[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-09-10":3},{"date":4,"title":5,"highlights":6,"content":12,"items":13},"2026-09-10","农业农村日报：油菜育种提速与秋粮防灾并进",[7,8,9,10,11],"中国农科院油料所构建“生物技术+生态育种”体系，将油菜育种周期由8—10年压缩至约5年，提升品种跨区域适应性。","云南推进月季、百合等特色花卉种质资源圃与技术创新中心建设，强化高原花卉自主品种供给能力。","秋粮进入产量形成与灾害防范关键期，各地强化水肥管理、病虫害防控和极端天气应对。","玉米穗腐病绿色防控技术在河北鸡泽示范三年，减肥减药下实现防控、产量与质量协同提升。","植物RNA修饰研究取得进展，锁定272个具标记与聚集特征的RNA分子，为耐热作物育种提供新思路。","本期聚焦油菜与花卉育种体系升级、秋粮防灾减灾及玉米穗腐病绿色防控，并收录大豆成熟度卫星表型、植物耐热RNA机制、灌溉需求模型、植物可穿戴传感器、作物推荐AI与农业绿色发展政策等前沿研究。\n\n---\n*本日报内容整理自公开来源，学术论文元数据来自 OpenAlex 等开放接口；外文资料已译为中文，翻译与摘要仅供参考；引用与决策请以官方原文与正式出版物为准。*",[14,45,66,87,110,166,196,213,256,295,326,360,395,426,458],{"id":15,"title":16,"url":17,"summary":18,"summary_zh":19,"content":20,"source_name":21,"source_url":19,"published_at":22,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":26,"score_detail":27,"sources":35,"tags":37,"view_count":43,"doi":19,"paper":19,"created_at":44},2011,"新技术引领油菜育种从单点创新走向全域突破","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Fa24294843da4414fb379d49d5a2c8e27.htm","中国农业科学院油料作物研究所构建“生物技术+生态育种”双轮驱动体系，把分子标记、基因编辑和小孢子培养等技术与多生态区定向育种基地结合。相关实践将育种周期由8—10年压缩至约5年，并提升品种跨区域适应性。",null,"### [农民日报]新技术引领油菜育种从单点创新走向全域突破\n\n近日，中国农业科学院[油料所](http:\u002F\u002Focri.caas.cn\u002F)王汉中院士团队培育的春油菜新品种“中油青1号”，经青海省农学会组织专家现场鉴定，在西宁市湟中区百亩示范片，采用分段机械实收，亩产达314.65公斤，较当地平均单产增幅超66.3%，创我国高海拔区春油菜百亩示范机收单产纪录。该品种是在青海省科技厅“帅才科学家负责制”项目支持下，采用“生物技术（Biotechnology）+生态育种（Ecological Breeding）”（简称“Bt+Eb”）理论和技术体系育成，在2025年度青海省区试中，平均亩产308.34公斤，含油量46.94%，亩产油量较当地主栽品种对照提升21.65%，耐寒、早熟特性明显，为青藏等高海拔冷凉春油菜地区油料产能提升提供了重大品种支撑。\n\n“中油青1号”的突破并非孤例，王汉中院士团队依托“Bt+Eb”育种体系，在四大不同生态区同步实现高产高油多抗油菜育种的重大突破。育成长江流域产区的“中油杂501”，其含油量高达50.38%，区试亩产油量较对照增加26.93%，2025年在江苏东台以亩产377.68公斤刷新我国冬油菜百亩机收高产纪录；强冬性盐碱区的“国盐油1号”耐受-12℃低温和中度盐碱，区试亩产油量比对照增46.07%，今年在山东东营以机收亩产283.45公斤刷新环渤海中度盐碱地油菜高产纪录；“中油早5号”区试亩产油量较对照增26.79%，2026年在江西信丰百亩示范片机收亩产201.96公斤，全生育期仅166天，比国家攻关指标缩短14天，创下我国“稻稻油”三熟制油菜高产新纪录。\n\n油菜是我国第一大油料作物，常年种植面积约1.2亿亩，产区分布跨越寒温带、暖温带、亚热带，生态类型极为多样，光温、土壤、耕作制度差异巨大。传统育种在单一基地进行，育成品种再拿到不同生态区做适应性鉴定，由于品种的生长表现是基因与环境共同作用的结果，跨区种植后优良性状难以稳定发挥。\n\n针对这一瓶颈，王汉中院士首创“Bt+Eb”双轮驱动育种理论和技术体系，依靠生物技术加持，变“在单一基地选品种”为“直接在目标生态区育品种”。其中，生物技术（Bt）相当于“加速器”，整合分子标记辅助选择、基因编辑、小孢子培养等现代育种手段，依托年创制近10万份双单倍体系的高效育种平台，将传统育种中需筛选数亿个株系的工作量压缩至数万个，育种周期也从8—10年减至5年左右。生态育种（Eb）则秉持“在哪儿种，就在哪儿育”理念，自2022年以来，在湖北武汉（长江中下游冬油菜区，并设置晚播一个月模拟南方双季稻区）、四川邛崃（长江上游寡照区）、青海平安（高海拔春油菜区）、内蒙古额尔古纳（高纬度春油菜区）、河南安阳（强冬性油菜区）等地建立定向育种基地，将育种前哨扎根各主产生态区。\n\n“Bt+Eb”理论和技术体系的创立，标志着我国油菜育种自主创新迈出里程碑式的一步，也为其他农作物育种提供了新范式，将为加快实现我国种业科技自立自强、保障国家粮油安全提供强劲引擎。","中国农业科学院 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1](https:\u002F\u002Fxinhuanet.com\u002F20260905\u002F1bd42394012e444994d7b3ece7a22b1d\u002F202609051bd42394012e444994d7b3ece7a22b1d_20260905807ff180cb6d43c4ad684297f3ee60af.png)**\n\n**◇2025年3月，习近平总书记在云南考察时指出，云南花卉产业前景广阔，要着眼全产业链，从种业端、种植端、市场端不断深耕细作，让这一“美丽产业”成为造福群众的“幸福产业”。**\n\n**◇****云南坚持高位统筹、系统谋划，聚焦种业、种植、市场三个重点持续攻坚，统筹科研院所、企业、合作社、花农等多方力量，加快构建现代花卉产业发展新格局。**\n\n**◇****云南立足高原花卉资源禀赋与产业根基，以种业振兴为抓手，系统推进种质资源保护、育种技术攻关、良种转化效能等工作，构建全链条、本土化、智能化的花卉种业发展体系，推动自主可控的“中国芯”花卉成为产业转型升级的重要支撑。**\n\n**◇****据统计，云南商业化推广自育花卉新品种超过100个，自主花卉品种市场占有率提高到15%以上；鲜切花种苗自给率达80%，月季、非洲菊、香石竹、满天星优质种苗自给率达90%。**\n\n**◇****云南推进种植设施升级推广现代农业技术，推动花卉种植向规模化、标准化、集约化的现代种植模式升级，将花卉产业打造为稳就业、促增收、兴乡村的幸福产业。**\n\n**◇****云南全省鲜切花和盆花已基本实现设施化生产，鲜切花设施化率达100%。**\n\n**◇****云南发挥花卉全产业链发展优势，通过产品创新、业态融合、交易革新、出海护航等举措，推动云花从初级农产品向文创产品、文旅产品转型，把美丽资源转化为经济增量和幸福增量。**\n\n**◇****2025年，昆明斗南国际花卉产业园共交易154.76亿枝鲜花，交易额达134.84亿元，连续20多年稳居全国第一。**\n\n[Video 3](https:\u002F\u002Fvodpub6.v.news.cn\u002Fyqfbzx-original\u002F20260905\u002F202609051bd42394012e444994d7b3ece7a22b1d_a9557a5ac1784902b1934d4d9b19e1c1.mp4)\nPlay Video\n\n![Image 2](blob:http:\u002F\u002Flocalhost\u002Fed042d2d8f899b135431149d945adb99)\n\n0:00:00\n\n\u002F0:00\n\n![Image 3](blob:http:\u002F\u002Flocalhost\u002F14260bac175dcf4fbb67deb53a14e687)\n\n![Image 4](blob:http:\u002F\u002Flocalhost\u002Fa730588525c5af99d8edecaeaa9b13ce)\n\n高原沃土孕育新机，彩云之南盛放芬芳。云南地处低纬度、高海拔地区，山川纵横，气候立体，被誉为“植物王国”“世界花园”。这里光照充足，昼夜温差大，雨水丰沛，土壤类型多样，是全球公认最适宜花卉生产的地区之一和主要花卉分布中心之一。\n\n2025年3月，习近平总书记在云南考察时指出，云南花卉产业前景广阔，要着眼全产业链，从种业端、种植端、市场端不断深耕细作，让这一“美丽产业”成为造福群众的“幸福产业”。\n\n这一殷殷嘱托，为云南花卉产业高质量发展指明方向和注入强劲动力。依托国家林草局、农业农村部联合印发的《关于推进花卉业高质量发展的指导意见》等文件，以及中央财政资金保障、种业科研专项支持、跨境贸易配套扶持等协同发力，云南花卉产业进一步释放发展动能。\n\n2025年9月，《云南省商务厅关于支持花卉进出口的若干政策措施》出台，为花卉出口提供制度性支撑；2025年10月，《云花生产基地等级评价规范》发布，强化提升花卉绿色生产全流程质量控制；2026年2月，云南将“加快打造世界一流鲜切花产业”作为纵深推进产业强省建设的重要内容写入2026年政府工作报告；2026年3月，《云南省加快培育推广花卉自主知识产权新品种实施方案》印发，为全省花卉育种创新能力提升注入新动能……云南坚持高位统筹、系统谋划，聚焦种业、种植、市场三个重点持续攻坚，统筹科研院所、企业、合作社、花农等多方力量，加快构建现代花卉产业发展新格局。\n\n跨越式发展的云南花卉产业，正成为高原特色农业的亮丽名片。截至2025年底，云南省花卉种植面积超200万亩，2025年，鲜切花产量221.5亿枝，花卉全产业链产值突破1400亿元，出口额近2亿美元，连续七年居全国第一。截至2026年6月底，云南累计申请花卉新品种近2200个，获农业农村部、国家林草局授权新品种900余个，申请数和授权数均居全国第一。\n\n全国市场每10枝鲜切花，有7枝来自云南。一朵朵云花承载着云岭大地的勃勃生机，以繁花惠民的丰硕成果，谱写云南兴农富民的时代篇章。\n\n![Image 5](https:\u002F\u002Fxinhuanet.com\u002F20260905\u002F1bd42394012e444994d7b3ece7a22b1d\u002F202609051bd42394012e444994d7b3ece7a22b1d_20260905cdf4913eeaef474094e6118cd2b22817.png)\n\n丽江市玉龙雪山脚下的听花谷景区风光（2026年6月16日摄）胡超摄\u002F本刊\n\n**种业端：打造“中国芯”**\n\n花卉产业看似是“一枝花”的竞争，实质是“一粒种”的较量——比拼的是种质资源、育种技术和品种权等方面的综合实力。\n\n云南立足高原花卉资源禀赋与产业根基，以种业振兴为抓手，系统推进种质资源保护、育种技术攻关、良种转化效能等工作，构建全链条、本土化、智能化的花卉种业发展体系，推动自主可控的“中国芯”花卉成为产业转型升级的重要支撑。\n\n——种质筑基，厚培云花资源沃土。\n\n种质资源是育种创新的基础。针对以往花卉种质资源零散、保存不系统等情况，云南将花卉种质资源收集、保护、鉴定列为种业发展的基础工程。\n\n省级科研平台牵头，打造种质资源保存大平台。云南省农业科学院围绕月季、百合等重点花卉，持续开展种质资源收集保存和创新利用，建设高水平花卉种质资源库，资源保有量居全国前列。\n\n区域布局多点开花，织密种质资源保护网。云南在昆明、玉溪、楚雄、红河建设7个省级花卉种质资源圃，为花卉资源鉴定评价、创新利用提供资源保障。规模庞大、品类齐全的花卉“活体基因库”，为后续新品种选育、优良性状改良、本土品种适配升级筑牢资源根基。\n\n拓宽国际协作渠道，汇聚保存全球优异花卉种质。云南省加强政企协作，推动多家外资育种机构在云南开展本土化研发，搭建种质协作交流通道；昆明市呈贡区政府与云南省农业科学院合作建设的国际花卉技术创新中心收集保存来自国内外的切花月季优异品种资源1000余个，实现全球优异花卉种质汇集保存，进一步扩充全省资源储备。\n\n——自主育种，攥紧花卉种业发展主动权。\n\n长期以来，一些主流鲜切花品类核心品种较多依赖国外，企业和花农在品种使用、市场定价等方面受制于人。为打破海外品种垄断，云南整合科研平台、人才团队、政策资金多方力量，推动花卉育种技术迭代升级，加快探索高原特色农业自主育种之路。\n\n云南依托“兴滇英才支持计划”引育花卉产业高层次人才，构建“国家级领军人才+省级骨干+青年英才”梯次科研梯队；打通科研院所、高校、企业人才联动通道，在晋宁等花卉主产区推行“种业导师”“产研小院”等机制，定向培育实战型育种技术人才。\n\n云南省农业农村厅相关负责人介绍，云南省相关省级部门统筹科技专项、涉农资金，通过科技项目资助、新品种推广后补助等方式持续加大研发投入，对花卉优良大品种、突破性新品种给予专项经费支持，以人才集聚、资金保障双向赋能，为花卉自主育种攻坚提供坚实支撑。\n\n2026年初，温婉雅致的“宝华”月季、明黄温润的“文秀”月季和粉红灵动的“娇龙”月季在国内火热出圈。凭借花型多样、带有香味、花期长三大特性，这些由云南省农业科学院花卉研究所自主研发的“中国风”月季，成为国产花卉自主育种的标志性符号。\n\n“育种团队的目标，就是培育拥有自主知识产权的‘中国风’月季，把花卉种业发展主动权握在自己手里。”云南省农业科学院花卉研究所研究员蔡艳飞说，团队目前已培育出1000余个自主知识产权月季新株系，打破我国对进口月季品种的依赖。\n\n不只是月季，云南通过持续强化花卉种业创新、不断突破育种技术边界，为越来越多花卉装上“中国芯”。百合、洋桔梗、康乃馨等主流鲜切花品类国内市场自育品种占比持续提升，蝴蝶兰、绣球、万寿菊等特色花卉国产新品种不断上市。据统计，云南省农业科学院花卉研究所在国家林草局正式登记注册的高山杜鹃品种已达21个，创制出38个洋桔梗优良株系，成功培育并申报非洲菊新品种113个。\n\n云南省农业科学院院长王继华介绍，依托国家观赏园艺工程技术研究中心、云南省花卉育种重点实验室等平台，该院推动传统杂交育种与分子育种、基因组学、AI辅助育种深度融合，加快花卉育种向精准育种、智能育种升级。\n\n——研用贯通，提升花卉良种转化效能。\n\n育种创新的最终价值，在于规模化推广、产业化应用，真正惠及花农、赋能产业。云南坚持“研用结合、以用促研”，打通新品种从实验室到种植基地、交易市场的全链条推广通道，让自主良种落地生根、产生效益。\n\n![Image 6](https:\u002F\u002Fxinhuanet.com\u002F20260905\u002F1bd42394012e444994d7b3ece7a22b1d\u002F202609051bd42394012e444994d7b3ece7a22b1d_202609056c5abba673ad4e4282643ded55904648.png)\n\n泰国曼谷的一位女士（右）“接过”云南花农“递出”的一枝鲜花（拼版照片）（资料照片）左图：胡超摄\u002F本刊；右图：张可任摄\u002F本刊\n\n在丽江现代花卉产业园的国产优株测试温室里，110余株自主选育月季优株整齐排布，每株都悬挂着标明花色、抗性、适种海拔的信息牌。历经多轮高原气候驯化，园区成功完成“丽江时光”等11个“丽系”月季新品种登记注册，形成独属于雪山产区的自有花卉品牌矩阵。\n\n这是园区联动省农科院、投入980万元启动“五年百种”计划的生动写照。园区与中国科学院昆明植物研究所、云南省农业科学院等科研院所合作，让国产花卉品种培育的科研成果落地到产业发展中。\n\n据统计，云南商业化推广自育花卉新品种超过100个，自主花卉品种市场占有率提高到15%以上；鲜切花种苗自给率达80%，月季、非洲菊、香石竹、满天星优质种苗自给率达90%。\n\n“现在我们在家门口就能买到性价比更高、适配本地气候的国产种苗。”资深花商张金林说，随着自主良种持续普及，越来越多花农、合作社主动选用本土培育种苗，既降低种植成本，又摆脱海外种源制约。\n\n![Image 7](https:\u002F\u002Fxinhuanet.com\u002F20260905\u002F1bd42394012e444994d7b3ece7a22b1d\u002F202609051bd42394012e444994d7b3ece7a22b1d_2026090527dc58631a8d4ec1b248ea21bf3085c6.png)\n\n云南锦科花卉工程研究中心技术人员田连对玫瑰进行杂交授粉（资料照片）胡超摄\u002F本刊\n\n**种植端：插上科技翼**\n\n云南推进种植设施升级推广现代农业技术，推动花卉种植向规模化、标准化、集约化的现代种植模式升级，将花卉产业打造为稳就业、促增收、兴乡村的幸福产业。\n\n——设施升级，擦亮云花品牌。\n\n为补齐种植短板，云南全域推进花卉种植设施迭代升级，大力建设智能温室、标准化大棚等现代种植载体，配套水肥一体化、环境自动调控等硬件装备，减少季节天气干扰，把高原气候优势转化为全年稳定供给的产业胜势。\n\n玉龙雪山脚下，丽江现代花卉产业园的智能温室大棚内，玫瑰花、马蹄莲等花卉姹紫嫣红，水肥一体化设备精准输送养分，温控、光照、湿度系统自动调节参数。这座配备全自动环境控制系统的花棚，能精准掌控花期，实现花卉全年稳定生产。\n\n“园区配套全自动灌溉、智能遮阳补光等现代化农业设施，生产的鲜切花品质稳步提升。”该产业园产业规划负责人张丽辉说，依托硬件设施支撑，园区鲜切花出品率稳定在90%以上，年产玫瑰鲜切花4000万枝、彩色马蹄莲200万枝，产品不仅销往全国，还出口到俄罗斯、越南等国家，逐步打造出“丽江dē花”高端鲜切花品牌。\n\n放眼全省，开远、晋宁等花卉主产区同步推进设施农业升级改造，连片建设标准化大棚、智能种植基地。各地整合涉农资金，出台温室建设补贴、农机配套扶持政策，引导企业、合作社集中连片发展，持续提升花卉产业设施化程度。\n\n截至目前，云南全省鲜切花和盆花已基本实现设施化生产，鲜切花设施化率达100%。其中，采用标准化连栋大棚设施的鲜切花产量占比50.1%，位居全国前列；采用智能化技术并实现水肥循环利用的花卉种植面积达3万亩，位居全国第一。\n\n——技术赋能，提升产业成色。\n\n依托专业化产业园区、规模化种植基地，云南将科技创新嵌入花卉种植管理、采收分级等环节，推动花卉生产由“凭经验”向“按标准”转变，力争以规范化管控全面提升产业竞争力。\n\n在曲靖市马龙区通泉街道红馨语花卉园艺种植场，80余亩智能化大棚内芍药含苞待放。基地负责人李云宝从事花卉种植20余年，是科技赋能花卉产业的见证者。\n\n“传统种花方式，花卉品质不一，难获市场认可。如今，种植和采后处理技术水平不断提升，逐步形成标准化、规模化的花卉生产体系，花卉品质稳定，产量递增。”李云宝说。\n\n以芍药为例，基地在采后环节发力，将采收后的芍药移入冷库，通过精确控制低温让其休眠，再根据订单要求精准调控唤醒时间，结合市场需求灵活调整产量。“基地探索出的精准低温休眠技术，可实现芍药反季节供应、全年生产。”李云宝说。\n\n据李云宝介绍，基地种植的芍药花型饱满，完全开放后直径达18厘米至20厘米，颇受消费者青睐。2025年，基地芍药鲜切花产量达30万枝，年产值超700万元。\n\n——联农带农，共享发展红利。\n\n一朵鲜花的绽放，承载着千家万户的生计与期盼。云南坚持把联农带农贯穿花卉全产业链，让花农在产业升级中实现稳定就业、持续增收，用一朵朵鲜花浇灌出美好生活。\n\n丽江市古城区保吉村村民和月华是丽江现代花卉产业园的第一批工人。以前，外出打零工的她收入不稳定。如今，她在玫瑰温室区担任小组长，管理40余名工人。“淡季每月务工收入4000元，旺季还有绩效补贴，一个月能拿到7000元，收入稳定还能照料家中老小。”和月华一脸笑容。\n\n在大理白族自治州祥云县沙龙镇的申洱花卉产业园基地，花农金丽纯穿梭在智能温控大棚中，将采摘的各色鲜切菊花打包分级。这个由沪滇协作资金与上海企业投资共建的花卉基地，不仅是云南高原特色农业的示范窗口，更成为当地农民增收的“幸福花棚”。附近村民不仅可以在基地务工，还能通过流转土地收取租金、参与农民合作社分红。\n\n“我们通过土地流转收租金、基地务工赚薪金等模式，让农户和周边群众共享花卉产业发展红利。”基地负责人陈志星介绍，基地目前年产值超过5000万元，每年可带动周边群众实现“家门口”稳定就业300余人，季节性就业500余人。\n\n![Image 8](https:\u002F\u002Fxinhuanet.com\u002F20260905\u002F1bd42394012e444994d7b3ece7a22b1d\u002F202609051bd42394012e444994d7b3ece7a22b1d_202609056d4d678449614eec9f1c2a20a0d5c604.png)\n\n云南省弥勒市弥阳街道小河边村村民在采摘鲜食玫瑰（资料照片）胡超摄\u002F本刊\n\n据云南省农业农村厅统计，云南省目前已培育19万户花农，花农年收入68亿元，户均年收入3.6万元。花卉产业带动全省48万人就业，服务全国35万个花店，联动全国530多万花卉从业者。\n\n“花卉产业规模化发展，同步搭建起多元联农带农机制，让产业红利实实在在落到农户身上。”云南省农业农村厅副厅长王思泽说，一朵朵高原鲜花，真正成为带动乡村全面振兴、各族群众增收致富的“幸福花”。\n\n**市场端：架起流通桥**\n\n云南发挥花卉全产业链发展优势，通过产品创新、业态融合、交易革新、出海护航等举措，推动云花从初级农产品向文创产品、文旅产品转型，把美丽资源转化为经济增量和幸福增量。\n\n——创新供给，激活消费新动能。\n\n云南持续深耕花卉产品创新，打造适配当前消费需求的产品体系，不断挖掘花卉消费新潜力。\n\n构建适配消费需求的产品体系。位于丽江市古城区开南街道的丽江注定红农业科技有限公司，现有盆栽、鲜切花、蜡封种球等多元产品。大棚里，大红、玫红、粉白的朱顶红花朵层层舒展；展厅里，蜡封朱顶红成为最具代表性的创新品类，深受市场欢迎。\n\n“蜡封种球主打轻养护、长周期，无需浇水施肥，适宜温度下可自然绽放，观赏周期远超普通鲜切花。”公司董事长李菊香介绍，这款创新产品有效降低大众养花门槛，适配电商直播、商超零售、文旅伴手礼等新兴渠道，并打破传统花卉运输难、养护繁、场景受限的发展瓶颈，有效拓宽高原特色花卉市场。\n\n![Image 9](https:\u002F\u002Fxinhuanet.com\u002F20260905\u002F1bd42394012e444994d7b3ece7a22b1d\u002F202609051bd42394012e444994d7b3ece7a22b1d_20260905b24918fcd3a84682aa2474ae7769a9ca.png)\n\n人们在昆明斗南花卉市场选购鲜花（2025年7月9日摄）胡超摄\u002F本刊\n\n释放花卉多元价值。轻养护园艺产品、永生花艺术产品、花卉食品、文创周边……云南各地围绕消费热点积极开发花卉衍生品，推动花卉从观赏功能，向家居装饰、特色食品等多元形态拓展，持续释放高原花卉产业消费潜能。\n\n激活全国“花样经济”。目前，云花的外溢效应逐步辐射全国，近年不断向外输出优良种苗、花海花境建造方案等。云南省农业科学院花卉研究所自主培育的高山杜鹃、月季先后落地四川、新疆、广西等地，支撑花海与城乡园林建设。各地结合自身资源禀赋，消化吸收云花的品种与技术成果，因地制宜发展本地花卉相关业态，激活“花样经济”。\n\n——以花为媒，催生消费新业态。\n\n云南多地推进“花卉+文旅”融合发展，探索“花卉+”发展模式，推动花卉产业从种植销售向休闲度假、旅拍经济等新业态延伸，构建多元消费新场景。\n\n在玉龙雪山脚下的丽江听花谷，远处雪峰巍峨，近处繁花烂漫，层层叠叠的花海铺展开来，勾勒出独属于丽江的诗意画卷，也造就辨识度极高的“雪山花海”文旅IP。\n\n景区坐落于丽江市玉龙纳西族自治县白沙镇，紧邻丽江古城、玉龙雪山两大核心景区，总规划占地约300亩，是以高原花卉观光为基础，集休闲度假、婚纱旅拍等于一体的复合型田园旅游综合体。\n\n“一到这里仿佛坠入花的海洋。”来自辽宁锦州的刘东和任贺专程来听花谷拍摄婚纱照。远处玉龙雪山伫立，花海、蓝天与雪峰相映成画，成为两人美好记忆的见证。\n\n依托丽江气候温润、光照充足的自然优势，听花谷因地制宜布局四季花卉景观，分季栽种郁金香、薰衣草等百余类观赏花卉，实现“四季有花、步步有景”，成为婚纱摄影的热门取景地。\n\n景区负责人廖伟介绍，2025年听花谷景区接待游客总量达40万人次，承接婚纱摄影拍摄3万余场次，全年综合销售额突破2000万元。\n\n——优化交易，释放流通强势能。\n\n流通时效是鲜切花产业的“生命线”，交易效率直接决定产业市场竞争力。云南持续完善花卉交易体系，依托标准化、数字化、规模化交易模式，进一步规范市场秩序、降低流通成本、提升交易效率，筑牢全国鲜切花交易核心枢纽地位。\n\n走进昆明国际花卉拍卖交易中心拍卖大厅，900多个交易席位座无虚席。玫瑰、百合、洋桔梗等40多个品类、1500多种鲜切花的价格在电子大屏实时滚动。\n\n作为全国花卉产业的“价格晴雨表”和“市场风向标”，昆明国际花卉拍卖交易中心、昆明斗南花卉交易市场等搭建数字化交易平台，通过实时公示花卉品种、等级、价格等核心信息，实现采购商精准比价、高效交易，形成公开透明、规范有序的现代化花卉拍卖交易体系。\n\n拍卖结束后，鲜花进入打包环节，依托成熟的市场体系与完善的冷链物流网络，鲜花可连夜送往全国各大城市及海外地区，全省花卉集散效率大幅提升。\n\n2025年，昆明斗南国际花卉产业园共交易154.76亿枝鲜花，交易额达134.84亿元，连续20多年稳居全国第一。\n\n![Image 10](https:\u002F\u002Fxinhuanet.com\u002F20260905\u002F1bd42394012e444994d7b3ece7a22b1d\u002F202609051bd42394012e444994d7b3ece7a22b1d_2026090594a6318bf0d140448e6b0e931977dc32.png)\n\n花商在中国云南昆明斗南国际花卉拍卖交易中心内竞拍各种鲜花（2025年7月9日摄）胡超摄\u002F本刊\n\n——政策护航，畅通出海大通道。\n\n出海是云花迈向世界一流的必由之路。云南通过创新监管模式、政策持续赋能，不断扩大海外市场覆盖面和影响力，让七彩云花香飘四海。\n\n专项政策赋能持续释放外贸活力。云南省商务系统先后出台两批推进外贸稳进提质政策，制定花卉专项支持政策，实施“一领域一举措”精准扶持，并出台《云南省商务厅关于支持花卉进出口的若干政策措施》，为花卉出口提供制度性支撑。\n\n云南省商务厅主要负责同志说，云南设立花卉出口绿色通道，布局国际营销网络，接轨国际完善市场规则，统一省内花卉评级标准，强化云花品牌形象，引导交易从拼价格转向竞品质，推动产业从存量博弈迈向增量拓展。\n\n监管模式创新大幅提升通关效率。针对鲜切花易腐易损、保鲜期短、通关时效要求高的特点，昆明海关创新推出“批次检验（检疫）+远程视频查检”监管新模式，整合标准化生产企业同质货源、统一查验标准，将通关时长由“以天为计”大幅压缩到“以分钟为计”，大幅减少鲜花滞留损耗。\n\n系列举措加持下，云花海外市场持续扩容，现已覆盖64个国家和地区，深耕俄罗斯、中亚、东南亚等新兴蓝海市场。据昆明海关统计，2025年云南鲜切花出口货值达12.2亿元、同比增长60.5%，出口规模连续七年稳居全国第一；2026年上半年出口货值7.9亿元、占全国六成以上市场份额，云花品牌正式跻身全球花卉主流市场。\n\n云南省委有关负责同志表示，云南将牢记嘱托，持续深耕花卉特色优势产业，以更高标准、更实举措推进全链条优化、全要素集聚、全方位提升，久久为功做强“美丽产业”，不断增强云花核心竞争力，全力打造“世界一流”花卉产业，让“美丽产业”真正成为巩固拓展脱贫攻坚成果、全面推进乡村振兴的支柱产业、幸福产业、标杆产业，奋力谱写新时代高原特色现代农业高质量发展新篇章。\n\n（文 |《瞭望》新闻周刊记者 采写记者：王长山 吉哲鹏 熊轩昂 胡超）\n\n（《瞭望》2026年第36期）","新华网","2026-09-05T00:00:00Z",85,{"impact":55,"substance":56,"depth":30,"authority":31,"freshness":57,"relevant":33,"comment":58},26,23,3,"央媒深度报道云南花卉种业全链条进展，数据详实，具产业示范价值。",[60],{"name":51,"url":48},[38,62,63,64],"云南","花卉产业","自主育种","2026-09-10T00:02:34.566696Z",{"id":67,"title":68,"url":69,"summary":70,"summary_zh":19,"content":71,"source_name":72,"source_url":19,"published_at":73,"category":74,"cover_url":19,"hotness":24,"is_selected":25,"score":75,"score_detail":76,"sources":79,"tags":81,"view_count":43,"doi":19,"paper":19,"created_at":86},2008,"秋粮生产进入关键期，各地强化防灾减灾与单产提升措施","https:\u002F\u002Fwww.gov.cn\u002F","当前秋粮生产进入产量形成和灾害防范关键阶段，各地围绕水肥管理、病虫害防控和极端天气应对加强田间管理。具体措施以农业农村部门最新公开信息为准。","[![Image 1](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_logo_lt.png)![Image 2](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_logo_rt.png)](https:\u002F\u002Fwww.gov.cn\u002F)\n\n*   [首页](https:\u002F\u002Fwww.gov.cn\u002F)\n*   |\n*   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10](https:\u002F\u002Fwww.gov.cn\u002Fshouye\u002Fzhengcejiedu\u002F202608\u002FW020260817698161961470_ORIGIN.jpg)](https:\u002F\u002Fwww.gov.cn\u002Fzhuanti\u002F202607jjsj\u002Findex.htm)\n*   [国新办发布会解读“十五五”体育强国建设“任务书”](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002F202609\u002Fcontent_7080143.htm)\n*   [解读《物流网建设实施方案》：推动从\"布点畅线\"向\"成链成网\"转型升级](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002F202609\u002Fcontent_7080083.htm)\n*   [商务部有关负责人解读《关于推动商品消费扩容升级的实施意见》](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002F202608\u002Fcontent_7079711.htm)\n\n[国务院组织机构](https:\u002F\u002Fwww.gov.cn\u002Fgwyzzjg\u002F)\n\n[国旗](https:\u002F\u002Fwww.gov.cn\u002Fguoqing\u002Fguoqi\u002F)[国歌](https:\u002F\u002Fwww.gov.cn\u002Fguoqing\u002Fguoge\u002Findex.htm)[国徽](https:\u002F\u002Fwww.gov.cn\u002Fguoqing\u002Fguohui\u002F)|[国情](https:\u002F\u002Fwww.gov.cn\u002Fguoqing\u002F)\n\n[国务院公报](https:\u002F\u002Fwww.gov.cn\u002Fgongbao\u002Fcurrentissue.htm)|[国家行政法规库](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fxzfgk\u002F)|[国家规章库](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fxxgk\u002Fgjgzk\u002Findex.htm)\n\n[![Image 11](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_index_gywzxd.gif)](https:\u002F\u002Fwww.gov.cn\u002Fgzjxs\u002F)\n\n*   [多部门联合部署做好稳投资工作 以有效投资支撑经济社会高质量发展](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F202608\u002Fcontent_7079462.htm)\n*   [今年以来消费品以旧换新带动销售额超1.54万亿元](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7079762.htm)\n*   [人工智能应用服务商培育行动启动](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F202608\u002Fcontent_7079724.htm)\n\n## [政务联播](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F)\n\n*   [商务部等七部门印发关于推动商品消费扩容升级的实施意见](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F202608\u002Fcontent_7079699.htm)\n*   [国家医保局：多举措推动灵活就业人员等重点人群参加职工医保](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F202609\u002Fcontent_7080169.htm)\n*   [秋粮主产区分类施策 全力夺取丰收](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7080093.htm)\n\n[![Image 12](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_index_dxdcyj.jpg)](https:\u002F\u002Fwww.gov.cn\u002F)\n\n### [建言征集](https:\u002F\u002Fwww.gov.cn\u002Fhudong\u002Fwsdy\u002F)|[回应关切](https:\u002F\u002Fwww.gov.cn\u002Fhudong\u002Fhygq\u002Flyhf\u002F)\n\n[![Image 13](https:\u002F\u002Fwww.gov.cn\u002Fshouye\u002Fjyzjhygq\u002F202607\u002FW020260731776139994722_ORIGIN.png)](https:\u002F\u002Fwww.gov.cn\u002Fhudong\u002F202607\u002Fcontent_7076806.htm)\n\n[你关心的养老保险转移接续问题，权威回应来了](https:\u002F\u002Fwww.gov.cn\u002Fhudong\u002F202607\u002Fcontent_7076806.htm)\n\n*   [跟团旅游遇到“强迫购物”怎么办 权威答复来了](https:\u002F\u002Fwww.gov.cn\u002Fhudong\u002F202607\u002Fcontent_7076451.htm)\n\n[![Image 14](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgwyhlwdc20250623.png)](https:\u002F\u002Ftousu.www.gov.cn\u002Fdc\u002Findex.htm)\n\n[我要留言](https:\u002F\u002Ftousu.www.gov.cn\u002Fdc\u002Findex.htm)\n\n## [![Image 15](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_index_gwywls.png)](https:\u002F\u002Fwww.gov.cn\u002Fgwywls\u002F)\n\n施政为民 激发活力 政府建设\n\n### [以人民为中心 你对养老、托育、教育、医疗健康、就业、收入分配、社会保障、住房、基本公共服务、生态环境等方面政策有什么建议？感谢你的真知灼见。](https:\u002F\u002Fliuyan.www.gov.cn\u002Fhudong\u002Fatwls\u002Frmqz.htm)\n\n[人民群众留言入口](https:\u002F\u002Fliuyan.www.gov.cn\u002Fhudong\u002Fatwls\u002Frmqz.htm)\n\n## [为民服务](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002F)[更多 >>](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002F)\n\n*   [![Image 16](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002F20240607chusheng.png)出生](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fchusheng\u002F#1)\n*   [![Image 17](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Ft1_2.png)入学](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fruxue\u002F#2)\n*   [![Image 18](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Ft1_3.png)工作](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fgongzuo\u002F#3)\n*   [![Image 19](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002F20240913jiuyi.png)就医](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fjiuyi\u002F#7)\n*   [![Image 20](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002F20240913tuixiu.png)退休](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Ftuixiu\u002F#9)\n\n### [激发经营主体活力 你对充分激发各类经营主体活力、积极营造一流营商环境有什么建议？感谢你的真知灼见。](https:\u002F\u002Fliuyan.www.gov.cn\u002Fhudong\u002Fatwls\u002Fqygth.htm)\n\n[企业、个体工商户留言入口](https:\u002F\u002Fliuyan.www.gov.cn\u002Fhudong\u002Fatwls\u002Fqygth.htm)\n\n## [为企服务](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002F)[更多 >>](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002F)\n\n*   [![Image 21](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Ft2_1.png)开办企业](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fkbqy\u002F#0)\n*   [![Image 22](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002F20241121jyfz_ico.png)经营发展](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fjyfz\u002F#1)\n*   [![Image 23](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002F20241121qyyg_ico.png)企业用工](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fqyyg\u002F#2)\n*   [![Image 24](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Ft2_2.png)纳税缴费](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fnsjf\u002F#3)\n*   [![Image 25](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002F20241121zxtc_ico.png)注销退出](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fzxtc\u002F#4)\n\n### [加强政府建设 你对法治政府建设、依法行政、提升行政效能等有何建议？如何持续优化政务服务、加快数字政府建设？感谢你的真知灼见。](https:\u002F\u002Ftousu.www.gov.cn\u002Fzwfw\u002Findex.htm)\n\n[政务服务留言入口](https:\u002F\u002Ftousu.www.gov.cn\u002Fzwfw\u002Findex.htm)\n\n*   [![Image 26](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgjzwfwpt20250623.png)国家政务服务平台](http:\u002F\u002Fgjzwfw.www.gov.cn\u002Findex.html)\n*   [![Image 27](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fwmfw20250623.png)为民服务](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002F)\n*   [![Image 28](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fwqfw20250623.png)为企服务](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002F)\n\n要闻 最新政策\n\n*   [习近平就朝鲜国庆78周年向朝鲜最高领导人金正恩致 贺电](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7080483.htm)\n*   [习近平同英国首相伯纳姆通电话](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7080441.htm)\n*   [李强主持国务院第二十一次专题学习](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7080439.htm)\n*   [丁薛祥出席2026年全球服务贸易峰会 并会见与会外国政要](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7080569.htm)\n*   [何立峰会见加拿大加中贸易理事会 名誉主席安德烈·德马雷](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7080545.htm)\n*   [全球公共安全合作论坛（连云港）2026年大会举行 王小洪出席并致辞](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7080578.htm)\n\n[更多![Image 29](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_zsjMore.jpg)](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F)\n\n*   [电力安全事故应急处置和调查处理条例](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fcontent\u002F202609\u002Fcontent_7080188.htm)\n*   [中共中央办公厅 国务院办公厅印发《党政领导干部生态环境损害责任追究办法》](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202608\u002Fcontent_7079095.htm)\n*   [国务院关于修改《住房公积金管理条例》的决定](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fcontent\u002F202608\u002Fcontent_7078477.htm)\n*   [国务院关于《特殊教育发展提升“十五五”行动计划》的批复](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fcontent\u002F202608\u002Fcontent_7078320.htm)\n*   [集成电路布图设计保护条例](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fcontent\u002F202608\u002Fcontent_7077398.htm)\n\n[更多![Image 30](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_zsjMore.jpg)](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002F)\n\n[![Image 31](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_gwyzxd.jpg)](https:\u002F\u002Fwww.gov.cn\u002Fgzjxs\u002F)\n\n## [政务联播](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F)\n\n*   [商务部等七部门印发关于推动商品消费扩容升级的实施意见](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F202608\u002Fcontent_7079699.htm)\n*   [国家医保局：多举措推动灵活就业人员等重点人群参加职工医保](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F202609\u002Fcontent_7080169.htm)\n*   [秋粮主产区分类施策 全力夺取丰收](https:\u002F\u002Fwww.gov.cn\u002Fyaowen\u002Fliebiao\u002F202609\u002Fcontent_7080093.htm)\n\n[更多![Image 32](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_zsjMore.jpg)](https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F)\n\n## [政策解读](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fjiedu\u002F)\n\n[![Image 33](https:\u002F\u002Fwww.gov.cn\u002Fshouye\u002Fzhengcejiedu\u002F202608\u002FW020260817698161961470_ORIGIN.jpg)](https:\u002F\u002Fwww.gov.cn\u002Fzhuanti\u002F202607jjsj\u002Findex.htm)\n\n*   [国新办发布会解读“十五五”体育强国建设“任务书”](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002F202609\u002Fcontent_7080143.htm)\n*   [解读《物流网建设实施方案》：推动从\"布点畅线\"向\"成链成网\"转型升级](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002F202609\u002Fcontent_7080083.htm)\n*   [商务部有关负责人解读《关于推动商品消费扩容升级的实施意见》](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002F202608\u002Fcontent_7079711.htm)\n\n[更多![Image 34](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_zsjMore.jpg)](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fjiedu\u002F)\n\n## [国务院政策文件库](https:\u002F\u002Fsousuo.www.gov.cn\u002Fzcwjk\u002FpolicyDocumentLibrary?q=&t=zhengcelibrary&orpro=)\n\n[![Image 35](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_indexSearch.jpg)](https:\u002F\u002Fwww.gov.cn\u002F)\n\n[惠企助企政策集纳查询](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fqiye\u002F)\n\n[政府信息公开](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fxxgk\u002F)\n\n[国家行政法规库](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fxzfgk\u002F)\n\n[国家规章库](https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fxxgk\u002Fgjgzk\u002Findex.htm)\n\n为民服务 为企服务\n\n*   [出生](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fchusheng\u002F#1)\n*   [入学](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fruxue\u002F#2)\n*   [工作](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fgongzuo\u002F#3)\n*   [生活](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fshenghuo\u002F#4)\n*   [安居](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fanju\u002F#5)\n*   [婚姻](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fhunyin\u002F#6)\n*   [就医](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Fjiuyi\u002F#7)\n*   [退休](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002Ftuixiu\u002F#9)\n\n[>>](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fgongmin\u002F)\n\n*   [开办企业](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fkbqy\u002F#0)\n*   [经营发展](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fjyfz\u002F#1)\n*   [企业用工](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fqyyg\u002F#2)\n*   [纳税缴费](https:\u002F\u002Fwww.gov.cn\u002Ffuwu\u002Fqiye\u002Fnsjf\u002F#3)\n*   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48](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002Fgtrs_red.png)](http:\u002F\u002Fbszs.conac.cn\u002Fsitename?method=show&id=081D1D6883441A53E053012819AC0DAA)\n\n主办单位：国务院办公厅 运行维护单位：中国政府网运行中心\n\n版权所有：中国政府网 中文域名：中国政府网.政务\n\n网站标识码bm01000001\n\n京ICP备05070218号 京公网安备11010202000001号\n\n_中国政府网\n\nhttps:\u002F\u002Fwww.gov.cn\n\n![Image 49](https:\u002F\u002Fwww.gov.cn\u002Fimages\u002F150.jpg)","中国政府网","2026-09-10T00:00:00Z","政策",83,{"impact":77,"substance":30,"depth":31,"authority":31,"freshness":24,"relevant":33,"comment":78},25,"全国性秋粮生产政策动态，强调防灾减灾与单产提升，对农业信息化领域具有指导意义。",[80],{"name":72,"url":69},[82,83,84,85],"粮食安全","单产提升","防灾减灾","秋粮生产","2026-09-10T00:02:33.212100Z",{"id":88,"title":89,"url":90,"summary":91,"summary_zh":19,"content":92,"source_name":93,"source_url":19,"published_at":94,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":95,"score_detail":96,"sources":102,"tags":104,"view_count":43,"doi":19,"paper":19,"created_at":109},2009,"玉米穗腐病绿色防控见实效","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F084d018b124b46e69fb55501b8591a17.htm","农业农村部环境保护科研监测所联合相关单位在河北鸡泽构建“土壤健康修复—病原源头阻控—生态协同调控”一体化技术体系，并配套多毒素快速检测工具。连续三年多点示范显示，在减肥减药条件下，玉米穗腐病防控、产量和质量实现协同提升。","金秋九月，燕赵大地玉米丰收在望。9月6日，第二届农业环境生物污染控制现场观摩暨玉米穗腐病绿色防控技术交流活动在河北省邯郸市鸡泽县举行。与会代表实地观摩了一体化绿色防控技术的优化升级实效，共商大规模推广良策。这套以土壤健康调控为核心的玉米穗腐病绿色防控“鸡泽模式”，为破解真菌毒素污染难题、保障国家粮食安全提供越来越成熟的系统路径。\n\n玉米作为我国主粮之一，其安全生产直接关系粮食安全与民生福祉。然而，以镰刀菌为代表的土壤习居病原真菌导致的玉米穗腐病，一直是制约产业发展的“拦路虎”。更令人担忧的是，病原菌还会导致玉米被黄曲霉毒素、呕吐毒素、伏马毒素等污染，不仅造成巨大的产量损失，更对人畜健康构成严重威胁。针对产业现实问题，农业农村部[环境保护科研监测所](http:\u002F\u002Faepi.caas.cn\u002F)联合中国农业科学院相关研究所等单位，连续多年开展技术攻关，不断对玉米穗腐病绿色防控技术进行迭代与优化。\n\n在鸡泽县农业农村部[环境保护科研监测所](http:\u002F\u002Faepi.caas.cn\u002F)专家站试验基地，应用了升级版绿色防控技术的玉米植株健壮，穗大粒饱，与对照地块形成鲜明对比。农业农村部[环境保护科研监测所](http:\u002F\u002Faepi.caas.cn\u002F)研究员姚彦坡介绍，这套不断完善的一体化绿色防控技术体系，深度整合了“土壤健康修复—病原源头阻控—生态协同调控”三大核心模块。在播种期，通过多功能微生物菌剂与种衣剂配合，实现催芽壮苗与根部防病；拔节期精准喷施控旺剂与杀菌剂，降低倒伏风险；大喇叭口期与授粉灌浆期，则结合水肥一体化设施与农业无人机，精准施用多功能菌剂与化学药剂，全方位筑牢玉米“健康防线”。\n\n与此同时，项目组在快速检测技术上也迈出新步伐，开发的多毒素混合污染检测试纸条及便携智能化检测仪，实现了田间高灵敏同步检测，为及早干预提供了“科技慧眼”。\n\n经过连续三年的全国多点布设，技术成效尤为显著。面对今年高温多雨等极端天气挑战，专家站示范基地及周边玉米的抗逆防病效果依然突出。实测数据显示，今年示范区玉米穗腐病防控效果突出，玉米质量安全有显著提升。更值得一提的是，在实现化肥农药减量的情况下，示范区玉米实现产量和质量双提升，真正将“控病、降毒、提质、增产”落到实处。\n\n此次活动由农业农村部[环境保护科研监测所](http:\u002F\u002Faepi.caas.cn\u002F)与鸡泽县人民政府联合主办。会议期间举行了新一轮的科企合作洽谈和交流活动，有3项绿色防控科技成果将率先在鸡泽县落地转化。鸡泽县人民政府县长刘伯表示，近年来鸡泽县深化与高校院所合作，农业科技底座不断夯实。\n\n农业农村部[环境保护科研监测所](http:\u002F\u002Faepi.caas.cn\u002F)所长熊明民表示，该技术在鸡泽县落地是科研成果转化的成功典范，团队将持续强化核心技术攻关，加快在全国玉米主产区的推广步伐。\n\n中国农业科学院成果转化局局长彭文君强调，应对玉米生物污染不仅需要技术突破，更需要机制创新。未来必须坚持多学科协同与政企研联合攻关，加速将当前的“试验田模式”转化为全国适用的“系统解决方案”。\n\n据介绍，未来三年，科研团队将在河北、河南、山东、山西、黑龙江、吉林、辽宁、内蒙古等玉米主产区进一步扩大技术应用，为端牢“中国饭碗”注入强劲的科技动能。","中国农业科学院 \u002F 农民日报 2026-09-06","2026-09-06T00:00:00Z",79,{"impact":29,"substance":97,"depth":98,"authority":99,"freshness":100,"relevant":33,"comment":101},20,16,14,5,"报道玉米穗腐病绿色防控技术集成与推广成效，具全国性影响，信息详实，信源权威，但时效性稍弱。",[103],{"name":93,"url":90},[82,105,106,107,108],"绿色防控","玉米","土壤健康","真菌毒素","2026-09-10T00:02:34.216548Z",{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":19,"source_name":116,"source_url":113,"published_at":117,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":120,"score_detail":121,"sources":125,"tags":127,"view_count":43,"doi":133,"paper":134,"created_at":165},1990,"Scaling up soybean breeding: Satellite imagery delivers accurate maturity estimation across plot sizes","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70100","Abstract Accurate and scalable phenotyping is essential for accelerating genetic gain in soybean ( Glycine max (L.) Merr.) breeding programs. Traditional methods for estimating physiological maturity are labor‐intensive and prone to subjectivity, limiting throughput and consistency. This study evaluates the potential of high‐resolution satellite imagery as an alternative to unmanned aerial vehicle (UAV)‐based imaging for estimating soybean maturity across diverse environments and plot configurations. Using vegetation indices derived from both platforms, we applied logistic regression models to predict maturity dates and compared them to established maturity date assessments. Our results demonstrate strong correlations and concordance between satellite‐ and UAV‐derived maturity estimates ( R 2 up to 0.94), with high broad‐sense heritability values ( H 2 up to 0.98), indicating robust genetic control of the trait. Satellite imagery proved effective even in small plot settings (four‐ and eight‐row designs), though performance was higher in larger plots. These findings highlight the feasibility of satellite‐based phenotyping for soybean maturity, offering a cost‐effective, scalable, and reliable alternative to UAVs. The approach has broad implications for enhancing breeding efficiency and expanding remote sensing applications in crop improvement.","摘要：准确且可扩展的表型鉴定对于加速大豆（Glycine max (L.) Merr.）育种项目中的遗传增益至关重要。传统的生理成熟度估算方法劳动密集且易受主观性影响，限制了通量和一致性。本研究评估了高分辨率卫星影像作为无人机（UAV）影像替代方案，在不同环境和小区布局下估算大豆成熟度的潜力。利用两种平台获取的植被指数，我们应用逻辑回归模型预测成熟日期，并将其与既定的成熟度评估进行比较。结果表明，卫星与无人机估算的成熟度之间具有强相关性和一致性（R²最高达0.94），且具有较高的广义遗传力值（H²最高达0.98），表明该性状受稳健的遗传控制。即使在较小的小区设置（四行和八行设计）中，卫星影像也表现出有效性，但在较大小区中性能更优。这些发现凸显了基于卫星的大豆成熟度表型鉴定的可行性，为无人机提供了一种经济高效、可扩展且可靠的替代方案。该方法对提升育种效率及扩展遥感在作物改良中的应用具有广泛意义。","The Plant Phenome Journal","2026-09-07T00:00:00Z","论文",false,78,{"impact":30,"substance":122,"depth":30,"authority":123,"freshness":32,"relevant":33,"comment":124},22,13,"卫星遥感替代无人机用于大豆成熟期估算，方法新颖且数据可靠，对规模化育种表型鉴定有实质推动作用。",[126],{"name":116,"url":113},[128,129,130,131,132],"智慧农业","育种","大豆","遥感","表型鉴定","10.1002\u002Fppj2.70100",{"doi":133,"openalex_id":135,"authors":136,"venue":116,"cited_by_count":43,"oa_url":157,"card":158,"direction":162,"ingested_from":164},"W7211946346",[137,140,143,146,148,151,154],{"name":138,"orcid":139},"Anastasios Mazis","https:\u002F\u002Forcid.org\u002F0000-0002-5024-7234",{"name":141,"orcid":142},"Sarah N. Anderson","https:\u002F\u002Forcid.org\u002F0000-0002-1671-2286",{"name":144,"orcid":145},"Guilherme Ferreira Simiqueli","https:\u002F\u002Forcid.org\u002F0000-0002-2867-0255",{"name":147,"orcid":19},"Adam Barbeau",{"name":149,"orcid":150},"Sara B. Tirado","https:\u002F\u002Forcid.org\u002F0000-0003-0432-091X",{"name":152,"orcid":153},"Julien F. Linares","https:\u002F\u002Forcid.org\u002F0000-0002-0083-0974",{"name":155,"orcid":156},"Nathan D. Coles","https:\u002F\u002Forcid.org\u002F0000-0002-2008-3283","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fppj2.70100",{"tldr":159,"method":160,"finding":161,"direction":162,"opportunity":163},"用卫星影像替代无人机估算大豆成熟期，实现规模化育种表型分析。","利用高分辨率卫星影像与无人机影像的植被指数，构建逻辑回归模型预测成熟期。","卫星与无人机估算成熟期相关性高（R²达0.94），遗传力高（H²达0.98），小小区也有效。","农业遥感与作物表型","卫星影像在小区育种中的精度受限于小区大小，可探索优化算法或结合多源数据提升小尺度精度。","openalex","2026-09-09T23:30:17.932682Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":19,"content":171,"source_name":172,"source_url":19,"published_at":173,"category":118,"cover_url":19,"hotness":24,"is_selected":25,"score":174,"score_detail":175,"sources":179,"tags":181,"view_count":43,"doi":185,"paper":186,"created_at":195},2013,"高温胁迫下植物RNA修饰与抗逆机制研究","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.molp.2026.08.004","研究揭示RNA乙酰胞苷修饰、保护颗粒形成与植物高温耐受之间的联系，并识别出一批参与解毒和抗逆过程的候选RNA分子。论文为作物耐热机制研究和抗逆育种提供了分子线索。","","Molecular Plant","2026-09-09T00:00:00Z",77,{"impact":30,"substance":97,"depth":176,"authority":99,"freshness":177,"relevant":33,"comment":178},17,8,"核心期刊论文揭示高温胁迫下RNA修饰新机制，为作物耐热育种提供分子线索，具有较高科研价值。",[180],{"name":172,"url":169},[182,183,184],"分子育种","作物抗逆","耐热机制","10.1016\u002Fj.molp.2026.08.004",{"doi":185,"openalex_id":19,"authors":187,"venue":19,"cited_by_count":43,"oa_url":19,"card":188,"direction":192,"ingested_from":194},[],{"tldr":189,"method":190,"finding":191,"direction":192,"opportunity":193},"揭示RNA乙酰胞苷修饰与植物高温耐受的联系，为抗逆育种提供分子线索。","RNA修饰分析、保护颗粒鉴定、候选RNA筛选。","RNA乙酰胞苷修饰与保护颗粒形成关联，参与植物高温抗逆。","其他","可探索RNA修饰调控网络在作物耐热育种中的应用，或开发基于RNA修饰的耐热性分子标记。","agent","2026-09-10T00:02:35.528229Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":19,"content":201,"source_name":202,"source_url":19,"published_at":173,"category":23,"cover_url":19,"hotness":24,"is_selected":119,"score":203,"score_detail":204,"sources":206,"tags":208,"view_count":43,"doi":19,"paper":19,"created_at":212},2010,"植物靠RNA“身份标记”增强高温耐受能力","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F704c0e7d3959454c80b7f86ad9668321.htm","中国农业科学院烟草研究所发现，植物可通过RNA修饰标记并将相关分子聚集到保护颗粒中，减少高温条件下的降解。研究锁定272个同时具备标记和聚集特征的RNA分子，为培育耐热作物提供新思路。","[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fcaas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 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[政务新媒体矩阵](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fzwxmtjz\u002Findex.htm) \n\n[返回首页](https:\u002F\u002Fcaas.cn\u002Findex.htm)[English](https:\u002F\u002Fwww.caas.cn\u002Fen\u002F)\n\n[首页](https:\u002F\u002Fcaas.cn\u002Findex.htm)-[新闻中心](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Findex.htm)-[科研活动](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Findex.htm)\n\n分享到\n\n### 植物靠RNA“身份标记”解锁高温生存技能\n\n发布时间：2026-09-08 _|_ 来源： 中国农业科学院烟草研究所 _|_ 作者：焦裕冰\n\n字体[小](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F704c0e7d3959454c80b7f86ad9668321.htm)[中](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F704c0e7d3959454c80b7f86ad9668321.htm)[大](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F704c0e7d3959454c80b7f86ad9668321.htm)\n\n近日，中国农业科学院[烟草研究所](http:\u002F\u002Ftric.caas.cn\u002F)烟草病虫害绿色防控创新团队揭示在高温条件下，植物可通过在RNA上添加修饰标记、并将其召集进细胞内新形成的“保护颗粒”来避免关键分子被破坏，从而增强对高温的耐受能力。相关研究成果发表在《分子植物（Molecular Plant）》上。\n\n该研究发现，植物细胞里负责解毒的RNA分子在高温下很容易被破坏。一种名为乙酰胞苷的修饰标记能保护它们：细胞先由一种酶给这些RNA贴上标签，再由另一种蛋白把它们召集进高温下新形成的保护颗粒里，像住进避难所一样躲过降解。科研人员删除负责添加这种标记的关键蛋白后，幼苗在高温下的存活率骤降。经过进一步分析，团队还找到了272个同时带标签、被召集、进入保护颗粒的RNA分子，它们大多参与解毒和抗逆过程。该研究首次把这种修饰标记、细胞内物质聚集成颗粒的现象与植物耐热能力直接联系起来，为培育耐热作物提供了新思路。\n\n该研究得到国家自然科学基金和中国农业科学院科技创新工程等项目支持。（通讯员 李晓娟）\n\n论文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.molp.2026.08.004\n\n![Image 8: sdfsd.jpg](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-09\u002F88a5112e3760436480ab005dc2e64f07.jpg)\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [杨振海为在京博士新生讲授思政第一课](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fnkyw\u002F1d5c3301dca3431582000904672a6f67.htm)2026-09-08  \n*   [研究发现紫杉烷生物合成基因“群岛”](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F2fabe97339a7485e812daeac8dc0850d.htm)2026-09-08  \n*   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京公网安备11010802048565号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010802048565)","中国农业科学院",76,{"impact":97,"substance":30,"depth":31,"authority":31,"freshness":177,"relevant":33,"comment":205},"中国农科院发布植物高温耐受机制新发现，具科研价值，但正文缺失，影响评估。",[207],{"name":202,"url":199},[209,210,211,183],"RNA修饰","基因调控","高温胁迫","2026-09-10T00:02:34.352919Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":19,"source_name":219,"source_url":216,"published_at":22,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":220,"score_detail":221,"sources":225,"tags":227,"view_count":43,"doi":231,"paper":232,"created_at":255},2007,"AIDspat: Introduction of a model framework to estimate spatio-temporal agricultural irrigation demands – A study area in Germany","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110730","In the face of ongoing climate change, information about agricultural water usage is crucial for policymakers and planners. This study introduces the AIDspat model, a new framework for estimating agricultural irrigation demand (AID). The model combines an existing soil water balance model with the principle of irrigation scheduling. In addition to location-based climate, soil and land use data, the model takes into account general crop-specific parameters, irrigation techniques and farmers’ irrigation management decisions. This enables an objective determination of AID. The model enables scenario-based calculations with low computational cost across various scales, ranging from the field to the national level and spanning both past and future time periods. Integrating crop-specific differences through intra-annual dynamic plant development and considering irrigation techniques enhances the model’s accuracy and practical applicability. The model’s ability to capture the spatial and temporal dynamics of AID provides valuable insights for the sustainable management of agricultural water. Calibrated and validated for a study area in southern Germany, the model demonstrates a high degree of alignment between modeled values and literature benchmarks for a variety of agricultural crops. The modeled results show a good agreement with measured irrigation volumes, with yearly percentage differences ranging from -17.2% to +47% and a mean absolute percentage error (MAPE) of approximately 16.9% over the period from 2017 to 2023. Thus, the model provides planners and policymakers with an irrigation decision support system to analyze the impact of agricultural systems, climatic changes and agricultural decision-making processes, thereby facilitating sustainable agricultural irrigation management.","面对持续的气候变化，农业用水信息对政策制定者和规划者至关重要。本研究引入了AIDspat模型，这是一个估算农业灌溉需求（AID）的新框架。该模型将现有的土壤水平衡模型与灌溉调度原理相结合。除了基于位置的气候、土壤和土地利用数据外，模型还考虑了通用的作物特定参数、灌溉技术以及农民的灌溉管理决策。这使得AID能够被客观地确定。该模型支持基于情景的计算，计算成本低，可适用于从田间到国家层面的各种尺度，并涵盖过去和未来的时间段。通过年内动态植物发育整合作物特异性差异，并考虑灌溉技术，增强了模型的准确性和实际应用性。模型捕捉AID时空动态的能力为农业水资源的可持续管理提供了宝贵的见解。该模型在德国南部的一个研究区域进行了校准和验证，显示出模拟值与多种农作物的文献基准高度一致。模拟结果与实测灌溉量吻合良好，2017年至2023年间年度百分比差异范围为-17.2%至+47%，平均绝对百分比误差（MAPE）约为16.9%。因此，该模型为规划者和政策制定者提供了一个灌溉决策支持系统，用于分析农业系统、气候变化和农业决策过程的影响，从而促进可持续的农业灌溉管理。","Agricultural Water Management",75,{"impact":30,"substance":122,"depth":222,"authority":99,"freshness":223,"relevant":33,"comment":224},19,2,"提出AIDspat模型，结合土壤水平衡与灌溉调度，实现时空尺度农业灌溉需水估算，对可持续水资源管理有参考价值。",[226],{"name":219,"url":216},[128,228,229,230],"农业灌溉","水资源管理","模型模拟","10.1016\u002Fj.agwat.2026.110730",{"doi":231,"openalex_id":233,"authors":234,"venue":219,"cited_by_count":43,"oa_url":216,"card":249,"direction":253,"ingested_from":164},"W4412168598",[235,237,239,241,243,246],{"name":236,"orcid":19},"Jacob Jeff Bernhardt",{"name":238,"orcid":19},"Frank Potts",{"name":240,"orcid":19},"Jan Bug",{"name":242,"orcid":19},"Maximilian Zinnbauer",{"name":244,"orcid":245},"Max Eysholdt","https:\u002F\u002Forcid.org\u002F0000-0001-9761-2991",{"name":247,"orcid":248},"Jochen Hack","https:\u002F\u002Forcid.org\u002F0000-0002-8060-7990",{"tldr":250,"method":251,"finding":252,"direction":253,"opportunity":254},"提出AIDspat模型，结合土壤水平衡与灌溉调度，估算时空农业灌溉需求。","结合土壤水平衡模型与灌溉调度原理，考虑气候、土壤、作物参数及灌溉管理决策。","模型在德国南部验证良好，MAPE约16.9%，能捕捉灌溉需求的时空动态。","农业人工智能与决策模型","可扩展至不同气候区，集成遥感数据或优化灌溉管理决策，提升模型适用性。","2026-09-09T23:30:46.078172Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":19,"source_name":262,"source_url":259,"published_at":22,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":220,"score_detail":263,"sources":265,"tags":267,"view_count":43,"doi":272,"paper":273,"created_at":294},1991,"Plant Wearable Sensors: A Comparative Review of Invasive and Non-Invasive Approaches for Real-Time Plant Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16181937","Plant wearable sensors have emerged as a transformative technology for precision agriculture and plant phenotyping, enabling in situ, real-time, and continuous acquisition of physiological signals from plant surfaces or internal tissues. However, existing reviews have organized the literature by monitoring targets, sensing functions, or material platforms, without systematically comparing technologies from the fundamental dimension of the degree of intervention imposed on plants. Drawing on representative studies identified through a structured literature search, this review establishes a three-tier classification framework—invasive, minimally invasive, and non-invasive—and conducts a head-to-head comparison across six dimensions: signal characteristics, plant disturbance, long-term stability, manufacturing complexity, field deployability, and biosafety. The results reveal that invasive sensors (nanobionic probes, implantable microelectrodes, and organic electrochemical transistors) achieve nM–pM detection limits, yet wound responses generally limit their effective monitoring duration to the order of days; non-invasive sensors (flexible patches, strain sensors, and multimodal platforms) support weeks-to-months of continuous monitoring and are amenable to scaled deployment, but the indirectness of surface signals confines detection limits to the μM level; minimally invasive technologies (microneedle arrays and ultra-thin microelectrodes) offer a compromise between the two extremes. On this basis, a decision framework based on three-layer selection is proposed to guide technology selection across laboratory research, field deployment, and controlled environment agriculture. Future efforts should focus on standardized performance evaluation protocols, biodegradable self-powered systems, and the integration of invasive–non-invasive hybrid sensing networks with plant digital twins.","植物可穿戴传感器已成为精准农业和植物表型分析中的变革性技术，能够在植物表面或内部组织中实现原位、实时和连续的生理信号采集。然而，现有综述多按监测目标、传感功能或材料平台对文献进行归类，尚未从对植物施加干预程度这一基本维度系统比较各类技术。基于结构化文献检索筛选出的代表性研究，本综述建立了侵入式、微侵入式和非侵入式三层分类框架，并从信号特征、植物干扰、长期稳定性、制造复杂度、田间部署能力和生物安全性六个维度进行了头对头比较。结果表明，侵入式传感器（纳米生物探针、植入式微电极和有机电化学晶体管）可实现纳摩尔至皮摩尔级别的检测限，但伤口响应通常将其有效监测时长限制在数天量级；非侵入式传感器（柔性贴片、应变传感和多模态平台）支持数周至数月的连续监测，并适合规模化部署，但表面信号的间接性将检测限限制在微摩尔级别；微侵入式技术（微针阵列和超薄微电极）则在两个极端之间提供了折中方案。在此基础上，提出了基于三层选择的决策框架，以指导实验室研究、田间部署和可控环境农业中的技术选型。未来工作应聚焦于标准化性能评估协议、可生物降解自供电系统，以及侵入式-非侵入式混合传感网络与植物数字孪生的集成。","Agriculture",{"impact":30,"substance":122,"depth":222,"authority":123,"freshness":57,"relevant":33,"comment":264},"系统比较侵入与非侵入式植物可穿戴传感器，提出三层分类框架，对精准农业技术选型有参考价值。",[266],{"name":262,"url":259},[128,268,269,270,271],"精准农业","实时监测","植物传感器","可穿戴设备","10.3390\u002Fagriculture16181937",{"doi":272,"openalex_id":274,"authors":275,"venue":262,"cited_by_count":43,"oa_url":259,"card":289,"direction":162,"ingested_from":164},"W7211935145",[276,278,281,283,285,287],{"name":277,"orcid":19},"Jialiang Zheng",{"name":279,"orcid":280},"Qingmin Pan","https:\u002F\u002Forcid.org\u002F0009-0002-7200-1734",{"name":282,"orcid":19},"Yixue Zhang",{"name":284,"orcid":19},"Chuandong Guo",{"name":286,"orcid":19},"Hanping Mao",{"name":288,"orcid":19},"Xiaodong Zhang",{"tldr":290,"method":291,"finding":292,"direction":192,"opportunity":293},"综述植物可穿戴传感器，按侵入性分类比较，提出选择框架。","结构化文献检索，建立侵入性三级分类，六维度对比。","侵入式灵敏度高但监测短，非侵入式持久但灵敏度低，微创折中。","可研究侵入-非侵入混合传感网络与植物数字孪生集成，及标准化评估协议。","2026-09-09T23:30:19.096117Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":300,"content":19,"source_name":301,"source_url":298,"published_at":94,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":220,"score_detail":302,"sources":305,"tags":307,"view_count":43,"doi":312,"paper":313,"created_at":325},1971,"Can industry 5.0’s human-centric approach fulfill industry 4.0’s unrealized promise to agriculture?","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102551","Agriculture sustains the world's growing population while facing a shrinking workforce increasingly reliant on temporary labor. Following the trajectory of industrial revolutions 1.0 through 3.0, agriculture has continuously adapted and improved its practices to meet rising food demand. Industry 4.0′s data-driven technologies, including artificial intelligence, smart sensors, robots, drones, and automated monitoring and decision-support systems, hold great promises for advancing sustainable agriculture and reducing dependency on human labor. However, the transition to Agriculture 4.0 has proven challenging, and the technologies have not been adopted as widely as expected. Adoption barriers stem from uncertainty about cost-effectiveness, driven by substantial initial investments in new technologies, and from uncertainty about how they integrate with existing workflows, alter operational processes, and affect efficiency, labor costs, and required skill sets, often exceeding farmers' knowledge bases and technical capacities. Recently, the Industry 5.0 framework emerged as an evolution, providing direction toward a human-centric, resilient, and sustainable industry. In this paper, I argue that Human Factors and Ergonomics (HF\u002FE), a scientific discipline concerned with human-system interactions and the application of evidence-based methods to inform human-centered design, has been largely absent from agricultural research and development discourse in Agriculture 4.0. Incorporating HF\u002FE methods into agricultural research and development has the potential to operationalize Industry 5.0′s human-centric vision, offering structured, evidence-based guidance on how to translate policy ambition into practical design and development, an approach that would benefit agricultural livelihoods and economic viability while helping agriculture meet global food demands.","农业支撑着全球不断增长的人口，却面临着日益缩减且愈发依赖临时劳动力的困境。沿循工业革命1.0至3.0的发展轨迹，农业不断调整和改进其生产方式，以满足日益增长的粮食需求。工业4.0时代的数据驱动技术，包括人工智能、智能传感器、机器人、无人机以及自动化监测和决策支持系统，为推进可持续农业、减少对人力的依赖带来了巨大希望。然而，向农业4.0的转型已被证明充满挑战，相关技术的普及程度远未达到预期。采用障碍源于对成本效益的不确定性——这由新技术所需的巨额初始投资驱动——以及对技术如何融入现有工作流程、改变操作过程、影响效率、劳动力成本和所需技能组合的不确定性，这些往往超出了农民的知识基础和技术能力。近期，工业5.0框架应运而生，作为一次演进，为以人为本、富有韧性和可持续的工业指明了方向。本文认为，人因学与工效学（Human Factors and Ergonomics, HF\u002FE）——一门关注人-系统交互、并应用循证方法为以人为本的设计提供依据的科学学科——在农业4.0的农业研究与发展讨论中基本缺席。将人因学与工效学方法纳入农业研究与发展，有望将工业5.0以人为本的愿景付诸实践，为如何将政策雄心转化为实际设计与发展提供结构化、循证的指导，这一路径将惠及农业生计和经济可行性，同时助力农业满足全球粮食需求。","Smart Agricultural Technology",{"impact":30,"substance":122,"depth":30,"authority":123,"freshness":303,"relevant":33,"comment":304},4,"论文深入探讨农业4.0采纳障碍，提出以人因工程落实工业5.0人本理念，视角新颖，对智慧农业推广有参考价值。",[306],{"name":301,"url":298},[128,308,309,310,311],"技术采纳","人因工程","农业4.0","工业5.0","10.1016\u002Fj.atech.2026.102551",{"doi":312,"openalex_id":314,"authors":315,"venue":301,"cited_by_count":43,"oa_url":319,"card":320,"direction":253,"ingested_from":164},"W7210273202",[316],{"name":317,"orcid":318},"Yael Salzer","https:\u002F\u002Forcid.org\u002F0000-0001-9771-0452","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007768\u002Fpdf",{"tldr":321,"method":322,"finding":323,"direction":253,"opportunity":324},"论文主张将人因工程融入农业4.0，以实现工业5.0以人为本的愿景，促进技术采纳。","论述人因工程（HF\u002FE）在农业研发中的缺失，提出将其作为实现工业5.0以人为本的","农业4.0技术采纳受阻，人因工程可提供结构化指导，促进技术应用，提升农业可持续性。","研究人因工程在农业技术设计中的应用，如用户中心设计，可提高技术采纳率，填补农业信息化中人的因素研究空白。","2026-09-09T23:30:03.531577Z",{"id":327,"title":328,"url":329,"summary":330,"summary_zh":331,"content":19,"source_name":332,"source_url":329,"published_at":173,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":333,"score_detail":334,"sources":336,"tags":338,"view_count":43,"doi":342,"paper":343,"created_at":359},2005,"KRISHI.AI: an explainable machine learning framework for data-driven crop recommendation","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-68815-w","Abstract Accurate crop selection is a fundamental determinant of farm productivity under varying soil characteristics and climatic conditions. Most current AI-based advisory systems are confined to predictive outputs or generic recommendations, providing little transparency into the reasoning behind a particular crop suggestion. This paper presents KRISHI.AI (derived from the Hindi word “krishi,”meaning“ agriculture,” and“ AI”for“ artificial intelligence), an agriculture-focused explainable artificial intelligence framework for data-driven crop recommendation. The framework recommends suitable crops from seven agronomic and environmental inputs, namely nitrogen (N), phosphorus (P), potassium (K), soil pH, temperature, humidity, and rainfall. It integrates a LightGBM-based multiclass prediction model with SHAP-based feature attribution and agronomically constrained counterfactual “what-if” analysis to explain why a crop is recommended and how feasible changes in the input conditions may alter the recommendation. The system uses a gradient-boosted tree ensemble (LightGBM) to produce high-confidence crop recommendations across 22 crop classes, achieving 99.39% test accuracy and a macro-averaged F1-score of 0.9900. These predictions are supplemented by SHAP (SHapley Additive exPlanations)-based feature-attribution visualizations and counterfactual “what-if” analysis to explain how input factors influence recommendations and how manipulating field conditions modifies crop suitability. Quantitative evaluation of the counterfactual module across 100 test instances exhibits a validity rate of 94.60%, a mean L1 feature-change distance of 0.18, and a mean of 2.10 features modified per scenario-confirming the practical credibility and sparsity of the generated explanations. These capabilities are delivered through an interactive Streamlit web dashboard that provides intuitive real-time feedback, enabling farmers and agricultural practitioners to explore scenario-based decisions without technical expertise. By integrating high-accuracy prediction with human-focused explainability in a single lightweight framework (4.3 MB model, 187 ms response latency), KRISHI.AI advances accessible and trustworthy decision support for technology-driven agriculture, with particular relevance to agricultural decision-support applications serving India’s smallholder farming community. The 189.3 ms value corresponds to the dedicated ablation profiling configuration, whereas 187 ms represents the representative end-to-end latency of the deployed pipeline reported in the main computational profiling experiment. The reported accuracy should be understood as a benchmark performance rather than as evidence of field-ready predictive reliability, because the uniformly balanced dataset does not capture the measurement noise, missing values, spatial heterogeneity, and seasonal variability present in real-world agricultural environments. However, the explainability pipeline and architectural contributions provide a reproducible basis for transparent agricultural AI.","准确的作物选择是决定不同土壤特性和气候条件下农业生产力的基本因素。当前大多数基于人工智能的咨询系统仅限于预测输出或通用建议，对特定作物建议背后的推理过程缺乏透明度。本文提出了KRISHI.AI（名称源自印地语词汇“krishi”，意为“农业”，以及“AI”，即“人工智能”），一个面向农业的可解释人工智能框架，用于数据驱动的作物推荐。该框架基于七项农艺与环境输入参数推荐适宜作物，即氮（N）、磷（P）、钾（K）、土壤pH值、温度、湿度及降雨量。它集成了基于LightGBM的多分类预测模型、基于SHAP的特征归因分析以及受农艺约束的反事实“假设”分析，以解释为何推荐某种作物，以及输入条件的可行变化如何改变推荐结果。该系统采用梯度提升树集成模型（LightGBM）在22个作物类别中生成高置信度的作物推荐，测试准确率达到99.39%，宏平均F1分数为0.9900。这些预测辅以基于SHAP（Shapley加性解释）的特征归因可视化和反事实“假设”分析，以解释输入因素如何影响推荐结果，以及田间条件的改变如何调整作物适宜性。对反事实模块在100个测试实例上的定量评估显示，其有效性率为94.60%，平均L1特征变化距离为0.18，每个场景平均修改2.10个特征——证实了所生成解释的实际可信度与稀疏性。上述功能通过交互式Streamlit网页仪表板实现，提供直观的实时反馈，使农民和农业从业者无需专业技术知识即可探索基于场景的决策。通过将高精度预测与以人为中心的可解释性整合于一个轻量级框架（模型大小4.3 MB，响应延迟187毫秒）中，KRISHI.AI推动了技术驱动型农业中可获取且可信赖的决策支持，尤其对服务印度小农户社区的农业决策支持应用具有重要价值。其中189.3毫秒对应专门的消融分析配置，而187毫秒则代表标准部署配置下的响应时间。","Scientific Reports",74,{"impact":30,"substance":122,"depth":30,"authority":99,"freshness":223,"relevant":33,"comment":335},"提出可解释的作物推荐框架，结合高精度与透明度，对农业AI落地有参考价值。",[337],{"name":332,"url":329},[128,339,340,341],"农业人工智能","可解释AI","作物推荐","10.1038\u002Fs41598-026-68815-w",{"doi":342,"openalex_id":344,"authors":345,"venue":332,"cited_by_count":43,"oa_url":329,"card":354,"direction":253,"ingested_from":164},"W7211943283",[346,349,351],{"name":347,"orcid":348},"Bhavya Dhingra","https:\u002F\u002Forcid.org\u002F0000-0002-9646-1869",{"name":350,"orcid":19},"Saarang Agarwal",{"name":352,"orcid":353},"Rishi Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1211-7989",{"tldr":355,"method":356,"finding":357,"direction":253,"opportunity":358},"提出可解释AI框架KRISHI.AI，基于土壤和气候数据推荐作物，并解释推荐原因。","LightGBM多分类模型结合SHAP特征归因和反事实分析，部署于Streaml","测试准确率99.39%，F1分数0.99，反事实有效性94.6%，模型轻量响应快。","可探索将可解释AI扩展到更多作物和地区，或集成实时传感器数据以增强动态推荐。","2026-09-09T23:30:37.344594Z",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":19,"source_name":366,"source_url":363,"published_at":22,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":333,"score_detail":367,"sources":370,"tags":372,"view_count":43,"doi":377,"paper":378,"created_at":394},1978,"Agricultural green development policy and agricultural export upgrading: evidence from China’s agricultural product trade","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1914900","How production-side agricultural green development policy affects agricultural exports remains insufficiently understood, particularly across the three distinct trade margins of export value, export volume, and export quality. Using China’s 2017 agricultural green development policy as a policy shock, this study employs panel data for 31 provincial-level regions from 2013 to 2024, measures pre-policy conditions using agricultural sustainability over 2013–2016, and applies a continuous difference-in-differences design to identify heterogeneous provincial export responses to the common policy shock. The results show that, following policy implementation, regions with higher pre-policy agricultural sustainability experienced larger relative increases in export value, export volume, and export quality, indicating that the policy response was not confined to either quality improvement or scale expansion alone. Dynamic estimates show that the positive differential in export quality emerged early in the implementation period, whereas export value and export volume adjusted more gradually. Robustness checks, counterfactual policy-timing tests, random permutation tests, and cluster-aware double machine learning estimates broadly corroborate the core findings. Among the three outcomes, the evidence is most stable for export quality, whereas export volume is estimated with relatively lower precision. Spatial analysis shows positive local direct effects across all three export dimensions. No stable significant cross-province indirect effects are found for export value or export volume, whereas export quality exhibits a statistically significant negative cross-province spatial association. Overall, the export adjustment associated with agricultural green development policy is characterized by multidimensional improvement, asynchronous adjustment, and limited spatial transmission, extending research on agricultural green development from domestic production and resource-environmental performance to international trade outcomes.","关于生产端农业绿色发展政策如何影响农产品出口，现有认知仍显不足，尤其是在出口价值、出口数量和出口质量这三个截然不同的贸易边际维度上。本研究以中国2017年农业绿色发展政策作为政策冲击，采用2013至2024年间31个省级行政区的面板数据，利用2013至2016年的农业可持续性水平度量政策前条件，并运用连续型双重差分设计，识别各省份对共同政策冲击的异质性出口反应。结果表明，政策实施后，政策前农业可持续性水平较高的地区，在出口价值、出口数量和出口质量上均经历了相对更大的增长，说明政策响应并非仅限于质量提升或规模扩张中的单一维度。动态估计显示，出口质量的积极差异在政策实施初期即已显现，而出口价值和出口数量的调整则相对渐进。稳健性检验、反事实政策时点测试、随机置换检验以及考虑聚类效应的双重机器学习估计，均大体印证了核心发现。在三个结果变量中，出口质量的证据最为稳定，而出口数量的估计精度相对较低。空间分析显示，三个出口维度均存在正向的本地直接效应。出口价值和出口数量未发现稳定显著的跨省间接效应，而出口质量则表现出统计上显著的负向跨省空间关联。总体而言，与农业绿色发展政策相关的出口调整呈现出多维改善、异步调整和有限空间传导的特征，将农业绿色发展研究从国内生产及资源环境绩效拓展至国际贸易结果领域。","Frontiers in Sustainable Food Systems",{"impact":97,"substance":122,"depth":30,"authority":368,"freshness":223,"relevant":33,"comment":369},12,"基于中国省级面板数据，实证分析农业绿色发展政策对出口的影响，方法严谨，结论具有政策参考价值。",[371],{"name":366,"url":363},[373,374,375,376],"农业绿色发展","农产品出口","政策评估","国际贸易","10.3389\u002Ffsufs.2026.1914900",{"doi":377,"openalex_id":379,"authors":380,"venue":366,"cited_by_count":43,"oa_url":363,"card":388,"direction":392,"ingested_from":164},"W7211958640",[381,383,386],{"name":382,"orcid":19},"Aijun Yi",{"name":384,"orcid":385},"Jianjie Zhang","https:\u002F\u002Forcid.org\u002F0009-0005-2403-324X",{"name":387,"orcid":19},"Yingqian Xu",{"tldr":389,"method":390,"finding":391,"direction":392,"opportunity":393},"研究农业绿色发展政策对中国农产品出口的影响，发现政策促进出口价值、数量和质量提升。","利用2017年政策冲击，采用连续双重差分和双机器学习等方法分析省级面板数据。","政策实施后，农业可持续性高的地区出口增长更显著，质量提升早于数量和价值。","农业绿色发展与碳","可探索政策对出口影响的微观机制，或研究不同农产品类别及贸易伙伴的异质性效应。","2026-09-09T23:30:07.149388Z",{"id":396,"title":397,"url":398,"summary":399,"summary_zh":400,"content":19,"source_name":301,"source_url":398,"published_at":94,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":333,"score_detail":401,"sources":403,"tags":405,"view_count":43,"doi":408,"paper":409,"created_at":425},1969,"A lightweight domain-specific appearance embedding for Turkey re-identification in dense barn environments","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102549","Individual-level monitoring of poultry in commercial barns requires identity-consistent tracking, which in turn depends on appearance features that can discriminate between visually homogeneous animals. Generic appearance models used in trackers such as DeepSORT are trained on human re-identification data and transfer poorly to livestock, where individuals lack the distinctive clothing and texture cues that drive pedestrian re-identification. In this work we present a lightweight domain-specific Siamese feature extractor for turkey re-identification, designed through a systematic, ablation-driven refinement process and trained with a combined batch-hard triplet and identity-classification objective. The final architecture combines pre-activation residual blocks, squeeze-and-excitation channel attention, and a BNNeck embedding head, and contains only 714K parameters — approximately 3.9 × fewer than the 2.8M-parameter mars-small128 model used by default in DeepSORT. On an open-set evaluation of 16 unseen turkey identities, the proposed embedding raises ROC AUC from 0.890 to 0.943, classification accuracy from 0.849 to 0.911, and the distribution separability index from 1.14 to 1.71, while reducing model storage by 3.8 × and inference latency relative to the baseline. A t-SNE projection of the learned space shows tight, well-separated clusters for the majority of identities, with residual overlap confined to the most visually similar individuals. When integrated into a DeepSORT tracking pipeline and evaluated on three commercial turkey-barn video sequences, the domain-specific embedding yields consistent gains in identity-related tracking metrics (IDF1 81.3 → 83.2%, association accuracy 68.8 → 69.9%). The results demonstrate that careful, domain-specific architectural design can substantially improve re-identification of visually homogeneous animals while simultaneously reducing computational cost. Within the scope of this controlled evaluation, the compact embedding is therefore well suited to resource-constrained edge hardware for continuous precision-livestock monitoring; large-scale field validation remains future work.","商业禽舍中对个体家禽的监测需要身份一致的跟踪，这依赖于能够区分视觉上同质动物的外观特征。DeepSORT等跟踪器中使用的通用外观模型基于行人重识别数据训练，难以有效迁移至牲畜场景，因为个体缺乏驱动行人重识别的独特服装和纹理线索。本研究提出了一种轻量级、面向特定领域的Siamese特征提取器，用于火鸡重识别，通过系统性的消融驱动优化流程设计，并采用批量硬三元组与身份分类联合目标进行训练。最终架构结合了预激活残差块、挤压激励通道注意力及BNNeck嵌入头，仅含71.4万参数——约为DeepSORT默认使用的280万参数mars-small128模型的3.9分之一。在包含16个未见火鸡身份的开放集评估中，所提出的嵌入将ROC AUC从0.890提升至0.943，分类准确率从0.849提升至0.911，分布可分性指数从1.14提升至1.71，同时模型存储量减少3.8倍，推理延迟相对基线降低。学习空间的t-SNE投影显示，大多数身份形成紧密且分离良好的簇，残余重叠仅限于视觉上最相似的个体。当集成至DeepSORT跟踪流程并在三段商业火鸡禽舍视频序列上评估时，该领域特定嵌入在身份相关跟踪指标上取得一致提升（IDF1从81.3%提升至83.2%，关联准确率从68.8%提升至69.9%）。结果表明，精细的领域特定架构设计能显著改善视觉同质动物的重识别性能，同时降低计算成本。在本受控评估范围内，该紧凑嵌入非常适合资源受限的边缘硬件，用于持续精准畜牧业监测；大规模现场验证仍为未来工作方向。",{"impact":30,"substance":122,"depth":30,"authority":368,"freshness":303,"relevant":33,"comment":402},"针对火鸡重识别的轻量级专用模型，显著提升密集养殖环境下的个体追踪精度并降低算力需求，对精准畜牧有实质推进。",[404],{"name":301,"url":398},[128,339,406,407],"边缘计算","畜禽识别","10.1016\u002Fj.atech.2026.102549",{"doi":408,"openalex_id":410,"authors":411,"venue":301,"cited_by_count":43,"oa_url":418,"card":419,"direction":423,"ingested_from":164},"W7210262200",[412,415],{"name":413,"orcid":414},"Debayan Sen","https:\u002F\u002Forcid.org\u002F0009-0009-0069-9548",{"name":416,"orcid":417},"Theo Lutz","https:\u002F\u002Forcid.org\u002F0000-0001-5405-044X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007744\u002Fpdf",{"tldr":420,"method":421,"finding":422,"direction":423,"opportunity":424},"提出轻量级火鸡重识别模型，提升密集圈舍中个体追踪精度并降低计算成本。","基于Siamese网络，结合预激活残差、SE注意力、BNNeck，用三元组和分类","模型参数仅714K，AUC从0.890升至0.943，IDF1提升至83.2%，优于通用模型。","智慧农业 \u002F 农业物联网","可扩展至其他家禽或牲畜，验证大规模现场部署，并探索跨品种泛化能力。","2026-09-09T23:30:03.401167Z",{"id":427,"title":428,"url":429,"summary":430,"summary_zh":431,"content":19,"source_name":432,"source_url":429,"published_at":22,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":433,"score_detail":434,"sources":436,"tags":438,"view_count":43,"doi":441,"paper":442,"created_at":457},1996,"An Interpretable BO-TCBDA Deep Learning Framework for Winter Wheat Yield Estimation Using Multi-Source Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183061","Reliable crop yield estimation is fundamental to food security and efficient agricultural management. However, current deep learning models still face limitations in selecting and integrating multi-source features, and their high predictive accuracy is often accompanied by limited interpretability. This study introduces a Bayesian Optimization–Temporal Convolutional Network–Bidirectional Long Short-Term Memory–Dual Attention (BO-TCBDA) deep learning framework for winter wheat yield estimation. Using Henan Province, China, as the study area, county-level winter wheat yield from 2013 to 2022 was estimated using the Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and climate data. The proposed model was compared with five commonly used machine learning and deep learning models. BO-TCBDA achieved the best performance, with an R2 of 0.823 and an RMSE of 561.26 kg\u002Fha. SIF improved the predictive performance of all models, with statistically significant gains observed in the deep learning models. The dual-attention mechanism provided interpretable insights by revealing relatively balanced contributions among the input features and highlighting the grain-filling stage through temporal attention. Furthermore, SHAP-based cross-validation analysis identified T12, corresponding to the latter part of the jointing stage, as the period with the highest contribution to yield prediction. The model also achieved an R2 of approximately 0.80 about 25 days before harvest. Overall, BO-TCBDA provides an accurate and interpretable approach for county-level winter wheat yield estimation and supports regional food security assessments and precision agriculture.","可靠的作物产量估算对于粮食安全和高效农业管理至关重要。然而，当前的深度学习模型在多源特征的选择与整合方面仍存在局限，且高预测精度往往伴随着有限的可解释性。本研究提出了一种基于贝叶斯优化–时间卷积网络–双向长短期记忆–双重注意力（BO-TCBDA）的深度学习框架，用于冬小麦产量估算。以中国河南省为研究区域，利用增强型植被指数（EVI）、叶面积指数（LAI）、太阳诱导叶绿素荧光（SIF）及气候数据，对2013至2022年县级冬小麦产量进行了估算。将所提模型与五种常用的机器学习和深度学习模型进行了比较。BO-TCBDA取得了最佳性能，其决定系数（R²）为0.823，均方根误差（RMSE）为561.26千克\u002F公顷。SIF提升了所有模型的预测性能，其中在深度学习模型中观察到了统计学上显著的增益。双重注意力机制通过揭示输入特征间相对均衡的贡献，并借助时间注意力突出灌浆期，提供了可解释性的见解。此外，基于SHAP的交叉验证分析识别出T12时段（对应拔节期后期）对产量预测的贡献最大。该模型在收获前约25天时，R²亦达到约0.80。总体而言，BO-TCBDA为县级冬小麦产量估算提供了一种准确且可解释的方法，并支持区域粮食安全评估和精准农业实践。","Remote Sensing",73,{"impact":30,"substance":122,"depth":30,"authority":123,"freshness":223,"relevant":33,"comment":435},"提出可解释的深度学习框架，结合多源遥感数据提升冬小麦估产精度与可解释性，对精准农业有实质贡献。",[437],{"name":432,"url":429},[128,339,131,439,440],"产量估算","冬小麦","10.3390\u002Frs18183061",{"doi":441,"openalex_id":443,"authors":444,"venue":432,"cited_by_count":43,"oa_url":429,"card":452,"direction":162,"ingested_from":164},"W7211938917",[445,447,449],{"name":446,"orcid":19},"Anqi Xue",{"name":448,"orcid":19},"Shufang Tian",{"name":450,"orcid":451},"Tingyan Fu","https:\u002F\u002Forcid.org\u002F0000-0003-4207-4211",{"tldr":453,"method":454,"finding":455,"direction":253,"opportunity":456},"提出BO-TCBDA深度学习框架，融合多源遥感数据估算冬小麦产量，兼具高精度与可解释性。","贝叶斯优化、TCN、BiLSTM、双注意力机制，结合EVI、LAI、SIF及气候","BO-TCBDA性能最优（R²=0.823），SIF提升预测，拔节后期贡献最大，收获前25天可预测。","可探索将双注意力与SHAP结合用于其他作物或区域，或开发实时预警系统，提升模型泛化与实用性。","2026-09-09T23:30:22.411579Z",{"id":459,"title":460,"url":461,"summary":462,"summary_zh":463,"content":19,"source_name":464,"source_url":461,"published_at":22,"category":118,"cover_url":19,"hotness":24,"is_selected":119,"score":433,"score_detail":465,"sources":467,"tags":469,"view_count":43,"doi":473,"paper":474,"created_at":499},1981,"Hydrochemistry and modeling nitrate concentration in farmland groundwater under different hydrological seasons by integrating hybrid quantum-classical ML, virtual sample generation and AlphaEarth Foundation","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fhess-30-5647-2026","Abstract. Precise seasonal prediction of groundwater nitrate concentrations in intensive agricultural areas faces challenges such as data sparsity, strong spatiotemporal heterogeneity, and complex hydro-biogeochemical processes. To address these issues, this study proposes an integrated prediction framework combining hybrid quantum-classical machine learning, advanced virtual sample generation (t-SNE-GMM-KNN), and remote sensing foundation model semantic embedding (AEF). Modeling was conducted across the 2022–2023 normal, dry, and wet seasons in Xiong'an New Area. Hydrochemical types were dominated by Ca-Mg-HCO3−, controlled by mineral dissolution and evaporation. Nitrate concentrations were highest in the dry season (mean 42.93 mg L−1), driven by evaporative concentration. Spatially, high-value zones shifted: southeast (normal), central (dry), and northwest (wet). MixSIAR modeling based on isotopes indicated domestic sewage and livestock manure (74.1 %) as dominant sources. The t-SNE-GMM-KNN strategy mitigated small-sample bias while preserving nonlinear structure. When virtual samples were augmented to 10-fold, the Random Forest R2 in the dry season increased from 0.284 to > 0.85. Furthermore, a hybrid quantum-classical Random Forest exhibited superior robustness for data sparsity, achieving peak performance in the normal season (R2 = 0.962, RMSE = 5.73 mg L−1). Additionally, using only AEF embeddings achieved screening-level accuracy (R2 up to 0.860), providing a feasible rapid survey scheme for extensive unmonitored regions. Correlation analysis identified TDS and EC as persistent top predictors (r > 0.8). This comprehensive framework offers a robust solution for seasonal nitrate prediction and sustainable water management.","摘要：集约化农业区地下水硝酸盐浓度的精准季节预测面临数据稀疏、时空异质性强及水文生物地球化学过程复杂等挑战。针对上述问题，本研究提出了一种集成预测框架，融合了混合量子-经典机器学习、先进虚拟样本生成技术（t-SNE-GMM-KNN）及遥感基础模型语义嵌入（AEF）。研究在雄安新区2022–2023年平水期、枯水期和丰水期开展了建模分析。水化学类型以Ca-Mg-HCO₃⁻为主，受矿物溶解和蒸发作用控制。硝酸盐浓度在枯水期最高（均值42.93 mg L⁻¹），主要受蒸发浓缩作用驱动。空间上，高值区呈迁移特征：平水期位于东南部，枯水期移至中部，丰水期则分布于西北部。基于同位素的MixSIAR模型表明，生活污水和畜禽粪便（74.1%）为主要污染源。t-SNE-GMM-KNN策略在保持非线性结构的同时缓解了小样本偏差；当虚拟样本扩充至10倍时，枯水期随机森林模型的R²从0.284提升至0.85以上。此外，混合量子-经典随机森林模型在数据稀疏条件下表现出优异的鲁棒性，在平水期达到最佳性能（R² = 0.962，RMSE = 5.73 mg L⁻¹）。仅使用AEF嵌入特征即可达到筛选级精度（R²最高达0.860），为广域无监测区域提供了可行的快速调查方案。相关性分析识别出TDS和EC为持续主导预测因子（r > 0.8）。该综合框架为季节性硝酸盐预测和可持续水资源管理提供了稳健的解决方案。","Hydrology and earth system sciences",{"impact":30,"substance":122,"depth":30,"authority":123,"freshness":223,"relevant":33,"comment":466},"研究提出结合量子机器学习与遥感基础模型的农田地下水硝酸盐预测框架，方法新颖，数据详实，对农业面源污染治理有参考价值。",[468],{"name":464,"url":461},[339,470,131,471,472],"机器学习","地下水","硝酸盐","10.5194\u002Fhess-30-5647-2026",{"doi":473,"openalex_id":475,"authors":476,"venue":464,"cited_by_count":43,"oa_url":461,"card":494,"direction":423,"ingested_from":164},"W7126035353",[477,479,482,484,487,489,491],{"name":478,"orcid":19},"Junjie Xu",{"name":480,"orcid":481},"Xin Wei","https:\u002F\u002Forcid.org\u002F0000-0002-5207-6141",{"name":483,"orcid":19},"Yilei Yu",{"name":485,"orcid":486},"Lihu Yang","https:\u002F\u002Forcid.org\u002F0000-0002-4580-4972",{"name":488,"orcid":19},"Yuanzheng Zhai",{"name":490,"orcid":19},"Cuicui Lv",{"name":492,"orcid":493},"Xinzhe Song","https:\u002F\u002Forcid.org\u002F0000-0002-6638-7805",{"tldr":495,"method":496,"finding":497,"direction":253,"opportunity":498},"提出集成混合量子-经典机器学习、虚拟样本生成和遥感基础模型的框架，预测雄安农田地下水硝酸盐浓度。","混合量子-经典随机森林、t-SNE-GMM-KNN虚拟样本、AlphaEarth","干季硝酸盐最高（42.93 mg\u002FL），虚拟样本扩增10倍使R2从0.284升至>0.85，混合模型","可探索将虚拟样本生成与基础模型嵌入结合，应用于其他数据稀疏的农业环境监测，如土壤养分或农药残留预测。","2026-09-09T23:30:08.416152Z"]