[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4765":3,"related-4765":93},{"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":38,"classification":39,"search_phrases":88,"slug":91,"view_count":22,"doi":8,"paper":8,"created_at":92},4765,"在农业农村现代化关键点上持续发力","https:\u002F\u002Fwww.qstheory.cn\u002F20261012\u002Fc6238e31742540c887fb80611fcdb460\u002Fc.html","求是网刊发关于兵团农业农村现代化的理论文章,部署三项重点。一是积极培育农业低空经济新业态,布局AI加农业专项行动;二是推动稳粮、优棉、强果、兴畜、创特色,扩大优质食味稻、专用小麦、高产高油大豆供给;三是推进农产品精深加工、发展智慧冷链物流,贯通仓储、运输、配送全环节。",null,"  持续深化团场改革，激发农业发展活力。一是持续深化团场综合配套改革，完善土地、职工、民兵“三位一体”机制，持续增强团场发展的内生动力和发展活力。针对团场连队基础设施与公共服务短板，推动城乡要素双向流动，促进人才、资金、技术等现代生产要素向团场连队有序集聚。健全人才引进与培育机制，鼓励科技人员、高校毕业生、退役军人等高素质人才到团场连队创业就业。持续推进公共服务均等化，助力实现区域全面融合、共同繁荣的发展目标。二是在规划建设方面，坚持精准施策，全面提升规划、建设、治理融合发展水平，统筹推进城镇高质量发展。各师市要立足自身资源禀赋与区位条件，科学编制区域发展规划，明确城市、团场、连队的功能定位与发展方向，加快构建城乡融合、功能互补、共同繁荣的城乡发展格局。三是充分发挥兵团农业发展优势，健全兵地共同推进乡村振兴的沟通协商机制，统筹推进兵地基础设施建设和人居环境改善，深化公共服务资源共享，推动兵团连队与地方邻近村开展结对共建，进一步推动兵地深度嵌入、融合发展。\n\n  强化农业科技创新，培育农业新质生产力。一是深入实施种业振兴行动，加快突破性品种选育推广，有序推进生物育种产业化应用。发挥兵团在棉花、玉米种子领域的基础优势，加大生物育种技术研发力度，推进分子标记辅助选择、基因编辑等现代育种技术应用，培育更多具有自主知识产权的优良品种。支持兵团围绕种业振兴、智慧农业、深度节水、土壤改良等领域开展农业关键核心技术攻关，高水平建设种业创新平台，加快培育突破性品种，破解兵团农业领域“卡脖子”技术难题。二是加快高端智能农机装备研发应用，持续巩固提升兵团农业机械化水平。积极培育农业低空经济新业态，拓展无人机在植保作业、农田监测、农资运输等领域应用场景。构建农业科技领军企业培育库，支持企业牵头组建创新联合体，加快培育龙头型、成长型农业科技企业。推动人工智能与农业生产经营深度融合，实施“人工智能+农业”专项行动，促进物联网、大数据、机器人等技术在精准施肥、智能灌溉、病虫害监测等生产环节规模化应用。三是进一步提升节水技术水平，在现有膜下滴灌、“干播湿出”等技术基础上，加快攻克深度节水、智能灌溉关键技术，推广应用智能水肥一体化技术，持续巩固兵团农业高效节水灌溉全国领先地位。探索建立农业用水总量控制与定额管理制度，深化农业水价综合改革，促进水资源可持续利用。\n\n  提升农业综合生产能力，夯实农产品稳产保供根基。一是稳定粮食生产。深入实施新一轮千亿斤粮食产能提升行动，持续推进蔬菜、林果、畜禽、水产等特色优势产业发展。牢固树立并深入践行大食物观，坚持农林牧渔协同发展，推动构建多元化食物供给体系，全方位、多途径开发食物资源，拓宽食物供给来源。按照“稳粮、优棉、强果、兴畜、创特色”的农业发展要求，将棉花种植面积稳定在千万亩级，持续巩固棉花产业优势地位，同时优化粮食品种结构，扩大优质食味稻、专用小麦、高产高油大豆等优质农产品供给。二是推进高标准农田建设。按照分区分类原则高质量推进高标准农田建设，完善立项、建设、验收与管护的全链条管理机制，推进大中型灌区现代化建设与改造，强化盐碱地治理，提升耕地质量。进一步提升高标准农田占耕地面积比重，从根本上改善农业生产的土地基础条件，为农业稳产高产提供坚实支撑。三是推动农产品品质提升。推动兵团农业向产量与品质并重转型，健全农业标准体系，完善农产品质量安全承诺达标合格证制度，构建以市场需求为导向的品质评价与质量分级制度。强化兵团农产品质量安全监管，确保农产品质量安全监测总体合格率稳定在较高水平，推动更多兵团优质农产品实现优质优价。\n\n  构建现代农业产业体系，加快推动产业转型升级。一是推动农产品精深加工。围绕“优、精、特”农业发展方向，坚持智能化升级、绿色化生产、融合化发展路径，促进种养环节增值，推动农产品供给由“原字号”初级产品向高附加值“制成品”转型。延伸拓展棉花、玉米、红枣、辣椒等特色农产品加工链条，发展精深加工与生物制造产业，挖掘农产品多元利用价值；加快智慧冷链物流网络建设，贯通仓储、运输、配送全环节，保障农产品跨区域高效安全流通；促进农业与文旅、康养等产业深度融合，培育发展田园综合体等新业态，拓展农业多元功能。充分开发利用荒漠、草场、林地与水域资源，把戈壁荒滩的潜在价值转化为从田间到车间的产业发展优势。二是壮大兴农富民产业。健全紧密型联农带农利益联结机制，发展壮大各具特色的团场连队经济。培育一批产业链条长、科技含量高、带动能力强的产业领军企业，做强农产品精深加工产业集群。持续做强农业优势产业，大力发展农产品加工流通业，促进农产品增值，让职工群众共享产业增值收益。做大做强棉花及纺织服装全产业链，积极承接东部地区产业转移，建设综合性纺织工业基地与特色服装产业集群。\n\n  建设宜居宜业和美连队，提升职工群众生活品质。一是学习运用“千万工程”经验，分区分类推进和美连队建设。接续实施乡村振兴“百连示范工程”扩面提质行动，进一步扩大示范覆盖范围、提升示范建设质量，推进高水平示范连队建设，发挥典型引领带动作用。持续健全连队治理长效机制，坚持和发展新时代“枫桥经验”，深入推进文明乡风建设工程。二是大力发展连队本土特色经济，保护传承军垦文化。立足各连队的区位条件、资源特色，因地制宜发展庭院经济、休闲农业、乡村旅游等新产业新业态，激活连队零散闲置的各类资源，以千家万户庭院“小美”赋能连队生态宜居、产业兴旺的“大美”建设，拓宽职工群众就地就近增收渠道。在连队建设全过程中，注重保护兵团特有的军垦文化风貌，守护并延续兵团精神的历史文脉，让红色军垦基因得到传承弘扬，使连队建设既充分体现新时代现代化发展水平，配套完善现代化基础设施与公共服务，又保留兵团屯垦戍边的独特标识，打造彰显军垦特色、生态宜居的田园式家园。\n\n  推进绿色低碳循环发展，擦亮农业生态底色。一是深入推进节水增效。健全高标准节水灌溉技术体系，在现有节水农业发展基础上，大面积推广应用智能水肥一体化技术，持续提高农业水资源利用效率，减少水资源无效消耗。探索建立符合兵团农业发展实际的农业用水总量控制与定额管理制度，以刚性制度约束引导农业合理配水用水，进一步促进区域水资源可持续利用。二是强化农业面源污染综合治理。深入推进化肥农药减量增效行动，大力推广有机肥替代化肥、病虫草害绿色防控等成熟技术，从源头减少农业面源污染排放。持续推进畜禽养殖粪污资源化利用，推动形成种养结合、循环发展的农业模式，实现养殖废弃物就地就近消纳、循环利用。健全农业废弃物回收利用体系。统筹推进农作物秸秆综合利用和农田残膜回收，全链条降低农业废弃物对生态环境的影响。","求是网 2026-10-12","2026-10-12T00:00:00Z","报道",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,18,16,15,9,1,"央媒刊发的兵团农业现代化系统部署，政策条款与产业路径清晰，但属方向性综合论述，新数据与独家信息有限。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","低空经济","高标准农田","种业振兴","农业现代化","智慧冷链",{"code":34,"slug":35,"name":36,"path":37,"parent_code":8},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"partial","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48,"reason":87},[45,53,60,66,72,77,82],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},"J2",12,true,[50],"农业农村现代化",[50],[31],{"code":54,"score":55,"supported":48,"title":56,"abstract":57,"tags":59},"A2",3,[],[58],"小麦",[],{"code":61,"score":55,"supported":48,"title":62,"abstract":63,"tags":65},"A4",[],[64],"大豆",[],{"code":67,"score":55,"supported":48,"title":68,"abstract":69,"tags":71},"E3",[],[70],"冷链",[],{"code":73,"score":22,"supported":14,"title":74,"abstract":75,"tags":76},"F1",[],[],[30],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"H1",[],[],[29],{"code":83,"score":22,"supported":14,"title":84,"abstract":85,"tags":86},"I1",[],[],[27],"一级领域明确，二级方向需要细分",[89,90],"兵团 种业振兴 生物育种","兵团 农业低空经济 无人机植保","兵团种业振兴生物育种-4765","2026-10-13T00:08:34.007796Z",{"total":94,"page":22,"page_size":94,"items":95},6,[96,164,222,354,404,443],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":8,"source_name":102,"source_url":99,"published_at":103,"category":104,"cover_url":8,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":110,"tags":112,"primary_topic":117,"secondary_topics":118,"classification":119,"search_phrases":143,"slug":146,"view_count":147,"doi":148,"paper":149,"created_at":163},5404,"Validation of a Day-of-Year Standardized Canopy Indicator for Regional Crop Monitoring: Suitability and Decoupling Boundaries","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.1707.v1","Translating satellite-derived crop canopy anomalies into regional production signals is critical for agricultural monitoring. However, standard anomaly indicators assume a stable, direct coupling between greenness and production, ignoring phenological shifts, sub-seasonal cropping dynamics, and harvested acreage fluctuations. I evaluate the validation boundaries and expected conditions of a pixel-level, day-of-year (DOY)-first Leaf Area Index (LAI) Z-score anomaly indicator with a leave- year-out baseline (2003–2022) across five contrasting global breadbaskets: Australia, Indo-Gangetic Plains (IGP), North China Plain (NCP), US Corn Belt, and Ukraine. The analysis shows that DOY- first standardization reduces temporal composite timing mismatches, though coupling with regional statistics varies systematically. Retrospective validation reveals that direct canopy-yield coupling remains robust exclusively in rainfed Australia (Pearson’s r = 0.90), where yield variability dominates production variance (explaining 90% of variance) and moisture stress limits canopy development. In contrast, three interpretation boundaries are identified: (1) greening trend-sensitivity in the IGP, where detrending reduces the association to r = 0.53; (2) temporal representativeness bias in the double-cropped NCP, requiring crop-calendar windowing to recover the wheat signal (r = 0.51); and (3) area-shock-sensitivity in Ukraine (where harvested area contractions explain 78% of production variance), decoupling canopy vigor from production (r = 0.40). Furthermore, the LAI Z-score provides a more interpretable canopy-response measure than the Vegetation Condition Index (VCI) in dense canopies where NDVI saturates. These validation levels are synthesized into a prototype diagnostic decision framework to support indicator configuration in operational monitoring systems.","将卫星反演的作物冠层异常转化为区域产量信号对农业监测至关重要。然而，标准异常指标假定绿度与产量之间存在稳定、直接的耦合关系，忽略了物候偏移、次季节作物动态以及收获面积波动。我评估了一种像元级、以年积日（DOY）为首要标准化维度的叶面积指数（LAI）Z分数异常指标在五个对比鲜明的全球粮仓——澳大利亚、印度河-恒河平原（IGP）、华北平原（NCP）、美国玉米带和乌克兰——的验证边界与预期适用条件，该指标采用留一年基线（2003—2022年）。分析表明，以DOY为首要标准化维度可减少时间合成时相不匹配，但其与区域统计数据的耦合关系存在系统性差异。回顾性验证显示，冠层与产量的直接耦合仅在雨养条件下的澳大利亚保持稳健（Pearson's r = 0.90），该地区产量变异性主导产量方差（解释90%的方差），且水分胁迫限制冠层发育。相比之下，识别出三个解释边界：（1）IGP的绿度趋势敏感性，去趋势后关联性降至r = 0.53；（2）双季种植的NCP的时间代表性偏差，需按作物日历开窗以恢复小麦信号（r = 0.51）；（3）乌克兰的面积冲击敏感性（收获面积缩减解释78%的产量方差），导致冠层活力与产量解耦（r = 0.40）。此外，在NDVI饱和的密集冠层中，LAI Z分数提供了比植被条件指数（VCI）更具可解释性的冠层响应度量。这些验证层级被综合为一个原型诊断决策框架，以支持业务化监测系统中的指标配置。","Preprints.org","2026-10-19T00:00:00Z","论文",65,{"impact":47,"substance":107,"depth":18,"authority":94,"freshness":108,"relevant":22,"comment":109},21,8,"预印本提出按年积日标准化的LAI异常指标，并在五大农业区验证其适用边界，方法有新意但尚未经同行评审，属细分领域进展。",[111],{"name":102,"url":99},[27,113,114,115,116],"遥感","作物监测","产量估测","叶面积指数",{"code":34,"slug":35,"name":36,"path":37,"parent_code":8},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":120},{"candidates":121,"agricultural_evidence":48,"reason":87},[122,127,134,139],{"code":54,"score":94,"supported":48,"title":123,"abstract":124,"tags":126},[],[58,125],"wheat",[],{"code":128,"score":94,"supported":48,"title":129,"abstract":130,"tags":133},"A3",[],[131,132],"玉米","corn",[],{"code":135,"score":55,"supported":14,"title":136,"abstract":137,"tags":138},"I3",[],[],[115,114,116,113],{"code":83,"score":22,"supported":14,"title":140,"abstract":141,"tags":142},[],[],[27],[144,145],"LAI Z-score 作物监测","遥感 作物产量 估测","LAIZ-score作物监测-5404",0,"10.20944\u002Fpreprints202610.1707.v1",{"doi":148,"openalex_id":150,"authors":151,"venue":102,"cited_by_count":147,"oa_url":99,"card":155,"direction":161,"ingested_from":162},"W7222967882",[152],{"name":153,"orcid":154},"Xiaolong Song","https:\u002F\u002Forcid.org\u002F0000-0002-2106-6629",{"tldr":156,"method":157,"finding":158,"direction":159,"opportunity":160},"验证了按年积日标准化的LAI Z-score冠层指标在五大农业区的适用边界。","用2003–2022年留一年基线，在五个全球粮仓区做LAI DOY标准化与产量相","仅雨养澳大利亚冠层-产量强耦合(r=0.90)，其余区域受趋势、物候和面积冲击影响而解耦。","农业遥感与作物表型","可发展融合物候窗口、去趋势与收获面积因子的分区自适应冠层异常指标诊断框架。","农业人工智能与决策模型","openalex","2026-10-20T23:30:40.675672Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":8,"content":169,"source_name":170,"source_url":8,"published_at":171,"category":12,"cover_url":8,"hotness":13,"is_selected":48,"score":172,"score_detail":173,"sources":176,"tags":178,"primary_topic":183,"secondary_topics":184,"classification":185,"search_phrases":217,"slug":220,"view_count":147,"doi":8,"paper":8,"created_at":221},5335,"中国农业科学院「农知育芯 BRIS」智能育种平台在洛阳发布 打造智能育种科研副脑","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Fa297dd7421fa4a3ba08c08863fbe3eb7.htm","10 月 14—16 日中国农学会图书情报分会 2026 年学术年会在河南洛阳举办。中国农业科学院农业信息研究所副所长赵瑞雪介绍,农知育芯(BRIS,Breeding Research Intelligent Service)集成 8 项分析工具、3 项自研算法,汇聚 2011—2025 年育种相关核心期刊文献,整合 6800 万条 NCBI 基因索引、57 万条 UniProt 专家校验蛋白信息;自主构建的育种知识图谱包含 7.9 万余个知识实体、12.6 万余条实体关系,覆盖水稻、玉米、小麦和拟南芥四类作物。","10月14日至16日，中国农学会图书情报分会2026年学术年会在河南洛阳举办。会议由中国农学会图书情报分会主办，中国农业科学院[农业信息研究所](http:\u002F\u002Faii.caas.cn\u002F)、河南省农业科学院农业信息技术研究所、洛阳市农林科学院联合承办。中国农业科学院[农业信息研究所](http:\u002F\u002Faii.caas.cn\u002F)副所长赵瑞雪在会上介绍农知育芯（BRIS，Breeding Research Intelligent Service）阶段性研究成果。农知育芯融合育种文献、权威数据库、知识图谱和分析算法，集成8项分析工具、3项自研算法，为研究人员提供知识获取、任务规划、证据推理全链条智能科研服务。\n\n智能育种是AI+农业的重要场景。随着高通量测序技术快速发展，育种科研数据快速增长，基因、性状和调控通路等知识分散于海量文献和数据库，候选基因筛选、功能解析和多组学研究面临知识检索繁琐、数据分析复杂、验证周期长和试错成本高等挑战。而通用大模型也难以充分满足专业知识、结果依据和溯源能力等科研要求。\n\n面向这一需求，农知育芯打通问答与计算、分析与决策、知识与工具三类能力。科研人员可通过自然语言提出问题或发起任务，系统自动调度工具与算法，汇聚文献、数据库、知识图谱及用户数据，形成分析结果与科研建议。在关键候选基因发现等任务中，平台可辅助科研人员将筛选过程由“大海捞针”转向“精准发现”，堪称智能育种的“科研副脑”。\n\n该平台以高质量知识图谱引导推理，结合分析工具计算、模型研判和知识增强开展协同验证，并对文献、数据库、工具和参数进行全链路核查，形成“问题—数据—结论”的科研闭环。\n\n据悉，农知育芯已汇聚2011年至2025年育种相关核心期刊文献，整合6800万条NCBI基因索引、57万条UniProt专家校验蛋白信息；自主构建的育种知识图谱包含7.9万余个知识实体、12.6万余条实体关系，覆盖水稻、玉米、小麦和拟南芥四类作物，形成文献、权威数据库和知识图谱互为校验的证据网络。\n\n该平台自主研发差异表达分析、GWAS、QTL定位、WGCNA共表达网络等8项经典生物信息与统计遗传分析工具，并构建多效基因挖掘、基因功能预测、通路与网络挖掘3项自研育种算法。\n\n依托上述知识资源、分析工具和算法能力，农知育芯面向育种科研人员提供智能问答和六大科研场景分析工具，涵盖关键候选基因发现、候选基因功能解析、分子机制与调控通路、基因型—表型—转录组联合研判、种质群体遗传解析和分析结果综合研判。\n\n该平台能提供快速问答、结构化分析和深度研究三类任务模式，可适配知识查询、多组学数据分析和综合育种前景评估等不同需求。系统可自动完成数据分析，汇聚多源文献证据，一键导出标准化PDF科研报告，并提供候选基因优先级排序和后续实验建议，支持PC端科研分析和移动端田间使用，服务育种科研人员和相关专业学生。","中国农业科学院 2026-10-14~16","2026-10-14T00:00:00Z",85,{"impact":17,"substance":174,"depth":18,"authority":20,"freshness":94,"relevant":22,"comment":175},22,"国家级科研机构发布的智能育种平台，知识资源与分析工具细节扎实，属AI+农业重要进展，值得精选。",[177],{"name":170,"url":167},[179,30,180,181,182],"农业人工智能","智能育种","知识图谱","科研平台",{"code":34,"slug":35,"name":36,"path":37,"parent_code":8},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":186},{"candidates":187,"agricultural_evidence":48,"reason":87},[188,194,198,202,208,212],{"code":189,"score":55,"supported":48,"title":190,"abstract":191,"tags":193},"A1",[],[192],"水稻",[],{"code":54,"score":55,"supported":48,"title":195,"abstract":196,"tags":197},[],[58],[],{"code":128,"score":55,"supported":48,"title":199,"abstract":200,"tags":201},[],[131],[],{"code":203,"score":204,"supported":14,"title":205,"abstract":206,"tags":207},"I2",2,[],[],[179,181],{"code":73,"score":22,"supported":14,"title":209,"abstract":210,"tags":211},[],[],[30],{"code":213,"score":22,"supported":14,"title":214,"abstract":215,"tags":216},"F2",[],[],[180],[218,219],"农知育芯 BRIS 智能育种","中国农科院 农业信息研究所 育种平台","农知育芯BRIS智能育种-5335","2026-10-20T02:47:43.320559Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":8,"source_name":228,"source_url":225,"published_at":229,"category":104,"cover_url":8,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":234,"tags":236,"primary_topic":240,"secondary_topics":241,"classification":242,"search_phrases":292,"slug":295,"view_count":147,"doi":296,"paper":297,"created_at":353},5314,"A data‐driven image extraction and analysis pipeline for plant phenotyping in controlled environments","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70116","Abstract Advances in automation, imaging, and artificial intelligence have enabled large‐scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Automated Precision Phenotyping Greenhouse and expanded plant growth and phenotyping (PGP v2) data across maize ( Zea mays L.), cotton ( Gossypium hirsutum ), rice ( Oryza sativa ), and sorghum ( Sorghum bicolor L.). The pipeline integrates pseudo‐red‐green‐blue composite image generation, plant detection and segmentation, image stitching, vegetation‐index analysis, texture analysis, morphological trait extraction, and temporal comparison of image‐derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, segment anything model (SAM) v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. Segment anything model for long sequences (SAM2Long)maintained plant‐instance associations across vertically stacked frames, while scale‐invariant feature transform–based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863‐dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment‐level temporal analysis of mutagenized sorghum lines and cold‐stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatment‐ or stress‐related responses. The combined dataset and workflow provide structured, automated, and well‐documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlled‐environment plant science and crop improvement.","摘要　自动化、成像和人工智能的进步使得大规模植物表型分析成为可能，但图像分析仍然是作物改良和生物学发现的关键瓶颈。我们利用德克萨斯农工大学农业生命科学自动化精准表型温室（Texas A&M AgriLife Automated Precision Phenotyping Greenhouse）的图像，开发了一个集成多光谱表型分析框架，并扩展了玉米（Zea mays L.）、棉花（Gossypium hirsutum）、水稻（Oryza sativa）和高粱（Sorghum bicolor L.）的植物生长与表型分析（plant growth and phenotyping，PGP v2）数据。该流程整合了伪红绿蓝合成图像生成、植物检测与分割、图像拼接、植被指数分析、纹理分析、形态性状提取以及图像衍生特征的时间比较，以量化植物结构、光谱反射率和纹理随时间的变化。在评估的分割方法中，segment anything model（SAM）v3在不同作物结构下提供了最高且最一致的准确性，尽管其所需计算时间多于经典方法。面向长序列的segment anything model（SAM2Long）在垂直堆叠帧之间维持了植物实例关联，而基于尺度不变特征变换（scale-invariant feature transform）的拼接在单株植物超出单一视野时重建了植物拼接图。对于每株植物和每个成像日期，该流程生成了一个863维特征向量，涵盖植被指数、光谱统计量、纹理描述符和形态性状。该框架通过两个案例研究进行了评估：诱变高粱品系的处理水平时间分析，以及使用独立成像系统对玉米进行的冷胁迫表型分析。在这两项研究中，提取的特征支持了表型变异的统计分析和多变量分析，并能够根据处理或胁迫相关响应区分植物。合并的数据集和工作流程为受控环境植物科学和作物改良提供了跨多种作物、实验设置和成像系统的结构化、自动化且记录完善的表型分析。","The Plant Phenome Journal","2026-10-18T00:00:00Z",81,{"impact":18,"substance":174,"depth":18,"authority":232,"freshness":21,"relevant":22,"comment":233},14,"该研究提出一套面向多作物的自动化多光谱表型分析流程，方法新颖、数据规模可观，对智慧育种与受控环境植物科学研究具有较高参考价值。",[235],{"name":228,"url":225},[27,179,237,238,239],"图像分割","表型组学","多光谱成像",{"code":34,"slug":35,"name":36,"path":37,"parent_code":8},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":243},{"candidates":244,"agricultural_evidence":48,"reason":87},[245,250,255,262,269,276,282,288],{"code":189,"score":94,"supported":48,"title":246,"abstract":247,"tags":249},[],[192,248],"rice",[],{"code":128,"score":94,"supported":48,"title":251,"abstract":252,"tags":254},[],[131,253],"maize",[],{"code":256,"score":94,"supported":48,"title":257,"abstract":258,"tags":261},"A5",[],[259,260],"高粱","sorghum",[],{"code":263,"score":94,"supported":48,"title":264,"abstract":265,"tags":268},"A6",[],[266,267],"棉花","cotton",[],{"code":270,"score":94,"supported":48,"title":271,"abstract":272,"tags":275},"B7",[],[273,274],"温室","greenhouse",[],{"code":203,"score":277,"supported":48,"title":278,"abstract":279,"tags":281},5,[],[280],"artificial intelligence",[179,237],{"code":135,"score":283,"supported":48,"title":284,"abstract":285,"tags":287},4,[],[286],"multispectral",[239],{"code":83,"score":22,"supported":14,"title":289,"abstract":290,"tags":291},[],[],[27],[293,294],"Texas A&M 自动化表型温室","玉米 高粱 多光谱表型","TexasAM自动化表型温室-5314","10.1002\u002Fppj2.70116",{"doi":296,"openalex_id":298,"authors":299,"venue":228,"cited_by_count":147,"oa_url":225,"card":348,"direction":159,"ingested_from":162},"W7222893982",[300,302,305,308,311,313,315,318,321,324,327,330,333,336,339,342,344,346],{"name":301,"orcid":8},"Fahimeh Orvati Nia",{"name":303,"orcid":304},"Joshua Peeples","https:\u002F\u002Forcid.org\u002F0000-0001-7861-7789",{"name":306,"orcid":307},"Seth C. Murray","https:\u002F\u002Forcid.org\u002F0000-0002-2960-8226",{"name":309,"orcid":310},"Andrew J. McFarland","https:\u002F\u002Forcid.org\u002F0009-0000-3516-8274",{"name":312,"orcid":8},"Troy Vann",{"name":314,"orcid":8},"Shima Salehi",{"name":316,"orcid":317},"Robert G. Hardin","https:\u002F\u002Forcid.org\u002F0000-0002-0714-8281",{"name":319,"orcid":320},"David D. Baltensperger","https:\u002F\u002Forcid.org\u002F0000-0003-4511-5680",{"name":322,"orcid":323},"Amir M. H. Ibrahim","https:\u002F\u002Forcid.org\u002F0000-0002-0584-753X",{"name":325,"orcid":326},"J. Alex Thomasson","https:\u002F\u002Forcid.org\u002F0000-0002-5557-105X",{"name":328,"orcid":329},"Henry Y. Fadamiro","https:\u002F\u002Forcid.org\u002F0000-0002-4530-9680",{"name":331,"orcid":332},"Nithya K. Subramanian","https:\u002F\u002Forcid.org\u002F0000-0002-1659-7396",{"name":334,"orcid":335},"Suresh D. Pillai","https:\u002F\u002Forcid.org\u002F0000-0002-9766-8765",{"name":337,"orcid":338},"Rebecca Roston","https:\u002F\u002Forcid.org\u002F0000-0002-3063-5002",{"name":340,"orcid":341},"Joslin Ishimwe","https:\u002F\u002Forcid.org\u002F0009-0003-6999-7718",{"name":343,"orcid":8},"Diptadeep Basak",{"name":345,"orcid":8},"Nazar Oladepo",{"name":347,"orcid":8},"Uday Santhosh Raju Vysyaraju",{"tldr":349,"method":350,"finding":351,"direction":159,"opportunity":352},"构建多光谱图像提取分析流程，实现多作物表型自动化量化。","用温室多光谱图像，集成SAM v3分割、SIFT拼接与863维特征提取。","SAM v3分割最准，特征可区分高粱诱变与玉米冷胁迫响应。","可探索轻量化分割模型与跨成像系统迁移，降低计算成本并拓展田间表型。","2026-10-19T23:30:17.276726Z",{"id":355,"title":356,"url":357,"summary":358,"summary_zh":8,"content":359,"source_name":360,"source_url":8,"published_at":171,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":105,"score_detail":361,"sources":364,"tags":366,"primary_topic":371,"secondary_topics":372,"classification":373,"search_phrases":399,"slug":402,"view_count":147,"doi":8,"paper":8,"created_at":403},5217,"\"金科育绿\"上海农业科技成果路演上海市农科院专场举办","https:\u002F\u002Fwww.saas.sh.cn\u002Fxwzx\u002Fzhxx\u002Fcontent_57112","2026年10月14日,\"金科育绿\"上海农业科技成果路演市农科院成果专场活动在南上海科创中心举行,现场带来申优331水稻、申科甜756玉米、沪晓砂梨、珍珠蜜1号甜瓜、沪翠甜6号等10项科技成果路演,8个项目签约总意向金额达705万元。","2026年10月14日，“金科育绿”上海农业科技成果路演——上海市农业科学院成果专场活动在南上海科创中心举行。活动以“科技赋能农科‘芯’势力”为主题，聚焦特色种源、农业生物制造、现代设施农业三大赛道，搭建产学研用金对接平台，推动农科成果与产业需求精准匹配，培育农业新质生产力。我院党委委员、副院长沈晓晖出席活动并致辞。\n\n沈晓晖表示，上海农业科技进步贡献率超80%，位居全国首位。我院搭建多层次科创平台，培育众多农作物新品种，食（药）用菌研究中心获评全球首个FAO食用菌参考中心，多项技术入选全国农业主推技术。近五年成果转化成效显著，多项成果创下国内转化纪录。我院正在推进体制机制改革，致力成为农业科技创新策源主力军和成果转移转化引领者，期待联合企业、金融机构深化协作，助力上海都市现代农业高质量发展，服务农业强国建设。\n\n活动设置成果路演、企业需求发布、项目集中签约、金融服务成效展示环节。我院科研人员带来10项科技成果路演，9家企业发布技术及融资诉求；现场完成8个项目签约，合作模式涵盖品种转让、技术许可、技术开发等，合作辐射安徽、山西、山东、湖北及上海地区，推动一批优质科技成果落地产业化。\n\n本次路演是打通成果转化“最后一公里”的重要实践。下一步，我院将持续深化科企、科金对接，推动更多新品种新技术落地应用。\n\n本次活动由上海市农业农村委员会指导，上海市农业科学院、上海市农业科技服务中心主办，多家技术交易、科技服务机构协办。上海市农业农村委员会相关处室、委属相关单位，各区农业农村委，各协办单位，我院各研究中心、职能部门，涉农科研院校、农业科技企业、金融机构参加本次活动。","上海市农业科学院(2026-10-14)",{"impact":18,"substance":19,"depth":47,"authority":362,"freshness":94,"relevant":22,"comment":363},13,"省级农科院成果路演，含签约金额与转化数据，对成果转化主题有参考价值，但属常规活动通稿。",[365],{"name":360,"url":357},[367,30,368,369,370],"设施农业","成果转化","产学研对接","农业生物制造",{"code":34,"slug":35,"name":36,"path":37,"parent_code":8},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":374},{"candidates":375,"agricultural_evidence":48,"reason":87},[376,380,384,390,394],{"code":189,"score":55,"supported":48,"title":377,"abstract":378,"tags":379},[],[192],[],{"code":128,"score":55,"supported":48,"title":381,"abstract":382,"tags":383},[],[131],[],{"code":385,"score":55,"supported":48,"title":386,"abstract":387,"tags":389},"B1",[],[388],"甜瓜",[],{"code":73,"score":22,"supported":14,"title":391,"abstract":392,"tags":393},[],[],[30],{"code":395,"score":22,"supported":14,"title":396,"abstract":397,"tags":398},"G1",[],[],[368],[400,401],"上海市农科院 成果路演","金科育绿 农业科技","上海市农科院成果路演-5217","2026-10-18T00:05:23.397954Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":8,"content":409,"source_name":410,"source_url":8,"published_at":11,"category":411,"cover_url":8,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":416,"tags":418,"primary_topic":421,"secondary_topics":422,"classification":423,"search_phrases":438,"slug":441,"view_count":147,"doi":8,"paper":8,"created_at":442},5197,"中华人民共和国农业农村部公告 第1072号:59个转基因玉米品种和1个转基因大豆品种审定通过","https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002Fnybzzj1\u002F202610\u002Ft20261016_6488661.htm","2026年10月12日,第五届国家农作物品种审定委员会第十二次会议审定通过敦玉323D等59个转基因玉米品种和中联豆2746GZ等1个转基因大豆品种并予以公告,持续推进转基因玉米大豆产业化应用。","[](https:\u002F\u002Fwww.moa.gov.cn\u002F)无障碍\n\n![Image 1](https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002Fimages\u002Ftopbg_0506_pc.jpg)![Image 2](https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002Fimages\u002Ftopbg_0506_m.jpg)\n\n![Image 3](https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002Fimages\u002Fg20_200404xxgksearch_1.png)\n\n[高级检索](https:\u002F\u002Fwww.moa.gov.cn\u002Fso\u002Fs?&siteCode=bm21000007&qt=&tab=gk&advancedSearchType=1)\n\n索 引 号 07B220203202600268 信息所属单位 种业管理司\n\n信息名称 中华人民共和国农业农村部公告 第1072号\n\n文 号 农业农村部公告第1072号 生效日期 2026年10月12日 发布日期 2026年10月16日\n\n内容概述 敦玉323D等59个转基因玉米品种、中联豆2746GZ等1个转基因大豆品种，业经第五届国家农作物品种审定委员会第十二次会议审定通过，现予公告。\n\n索 引 号 07B220203202600268 信息名称 中华人民共和国农业农村部公告 第1072号 文 号 农业农村部公告第1072号 信息所属单位 种业管理司 生效日期 2026年10月12日 发布日期 2026年10月16日 内容概述 敦玉323D等59个转基因玉米品种、中联豆2746GZ等1个转基因大豆品种，业经第五届国家农作物品种审定委员会第十二次会议审定通过，现予公告。\n\n## 中华人民共和国农业农村部公告 第1072号\n\n发布时间：2026年10月16日\n\n字体：[大 中 小]\n\n敦玉323D等59个转基因玉米品种、中联豆2746GZ等1个转基因大豆品种，业经第五届国家农作物品种审定委员会第十二次会议审定通过，现予公告。\n\n附件：敦玉323D等60个转基因品种审定信息\n\n农业农村部\n\n2026年10月12日\n\n附件：\n\n*   [中华人民共和国农业农村部公告 第1072号.ofd](https:\u002F\u002Fwww.moa.gov.cn\u002Fzxfile\u002Freader?file=https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002Fnybzzj1\u002F202610\u002FP020261016521444980506.ofd)\n\n相关新闻：\n\n扫一扫在手机打开当前页\n\n*   [机关子站![Image 4](https:\u002F\u002Fwww.moa.gov.cn\u002Fimages\u002Fgov-links-arrow-icon1_03.png)](https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002Fnybzzj1\u002F202610\u002Ft20261016_6488661.htm)\n*   [直属单位网站![Image 5](https:\u002F\u002Fwww.moa.gov.cn\u002Fimages\u002Fgov-links-arrow-icon1_03.png)](https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002Fnybzzj1\u002F202610\u002Ft20261016_6488661.htm)\n*   [![Image 6](https:\u002F\u002Fwww.moa.gov.cn\u002Fimages\u002Fgov-links-pic_1_03.png)](https:\u002F\u002Fwww.gov.cn\u002F)\n*   [国务院各部门网站![Image 7](https:\u002F\u002Fwww.moa.gov.cn\u002Fimages\u002Fgov-links-arrow-icon1_03.png)](https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002Fnybzzj1\u002F202610\u002Ft20261016_6488661.htm)\n*   [地方农业管理部门网站![Image 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[农村合作经济指导司](https:\u002F\u002Fhzjjs.moa.gov.cn\u002F \"农村合作经济指导司\")\n14.   [帮扶司](https:\u002F\u002Fbfs.moa.gov.cn\u002F \"帮扶司\")\n15.   [区域协作促进司（革命老区工作办公室）](https:\u002F\u002Fqys.moa.gov.cn\u002F \"区域协作促进司（革命老区工作办公室）\")\n16.   [监督检查司](https:\u002F\u002Fjdjcs.moa.gov.cn\u002F \"监督检查司\")\n17.   [市场与信息化司](https:\u002F\u002Fscs.moa.gov.cn\u002F \"市场与信息化司\")\n18.   [国际合作司](https:\u002F\u002Fgjs.moa.gov.cn\u002F \"国际合作司\")\n19.   [科学技术司（农业转基因生物安全管理办公室）](https:\u002F\u002Fkjs.moa.gov.cn\u002F \"科学技术司（农业转基因生物安全管理办公室）\")\n20.   [农产品质量安全监管司](https:\u002F\u002Fjgs.moa.gov.cn\u002F \"农产品质量安全监管司\")\n21.   [农垦局](https:\u002F\u002Fnkj.moa.gov.cn\u002F \"农垦局\")\n22.   [种业管理司](https:\u002F\u002Fzys.moa.gov.cn\u002F \"种业管理司\")\n23.   [农业机械化管理司](https:\u002F\u002Fnjhs.moa.gov.cn\u002F \"农业机械化管理司\")\n24.   [农田建设管理司](https:\u002F\u002Fntjss.moa.gov.cn\u002F \"农田建设管理司\")\n25.   [机关党委](https:\u002F\u002Fdangwei.moa.gov.cn\u002F \"机关党委\")\n26.   [离退休干部局](https:\u002F\u002Fltxgbj.moa.gov.cn\u002F \"离退休干部局\")\n27.   [长江流域渔政监督管理办公室](https:\u002F\u002Fcjyzbgs.moa.gov.cn\u002F \"长江流域渔政监督管理办公室\")\n\n1.   [农业农村部机关服务中心( 农业农村部机关服务局 )](http:\u002F\u002Fwww.jgfwj.agri.cn\u002F \"农业农村部机关服务中心( 农业农村部机关服务局 )\")\n2.   [中国农业科学院](https:\u002F\u002Fwww.caas.cn\u002F \"中国农业科学院\")\n3.   [中国水产科学研究院](https:\u002F\u002Fwww.cafs.ac.cn\u002F \"中国水产科学研究院\")\n4.   [中国热带农业科学院](https:\u002F\u002Fwww.catas.cn\u002F \"中国热带农业科学院\")\n5.   [全国农业展览馆（中国农业博物馆）](http:\u002F\u002Fwww.ciae.com.cn\u002F \"全国农业展览馆（中国农业博物馆）\")\n6.   [中国农业电影电视中心](http:\u002F\u002Fwww.ntv.cn\u002F \"中国农业电影电视中心\")\n7.   [农民日报社](https:\u002F\u002Fwww.farmer.com.cn\u002F \"农民日报社\")\n8.   [中国农业出版社有限公司（农村读物出版社）](http:\u002F\u002Fwww.ccap.com.cn\u002F \"中国农业出版社有限公司（农村读物出版社）\")\n9.   [中国农村杂志社](https:\u002F\u002Fwww.crnews.net\u002F \"中国农村杂志社\")\n10.   [全国乡村振兴宣传教育中心](https:\u002F\u002Fnpecrr.agri.cn\u002F \"全国乡村振兴宣传教育中心\")\n11.   [中央农业干部教育培训中心（农业农村部管理干部学院、中国共产党农业农村部党校）](http:\u002F\u002Fwww.gbxy.agri.cn\u002F \"中央农业干部教育培训中心（农业农村部管理干部学院、中国共产党农业农村部党校）\")\n12.   [农业农村部人力资源开发中心、中国农学会](https:\u002F\u002Fwww.rlzy.moahr.cn\u002F \"农业农村部人力资源开发中心、中国农学会\")\n13.   [农业农村部农村经济研究中心](http:\u002F\u002Fwww.rcre.agri.cn\u002F \"农业农村部农村经济研究中心\")\n14.   [农业农村部信息中心](https:\u002F\u002Fwww.agri.cn\u002F \"农业农村部信息中心\")\n15.   [农业农村部农产品质量安全中心](http:\u002F\u002Fwww.aqsc.agri.cn\u002F \"农业农村部农产品质量安全中心\")\n16.   [中国绿色食品发展中心](http:\u002F\u002Fwww.greenfood.agri.cn\u002F \"中国绿色食品发展中心\")\n17.   [农业农村部规划设计研究院](http:\u002F\u002Fwww.aape.org.cn\u002F \"农业农村部规划设计研究院\")\n18.   [中国乡村振兴发展中心](https:\u002F\u002Fxczx.agri.cn\u002F \"中国乡村振兴发展中心\")\n19.   [农业农村部大数据发展中心](http:\u002F\u002Fwww.abdc.agri.cn\u002F \"农业农村部大数据发展中心\")\n20.   [农业农村部工程建设服务中心](http:\u002F\u002Fwww.gczx.agri.cn\u002F \"农业农村部工程建设服务中心\")\n21.   [农业农村部财会服务中心、中华农业科教基金会](http:\u002F\u002Fwww.ckzx.agri.cn\u002F \"农业农村部财会服务中心、中华农业科教基金会\")\n22.   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[网站声明](https:\u002F\u002Fwww.moa.gov.cn\u002Fsy_23468\u002Fhomeinfo\u002Fwzsm\u002F)\n*   |\n*   [网站地图](https:\u002F\u002Fwww.moa.gov.cn\u002Fsy_23468\u002Fhomeinfo\u002Fwzdt\u002F)\n*   |\n*   [联系我们](https:\u002F\u002Fwww.moa.gov.cn\u002Fsy_23468\u002Fhomeinfo\u002Flxwm\u002F)\n*   |\n*   [访问分析](https:\u002F\u002Freport.moa.gov.cn\u002Freport\u002Findex.html)\n\n主办单位：中华人民共和国农业农村部 承办单位：[农业农村部信息中心](https:\u002F\u002Fwww.agri.cn\u002F)\n\n网站识别码bm21000007[京ICP备05039419号-2](https:\u002F\u002Fbeian.miit.gov.cn\u002F)[京公网安备 11010502037559号![Image 11](https:\u002F\u002Fwww.moa.gov.cn\u002Fimages\u002Fbajgwa.png)](https:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010502037559)\n\n网站保留所有权，未经允许不得复制镜像\n\n[![Image 12](https:\u002F\u002Fwww.moa.gov.cn\u002Fimages\u002Fjiucuo.png)](https:\u002F\u002Fzfwzgl.www.gov.cn\u002Fexposure\u002Fjiucuo.html?site_code=bm21000007&url=https%3A%2F%2Fwww.moa.gov.cn%2F)\n\n[按回车键在新窗口打开无障碍说明页面,按Alt+~键打开导盲模式。](javascript:void(0);)","农业农村部(2026-10-12)","政策",87,{"impact":414,"substance":174,"depth":19,"authority":20,"freshness":94,"relevant":22,"comment":415},28,"农业农村部公告审定通过60个转基因玉米、大豆品种，属全国性种业政策节点事件，信息具体、权威性强，值得进入每日精选。",[417],{"name":410,"url":407},[30,131,64,419,420],"品种审定","转基因育种",{"code":34,"slug":35,"name":36,"path":37,"parent_code":8},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":424},{"candidates":425,"agricultural_evidence":48,"reason":87},[426,430,434],{"code":73,"score":362,"supported":48,"title":427,"abstract":428,"tags":429},[419],[419],[419,30],{"code":128,"score":47,"supported":48,"title":431,"abstract":432,"tags":433},[131],[131],[131],{"code":61,"score":47,"supported":48,"title":435,"abstract":436,"tags":437},[64],[64],[64],[439,440],"农业农村部 第1072号 转基因品种","敦玉323D 中联豆2746GZ","农业农村部第1072号转基因品种-5197","2026-10-18T00:05:13.361361Z",{"id":444,"title":445,"url":446,"summary":447,"summary_zh":448,"content":8,"source_name":449,"source_url":446,"published_at":11,"category":104,"cover_url":8,"hotness":13,"is_selected":14,"score":450,"score_detail":451,"sources":455,"tags":457,"primary_topic":460,"secondary_topics":461,"classification":462,"search_phrases":494,"slug":497,"view_count":147,"doi":498,"paper":499,"created_at":518},4901,"Feature-level fusion of Sentinel-1 SAR and Sentinel-2 optical imagery for crop-type mapping in humid tropical mountainous terrain","https:\u002F\u002Fdoi.org\u002F10.64960\u002Feasr.2026.263460","Accurate crop-type mapping in humid tropical mountains is challenged by persistent cloud cover, rugged topography, and fragmented smallholder parcels. This study evaluates a feature-level fusion workflow that integrates Sentinel-2 optical indices and Sentinel-1 SAR backscatter for crop-type mapping in Candipuro District, Lumajang, East Java, Indonesia. Sentinel-2 surface reflectance and Sentinel-1 IW data acquired in June–July 2023 were harmonised in Google Earth Engine to a common 10 m pixel grid, so that all comparison maps were evaluated on the same spatial support. The optical component comprised NDVI, NDBI, and NDWI, whereas the radar component used the VH\u002FVV polarisation ratio. A DEM was used only to characterise the mountainous setting and to assist terrain interpretation, and was not included as a predictor in the classifier. The stacked feature image was classified using a Random Forest model trained with 101 field-survey ground control points representing eight land-cover\u002Fcrop-related classes. Accuracy was assessed using 393 stratified random validation points. The fused-image classification achieved an overall accuracy of 93% and a kappa coefficient of 0.90. The fusion workflow yielded richer thematic differentiation than the optical-only comparison scenarios, particularly for water bodies, sugarcane, and shrubland. However, class-level results reveal substantial uncertainty for corn fields and moderate confusion in built-up and water classes. Therefore, the present workflow should be interpreted as a site-specific, single-date proof of concept rather than a fully transferable operational solution. The results indicate that Sentinel-1\u002FSentinel-2 feature fusion is promising for crop-type mapping in cloud-prone mountainous landscapes, while future work should incorporate multi-temporal phenology, richer vegetation indices, SAR texture features, uncertainty mapping, and broader field sampling.","在湿润的热带山区，准确的作物类型制图面临持续云覆盖、崎岖地形和破碎的小农户地块等挑战。本研究评估了一种特征级融合工作流，该工作流整合了Sentinel-2光学指数与Sentinel-1 SAR后向散射数据，用于印度尼西亚东爪哇省卢马姜县坎迪普罗区的作物类型制图。2023年6月至7月获取的Sentinel-2地表反射率和Sentinel-1 IW数据在Google Earth Engine中统一至共同的10 m像元网格，以确保所有对比制图均在相同的空间支撑上进行评估。光学组分包括NDVI、NDBI和NDWI，雷达组分则使用VH\u002FVV极化比值。DEM仅用于表征山区环境并辅助地形解译，未作为预测变量纳入分类器。堆叠特征影像采用随机森林模型进行分类，该模型以101个野外调查地面控制点进行训练，代表八个土地覆盖\u002F作物相关类别。精度评估使用393个分层随机验证点。融合影像分类的总体精度达到93%，Kappa系数为0.90。与仅光学对比方案相比，融合工作流产生了更丰富的专题区分能力，尤其在水体、甘蔗和灌木地方面。然而，类别层面的结果揭示了玉米田存在较大不确定性，建成区和水体类别存在中等程度的混淆。因此，本工作流应被解读为特定站点的单日期概念验证，而非完全可迁移的业务化方案。结果表明，Sentinel-1\u002FSentinel-2特征融合在多云山区景观的作物类型制图中具有前景，未来工作应纳入多时相物候、更丰富的植被指数、SAR纹理特征、不确定性制图以及更广泛的野外采样。","Engineering and Applied Science Research",66,{"impact":108,"substance":452,"depth":453,"authority":47,"freshness":21,"relevant":22,"comment":454},20,17,"方法扎实、精度可观的遥感作物制图概念验证研究，但属单点单时相案例，公共影响有限，适合作为技术参考而非每日精选头条。",[456],{"name":449,"url":446},[27,179,113,458,459],"作物识别","哨兵卫星",{"code":34,"slug":35,"name":36,"path":37,"parent_code":8},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":463},{"candidates":464,"agricultural_evidence":48,"reason":87},[465,469,475,482,486,490],{"code":128,"score":94,"supported":48,"title":466,"abstract":467,"tags":468},[],[131,132],[],{"code":263,"score":94,"supported":48,"title":470,"abstract":471,"tags":474},[],[472,473],"甘蔗","sugarcane",[],{"code":476,"score":94,"supported":48,"title":477,"abstract":478,"tags":481},"G2",[],[479,480],"小农户","smallholder",[],{"code":83,"score":22,"supported":14,"title":483,"abstract":484,"tags":485},[],[],[27],{"code":203,"score":22,"supported":14,"title":487,"abstract":488,"tags":489},[],[],[179],{"code":135,"score":22,"supported":14,"title":491,"abstract":492,"tags":493},[],[],[113],[495,496],"Sentinel-1 Sentinel-2 作物分类","印尼 东爪哇 作物制图","Sentinel-1Sentinel-2作物分类-4901","10.64960\u002Feasr.2026.263460",{"doi":498,"openalex_id":500,"authors":501,"venue":449,"cited_by_count":147,"oa_url":512,"card":513,"direction":159,"ingested_from":162},"W7221544367",[502,505,508,510],{"name":503,"orcid":504},"Indarto Indarto","https:\u002F\u002Forcid.org\u002F0000-0001-6319-6731",{"name":506,"orcid":507},"Gunawan Gunawan","https:\u002F\u002Forcid.org\u002F0000-0001-7914-3669",{"name":509,"orcid":8},"Mahrus Irsyam",{"name":511,"orcid":8},"Fahmi Arif Kurnanto","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F263460\u002F176575",{"tldr":514,"method":515,"finding":516,"direction":159,"opportunity":517},"融合Sentinel-1 SAR与Sentinel-2光学特征，在印尼热带山地实现93%精度的作物类","Google Earth Engine中10米格网特征级融合NDVI\u002FNDBI\u002F","融合影像总体精度93%、kappa 0.90，优于仅光学方案，但玉米和建成区仍存在明显混淆。","可引入多时相物候、SAR纹理与不确定性制图，提升热带多云山区小农户破碎地块的作物识别与可迁移性。","2026-10-14T23:30:28.943594Z"]