary":382,"summary_zh":383,"content":8,"source_name":384,"source_url":381,"published_at":385,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":395,"secondary_topics":396,"classification":401,"search_phrases":423,"slug":426,"view_count":109,"doi":427,"paper":428,"created_at":459},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":235,"substance":132,"depth":17,"authority":92,"freshness":303,"relevant":21,"comment":388},"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。",[390],{"name":384,"url":381},[55,392,393,30,394],"无人机","农业遥感","叶面积指数",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[397],{"code":68,"slug":398,"name":399,"path":400,"parent_code":159},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":402},{"candidates":403,"agricultural_evidence":46},[404,413,419],{"code":68,"score":405,"supported":46,"title":406,"abstract":408,"tags":412},19,[407],"multispectral",[409,410,411,407],"遥感","无人机多光谱","remote sensing",[393,394],{"code":32,"score":17,"supported":46,"title":414,"abstract":416,"tags":418},[415],"kiwifruit",[30,417,415],"orchard",[30],{"code":51,"score":21,"supported":14,"title":420,"abstract":421,"tags":422},[],[],[55],[424,425],"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":427,"openalex_id":429,"authors":430,"venue":384,"cited_by_count":173,"oa_url":381,"card":453,"direction":457,"ingested_from":180},"W7213243341",[431,433,435,437,440,442,444,446,448,451],{"name":432,"orcid":8},"Wenjie Li",{"name":434,"orcid":8},"Hongen Liu",{"name":436,"orcid":8},"Linghuan Ouyang",{"name":438,"orcid":439},"Qian Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7916-1370",{"name":441,"orcid":8},"Tianqi Lv",{"name":443,"orcid":8},"Jiali Li",{"name":445,"orcid":8},"Xintao Lin",{"name":447,"orcid":8},"Chen Yao",{"name":449,"orcid":450},"Yongqiang Zheng","https:\u002F\u002Forcid.org\u002F0000-0003-3246-2800",{"name":452,"orcid":8},"Jianping Qian",{"tldr":454,"method":455,"finding":456,"direction":457,"opportunity":458},"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）。","农业遥感与作物表型","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"]