[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3245":3,"related-3245":95},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":79,"slug":82,"view_count":83,"doi":8,"paper":84,"created_at":94},3245,"Shaanxi Kiwi Farmers Break Free from Yield-Security FoMO（陕西猕猴桃农户摆脱\"产量安全错失恐惧\"）——Frontiers Sustain Food Syst 试错经验可破除数字养分管理技术采纳障碍","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F19\u002Fshaanxi-kiwi-farmers-break-free-from-yield-security-fomo","Frontiers in Sustainable Food Systems 发表研究：马来西亚博特拉大学Xia Li等学者针对中国陕西省675户猕猴桃种植户开展调研，发现\"产量安全错失恐惧（Yield-Security 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。",null,"Frontiers in Sustainable Food Systems","2026-09-19T00:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,22,18,13,8,1,"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。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农户采纳","猕猴桃","陕西","数字农业技术",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":45,"locked":13,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,56,62,69,74],{"code":32,"score":51,"supported":52,"title":53,"abstract":54,"tags":55},12,true,[28],[28],[28],{"code":39,"score":57,"supported":52,"title":58,"abstract":60,"tags":61},11,[59],"养分管理",[59],[],{"code":63,"score":64,"supported":52,"title":65,"abstract":66,"tags":68},"H2",3,[],[67],"土壤",[],{"code":70,"score":21,"supported":13,"title":71,"abstract":72,"tags":73},"G2",[],[],[27],{"code":75,"score":21,"supported":13,"title":76,"abstract":77,"tags":78},"I1",[],[],[26],[80,81],"陕西 猕猴桃 数字养分管理","产量安全错失恐惧 农户采纳","陕西猕猴桃数字养分管理-3245",2,{"doi":8,"openalex_id":8,"authors":85,"venue":8,"cited_by_count":86,"oa_url":8,"card":87,"direction":91,"ingested_from":93},[],0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"陕西675户猕猴桃种植户调研发现，产量安全错失恐惧阻碍数字养分管理技术采纳，试错体验可缓解。","对陕西675户猕猴桃种植户问卷调查，分析FoMO与三种数字技术采纳意向关系。","产量安全错失恐惧持续负向影响采纳；动手试错体验提升土壤检测仪和叶绿素仪采纳，对App影响小。","数字乡村与农业信息化","可探究不同数字农业技术中试错体验的差异化机制，设计降低农户FoMO的干预策略。","agent","2026-09-23T00:04:33.090863Z",{"total":96,"page":21,"page_size":96,"items":97},6,[98,177,233,281,364,406],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":8,"source_name":9,"source_url":101,"published_at":104,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":105,"score_detail":106,"sources":111,"tags":113,"primary_topic":115,"secondary_topics":116,"classification":117,"search_phrases":148,"slug":151,"view_count":83,"doi":152,"paper":153,"created_at":176},2915,"Does hands-on trial experience weaken the negative association between yield-security FoMO and digital nutrient-management technology adoption? Evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1898755","Excessive fertilizer use remains a major challenge for environmentally sustainable agricultural development in China, particularly in smallholder-based perennial production systems characterized by delayed feedback and production uncertainty. Although portable digital nutrient decision-support technologies may help improve fertilizer-use efficiency, their adoption among farmers remains uneven. Drawing on survey data from 675 kiwifruit farmers in Shaanxi Province, this study applies a multivariate probit model to examine the associations of cognitive evaluations, yield-security fear of missing out (FoMO), and hands-on trial experience with farmers’ adoption of soil testers, chlorophyll meters, and agro-advisory mobile applications. The results show that perceived ease of use and perceived relative advantage are positively associated with adoption, whereas yield-security FoMO is negatively associated with all three adoption outcomes in the baseline models. However, when the item most directly related to individual production-risk concerns is excluded, the negative association remains significant for soil testers and chlorophyll meters but becomes insignificant for agro-advisory mobile applications. Hands-on trial experience was positively associated with the adoption of soil testers and chlorophyll meters, but its association with agro-advisory mobile application adoption was not statistically significant. It was also associated with a weaker negative FoMO–adoption relationship for soil testers and chlorophyll meters, but no statistically significant moderating relationship was observed for agro-advisory mobile applications. These findings highlight the potentially important, but technology-specific, role of structured experiential learning in addressing behavioral barriers and supporting digital nutrient-management transitions in perennial smallholder agriculture.","过量施肥仍然是中国农业环境可持续发展面临的一大挑战，在以小农为基础、具有反馈滞后和生产不确定性的多年生作物生产体系中尤为突出。尽管便携式数字养分决策支持技术可能有助于提高肥料利用效率，但农户对其采用仍不均衡。本研究基于陕西省675户猕猴桃种植户的调查数据，采用多元Probit模型，考察认知评价、产量安全错失恐惧（FoMO）以及亲身试用经历与农户采用土壤测试仪、叶绿素仪和农技咨询移动应用程序之间的关联。结果表明，感知易用性和感知相对优势与采用行为呈正相关，而产量安全错失恐惧在基准模型中与三种技术的采用结果均呈负相关。然而，当剔除与个体生产风险担忧最直接相关的题项后，这种负相关对土壤测试仪和叶绿素仪仍然显著，但对农技咨询移动应用程序则不再显著。亲身试用经历与土壤测试仪和叶绿素仪的采用呈正相关，但其与农技咨询移动应用程序采用之间的关联在统计上不显著。亲身试用经历还弱化了产量安全错失恐惧与土壤测试仪和叶绿素仪采用之间的负相关关系，但对农技咨询移动应用程序未观察到统计上显著的调节关系。这些发现凸显了结构化体验式学习在应对行为障碍、支持多年生小农农业数字养分管理转型方面可能具有重要作用，但这种作用因技术类型而异。","2026-09-18T00:00:00Z",72,{"impact":51,"substance":107,"depth":108,"authority":19,"freshness":109,"relevant":21,"comment":110},21,17,9,"基于陕西675户猕猴桃果农调查，揭示产量安全FoMO对数字施肥技术采纳的抑制作用及试用体验的调节效应，对数字农业推广有实证参考价值。",[112],{"name":9,"url":101},[26,27,28,114,30],"化肥减量",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":13,"version":46,"method":47,"evidence":118},{"candidates":119,"agricultural_evidence":52},[120,127,133,138,144],{"code":39,"score":12,"supported":52,"title":121,"abstract":122,"tags":126},[],[123,124,59,125],"施肥","肥料","fertilizer",[114],{"code":32,"score":128,"supported":52,"title":129,"abstract":130,"tags":132},7,[],[28,131],"kiwifruit",[28],{"code":63,"score":96,"supported":52,"title":134,"abstract":135,"tags":137},[],[67,136],"soil",[],{"code":70,"score":139,"supported":52,"title":140,"abstract":141,"tags":143},4,[],[142],"smallholder",[27],{"code":75,"score":21,"supported":13,"title":145,"abstract":146,"tags":147},[],[],[26],[149,150],"陕西 猕猴桃 数字施肥","农户 数字营养管理 采纳","陕西猕猴桃数字施肥-2915","10.3389\u002Ffsufs.2026.1898755",{"doi":152,"openalex_id":154,"authors":155,"venue":9,"cited_by_count":86,"oa_url":101,"card":170,"direction":91,"ingested_from":175},"W7213534592",[156,159,162,165,168],{"name":157,"orcid":158},"Xia Li","https:\u002F\u002Forcid.org\u002F0000-0001-9933-6679",{"name":160,"orcid":161},"Ahmad Hanis Izani Abdul Hadi","https:\u002F\u002Forcid.org\u002F0000-0001-6512-9938",{"name":163,"orcid":164},"Yeong Sheng Tey","https:\u002F\u002Forcid.org\u002F0000-0002-1252-2485",{"name":166,"orcid":167},"Shaufique Fahmi Sidique","https:\u002F\u002Forcid.org\u002F0000-0002-7545-2816",{"name":169,"orcid":8},"Nolila Mohd Nawi",{"tldr":171,"method":172,"finding":173,"direction":91,"opportunity":174},"基于陕西675户猕猴桃果农调查，分析产量安全FoMO与亲身体验对数字养分管理技术采纳的影响。","多元Probit模型，675份陕西猕猴桃果农问卷数据。","产量安全FoMO负向关联采纳，亲身体验可弱化该负向关系，但作用因技术类型而异。","可探究不同技术类型下体验式学习干预的设计与效果差异，及其对农户数字养分管理采纳的长期影响。","openalex","2026-09-19T23:30:08.149310Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":8,"content":182,"source_name":183,"source_url":8,"published_at":184,"category":185,"cover_url":8,"hotness":12,"is_selected":13,"score":186,"score_detail":187,"sources":190,"tags":192,"primary_topic":198,"secondary_topics":199,"classification":207,"search_phrases":228,"slug":231,"view_count":86,"doi":8,"paper":8,"created_at":232},4639,"广东省农科院果树所农村科技特派员赴南沙开展番石榴科学施肥培训200余人参加","https:\u002F\u002Fwww.nfnews.com\u002Fcontent\u002FKyllAKRgyD.html","广东省农业科学院果树研究所联合广州市南沙区万顷沙镇农业农村技术服务中心,开展农村科技特派员技术下乡活动,分两场面向辖区种植户开展番石榴品质提升科学施肥专题培训,来自周边10多个村近200种植户参加。果树所研究员邵雪花聚焦科学施肥这一核心环节开展授课,围绕果园有机肥合理施用、不同物候期养分需求、化肥减量增效等实操内容展开讲解,引导种植户转变传统用肥观念。培训同步免费赠送有机肥,科技服务落地最后一公里。","近日，广东省农业科学院果树研究所（以下简称“果树所”）联合广州市南沙区万顷沙镇农业农村技术服务中心，开展农村科技特派员技术下乡活动，分两场面向辖区种植户开展番石榴品质提升科学施肥专题培训，把施肥管护实用技术送到农户家门口。来自周边10多个村近200种植户参加了培训。\n\n番石榴是南沙万顷沙镇颇具规模的特色水果，也是当地不少农户的重要收入来源。生产中部分果农存在施肥配比不合理、用肥方式粗放等问题，容易造成土壤地力退化，直接影响果实品质与商品价值。\n\n针对这一产业现实难题，广州市农村科技特派员、果树所研究员邵雪花结合万顷沙本地气候、土壤条件以及番石榴果园生产现状，聚焦科学施肥这一核心环节开展授课。围绕果园有机肥合理施用、不同物候期养分需求、化肥减量增效等实操内容展开讲解，引导种植户转变传统用肥观念，通过优化施肥方案改良园地土壤，从源头改善番石榴果品，提升市场竞争力。\n\n培训现场，番石榴种植户结合自家果园遇到的实际生产困惑踊跃提问，专家就地分析问题症结，一对一给出针对性管理建议。活动同步为到场参训农户免费赠送有机肥，将技术指导与生产物资支持相结合，让果农看得见理论、拿得到物资、学得到本领。“培训贴合日常果园管理需求，厘清了过去施肥上的误区，今后，我们对管好果园、种出好果更有把握。”现场农户表示。\n\n培训结束后，农村科技特派员团队与万顷沙镇农业农村技术服务中心人员一同前往万顷沙镇智正农场与番石榴种植基地，实地查看红宝石番石榴的开花坐果情况，并就番石榴病害发生情况与防治处理进行了现场交流与技术指导。\n\n参加培训的农村科技特派员表示，此次培训发挥农村科技特派员的专业优势，直面产业一线的现实问题，把实验室的科研成果转化为农户可落地的田间技术，是落实“百县千镇万村高质量发展工程”的具体实践，接下来，将立足万顷沙番石榴产业发展需求，持续开展技术培训、现场咨询等系列科技服务，补齐种植技术短板，打通农业科技服务落地的 “最后一公里”，以农科力量助力特色水果产业提质增效，夯实乡村产业振兴根基。","南方+ 2026-10-09","2026-10-09T00:00:00Z","报道",54,{"impact":12,"substance":188,"depth":51,"authority":12,"freshness":20,"relevant":21,"comment":189},14,"地方性农技培训报道，内容具体、有实操细节与多方信源，但影响层级限于地市，适合作为区域农技服务案例收录。",[191],{"name":183,"url":180},[193,194,195,196,197],"百千万工程","科技特派员","科学施肥","番石榴","化肥减量增效",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[200,201],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":202,"slug":203,"name":204,"path":205,"parent_code":206},"G1","extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","G",{"status":45,"locked":13,"version":46,"method":47,"evidence":208},{"candidates":209,"agricultural_evidence":52},[210,214,219,223],{"code":39,"score":19,"supported":52,"title":211,"abstract":212,"tags":213},[123],[123],[197,195],{"code":202,"score":51,"supported":52,"title":215,"abstract":217,"tags":218},[216],"科技特派",[216],[194],{"code":32,"score":57,"supported":52,"title":220,"abstract":221,"tags":222},[34],[34],[],{"code":224,"score":21,"supported":13,"title":225,"abstract":226,"tags":227},"G4",[],[],[193],[229,230],"广东农科院 番石榴 科学施肥","南沙 万顷沙 番石榴 科技特派员","广东农科院番石榴科学施肥-4639","2026-10-11T00:09:42.477263Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":8,"content":238,"source_name":239,"source_url":8,"published_at":240,"category":185,"cover_url":8,"hotness":12,"is_selected":13,"score":241,"score_detail":242,"sources":244,"tags":246,"primary_topic":251,"secondary_topics":252,"classification":254,"search_phrases":276,"slug":279,"view_count":64,"doi":8,"paper":8,"created_at":280},1486,"湖南农科院柑橘专家团队赴保靖县比耳镇开展为期三天培训：覆盖修枝整形、病虫防控、科学施肥","https:\u002F\u002Fwww.baojingrm.cn\u002Fcontent\u002F646042\u002F75\u002F16222356.html","8 月 26 日至 28 日，比耳镇特邀湖南省农业科学院园艺研究所柑橘栽培专家团队，开展为期三天的柑橘栽培技术专项培训。本次培训采用室内理论精讲+果园实景实操相结合的模式，精准围绕修枝整形、病虫防控、科学施肥三大核心技术授课。杨水芝专家讲解柑橘科学修枝整形技巧；龚碧涯专家重点围绕柑橘病虫害绿色防控及黄龙病综合管控授课；邓素枫专家聚焦树体营养诊断与科学施肥。比耳镇柑橘产业是群众就近就业、稳定增收的特色支柱产业。","## 借智引技到田间 精准培训促发展——省农科院柑橘专家团队在比耳镇开展技术培训\n\n来源：保靖县融媒体中心 作者：向燕玲 编辑：田清 2026-08-31 12:53:26\n\n保靖县融媒体中心8月31日讯（通讯员 向燕玲）夏末秋初，正值柑橘果实膨大、秋梢管护的关键窗口期，是决定果品品质和来年产量的重要阶段。为破解农户种植技术难题，助力柑橘产业提质增效，8月26日至28日，比耳镇特邀湖南省农业科学院园艺研究所柑橘栽培专家团队，开展为期三天的柑橘栽培技术专项培训。本次培训采用室内理论精讲+果园实景实操相结合的模式，精准围绕修枝整形、病虫防控、科学施肥三大核心技术授课，为全镇柑橘产业高质量发展注入科技动能。\n\n比耳镇作为保靖县柑橘核心主产区，柑橘产业是群众就近就业、稳定增收的特色支柱产业。针对辖区果农长期存在的修剪凭经验、施肥不科学、病虫识别不准、黄龙病防控薄弱等突出问题，比耳镇前期深入田间走访摸排，精准梳理种植短板与技术需求，按需定制培训课程，摒弃传统“大水漫灌”模式，实现精准施教、对症解惑，让培训更贴合生产、更贴合民心。\n\n![Image 1: 8月31日(1).jpg](https:\u002F\u002Fwww.baojingrm.cn\u002Fcontent\u002F646042\u002F75\u002F8%E6%9C%8831%E6%97%A5(1).jpg)\n\n本次培训内容紧凑务实、针对性极强。室内授课中，专家们用通俗易懂的语言拆解专业技术，直击种植痛点难点。杨水芝专家以“给树做减法”为切入点，系统讲解柑橘科学修枝整形技巧，指导农户通过合理疏枝、优化树形，改善通风透光条件，集中树体养分、促进优质果生长。龚碧涯专家重点围绕柑橘病虫害绿色防控及黄龙病综合管控授课，详细讲解木虱监测、病株甄别方法，精准区分缺素黄化与病害黄化，补齐农户黄龙病防控认知短板。邓素枫专家聚焦树体营养诊断与科学施肥，纠正盲目施肥、随意用肥的老旧陋习，引导果农树立看土施肥、看树补肥的精细化管护理念。\n\n![Image 2: 8月31日.jpg](https:\u002F\u002Fwww.baojingrm.cn\u002Fcontent\u002F646042\u002F75\u002F8%E6%9C%8831%E6%97%A5.jpg)\n\n田间实操环节，专家团队下沉橘园一线手把手教学、点对点指导。杨水芝现场示范标准修剪手法，逐一纠正不规范操作，切实解决果农“不会剪、不敢剪”的管护难题；龚碧涯现场辨认病虫枝叶，讲解病虫害高发规律，阐述黄龙病早发现、早处置、早除治的关键要点；邓素枫通过观察叶片长势，直观教学缺素识别、补肥实操方法，让科学施肥技术落地见效。整套教学理论贴合实际、实操简单易学，真正让农户学得懂、用得上、有成效。\n\n“种橘多年全靠老经验，这次专家讲得细、教得实，解决了我们多年的种植困惑。”参训果农纷纷表示，本次培训恰逢其时、干货满满，既解决了当前管护难题，也为来年稳产丰产筑牢基础。\n\n此次省级专家下沉授课，是比耳镇借智引技、服务产业的务实举措，有效打通了农技推广“最后一公里”。下一步，比耳镇将持续深化与省农科院的产学研合作，建立健全农技常态化下乡指导机制，加大柑橘标准化、精细化、科学化种植技术推广力度，推动全镇柑橘产业品质与效益双提升。\n\n一审：张晓莉\n\n二审：何晗竹珺\n\n三审：高 伟\n\n来源：保靖县融媒体中心\n\n编辑：田清\n\n本文为[保靖新闻网](https:\u002F\u002Fwww.baojingrm.cn\u002F \"保靖新闻网\")原创文章，转载请附上原文出处链接和本声明。\n\n本文链接：[https:\u002F\u002Fwww.baojingrm.cn\u002Fcontent\u002F646042\u002F75\u002F16222356.html](https:\u002F\u002Fwww.baojingrm.cn\u002Fcontent\u002F646042\u002F75\u002F16222356.html)","保靖新闻网","2026-08-31T04:53:26Z",46,{"impact":20,"substance":188,"depth":51,"authority":96,"freshness":96,"relevant":21,"comment":243},"地方农技培训报道，内容具体，但影响范围有限，时效性一般。",[245],{"name":239,"url":236},[247,248,249,195,250],"技术培训","柑橘产业","黄龙病防控","修枝整形",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[253],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":255},{"candidates":256,"agricultural_evidence":52},[257,261,266,272],{"code":39,"score":51,"supported":52,"title":258,"abstract":259,"tags":260},[123],[123],[195],{"code":32,"score":57,"supported":52,"title":262,"abstract":264,"tags":265},[263],"柑橘",[263],[],{"code":267,"score":64,"supported":52,"title":268,"abstract":269,"tags":271},"F4",[],[270],"绿色防控",[],{"code":202,"score":21,"supported":13,"title":273,"abstract":274,"tags":275},[],[],[247],[277,278],"黄龙病防控 修枝整形 技术培训 柑橘产业","黄龙病防控 修枝整形","黄龙病防控修枝整形技术培训柑橘产业-1486","2026-09-03T00:06:42.756308Z",{"id":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":8,"source_name":287,"source_url":284,"published_at":288,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":289,"score_detail":290,"sources":292,"tags":294,"primary_topic":298,"secondary_topics":299,"classification":306,"search_phrases":327,"slug":330,"view_count":139,"doi":331,"paper":332,"created_at":363},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":16,"substance":17,"depth":18,"authority":188,"freshness":109,"relevant":21,"comment":291},"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。",[293],{"name":287,"url":284},[26,295,296,28,297],"无人机","农业遥感","叶面积指数",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[300],{"code":301,"slug":302,"name":303,"path":304,"parent_code":305},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":13,"version":46,"method":47,"evidence":307},{"candidates":308,"agricultural_evidence":52},[309,318,323],{"code":301,"score":310,"supported":52,"title":311,"abstract":313,"tags":317},19,[312],"multispectral",[314,315,316,312],"遥感","无人机多光谱","remote sensing",[296,297],{"code":32,"score":18,"supported":52,"title":319,"abstract":320,"tags":322},[131],[28,321,131],"orchard",[28],{"code":75,"score":21,"supported":13,"title":324,"abstract":325,"tags":326},[],[],[26],[328,329],"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":331,"openalex_id":333,"authors":334,"venue":287,"cited_by_count":86,"oa_url":284,"card":357,"direction":361,"ingested_from":175},"W7213243341",[335,337,339,341,344,346,348,350,352,355],{"name":336,"orcid":8},"Wenjie Li",{"name":338,"orcid":8},"Hongen Liu",{"name":340,"orcid":8},"Linghuan Ouyang",{"name":342,"orcid":343},"Qian Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7916-1370",{"name":345,"orcid":8},"Tianqi Lv",{"name":347,"orcid":8},"Jiali Li",{"name":349,"orcid":8},"Xintao Lin",{"name":351,"orcid":8},"Chen Yao",{"name":353,"orcid":354},"Yongqiang Zheng","https:\u002F\u002Forcid.org\u002F0000-0003-3246-2800",{"name":356,"orcid":8},"Jianping Qian",{"tldr":358,"method":359,"finding":360,"direction":361,"opportunity":362},"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",{"id":365,"title":366,"url":367,"summary":368,"summary_zh":8,"content":369,"source_name":370,"source_url":8,"published_at":371,"category":185,"cover_url":8,"hotness":12,"is_selected":13,"score":372,"score_detail":373,"sources":376,"tags":378,"primary_topic":382,"secondary_topics":383,"classification":384,"search_phrases":401,"slug":404,"view_count":64,"doi":8,"paper":8,"created_at":405},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*   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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",75,{"impact":18,"substance":374,"depth":108,"authority":188,"freshness":96,"relevant":21,"comment":375},20,"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。",[377],{"name":370,"url":367},[26,379,380,28,381],"农业人工智能","农产品溯源","光谱成像",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":13,"version":46,"method":47,"evidence":385},{"candidates":386,"agricultural_evidence":52},[387,391,395],{"code":32,"score":51,"supported":52,"title":388,"abstract":389,"tags":390},[28],[28],[28],{"code":75,"score":139,"supported":52,"title":392,"abstract":393,"tags":394},[],[26],[26],{"code":396,"score":139,"supported":52,"title":397,"abstract":398,"tags":400},"I2",[],[399],"artificial intelligence",[379],[402,403],"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":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":8,"source_name":412,"source_url":409,"published_at":413,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":289,"score_detail":414,"sources":416,"tags":418,"primary_topic":422,"secondary_topics":423,"classification":424,"search_phrases":451,"slug":454,"view_count":86,"doi":455,"paper":456,"created_at":472},2122,"Heterogeneous behaviours towards precision agriculture adoption among Italian winegrowers: insights from latent class analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10448-0","Abstract Purpose Precision Agriculture technologies including satellite data, drones, field robots and automatic guidance, are increasingly promoted as tools to improve sustainability, competitiveness, and environmental efficiency in viticulture. However, the adoption of these practices among Italian winegrowers exhibits significant heterogeneity, influenced by a combination of structural and behavioural factors. This study aims to identify distinct groups of Italian winegrowers based on their intentions to adopt PATs, examining how these intentions relate to four behavioural constructs that capture cognitive, social, and risk-related influences. Method The present study integrates a profile-based perspective by estimating a latent-class generalised structural equation model on 272 Italian winegrowers. The model of class membership is predicated upon a multifaceted conceptualisation encompassing risk perception, risk tolerance, perceived ease of use, and subjective norms and the technology-specific five-year intentions are incorporated as class-specific probit intercepts. Results Three qualitatively distinct profiles emerge: risk-sensitive sceptics, selective pragmatists and usability-oriented high adopters with sharply different adoption patterns across technologies. Participation in rural development schemes is the strongest predictor of membership in the higher-propensity latent classes, namely the selective-pragmatist and usability-oriented high-adopter profiles. In addition, familiarity with PATs, male gender and participation in consortium are positively associated with the higher-propensity class, while age shows a weak negative correlation. Conclusion These results inform the design of extension and policy for specific segments, prioritising risk-mitigation trials for cautious growers, targeted information services for select profiles, and integrated PATs bundles for those with a high propensity.","摘要 目的 精准农业技术（Precision Agriculture Technologies，PATs），包括卫星数据、无人机、田间机器人和自动导航，日益被视为提升葡萄栽培可持续性、竞争力和环境效率的工具。然而，意大利葡萄种植者对上述技术的采纳呈现出显著异质性，受到结构性因素与行为因素的共同影响。本研究旨在基于意大利葡萄种植者采纳PATs的意愿识别不同群体，并考察这些意愿如何与四个行为构念相关联，这四个构念分别捕捉认知、社会和风险相关的影响。方法 本研究整合了基于剖面的视角，对272名意大利葡萄种植者估计了潜在类别广义结构方程模型。类别归属模型建立在涵盖风险感知、风险容忍度、感知易用性和主观规范的多维概念化基础之上，并将技术特定的五年意愿作为类别特定的probit截距纳入模型。结果 研究识别出三种性质不同的剖面：风险敏感型怀疑者、选择性实用主义者和以易用性为导向的高采纳者，其在不同技术上的采纳模式差异显著。参与农村发展计划是归属于较高倾向潜在类别（即选择性实用主义者和以易用性为导向的高采纳者剖面）的最强预测因素。此外，对PATs的熟悉程度、男性性别和参与合作社与较高倾向类别呈正相关，而年龄则表现出较弱的负相关。结论 上述结果为针对特定群体的推广和政策设计提供了依据，应优先为谨慎型种植者开展风险缓解试验，为特定剖面提供有针对性的信息服务，并为高倾向群体提供整合的PATs技术组合。","Precision Agriculture","2026-09-10T00:00:00Z",{"impact":16,"substance":17,"depth":18,"authority":188,"freshness":109,"relevant":21,"comment":415},"基于272户意大利葡萄种植者的潜类别分析，揭示精准农业采纳的异质性行为分群，对农户分类推广与政策设计有实质参考价值。",[417],{"name":412,"url":409},[26,27,419,420,421],"精准农业","遥感监测","葡萄种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":13,"version":46,"method":47,"evidence":425},{"candidates":426,"agricultural_evidence":52},[427,432,438,443,447],{"code":75,"score":16,"supported":52,"title":428,"abstract":430,"tags":431},[429],"precision agriculture",[419,429],[26,419],{"code":206,"score":96,"supported":52,"title":433,"abstract":434,"tags":437},[],[435,436],"农村发展","rural development",[],{"code":32,"score":139,"supported":52,"title":439,"abstract":440,"tags":442},[],[441],"葡萄",[421],{"code":70,"score":21,"supported":13,"title":444,"abstract":445,"tags":446},[],[],[27],{"code":301,"score":21,"supported":13,"title":448,"abstract":449,"tags":450},[],[],[420],[452,453],"农户采纳 智慧农业 精准农业 葡萄种植","农户采纳 智慧农业","农户采纳智慧农业精准农业葡萄种植-2122","10.1007\u002Fs11119-026-10448-0",{"doi":455,"openalex_id":457,"authors":458,"venue":412,"cited_by_count":86,"oa_url":409,"card":467,"direction":91,"ingested_from":175},"W7212151394",[459,461,464],{"name":460,"orcid":8},"Adriano Biondo",{"name":462,"orcid":463},"Antonino Galati","https:\u002F\u002Forcid.org\u002F0000-0003-0753-2934",{"name":465,"orcid":466},"Francesco Caracciolo","https:\u002F\u002Forcid.org\u002F0000-0001-9430-7529",{"tldr":468,"method":469,"finding":470,"direction":91,"opportunity":471},"基于272名意大利葡萄种植者，用潜类别分析识别精准农业技术采纳意向的异质性群体。","潜类别广义结构方程模型，纳入风险感知、风险容忍、易用性和主观规范。","分出风险敏感怀疑者、选择性实用主义者和易用性高采纳者三类，参与农村发展计划是最强预测因素。","可针对不同农户群体设计差异化推广策略，并研究政策参与如何通过行为路径影响技术采纳。","2026-09-11T23:30:02.943184Z"]