[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-09-05":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-09-05","种业振兴成果丰硕，智慧育种引领未来",[7,8,9,10,11,12],"第三十三届北京种业大会开幕，145项自主知识产权成果亮相，AI渗透率达90%以上，发布国内首款番茄育种芯片'京番芯1.0'，打破进口依赖。","云南农科院花卉所培育出1000余个月季新株系，'中国风'月季走红，打破进口依赖，助力'美丽产业'成为'幸福产业'。","万建民院士团队破解水稻抽穗信号传导关键链路，成果发表于《Plant Cell》，为水稻高产稳产提供新基因资源。","国内首个矮秆玉米品种LC6569将过审，株高2.3米，耐密高产，明年推广20万亩，实现全程机械化。","农业农村部公示第二批全国名特优新农产品540个，公示至9月11日；第9批拟批准登记农药产品707个，公示至9月6日。","黑龙江农业数字化转型成效显著，机械化率99.28%，AI、无人机、机器人重塑耕作，粮食产量连续16年全国第一。","本期日报聚焦种业创新与智慧农业，涵盖北京种业大会、番茄芯片发布、矮秆玉米签约等重大产业事件，同时关注水稻抽穗机制、花卉自主育种等科研进展，以及名特优新农产品和农药登记等政策动态，展现农业全领域数智化转型与高质量发展态势。\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。*",[15,95,142,187,245,292,334,424,488,537,570,604,665,726,778],{"id":16,"title":17,"url":18,"summary":19,"summary_zh":20,"content":21,"source_name":22,"source_url":20,"published_at":23,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":27,"score_detail":28,"sources":36,"tags":38,"primary_topic":44,"secondary_topics":50,"classification":51,"search_phrases":90,"slug":93,"view_count":73,"doi":20,"paper":20,"created_at":94},1674,"第三十三届中国北京种业大会 9 月 4 日丰台开幕：145 项自主知识产权成果集中亮相","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7681595568245834250\u002F","第三十三届中国北京种业大会以\"AI 赋能智慧育种数智引领种业未来\"为主题，首次将智慧育种作为主线。大会推出 145 项具有自主知识产权的育种芯片、大模型、表型采集智能设备等成果，育种各环节人工智能渗透率达 90% 以上。在\"基因解码\"板块，50 多款\"京牌\"育种芯片覆盖农作物、畜禽、水产、林果全品类；\"模型演算\"板块展示 30 多款种业大模型，超三分之一由在京单位牵头研发。",null,"2026-09-04 16:34·[光明网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAA9Lz0MeLdJDmqpU26Xi9O_M-cYI9z530wjM7eDKvzZTw\u002F?source=tuwen_detail)\n\n“先在大模型里育一遍种，再让种子走进田间。”正在举行的第三十三届中国北京种业大会上，这样的场景已不再是设想：12款种业大模型集中亮相，全球首款人形具身智能育种机器人“吉儿”2.0正式发布，一款款国产育种芯片宣告关键技术突破……融合生物技术、大数据信息技术、人工智能的智慧育种，正以“看得见、摸得着”的方式加速到来。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F278302eb35ef438e8ac88769770f1073~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171503&x-signature=OoGKiN1uiS46jtDAyF3JCs23ASc%3D)\n本届大会首次以智慧育种为主线，推出145项具有自主知识产权的育种芯片、大模型、表型采集智能设备等成果，育种各环节人工智能渗透率达90%以上，为智慧育种变革提供可复制、可推广的“北京范式”。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F0eef24c9c131bc4a98514a93ff85d258~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171503&x-signature=zFckRUYS9P%2F5YgN7equYQQU67jY%3D)\n“基因解码”板块，50多款“京牌”育种芯片覆盖农作物、畜禽、水产、林果全品类。拉索生物是国内首家实现高密度固相基因芯片自主研发、生产及商业化的企业，打破国外长达20年的技术垄断；博睿迪打造了全球首个基于液相芯片、全面国产化的智慧检测平台；“京科芯6H-60K”芯片填补国内玉米EDV标准化鉴定技术空白，全产业应用度最广；北京市农林科学院育成首款国产化小麦65K固相芯片，技术整体达到国际先进水平。从“一粒芯”的突围中，可以读懂种源自主可控的坚实足印。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F1fd578559d383d18a3441b536959d11b~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171503&x-signature=qzGHUI6Gjru6tClk6G9bFWzSEuQ%3D)\n“模型演算”板块，全国各地陆续涌现的30多款种业大模型中，超三分之一由在京单位牵头研发，涵盖大会首次发布的“神农育种Studio”一体化智能育种工作台、全国首个种业大语言模型“丰登”等。“金种子”智能育种平台已落地多家种业龙头企业，累计支撑400余个优质品种选育，实现节本增效5亿元以上。\n\n育种全链条的智能化，同样离不开“看得见的装备”。在“表型感知”板块，北京市农林科学院农作物表型组大数据工厂可对田间作物开展大规模立体观测，数据采集效率比人工提升成百上千倍；在“场景执行”板块，全球首款人形具身智能育种机器人“吉儿”2.0发布，补齐智能育种终端装备短板，构建“种机协同”全新育种范式。\n\n主分会场联动搭建全场景智慧育种展示平台，135位行业专家围绕玉米、大豆、蔬菜、水产等细分领域分享数智育种实践，全方位展现人工智能赋能种业全产业链变革的全新图景。（光明网记者田新宇宋雅娟肖春芳）\n\n来源：光明网","光明网","2026-09-04T00:00:00Z","报道",10,true,92,{"impact":29,"substance":30,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":35},28,24,18,13,9,1,"全国性种业大会集中展示145项自主知识产权成果，涵盖育种芯片、大模型等关键突破，信息量大且具产业引领价值。",[37],{"name":22,"url":18},[39,40,41,42,43],"农业人工智能","种业振兴","育种芯片","智慧育种","种业大模型",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":56},"classified",false,"2026-10-22.2","rules",{"candidates":57,"agricultural_evidence":26},[58,64,71,78,84],{"code":45,"score":59,"supported":26,"title":60,"abstract":62,"tags":63},12,[61],"种业",[61],[40],{"code":65,"score":66,"supported":26,"title":67,"abstract":68,"tags":70},"I2",4,[],[69],"大模型",[39],{"code":72,"score":73,"supported":26,"title":74,"abstract":75,"tags":77},"C",3,[],[76],"畜禽",[],{"code":79,"score":73,"supported":26,"title":80,"abstract":81,"tags":83},"D",[],[82],"水产",[],{"code":85,"score":86,"supported":53,"title":87,"abstract":88,"tags":89},"F2",2,[],[],[42,41],[91,92],"农业人工智能种业大模型智慧育种种业振兴","农业人工智能种业大模型","农业人工智能种业大模型智慧育种种业振兴-1674","2026-09-05T00:04:37.698542Z",{"id":96,"title":97,"url":98,"summary":99,"summary_zh":20,"content":100,"source_name":101,"source_url":20,"published_at":23,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":102,"score_detail":103,"sources":106,"tags":108,"primary_topic":112,"secondary_topics":118,"classification":119,"search_phrases":137,"slug":140,"view_count":73,"doi":20,"paper":20,"created_at":141},1699,"云南农科院花卉所：让\"美丽产业\"成为\"幸福产业\"——\"中国风\"月季打破进口依赖","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7681829249644331561\u002F","2026 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。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。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。云南省农科院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。","![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Fdaca6cdf321845a0a0829ad715078aab~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171780&x-signature=e2C4o07zmU6JcnB%2BiBL%2FNSfnFyY%3D)\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视频加载中...\n\n**文 |《瞭望》新闻周刊记者**\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 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F5adccd951c93e291388f8be5972d5fb3~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171780&x-signature=HCDBww%2FWA0MZwiPntEaQJVRhrzs%3D)\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 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F756c7752ce34c7d813dea6eed77f703a~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171780&x-signature=PM%2F8LaJFOzKnFrLzJA3ENQBMIRY%3D)\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 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fffe3ac9cfa139ed399d05070321b67b2~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171780&x-signature=FZkb7GAaDIEh7zTtVHQStEHhY7A%3D)\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 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F1e1c3db932e2bf73deebbbe3ed62e4a4~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171780&x-signature=%2BB%2Bdu4MwXWIF2sZhNHs54MCr6eo%3D)\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 6](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fc288133b9a3becc3e70f125eded354d6~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171780&x-signature=wpZmxQh5t5isjQXhQ1UpHNnvuB4%3D)\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 7](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F0f4fdfa1f64104b1fcbc4faaa819a5c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171780&x-signature=prl%2Fc0t8lcZWgl82yQQOhv%2BeYng%3D)\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（采写记者：王长山吉哲鹏熊轩昂胡超）\n\n（《瞭望》2026年第36期）","人民日报",87,{"impact":29,"substance":30,"depth":31,"authority":104,"freshness":86,"relevant":34,"comment":105},15,"央媒深度报道云南花卉全产业链成就，聚焦种业自主创新，数据详实，影响深远。",[107],{"name":101,"url":98},[40,109,110,111],"云南","花卉产业","自主育种",{"code":113,"slug":114,"name":115,"path":116,"parent_code":117},"B4","flowers","花卉与观赏植物","\u002Ftopic\u002Fhorticulture\u002Fflowers","B",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":120},{"candidates":121,"agricultural_evidence":26},[122,129,133],{"code":113,"score":123,"supported":26,"title":124,"abstract":127,"tags":128},23,[125,126],"花卉","月季",[125,126],[110],{"code":45,"score":34,"supported":53,"title":130,"abstract":131,"tags":132},[],[],[40],{"code":85,"score":34,"supported":53,"title":134,"abstract":135,"tags":136},[],[],[111],[138,139],"种业振兴自主育种花卉产业云南","种业振兴自主育种","种业振兴自主育种花卉产业云南-1699","2026-09-05T00:04:39.271457Z",{"id":143,"title":144,"url":145,"summary":146,"summary_zh":20,"content":20,"source_name":147,"source_url":20,"published_at":148,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":102,"score_detail":149,"sources":154,"tags":156,"primary_topic":160,"secondary_topics":166,"classification":167,"search_phrases":182,"slug":185,"view_count":86,"doi":20,"paper":20,"created_at":186},1672,"中国农科院作科所万建民院士团队：破解水稻抽穗信号传导的关键链路","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyjd\u002F202609\u002Ft20260902_d013.html","近日，万建民院士领衔的中国农业科学院作物科学研究所作物功能基因组研究创新团队解析了关键基因介导水稻光周期信号传递、精准调控抽穗期的分子机制。相关成果发表在国际权威期刊《Plant Cell》。该研究为水稻品种适应性改良与高产稳产栽培提供了新的基因资源和理论支撑。","中国农业科学院","2026-09-02T00:00:00Z",{"impact":150,"substance":151,"depth":31,"authority":104,"freshness":152,"relevant":34,"comment":153},25,22,7,"国家级科研团队在权威期刊发表水稻抽穗期调控新机制，为品种改良提供基因资源，影响深远。",[155],{"name":147,"url":145},[40,157,158,159],"水稻育种","基因调控","抽穗期",{"code":161,"slug":162,"name":163,"path":164,"parent_code":165},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":168},{"candidates":169,"agricultural_evidence":26},[170,174,178],{"code":161,"score":59,"supported":26,"title":171,"abstract":172,"tags":173},[163],[163],[157],{"code":45,"score":34,"supported":53,"title":175,"abstract":176,"tags":177},[],[],[40],{"code":85,"score":34,"supported":53,"title":179,"abstract":180,"tags":181},[],[],[158],[183,184],"基因调控水稻育种种业振兴抽穗期","基因调控水稻育种","基因调控水稻育种种业振兴抽穗期-1672","2026-09-05T00:04:37.593488Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":20,"content":192,"source_name":193,"source_url":20,"published_at":23,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":194,"score_detail":195,"sources":198,"tags":200,"primary_topic":203,"secondary_topics":208,"classification":209,"search_phrases":240,"slug":243,"view_count":66,"doi":20,"paper":20,"created_at":244},1676,"北京市农林科学院玉米所：国内首个即将通过国家审定的矮秆玉米品种 LC6569 现场签约","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7681650592251150882","第三十三届中国北京种业大会上，北京市农林科学院玉米研究所首席科学家赵久然带来最新成果矮秆 LC6569 和京科 857D，并与北京联创种业有限公司签署科技成果转化合作协议。LC6569 是国内首个即将通过国家审定的矮秆玉米品种，株高仅 2.3 米左右，比常规玉米矮一大截，却能在每亩 6000 株的高密度下保持大果穗，实现矮秆、耐密、高产、抗倒伏和全程机械化，明年推广面积预计达 20 万亩以上。","一颗玉米种子的育种旅程，曾需要十年光阴，如今在AI的加持下，这个周期正在被缩短一半。9月4日，第三十三届中国北京种业大会在京开幕，AI大模型公开征集、矮秆玉米品种被抢订一空、沙漠小麦烤馕成“爆款”……145项自主知识产权成果集中亮相，育种各环节人工智能渗透率已达90%以上，会场交易额预计突破5亿元。智能育种元年，中国种业正跑出加速度。\n\n**智能育种元年已到来，育种家：“AI大幅缩减育种周期”**\n\n在本届种业大会上，北京市农林科学院玉米研究所首席科学家赵久然带来了最新成果，矮秆LC6569和京科857D，并且现场签署了科技成果转化合作协议。北京市农林科学院玉米所将与北京联创种业有限公司，共同开发LC6569、LP6585两个矮秆玉米品种，以灵活许可模式推动科研成果向市场转化，打通从实验室到田间地头的种业产业化路径。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Fe60dfd44c22143c28f79c5686e4fecfb~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171516&x-signature=WiGxshzcArhqgQHC0F9aTR6TqYU%3D)\n会场展出的矮秆LC6569和京科857D。新京报记者耿子叶摄\n\n赵久然从事玉米科研育种工作已有40年，其团队育成的数百个品种在生产上大面积应用，每年推广应用约6000万亩，累计超6亿亩。本次重点推出的LC6569是国内首个即将通过国家审定的矮秆玉米品种，株高仅2.3米左右，比常规玉米矮一大截，却能在每亩6000株的高密度下保持大果穗，实现矮秆、耐密、高产、抗倒伏和全程机械化。该品种已被头部种业企业列为主推品种，今年的制种早已经被种植大户订购一空，明年推广面积预计达20万亩以上。\n\n据悉，在本届大会成果发布区，35家单位发布了191项新品种、新技术、新设备等成果，通过线下发布、线上直播、短视频宣发相结合的方式，大会重点推介10项关键技术与14个突破性新品种，提升种业创新成果传播影响力。大会成功促成12项合作签约。\n\n该玉米品种的诞生，背后还采用了AI智能育种技术，精准聚合了矮秆大穗、耐密抗倒、宜机收等性状。赵久然表示，“原来培育一个新品种至少需要十年时间，通过AI育种技术，如今的育种周期大幅缩减，将传统育种周期缩短至5～6年。”赵久然还发出了征集令，“目前这套方案已经大幅提升了育种效率，但是我们也想发出邀请，如果有更好更适合育种的AI大模型，请与我们联系，我们将积极测试和使用。”\n\n值得一提的是融合生物技术、大数据信息技术、人工智能的智能育种元年已到来，本届大会邀请业内顶尖专家齐聚交流，聚焦智能育种体系构建，分享基因编辑、智能育种等前沿技术，充分发挥大会技术引领风向标作用，推出145项具有自主知识产权的育种芯片、大模型、表型采集智能设备等，育种各环节人工智能渗透率达90%以上，为智能育种变革提供可复制、可推广的“北京范式”。\n\n**在沙漠丰产的京字号小麦，做成的烤馕颇受欢迎**\n\n在主会场丰台京丰宾馆的京品体验区上，烤馕、吐司面包、油鸡等美食各种香气扑鼻，16家单位带来了近100项以新品种培育的主粮、蔬菜、肉鱼等优质农产品，以及延伸开发的各类特色加工产品，上万名参会者和市民现场品鉴体验“京种京味”产品。\n\n最让参会者挪不动步的，不是展板上的介绍数据，而是展台前杂交小麦“京麦189”磨的面粉制作的烤馕。北京市农林科学院杂交小麦研究所苑少华向记者介绍，这批馕是今天早上现烤的，供不应求，“放出来就没了，已经跟不上大家吃的节奏了”。不断有参观者循着香气聚拢过来，尝过之后都想买，苑少华只好反复解释“只展示不售卖”。有位参观者边吃边评价：“这烤馕味道很正，越嚼越香。”\n\n杂交小麦“京麦189”是我国首批通过国家审定的耐盐碱小麦品种之一，它耐干旱、耐贫瘠、耐盐碱，在中重度盐碱地也能稳产。正是这些“皮实”特性，让它从北京顺义的育种基地出发，一路扎根到新疆喀什。今年麦收季，“京麦189”在塔克拉玛干沙漠改良地块，亩产超过350公斤，同时在新疆喀什地区麦盖提县的一块改良盐碱地实测亩产更是达到768公斤。\n\n在种业大会上一抢而空的烤馕，也道出了京麦189最独特的地方，既能在沙漠盐碱地上高产稳产，磨出的面粉还能烤出地道的馕。农业科技的温度，藏进了观众喜爱的这口馕里。\n\n本次在主会场举办的科技成果交流体验活动上，首次聚焦智能育种，设置了“1核4区”，有京品体验区、成果发布区、重点企业区、交流交易区等，共汇集了北京及国内外138家科研院所、种业企业、技术服务商的621项前沿技术成果。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fec5ba31d64e1311b40c1d98bbb8c80d8~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171516&x-signature=HlwG%2BmZTa4T4Nc6KZiEhqMZ%2BaK8%3D)\n会场展出的农产品。新京报记者耿子叶摄\n\n现场，一款机器人“吉儿”2.0同样引人关注，这也是全球首款具身智能育种机器人，今年的“吉儿”2.0升级有了双手，可以专门为作物牵线搭桥，实现15秒“撮合”一朵花，柱头识别准确率约90%，单次巡航授粉成功率82%以上。“最重要的是，吉儿可以白加黑24小时不间断地工作。”相关负责人介绍，未来还可以在去除老叶、采摘、监测病虫害、施肥等发挥作用，为破解种源“卡脖子”难题提供装备支撑。\n\n**京种再出海，“朋友圈”越来越大**\n\n来自平谷区的北京市华都峪口禽业有限责任公司总经理杜守山，在本届大会上有了新收获。公司与乌兹别克斯坦巴拉卡·哈姆科尔·帕兰达有限责任公司签署蛋鸡种业国际战略合作协议，计划未来三年在乌兹别克斯坦建成6万套祖代鸡产能的项目，项目达产后向中亚五国、俄罗斯、白俄罗斯及阿富汗地区推广约5亿只商品代白羽肉鸡。\n\n京种扬帆出海的脚步加快，俄罗斯联邦国家科学中心渔业与海洋学研究所与北京市农林科学院水产所共建国际合作实验室，达成了渔业与海洋学领域项目研究、人员交流等四项合作，实现鲟鱼种质资源保护、遗传多样性研究与国际可持续贸易监管的协同攻关。\n\n本届大会共邀请联合国粮农组织、植物新品种保护联盟、国际种子联盟、亚太种子协会等国际组织和19个国家的45名外宾，引导本地种业企业走出国门、开拓国际市场，合作伙伴从东南亚、非洲、中南美洲，向北美、中西亚、东欧、西欧拓展。同时，8场论坛密集呈现国际化元素。在论坛上，联合国粮农组织原总干事特别顾问、国际植物保护公约原秘书长夏敬源分享了全球种业发展趋势与中国种业国际化的战略思考，倡导中国品种、中国标准走向世界舞台。\n\n**农企现场洽谈合作，预计会场交易额突破5亿元**\n\n现场一家来自内蒙古的蔬菜企业，其企业推出的盆栽辣椒很新颖。一盆辣椒价格和普通绿植相差无几，但是可以同时满足观赏和采摘的双重价值。“这盆辣椒可观赏，也可以食用，养得好可以达到半年甚至更久。”企业负责人常瑞青向记者介绍，“这些盆栽辣椒，在内蒙古的水果店、花店里可以买得到。目前，尚未进入北京市场，但在这次种业大会上有几家企业也看中了，正在洽谈合作，相信很快北京的市民也可以买到。”\n\n据悉，本届种业大会采用“实物展示＋现场互动体验＋商务洽谈”的形式，推动合作项目和商业订单签约。在主会场“重点企业区”“交易交流区”，先正达、大北农等220家种业企业现场展示最新品种，通过种子交流交易等活动，与潜在客户进行面对面洽谈，开展精准供需对接。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F114727dc63670ace4b8afa1c1f3896db~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171516&x-signature=JUbdAOl%2FQkffmcFrgFjoE5rVPn0%3D)\n种业大会现场。新京报记者耿子叶摄\n\n预计今年会场交易额将远超去年，突破5亿元。此外，会议配套引入金融服务和知识产权服务，推动北京农商银行与北京拉索生物科技有限公司达成1项金融支持种业发展签约，打通资金链、创新链、产业链融合通道。\n\n为了办成“永不落幕”的大会，5月-10月大会因时因地举办10项展示观摩活动，开设“田间会客厅”。截至目前，共展示玉米、小麦、春季蔬菜新优特品种2400余个，在京郊示范种植4000亩，通过专家、市场、消费者多方评选，发布玉米、小麦、蔬菜优新品种166个，吸引来自全国各地的2万余名专业人员看“禾”选种，“京紫糯519”鲜食玉米、“农大918”强筋小麦、“红心蜜”黄瓜、“京番串红1号”番茄等一批新品种受到京津冀种植大户追捧，成为爆款新品种。\n\n新京报记者耿子叶\n\n编辑张树婧校对李立军","新京报",85,{"impact":150,"substance":151,"depth":31,"authority":196,"freshness":33,"relevant":34,"comment":197},11,"报道北京种业大会，聚焦AI育种缩短周期及矮秆玉米品种突破，产业影响大，信息详实。",[199],{"name":193,"url":190},[39,40,201,202],"智能育种","矮秆玉米",{"code":204,"slug":205,"name":206,"path":207,"parent_code":165},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":210},{"candidates":211,"agricultural_evidence":26},[212,216,220,226,232,236],{"code":204,"score":196,"supported":26,"title":213,"abstract":214,"tags":215},[206],[206],[],{"code":45,"score":66,"supported":26,"title":217,"abstract":218,"tags":219},[],[61],[40],{"code":221,"score":73,"supported":26,"title":222,"abstract":223,"tags":225},"F5",[],[224],"机械化",[],{"code":227,"score":73,"supported":26,"title":228,"abstract":229,"tags":231},"G1",[],[230],"成果转化",[],{"code":85,"score":34,"supported":53,"title":233,"abstract":234,"tags":235},[],[],[201],{"code":65,"score":34,"supported":53,"title":237,"abstract":238,"tags":239},[],[],[39],[241,242],"农业人工智能智能育种矮秆玉米种业振兴","农业人工智能智能育种","农业人工智能智能育种矮秆玉米种业振兴-1676","2026-09-05T00:04:37.833878Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":20,"content":20,"source_name":250,"source_url":20,"published_at":251,"category":24,"cover_url":20,"hotness":25,"is_selected":53,"score":252,"score_detail":253,"sources":258,"tags":260,"primary_topic":264,"secondary_topics":269,"classification":270,"search_phrases":287,"slug":290,"view_count":34,"doi":20,"paper":20,"created_at":291},1675,"国内首款番茄育种固相基因芯片\"京番芯 1.0\"发布——结束进口依赖","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20260901A09T2F00","8 月 31 日，北京市通州区国际种业科技园区召开新闻发布会，我国首款具有完全自主知识产权的高精度番茄育种固相基因芯片\"京番芯 1.0\"正式发布。该芯片包含近 5000 个核心位点，芯片检出率达 99.12%，检测重复性达 99.85%，单轮实验可在 72 小时内完成并直接获得基因型数据。芯片由北京通州国际种业科技、北京市农林科学院蔬菜研究所、山东农业大学、苏州拉索生物等单位协同攻关完成。","北京城市副中心报","2026-09-01T00:00:00Z",82,{"impact":29,"substance":30,"depth":254,"authority":255,"freshness":256,"relevant":34,"comment":257},16,8,6,"国内首款番茄育种固相基因芯片发布，打破进口依赖，具有产业级突破意义。",[259],{"name":250,"url":248},[40,261,262,263],"基因芯片","番茄育种","自主创新",{"code":265,"slug":266,"name":267,"path":268,"parent_code":117},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":271},{"candidates":272,"agricultural_evidence":26},[273,279,283],{"code":265,"score":274,"supported":26,"title":275,"abstract":277,"tags":278},14,[276],"番茄",[267,276],[],{"code":45,"score":66,"supported":26,"title":280,"abstract":281,"tags":282},[],[61],[40],{"code":85,"score":34,"supported":53,"title":284,"abstract":285,"tags":286},[],[],[261],[288,289],"基因芯片番茄育种种业振兴自主创新","基因芯片番茄育种","基因芯片番茄育种种业振兴自主创新-1675","2026-09-05T00:04:37.766306Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":20,"content":297,"source_name":298,"source_url":20,"published_at":251,"category":299,"cover_url":20,"hotness":25,"is_selected":26,"score":252,"score_detail":300,"sources":303,"tags":305,"primary_topic":309,"secondary_topics":315,"classification":316,"search_phrases":329,"slug":332,"view_count":86,"doi":20,"paper":20,"created_at":333},1662,"第二批全国名特优新农产品公示：540 个产品拟纳入名录，公示至 9 月 11 日","http:\u002F\u002Fwww.aqsc.org\u002Fmtyx\u002Ftzgg\u002F202609\u002Ft20260901_474171.htm","经县市人民政府批准、县级农业农村部门申请、地市级农业农村部门审核、省级农产品质量安全工作机构审核，农业农村部农产品质量安全中心组织专家技术评审，认定北京市顺义区农业农村局等单位申请的\"顺义草莓\"等 540 个产品符合《全国名特优新农产品管理办法》要求，拟纳入\"全国名特优新农产品\"名录。公示期至 2026 年 9 月 11 日。","��أ��С��׼��ؼ�ũҵũ�岿��롢��м�ũҵũ�岿��ˡ�ʡ��ũ��Ʒ��ȫ��ũ��Ʒ��ˣ�ũҵũ�岿ũ��Ʒ��ȫ��֯ר�Ҽ��϶��˳��ũҵũ��ֵȵ�λ��ġ�˳��ݮ��540��Ʒ��ϡ�ȫ��ũ��Ʒ��취��Ҫ��롰ȫ��ũ��Ʒ��¼��ֽ��Ϣ��Թ�ʾ��\n\n��йص�λ�͸��˶Թ�ʾ��ģ��2026��9��11��ǰʵ��ũҵũ�岿ũ��Ʒ��ȫ��˵��ľ��ݺ��ɡ�\n\n��ͨѶ��ַ��г��223�� ũҵũ�岿ũ��Ʒ��ȫ��ũ��Ʒ��չ��ʱࣺ100020��ϵ�绰��010-59198569��棺010-59198522��\n\n2026��ڶ��ȫ��ũ��Ʒ��¼�ռ��¼��ʾ��Ϣ\n��ʡ��Ʒ��뵥λȫ��ϵ��ϵ��ʽ��ģ��ꡢ��ͷ��ֻ��ȣ��Ҫ��Ӫ��λ\n1��˳��˳��ݮ��˳��ũҵũ��15510189518 65��ũ��ũҵ��޹�˾\n\n ��ֻС��л�ũҵ��ι�˾\n\n ��̰��߲˺��\n\n ��ֵ�ũҵ�Ƽ��޹�˾\n\n ��һ��ũҵרҵ��\n\n ��˳��ũҵ�Ƽ��չ��޹�˾\n2��˳��С��˳��ũҵũ��Ž�13651064966 35��֮��ũҵ�Ƽ��޹�˾\n\n ��ׯԲ��ֲרҵ��\n3��˳��˳�巬��˳��ũҵũ��15510189518 8��̰��߲˺��\n\n ��ʳΪ��̬ũҵ԰��ι�˾\n\n ��ܰ�Ƽ��޹�˾\n\n ��˳��߲��ֲ��޹�˾\n4��ƽ��ƽ��ݮ��в�ƽ��ũҵ��18519686580 200��ũҵ�Ƽ��޹�˾\n\n ��԰��ݮרҵ��\n\n ��ٲ�ݮרҵ��\n\n ��γ�Ե��Ʒרҵ��\n\n ��԰ũҵ�Ƽ��չ��޹�˾\n\n ��ũũҵרҵ��\n\n ��ֲ԰\n\n ��ũҵרҵ��\n\n ��ũ��ι۹��޹�˾\n5��Ʒ��ũҵũ��Ӣ 13716525261 38.2��˶ũ��Ʒ��רҵ��\n\n ��ֲרҵ��\n\n ��ֲרҵ��\n\n ��ɽ��ũ�߲��ֲרҵ��\n\n ��԰ũ��Ʒ��רҵ��\n6��巬��ũҵ��չ��13662118386 100��߹�רҵ��\n\n ��б��߲�רҵ��\n\n ��һ��ũҵ�Ƽ��չ��޹�˾\n\n ��Ӫǿ�帻��ũҵ�Ƽ��޹�˾\n\n ��Դ��̬ũҵ�Ƽ��޹�˾\n7��б��ũҵ��չ��18512241539 30��ˮ��ũ��ֲרҵ��\n\n ��б��ͥũ��\n8��ݮ��м��ɫʳƷ��չ��ѩ÷13652072585 190��к��ݮ��ֲרҵ��\n\n ��м��ͥũ��\n\n ��м��ͥũ��\n9��컨��ɣ��м��ɫʳƷ��չ��ѩ÷13652072585 513.33��м��ɽ��ʷ��ׯ��ɷݾ��ú��\n\n ��м��ɽ��ʷ��ׯ��ɷݾ��ú��\n\n ��м��ԣũ��ֲרҵ��\n10�ӱ�ʡ ƽɽ��ƽɽ�㹽 ƽɽ��ũҵũ��0311-82911135 600��Ԫũҵ�Ƽ�ʯ��ׯ��޹�˾\n\n ƽɽ��ũҵ�Ƽ��޹�˾\n\n ƽɽ�̱��ũҵ�Ƽ��޹�˾\n\n ƽɽ��ӵ��ũҵ��޹�˾\n\n ƽɽ�г�ׯ��ũҵ��޹�˾\n\n ƽɽ�ܼ�ׯ��ũҵ��޹�˾\n\n �ӱ��ũҵ��޹�˾\n11�ӱ�ʡ ʯ��ׯ��¹Ȫ��¹Ȫ�㴻 ʯ��ׯ��¹Ȫ��ũҵũ��15333290378 866.7��ʯ��ׯ��¹Ȫ��ȼ��㴻רҵ��\n\n ʯ��ׯ�йȼ��̬ũҵ��޹�˾\n\n ʯ��ׯ��¹Ȫ��ʢ��ֲרҵ��\n12�ӱ�ʡ��ɽ�в��Ϻ��ɽ�в��ũҵũ��13623156995 12000��ɽ��ͨˮ��Ƽ��޹�˾\n\n ��ɽ�в��ˮ��޹�˾\n13�ӱ�ʡ��ɽ�з��Ϻ��ɽ�з��ũҵũ��÷18330451855 1.5��ͷ��ɽ�з��ׯ��ֳ��\n\n ��ɽ�з��ׯ��ũ�˼�ͥũ��\n14�ӱ�ʡ��ɽ�з��Ϻ��Ӷ�Ϻ��ɽ�з��ũҵũ��÷18330451855 70��к��루��ɽ��ˮ��Ƽ��޹�˾\n\n ��ɽ�з��ʴ�ˮ��ֳ��޹�˾\n15�ӱ�ʡ Ǩ��Ǩ��Ǩ��ũҵũ��¹�17732561255 7000��ɽ��̬ũҵ��޹�˾\n\n ��ɽ��Ѿʵҵ�ɷ��޹�˾\n\n Ǩ��д��羰��ͥũ��\n16�ӱ�ʡ¬��¬��¬��ũҵũ��13833569867 210��¬��ع��ˮ��ֲרҵ��\n\n ¬��һ˳��ֲרҵ��\n17�ӱ�ʡ��к�ɽ��ɽӣ�ҷ��ɽ��ũҵũ��18832071888 110��н�Դũҵ�Ƽ��޹�˾\n\n ��к�ɽ��߲�רҵ��\n\n �ӱ��ũҵ�Ƽ��޹�˾\n18�ӱ�ʡ��ũҵũ��ϼ 15075043667 580��ص�·��ҩ��ֲ��޹�˾\n\n �ӱ��˿�ũҵ�Ƽ��޹�˾\n\n ��ũ��ũҵ�Ƽ��޹�˾\n19�ӱ�ʡ��з��ƽ��з��ũҵ��ƹ��־Ӣ 13603202462 800��о��ʳ�þ��ֲ��޹�˾\n\n ��һ��ʳ�þ��޹�˾\n\n ��з��˶ʳ�þ��ֲרҵ��\n20�ӱ�ʡ��ũҵũ��¬ѩ��15175096107 150��ӱ��˼ũҵ�Ƽ��޹�˾\n\n ��ϼ�ͥũ��\n\n ��˶�̼�ͥũ��\n\n ��ũҵ�Ƽ��޹�˾\n21�ӱ�ʡ��Բ��ũҵũ��˼15932799911 140��߲��ֲרҵ��\n\n ��ũҵרҵ��\n\n ��̩��ͥũ��\n\n ��Ƽ��޹�˾\n22�ӱ�ʡ��ܸ��ũҵũ��˼15932799911 150��̩��ͥũ��\n\n ��ũҵרҵ��\n\n ��߲��ֲרҵ��\n23�ӱ�ʡ��о��ü��ϴ󱤻�Ҭ��ü��ܷ�18631071818 326��ũ��רҵ��\n\n ��ÿ��ʼ�ͥũ��\n\n ��ʱũҵ�Ƽ��޹�˾\n24�ӱ�ʡ��ũҵũ��ΰ 19831969919 6670��̨��ʳƷ��޹�˾\n\n ��ѻ��ʹ��޹�˾\n\n ��ũ��Ʒ�ӹ��\n\n ��ʣ��̨��ҩ��޹�˾\n\n �ӱ��ƿƼ��޹�˾\n\n �ӱ��ׯũҵ�Ƽ��޹�˾\n\n ��ʤ��ʹ��޹�˾\n\n ��ع��̬԰��޹�˾\n25�ӱ�ʡ��ʷ��ũҵũ��ΰ 19831969919 6670��ũ��Ʒ�ӹ��\n\n ��ʤ��ʹ��޹�˾\n\n �ӱ��ׯũҵ�Ƽ��޹�˾\n\n ��ع��̬԰��޹�˾\n\n �ӱ��ƿƼ��޹�˾\n\n ��ѻ��ʹ��޹�˾\n\n ��̨��ʳƷ��޹�˾\n\n ��ʣ��̨��ҩ��޹�˾\n26�ӱ�ʡ��ѿ��ũҵũ��ΰ 19831969919 6670��ع��̬԰��޹�˾\n\n �ӱ��ƿƼ��޹�˾\n\n ��ũ��Ʒ�ӹ��\n\n ��ѻ��ʹ��޹�˾\n\n ��̨��ʳƷ��޹�˾\n\n �ӱ��ׯũҵ�Ƽ��޹�˾\n\n ��ʤ��ʹ��޹�˾\n27�ӱ�ʡ��ũҵũ��ռ��19031929200 440��ɽ��Դ��ɽ��޹�˾\n\n ��ֲũ��רҵ��\n28�ӱ�ʡ��ƽ��ƽ��ƽ��ũҵũ��ˮ��15033777717 350��ӱ��Ƽ��޹�˾\n\n ��ƽʢ��ľ��ũҵ��ι�˾\n\n �ӱ��ҵũҵ��޹�˾\n29�ӱ�ʡ��ƽ��ƽ��ƽ��ũҵũ��ˮ��15033777717 9.3��ͷ��ƽ�ؼ̵�ũҵ��޹�˾\n\n ��ƽ�ذ��̨��רҵ��\n30�ӱ�ʡ�żҿ��ȫ��ȫ��²��żҿ��ȫ��ũҵ��ƹ��Фռ��--�żҿ��ȫ��ũҵũ��ֻ��վ 13784593228 110��żҿ�÷��ֲרҵ��\n\n �żҿھ¸�ũҵרҵ��\n\n �żҿ��ȫ��ֲרҵ��\n31�ӱ�ʡ��ͷ��ͷɣ��ͷ��ũҵũ��ΰ 13833719656 410��ͷ�й�ũ��ͥũ��\n\n ��ͷ��Ƽ��޹�˾\n\n ��ͷ��ֲרҵ��\n\n ��ͷ��ũҵ��չ��޹�˾\n\n �ӱ��˼�ʳƷ��޹�˾\n32�ӱ�ʡ��躵��ݲ��ũҵũ�巢չ��ߺ��15612709789 660��ų�ũ��ֲרҵ��\n\n ��ľ��ֲרҵ��\n33 ɽ��ʡ��ز��ũҵũ��18635702114 560��ֺ��ҩ��ֲ��޹�˾\n\n ɽ��Ƽ��޹�˾\n34 ɽ��ʡ��Զ־��ũҵũ��18635702114 580��ֺ��ҩ��ֲ��޹�˾\n\n ɽ��̬ũҵ�Ƽ��޹�˾\n35 ɽ��ʡ��կ��կ��Ŵ��կ��ũҵũ��¹�3504338512 850��ɽ��ũҵ�ɷ��޹�˾\n\n ɽ��Դũҵ�ɷ��޹�˾\n36 ɽ��ʡ ƫ��ƫ��С��ƫ��ũҵũ��Ѧ��13313502660 2000��ƫ��Ӽӹ�רҵ��\n\n ƫ��ũ��Ʒ��޹�˾\n\n ɽ��ʡƫ��ˮȪ��۾�ũ�ֿƼ��չ��޹�˾\n\n ƫ��ؽ��ũ��ҵ��չ��޹�˾\n37 ɽ��ʡ��ִ�ũҵ��ҵ��չ����15935655187 3000��ʳ��֬��޹�˾\n\n ɽ��ҽʳƷ�Ƽ��޹�˾\n38 ɽ��ʡ�˳��κ��κ��²��˳��κ��߷�չ��13935910098 100��˳��κ��²�רҵ��ֲ��\n\n �˳��κ��׿�߲��ֲ��޹�˾\n39 ɽ��ʡ��ƻ��ũҵũ��߹�ƽ 13935995290 2380��ؽ��ũҵ��չ��޹�˾\n\n ɽ��޹��רҵ��\n40 ɽ��ʡ ƽ½��ƽ½ƻ��ƽ½�ع�ҵ��չ��Ƽ 15035961107 8400��ƽ½�ؽ�ͯ��ҵרҵ��\n\n ɽ��ũҵ�ɷ��޹�˾\n41��ɹ��Ĭ��Ĭ��캣�Ĺ��Ĭ��ũ��Ʒ��ȫ��ʷ�λ�18843181724 100��Ĭ��Ϻ��ֳרҵ��\n\n ��Ĭ��ϲ��ֳ��ͥũ��\n\n ��Ĭ��ũ��רҵ��\n42��ɹ��ͨ��пƶ��ƶ��Ϲ�ͨ��пƶ��ũ��Ʒ��ȫ��13488559876 130��ɹ�ͨ��пƶ��ũ��ֳרҵ��\n\n ͨ��пƶ��ϲ��ֲרҵ��\n43��ɹ��п�ǰ��п�ǰ��п�ǰ��ũ��Ʒ��ȫ��Ʒ��Ӫ��15047765285 1��ֻ��˹�кո��ɫʳƷ��޹�˾\n\n ��п�ǰ��ϣ��̫��רҵ��\n44��ɹ��Ƥ��ũ��δ�ϼ 13947786877 0.5��ͷ��ũ��ҵ��ι�˾\n\n ��ɹŶ�ţ��ҵ��ι�˾\n45��ɹ��ũ��ҵ��չ��˹��һ�㹤��Ա 15248450474 667��ղ��ɻ�ũ��ҵרҵ��\n\n ��ũ��ҵ��޹�˾\n46��ɹ��ţ��ũ��Ʒ��ȫ��13347011660 0.32��ͷ��ɹ��ɽ��ҵ��޹�˾\n\n ��ɹź��ó��޹�˾\n\n ��ΰ��ֳũ��רҵ��\n47��ɹ��ũ��Ʒ��ȫ��13347011660 1.6��ֻ��ɹ��ɽ��ҵ��޹�˾\n\n ��ɹź��ó��޹�˾\n\n ��ΰ��ֳũ��רҵ��\n48��ʡ��ռ��ռ��ũҵũ��ܰԪ 13840399349 220��б�ǿ�߲��ֲרҵ��\n\n ��֣��ֲרҵ��\n49��ʡ��н��ݻ��ҹ�ͷ��ũҵũ��13940808646 630��ּ��ʳƷ�ɷ��޹�˾\n\n ��δ��ʳƷ��޹�˾\n50��ʡ��ũҵũ��13942695588 200��ũҵ��޹�˾\n\n ��ũҵרҵ��\n\n ��֣��ȫ��רҵ��\n51��ʡ�߷��߷��⼦��߷��⼦��ҵЭ��ʯ��13795183181 31000��߷��ʳƷ�ӹ��޹�˾\n\n ��º�ʳƷ��޹�˾\n\n ��һʳƷ�ӹ��޹�˾\n\n �߷��д�ʳƷ��޹�˾\n\n ��ʳƷ��޹�˾\n\n ��м�ʳƷ��޹�˾\n\n ��ةʳƷ��޹�˾\n52��ʡ ׯ��ׯ��ɫ��ׯ��к��չ��15898116312 9000��ˮ��޹�˾\n\n ��ع�Ӧ��޹�˾\n\n ��Ӧ��޹�˾\n53��ʡ��ӣ�ҷ��ũҵũ��Ʒ�13889770788 220��ŵũҵ��޹�˾\n\n ��ũ��С��ֲרҵ��\n54��ʡ��Ǵ��ũҵũ��ܺ�13842233123 1620��ͥ��ֲũ��\n\n ��򷽻��ֲרҵ��\n\n ��д��ҵ��޹�˾\n\n ��к��ͥũ��\n\n ��¡��ʳ�ӹ��\n\n ��ˮ��ֲ��ͥũ��\n55��ʡ��ɽ��ũҵũ����13464533771 6700��Դ��ҵ�ɷ��޹�˾\n\n ��޹�˾\n56��ʡ��ũҵũ����15704157513 240��س��泡\n\n ��Ӣ��Ѽ�̽��̬�Ƽ��ɷ��޹�˾\n57��ʡ��ɽ��ɽ��ɽ��ũҵũ��ۺϷ��ո�13941633178 800��ɽ��ҵ��ӹ��\n\n ��ɫ��ũҵ��չ��޹�˾\n\n ��ɽ�ؽ��Դ��ҵ��޹�˾\n58��ʡ��ɽ��ɽţ��ɽ��ũҵũ��ۺϷ��13384162928 1.8��ͷ��Դ��ҵ��޹�˾\n\n ��ɽ��׳�\n59��ʡ��ͷ��ũҵũ��ۺϷ��ģ��ֲ�ﱣ��ģ��13941611039 103��򱦹��ֲ��幤�̻��\n\n ��̺��ֲũ��幤�̻��\n60��ʡ��ʯ��ʯ��ʯ��ũҵũ��18741700979 208��ʯ��в��̽��Ϲ��ֲũ��\n\n ��ʯ��к�ϼ�߲�ˮ��רҵ��\n61��ʡ��ɹ��ɹ��ũҵũ��13464813166 100��̿��ʳƷ��޹�˾\n\n ��ɭ��̬ũ��޹�˾\n62��ʡ��С��ũҵ��չ��Ȼ 13942139257 720��غ�ͼ��ҵ��޹�˾\n\n ��ػ��˼��عɷ�רҵ��\n\n ��ʳ�մ��޹�˾\n\n ��̹�ũҵ��չ��޹�˾\n63��ʡ��ļ��ũҵ��չ��Ȼ 13942139257 650��ذ�ɽ��Ʒרҵ��\n\n ��عɷ�רҵ��\n64��ʡ��Ʊ��Ʊ��Ʊ��ũ��Ʒ��ȫ��վ��15724675433 550��Ʊ��¡ũ��רҵ��\n\n ��Ʊ�з��ũҵ�Ƽ��չ��޹�˾\n\n ��Ʊ�к��ֲרҵ��\n\n ��Ʊ��ʤũ��ֲרҵ��\n\n ��Ʊ��ũ��ʵҵ��޹�˾\n65��ʡ��Դ��Դ��ݮ��Դ��ũҵ��չ��ģ��Դ��ֲ�ﱣ��վ��Ծ�16604216669 240��Դ��؈��߲��ֲרҵ��\n\n ��Դ��Ұũҵ�Ƽ��޹�˾\n\n ��Դ��ļ�Դ��רҵ��\n66��ʡ��о�̨��̨��о�̨��ũҵũ��18843055578 22533��о�̨��׼��޹�˾\n\n ��ʡ��ʮũҵ�Ƽ��չ��޹�˾\n\n ��ҵ��޹�˾\n\n 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��ؿ��в�ҩ��ֲũ��רҵ��\n\n Į��ɽ��Ʒ��޹�˾\n\n ��˰��Դҩʳֲ��Ƽ��޹�˾\n76��ʡ��˰��˰��ţ��˰��ˮ��15245766600 0.65��ͷ��ƣ��˰��룩��ҵ��޹�˾\n\n ��˰��볣��ũ��޹�˾\n77��ʡ��Ӽ��ũҵũ��18845597017 10��˰��ɭ��ʳƷ��޹�˾\n\n ��ȫ��\n78�Ϻ��ζ��ζ��Ϻ��мζ��ũҵ��ƹ��ຣ��15921886671 50��Ϻ��ũҵרҵ��\n\n �Ϻ��ʳ��רҵ��\n79�Ϻ��ɽ��ɽ��Ϻ��н�ɽ��ũ��רҵ��ϻ��13061699322 30��Ϻ��߲��ֲרҵ��\n\n �Ϻ��Ұ��רҵ��\n\n �Ϻ�˼��߲��ֲרҵ��\n\n �Ϻ��Ӽ�ũҵרҵ��\n\n �Ϻ��ũҵ�Ƽ��չ��޹�˾\n\n �Ϻ��ϣ��רҵ��\n80��ʡ�Ͼ��ˮ��÷�Ͼ��ˮ��ֵ��´��18851622208 300��Ͼ��ұ߿Ƽ�԰��޹�˾\n\n �Ͼ��ũҵ��չ��޹�˾\n81��ʡ�Ͼ��иߴ��Ϫ��Ͼ��иߴ��Ϫ�ֵ��´��13151540888 20��ߴ��Ϫ�򶨲��»��\n\n �Ͼ��иߴ��˾��ҵ��ͥũ��\n82��ʡ��Ϻ��ũҵũ��л�ƾ�13861202003 334��ƽ��ͥũ��\n\n ��ҵ��޹�˾\n\n ��ũ��ֲ��ͥũ��\n\n ��ΤΤ��ͥũ��\n\n ��Ա��ͥũ��\n83��ʡ��⽭��߶��⽭��߶��Դ 18962531908 110��ݻ��̬ũҵ��޹�˾\n\n ��רҵ��\n\n ��Ϝ��̬ũҵ��޹�˾\n84��ʡ��⽭��⽭��⽭��ʢ��С��13913099898 700��˴��ˮ��ֳ��޹�˾\n\n ��ʢ��Ͷ�ʷ�չ��޹�˾\n85��ʡ��ɽ�ײ��ɽ�ֵ��´��18051812126 510��ɽ�̲��޹�˾\n\n ��άĦ��̲��޹�˾\n86��ʡ��÷��С��÷��¹��15506232918 108��÷��רҵ��\n\n ��о�ɳ��ִ�ũҵ�Ƽ��޹�˾\n\n ��н�ũҵ�Ƽ��޹�˾\n87��ʡ��ɽ��ְ��ɽ��ǿ 13358057149 55��Ƽ��޹�˾\n\n ��ɽ��ʳ�þ��޹�˾\n88��ʡ�綫��綫��Ϻ�綫�ض�ϺЭ��ʯ��ɽ 13921693588 20000��ͨ��Ʒ��ֳ��޹�˾\n\n �綫�ؾ��ˮ��ֳרҵ��\n\n �綫�ػԕNˮ��ֳ��糡\n\n ��պ��ˮ��Ƽ��޹�˾\n\n ��ͨ��ũҵ��չ��޹�˾\n89��ʡ��ĸ��ˮ��ѧ��13862817428 1210��к�Զˮ��ֳ��޹�˾\n\n ��޹�˾\n90��ʡ��ϲ��ϲ�Э��ë��13606283101 2000��к�Զˮ��ֳ��޹�˾\n\n ��ͨ��ʳƷ��޹�˾\n91��ʡ��Ƹ��к��Ƹ��к��ܺ��15961399658 933��Ƹ��Ӣ��е��רҵ��\n\n ��ҫ��ͥũ��\n92��ʡ��彭��彭�ֲ��彭��ũҵũ��ˮ��밮��13915117229 675��̬ũ��޹�˾\n\n ��彭��ںϽ��跢չ��޹�˾\n93��ʡ�γ��ζ��ׯ��γ��ζ��ũ��Ʒ��ȫ��ﰮ��15371111127 60��γ��к��η�չ��޹�˾\n\n �γ��ũ��רҵ��\n94��ʡ��Ѽ��ؾ��ۺϷ��15862029533 20��غϵ��ز��޹�˾\n\n ��ֳ��ͥũ��\n95��ʡ��Ӧ��Ӧ��Э��13365133168 107��Ӧ��ᴺ��ũҵ�Ƽ��չ��޹�˾\n\n ��ŵ¿�ũҵ�Ƽ��޹�˾\n\n ��ũҵ�Ƽ��޹�˾\n\n ��Ӧ��ɫũ��Ʒרҵ��\n\n ��Ӧ��Ҷ��ʳ��ͥũ��\n96��ʡ��ũҵũ��13952899656 107��Ʊ��԰\n\n ��ۼ�ͥũ��\n\n ��й�֮Դ��ͥũ��޹�˾\n\n ��֪��̲��ͥũ��\n97��ʡ̩��н��ݮ̩��н��18252616918 103��̩��н��ׯ��ֲרҵ��\n\n ̩��н��·ݮ��ֲרҵ��\n\n ̩��С��Ҷ��̬ũҵ��޹�˾\n98�㽭ʡ��ƽ��ƽ��ƽ��ũҵũ��Ͻ�15258182325 150��ݼ��ũҵ�Ƽ��޹�˾\n\n ��Ƽ��ι�˾\n99�㽭ʡ��ж�ͷ��ͷ��ж�ͷ��ũҵũ��15356529692 3500��β��ả��㽭��޹�˾\n\n ��㵺��ҵ��޹�˾\n\n �㽭��һ��޹�˾\n\n ��¹�ۺ��ҵ��޹�˾\n\n ��ر��ˮ��޹�˾\n100�㽭ʡ��Ϻ��·��Ϻ��·��13967324387 52��·��޹�˾\n\n ��Ϻ��·��ų��ͥũ��\n101�㽭ʡ��ũҵũ��ˮ���18751898629 253.6��ˮ��רҵ��\n\n ��ټ�ͥũ��\n\n ��ִ��̬ũҵ��޹�˾\n102�㽭ʡ��ͨԪ��ͨԪ��ũ��֯��ϻ��С��13706839117 1.5��ͷ��ط��ҵ��ι�˾\n\n ��˻��ũҵ��޹�˾\n\n ��ɽũҵ�Ƽ��޹�˾\n103�㽭ʡ��ε��С��Ϻ��ũҵũ��С��13736832828 800��ֵ��ݼ�ͥũ��\n\n ��ڳ��ʥũ��\n\n ��ͨԪ��ͥũ��\n\n ��ֵ��¸�ũ��\n\n ��Ϻũҵ�Ƽ��޹�˾\n\n ��ν��ͥũ��\n\n ��԰��ͥũ��\n\n ��˫��ũ��\n\n ��ԭ�ֵ�ȫ��ũ��\n\n ��ذ�ʤũ��\n104�㽭ʡ ƽ��ƽ��צ��ƽ��ũҵũ��13615730528 50��ƽ��Ұ��רҵ��\n\n ƽ��к��Դ��רҵ��\n\n ƽ��з��ͥũ��\n105�㽭ʡ��ʳ��ũҵũ��̻�19883983810 1000��ũҵ��չ��ι�˾\n\n ��ж��ܼ�ͥũ��\n\n ��гǶ��ֵ��ʼ�ͥũ��\n\n ��޹�˾\n106�㽭ʡ��ʸ��ũҵũ����13735052796 10��˳ʳƷ��޹�˾\n\n ��ʵ¶�רҵ��\n107�㽭ʡ��ɽ��ɽ��ɽ��ũҵũ��Ҷ��ΰ 13506701649 12��ͷ�㽭��ҵ��޹�˾\n\n ��ɽ��Զ��ֳ��\n108�㽭ʡ��ɽ��ɽ�׶��Ƽ��ɽ��ũҵũ��Ҷ��ΰ 13506701649 120��ɽ��޹�˾\n\n ��ɽ��ַ�ũҵ��޹�˾\n\n ��ɽ�п�Դ��޹�˾\n\n ��ɽ��鶼��ͥũ��\n109�㽭ʡ��ɽ��ɽ�󻱷��ɽ��ũҵũ��Ҷ��ΰ 13506701649 10.8��Ⱥ�㽭��ɽ��Ʒ�ɷ��޹�˾\n\n �㽭��ɽ��ҵ��޹�˾\n\n �㽭��͵��Ƽ��޹�˾\n\n ��ͬ��÷��Ʒ��ɽ��޹�˾\n\n �㽭ǧ��Ʒ��޹�˾\n\n �㽭��ɽ�з�Ե��ҵ��޹�˾\n110��򺣷��ũҵũ��13486028335 50��ׯ�нֵ��ٹϹ��߲��ʾ��\n\n ��򺣼�Բũ��\n\n ��򺣽��԰�߹�רҵ��\n\n ��ʱ��̬ũҵ��޹�˾\n\n ��̬ũ��\n111��ɽ��ɽ��ũҵũ����13732122926 110��ɽ��ũ��ͥũ��\n\n ��ɽ��ƽ��ͥũ��\n\n ��ɽ��߼�ͥũ��\n\n ��ɽ��ͥũ��\n112��ˮ��ҵЭ��ϲ�13806822068 100��غ��԰ˮ��רҵ��\n\n ��һ�о��ͥũ��\n\n ��ɽˮ��רҵ��\n\n ��һ��׼��רҵ��\n113��ʡ��ũҵũ��19156529186 136��ս��ˮ��ֳ��޹�˾\n\n �Ϸ��Դ��ֲ��ͥũ��\n\n �Ϸ�̫��ũҵ�Ƽ��޹�˾\n\n ��̫��ũ��ֲ��ͥũ��幤�̻��\n114��ʡ�ߺ��𯽭��𯽭��޷�ߺ��𯽭��ũҵũ��13655597519 500��𯽭��ż�ͥũ��\n\n �ߺ��ΰ��ũ��Ʒ��רҵ��\n\n �ߺ��«��߲�רҵ��\n\n �ߺ��н��߲��ֲרҵ��\n\n �ߺ��ũҵ��չרҵ��\n115��ʡ��ƾ��ũҵũ��̳�18949515399 350��ذ��ҵ��ι�˾\n\n ��̬ũҵ��޹�˾\n\n ��μ��ҩ��ֲרҵ��\n116��ʡ��ʷԺ��ũҵũ��ˮ��ΰ 19855483496 66��д��߲�ũ��רҵ��\n\n ��ʷԺάǿ��ͥũ��\n117��ʡ��ë��߲˲�ҵ��չ�ٽ��17375064978 533��߲˹Ϲ��ֲ��ͥũ��\n\n ��ɽ��ũҵ�Ƽ��޹�˾\n\n ��غ��ũҵ��չ��޹�˾\n\n ��ɽ��԰ũҵ��չ�ɷ��޹�˾\n\n ��Դũҵ�Ƽ��ɷ��޹�˾\n\n ��ũҵ�Ƽ��޹�˾\n\n ��ʡ��̬ũҵ��޹�˾\n118��ʡ��Ϻ��ũҵũ��ղ��ģ��ũҵũ��ָ߼�ũ��ʦ 18255564560 2670��ƺ��ˮ��ֲ��ͥũ��\n\n ��ƺ��ŷ��ֳ��ͥũ��\n\n ��ֳרҵ��\n\n ��ƺ�ʤ��ˮ��ֲ��ͥũ��\n\n ��ش��ˮ��Ʒ��ֳרҵ��\n\n ��ľҲˮ��ֲרҵ��\n\n ��ˮ��ֲ��ͥũ��\n\n ��ؽ�֦��ֲ��ͥũ��\n\n ��ƺ��ֳ��ͥũ��\n\n ��س��Ϻ��ֳרҵ��\n119��ʡ�Ϫ��ϪС��Ϫ��ũҵũ��ľ�15256132834 16000��Ϫ��ֲ��ͥũ��\n\n ��˫��ҵ��ι�˾\n120��ʡ Ǳɽ��Ǳɽɣ��Ǳɽ��ũҵũ��13955672230 150��ũҵ��չ�ɷ��޹�˾\n\n Ǳɽ��Ұ��м�ͥũ��\n\n Ǳɽ�й��ͥũ��\n121��ʡ Ǳɽ��Ǳɽ��ź Ǳɽ��ũҵũ��13955672230 200��Ǳɽ��ԭ��ź��ֲרҵ��\n\n Ǳɽ��ͥũ��\n\n Ǳɽ��ͥũ��\n\n Ǳɽ��ͥũ��\n122��ʡ̫��̫��̫��ũҵũ��13966939123 1417.7��̫��ش�Ұ��̬��ι�˾\n\n ̫��ع��ҵ��޹�˾\n\n ��ʡ��ũҵ�Ƽ��ɷ��޹�˾\n\n ��ò��޹�˾\n\n ̫��ͷɽũҵ��չ��޹�˾\n\n ̫��̴��ҵ��չ��ι�˾\n\n ̫��̻�԰�ּ�ͥũ��\n123��ʡ��ܲ��ũҵũ��13955975528 550��ɽ��ܲ�ҵ�ɷݹ�˾\n\n ��޷��ͥũ��\n\n ��ɽԴ��Ų�ҵ��޹�˾\n124��ʡ��Ѽ��ũҵũ����13505500400 100��ع��Ҽ�ͥũ��\n\n ��غ��̬ũҵ��޹�˾\n\n ��ؽ��־��ʳƷ��޹�˾\n125��ʡ��Զ��Զ��Զ��ũҵũ��־��18055097410 5��ͷ��Զ��\n\n ��Զ��ɫ��ֳרҵ��\n126��ʡ��Ǿ��ũҵũ��19166115658 190��ʡ��ũҵ��չ��޹�˾\n\n ��н��̬ũҵ�Ƽ��޹�˾\n\n ��־��ֲ��ͥũ��\n127��ʡ��н��н��ũ��Ʒ��»۾�18056418126 110��ɫ��̬ũҵ��޹�˾\n\n ��һ�԰��̬ũҵ��޹�˾\n\n ��ɽ��̬ũҵ�Ƽ��޹�˾\n128��ʡ��н��𰲷��н��ũ��Ʒ��»۾�18056418126 117��ũ��ҵ��޹�˾\n\n ��н��ľ��ϸڴ��԰��ͥũ��\n\n ��н��ũҵרҵ��\n129��ʡ��ԣ��ԣ��ԣ��ũ��Ʒ��ȫ��ʿ��18919796650 330��кͳ�ũҵ��չ��޹�˾\n\n ��޹�˾\n130��ʡ��к��ͷ��к��ũҵũ��18259060691 1583��к��Ʒũҵ��չרҵ��\n\n ��к��߷�ˮ��ֳ��\n131��ʡ��Ϫ��Ϫ��ɽ��Ϫ��ũҵũ��ˮ��÷15280590068 112��Ϫ�ؽ�ľ��̬ũ��\n\n ��Ϫ��Ƕ��ͥũ��\n\n ��Ϫ��֮Դ��רҵ��\n132��ʡ��Ϫ��Ϫ��Ϫ��ũҵũ��־13860530257 2320��ʡ��Ϫ�ع��˲�ҵ��޹�˾\n\n ��Ϫ��Ƹ��ҵ��޹�˾\n\n ��Ϫ�ر�Ҷܰ��Ҷרҵ��\n\n ��Ϫ��Ҷרҵ��\n\n ��Ϫ��Ҷרҵ��\n\n ��Ҷ��޹�˾\n\n ��Ҷ��޹�˾\n133��ʡ��ԭ��ũҵũ��Ϲ��18960526266 2.6��Ⱥ��ر�̨ɽ�з��ֳũ��רҵ��\n\n ��ԴҰ��̬ũ�ַ�չ��޹�˾\n\n ��۷�԰��ֳ��ͥũ��\n134��ʡ��Ϫ��Ϫɽ��Ϫ��ũҵũ��13850732891 660��Ϫ�ذ��Ϲ��Ͳ�רҵ��\n\n ��Ϫ�ظ��Դ�Ͳ��ֲ��\n135��ʡ��Ϫ��Ϫ��ɽ��Ϫ��ũҵũ��13850732891 700��˫�Ų�ҵ��޹�˾\n\n ��Ϫ��ҵ��޹�˾\n\n ��ʡ��Ϫ�ؽ","农业农村部农产品质量安全中心","政策",{"impact":150,"substance":301,"depth":104,"authority":104,"freshness":152,"relevant":34,"comment":302},20,"全国性农产品名录公示，涉及540个产品，影响广泛，信息具体，权威性高。",[304],{"name":298,"url":295},[306,307,308],"农产品质量安全","名特优新农产品","全国公示",{"code":310,"slug":311,"name":312,"path":313,"parent_code":314},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":317},{"candidates":318,"agricultural_evidence":26},[319,324],{"code":310,"score":66,"supported":26,"title":320,"abstract":321,"tags":323},[],[322],"农产品质量",[306],{"code":265,"score":73,"supported":26,"title":325,"abstract":326,"tags":328},[],[327],"草莓",[],[330,331],"农产品质量安全名特优新农产品全国公示","农产品质量安全名特优新农产品","农产品质量安全名特优新农产品全国公示-1662","2026-09-05T00:04:35.580909Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":20,"source_name":340,"source_url":337,"published_at":341,"category":342,"cover_url":20,"hotness":25,"is_selected":53,"score":252,"score_detail":343,"sources":345,"tags":347,"primary_topic":350,"secondary_topics":356,"classification":357,"search_phrases":384,"slug":387,"view_count":73,"doi":388,"paper":389,"created_at":423},1635,"Invisible Threats, Relentless Hunters: Biosurveillance of Airborne Plant Pathogens","https:\u002F\u002Fdoi.org\u002F10.1146\u002Fannurev-phyto-011325-093123","Airborne dispersal enables plant pathogens to travel across fields, regions, and continents, fueling rapid epidemics and emerging disease threats. Biosurveillance, the systematic monitoring of airborne inoculum, offers the opportunity to detect pathogens before symptoms appear and informs timely, risk-based management. Recent advances in air sampling, molecular diagnostics, metagenomics, and imaging technologies have expanded the scale and resolution of pathogen monitoring, from single-species qPCR assays to community-level aerobiome surveys. Integration of biosurveillance data with decision-support systems, remote sensing, and artificial intelligence is transforming early-warning capabilities and providing novel insights into pathogen ecology, evolution, and fungicide resistance. Yet major challenges remain, including assay standardization, data interpretation, and translation into actionable tools for growers. This review synthesizes current approaches, highlights case studies in which biosurveillance has advanced disease management, and outlines future directions toward coordinated surveillance networks and precision agriculture applications.","空气传播使植物病原体能够跨越田间、区域乃至大陆进行扩散，从而引发快速蔓延的流行病和新兴病害威胁。生物监测（Biosurveillance），即对空气传播接种体的系统性监测，为在症状出现前检测病原体提供了可能，并支持基于风险的及时管理决策。近年来，空气采样、分子诊断、宏基因组学及成像技术的进步，已将病原体监测的规模与分辨率从单一物种的qPCR检测拓展至群落层面的空气微生物组调查。将生物监测数据与决策支持系统、遥感技术及人工智能相结合，正不断提升早期预警能力，并为病原体生态学、进化及杀菌剂抗性提供新的见解。然而，重大挑战依然存在，包括检测方法的标准化、数据解读以及将其转化为种植者可操作的工具。本综述综合了当前的研究方法，重点介绍了生物监测推动病害管理的典型案例，并展望了迈向协同监测网络与精准农业应用的未来方向。","Annual Review of Phytopathology","2026-09-03T00:00:00Z","论文",{"impact":301,"substance":151,"depth":31,"authority":104,"freshness":152,"relevant":34,"comment":344},"系统综述气传病害生物监测技术进展，对智慧植保与精准农业有重要参考价值。",[346],{"name":340,"url":337},[39,348,349],"植物病害","生物监测",{"code":351,"slug":352,"name":353,"path":354,"parent_code":355},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":358},{"candidates":359,"agricultural_evidence":26},[360,367,373,378],{"code":361,"score":256,"supported":26,"title":362,"abstract":363,"tags":366},"I1",[],[364,365],"精准农业","precision agriculture",[],{"code":351,"score":256,"supported":26,"title":368,"abstract":369,"tags":372},[],[370,371],"遥感","remote sensing",[],{"code":65,"score":66,"supported":26,"title":374,"abstract":375,"tags":377},[],[376],"artificial intelligence",[39],{"code":379,"score":73,"supported":26,"title":380,"abstract":381,"tags":383},"I7",[],[382],"研究方法",[],[385,386],"农业人工智能植物病害生物监测","农业人工智能植物病害","农业人工智能植物病害生物监测-1635","10.1146\u002Fannurev-phyto-011325-093123",{"doi":388,"openalex_id":390,"authors":391,"venue":340,"cited_by_count":414,"oa_url":337,"card":415,"direction":421,"ingested_from":422},"W7207561022",[392,394,396,398,400,402,404,406,408,410,412],{"name":393,"orcid":20},"L. M. Quesada-Ocampo",{"name":395,"orcid":20},"T. Miles",{"name":397,"orcid":20},"M. Prieto-Torres",{"name":399,"orcid":20},"M. Chilvers",{"name":401,"orcid":20},"S. G. Crandall",{"name":403,"orcid":20},"D. Gent",{"name":405,"orcid":20},"K.M. Gold",{"name":407,"orcid":20},"L. Heger",{"name":409,"orcid":20},"M. Kudenov",{"name":411,"orcid":20},"R. P. Naegele",{"name":413,"orcid":20},"L. Xiang",0,{"tldr":416,"method":417,"finding":418,"direction":419,"opportunity":420},"综述空气传播植物病原体的生物监测技术及其在病害预警中的应用。","综述空气采样、分子诊断、宏基因组学及成像技术，结合决策支持系统、遥感和AI。","生物监测可提前检测病原体，但标准化、数据解读和实用工具转化仍是挑战。","智慧农业 \u002F 农业物联网","可研究将生物监测数据与遥感、AI结合，开发标准化、可操作的病害预警系统，并探索病原体传播的生态学机制。","农业遥感与作物表型","openalex","2026-09-04T23:30:31.964985Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":20,"content":429,"source_name":430,"source_url":20,"published_at":341,"category":24,"cover_url":20,"hotness":25,"is_selected":53,"score":431,"score_detail":432,"sources":434,"tags":436,"primary_topic":440,"secondary_topics":445,"classification":446,"search_phrases":483,"slug":486,"view_count":66,"doi":20,"paper":20,"created_at":487},1706,"AI, Drones, Robots Reshape Farming in Heilongjiang, China's Grain Heartland（CGTN 9-3 报道）","https:\u002F\u002Fnews.cgtn.com\u002Fnews\u002F2026-09-03\u002FAI-drones-robots-reshape-farming-in-China-s-grain-heartland-1Q8bEeCDD7a\u002Fp.html","CGTN 报道黑龙江农业数字化转型。在北大荒信息公司，一面墙大小的数字地图追踪约 320 万公顷农田的状况。土壤条件、作物生长和拖拉机运动数据在屏幕上更新。黑龙江省耕种收综合机械化率达 99.28%，已连续 16 年粮食总产量位居全国第一。激光除草机器人在田间作业可清除 95% 以上的杂草，作物损伤低于 0.1%；电动驱动精量播种机比传统机械式播种机减少种子损伤、作业速度快约 50%。\"十五五\"期间黑龙江将发展农业机器人和智能农业装备产业。","Inside Beidahuang Information Company in Harbin, capital of northeast China's Heilongjiang Province, a wall-sized digital map tracks conditions across some 3.2 million hectares of farmland.\n\nData on soil conditions, crop growth and tractor movements update across the screen, while satellite remote sensing, the Internet of Things and AI are being integrated into different stages of farming.\n\nIt is one example of how agriculture is changing in Heilongjiang, China's top grain-producing province.\n\nHeilongjiang has ranked first in the country in total grain output for 16 consecutive years. The comprehensive mechanization rate for plowing, sowing and harvesting has reached 99.28%.\n\nNow, AI, drones, remote sensing and smarter machinery are pushing that highly mechanized system into a more digital phase.\n\n![Image 1: A worker assembles agricultural machinery at Heilongjiang Dewo Technology Development Co., Ltd. in Harbin, Heilongjiang Province, China, September 1, 2026. \u002FVCG](https:\u002F\u002Fnews.cgtn.com\u002Fnews\u002F2026-09-03\u002FAI-drones-robots-reshape-farming-in-China-s-grain-heartland-1Q8bEeCDD7a\u002Fimg\u002F42432a4bc73b42df9e400419652e7664\u002F42432a4bc73b42df9e400419652e7664.jpeg)\n\nA worker assembles agricultural machinery at Heilongjiang Dewo Technology Development Co., Ltd. in Harbin, Heilongjiang Province, China, September 1, 2026. \u002FVCG\n\nA worker assembles agricultural machinery at Heilongjiang Dewo Technology Development Co., Ltd. in Harbin, Heilongjiang Province, China, September 1, 2026. \u002FVCG\n\nFarming by data\n\nWang Hao, deputy general manager of Beidahuang Information Company's market operation center, said a farmer inspecting crops on foot could previously cover only about 200 mu, or roughly 13 hectares, a day.\n\nDrones and remote sensing can now monitor much larger areas and allow crop data across the monitored farmland to be updated every five days, he said.\n\nThe company's AI model for cold-region crops analyzes soil, weather and crop-growth data to generate planting plans and recommendations for variable-rate fertilization, which allows fertilizer use to be adjusted according to conditions in different parts of a field.\n\nBut scaling up agricultural AI remains challenging.\n\nWang said AI models require large amounts of local data and repeated testing because soil, weather and other environmental conditions can vary significantly from place to place.\n\nDigital technology is also being used farther along the production chain.\n\nIn Wuchang, a major rice-producing area in Harbin, robots at Qiaofu Dayuan Agricultural Co., Ltd. stack packaged rice for shipment. QR codes on the products provide information including where the rice was produced and its nutritional content.\n\nSmarter machines\n\nOther technologies are changing the machinery working directly on farmland.\n\nAt a planting base in Heilongjiang, a four-wheel laser-weeding robot moves between crop rows. Multispectral cameras and sensors scan the ground, while algorithms distinguish crops from weeds and direct laser beams at the unwanted plants.\n\nThe system offers a chemical-free alternative to herbicides while reducing reliance on manual weeding.\n\nLi Yongwei, deputy general manager of Harbin Huagong Zhiyun Technology Co., Ltd., said the technology can remove more than 95% of weeds while keeping crop damage below 0.1%. He said it can also cut weeding costs by up to 70% compared with manual labor.\n\nSeeders are becoming more intelligent as well.\n\nElectric-drive precision seeders produced by Heilongjiang Dewo Technology Development Co., Ltd. allow operators to monitor and adjust air pressure and seed spacing in real time through smart terminals.\n\nDu Mujun, the company's chief engineer, said the system can reduce seed damage and operate about 50% faster than conventional mechanical seeders.\n\n![Image 2: The 25th Heilongjiang Agricultural Machinery Exhibition opens in Harbin, Heilongjiang Province, China, March 14, 2026. \u002FVCG](https:\u002F\u002Fnews.cgtn.com\u002Fnews\u002F2026-09-03\u002FAI-drones-robots-reshape-farming-in-China-s-grain-heartland-1Q8bEeCDD7a\u002Fimg\u002F87095d0d3fda456b868a8a85cb0c2682\u002F87095d0d3fda456b868a8a85cb0c2682.jpeg)\n\nThe 25th Heilongjiang Agricultural Machinery Exhibition opens in Harbin, Heilongjiang Province, China, March 14, 2026. \u002FVCG\n\nThe 25th Heilongjiang Agricultural Machinery Exhibition opens in Harbin, Heilongjiang Province, China, March 14, 2026. \u002FVCG\n\nBeyond mechanization\n\nHeilongjiang produced a record 82 million tonnes of grain in 2025, marking its 22nd consecutive bumper harvest. The province also had 676,000 intelligent agricultural machines that year, ranking first nationwide.\n\nDuring the 15th Five-Year Plan period from 2026 to 2030, Heilongjiang plans to develop industries including agricultural robots and intelligent farming equipment.\n\nConstruction began in August on a national pilot base for AI applications in crop planting, aimed at tackling common technical bottlenecks and developing technologies that can be deployed on a larger scale.\n\nThe shift is part of China's broader push for agricultural modernization. National plans call for wider use of technologies including drones, the Internet of Things and robots in agriculture and rural areas.\n\nFor Heilongjiang, where mechanization of plowing, sowing and harvesting is already above 99%, the focus is increasingly shifting from simply mechanizing farm work to making equipment and agricultural decisions more intelligent.\n\nHu Jinyou, a professor at China Agricultural University, said smart agriculture is still being refined, but deeper integration between AI and agriculture is an \"irreversible trend.\"\n\nAs a new generation of better-educated farmers enters the sector, digital management is likely to become increasingly common, he added.\n\n(With input from Xinhua)","CGTN",81,{"impact":150,"substance":301,"depth":254,"authority":32,"freshness":152,"relevant":34,"comment":433},"报道黑龙江智慧农业综合进展，含AI、激光除草等具体案例，具产业参考价值。",[435],{"name":430,"url":427},[437,39,438,439,364],"智慧农业","农业机器人","黑龙江",{"code":441,"slug":442,"name":443,"path":444,"parent_code":49},"F6","agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":447},{"candidates":448,"agricultural_evidence":26},[449,453,459,465,469,475,479],{"code":441,"score":66,"supported":26,"title":450,"abstract":451,"tags":452},[],[438],[438],{"code":454,"score":73,"supported":26,"title":455,"abstract":456,"tags":458},"A7",[],[457],"间作",[],{"code":460,"score":73,"supported":26,"title":461,"abstract":462,"tags":464},"F4",[],[463],"除草",[],{"code":221,"score":73,"supported":26,"title":466,"abstract":467,"tags":468},[],[224],[],{"code":470,"score":73,"supported":26,"title":471,"abstract":472,"tags":474},"H2",[],[473],"土壤",[],{"code":361,"score":86,"supported":53,"title":476,"abstract":477,"tags":478},[],[],[437,364],{"code":65,"score":34,"supported":53,"title":480,"abstract":481,"tags":482},[],[],[39],[484,485],"农业人工智能农业机器人智慧农业精准农业","农业人工智能农业机器人","农业人工智能农业机器人智慧农业精准农业-1706","2026-09-05T00:04:40.911477Z",{"id":489,"title":490,"url":491,"summary":492,"summary_zh":20,"content":493,"source_name":494,"source_url":20,"published_at":341,"category":24,"cover_url":20,"hotness":25,"is_selected":53,"score":495,"score_detail":496,"sources":498,"tags":500,"primary_topic":505,"secondary_topics":509,"classification":510,"search_phrases":532,"slug":535,"view_count":86,"doi":20,"paper":20,"created_at":536},1697,"2026 全国种子双交会 9 月 10 日合肥启幕——首次独立设立政策解读专场","https:\u002F\u002Fapi.app.anhuinews.com\u002Fpc\u002Fcontent\u002F9552973.html","第十八届中国国际种业博览会暨第二十三届全国种子信息交流与产品交易会将于 9 月 10 日至 12 日在合肥滨湖国际会展中心举办。本届国家级种业盛会以\"数智强芯促突破全链振兴筑粮安\"为主题，首次单独开设政策解读专场；首次发布\"十四五\"育种攻关成果 90 项；首次设立\"数智种业与生物育种展区\"，崖州湾国家实验室、中国科学院、拉索生物等 15 家科研单位和科创企业将集中展示 AI 育种大模型、育种机器人、国产基因芯片等前沿成果。","9月3日上午，记者从合肥市政府新闻发布会获悉，2026第十八届中国国际种业博览会暨第二十三届全国种子信息交流与产品交易会（以下简称“全国种子双交会”），将于9月10日至12日在合肥滨湖国际会展中心举办。本届国家级种业盛会以“数智强芯促突破全链振兴筑粮安”为主题，恰逢种业振兴攻坚关键之年，展会推出多项首创举措，聚焦AI育种、生物育种前沿领域，汇聚全行业资源助力种业创新与粮食安全。\n\n**首次独立设立政策解读专场**\n\n本届种子双交会首次单独开设政策解读专场，将围绕“十五五”种业发展总体考虑、种质资源进出口管理、育种攻关任务目标、企业扶优政策、生物育种产业化示范及市场监管新规六大议题进行系统阐释，为行业提供清晰的政策指引和发展方向。\n\n**首次发布“**十四五**”育种攻关成果90项**\n\n信息发布板块将首次发布“十四五”育种攻关成果90项，涵盖突破性品种选育、育种技术创新、骨干亲本创制等领域；同时发布2025年度重点农作物品种信息、2026年国家农作物新品种核心展示评价结果，以及种子供需形势和质量形势报告。\n\n**首设“**数智种业与生物育种展区**”，AI育种产业化**\n\n技术展览方面，今年首次设立“数智种业与生物育种展区”，崖州湾国家实验室、中国科学院、拉索生物等15家科研单位和科创企业将集中展示AI育种大模型、育种机器人、国产基因芯片、智能育种加速器等前沿成果。配套举办的“人工智能＋种业”对接活动，将围绕高通量表型组学、全基因组选择等场景分享实践案例，推动AI技术从概念验证走向规模化产业应用。\n\n**首次设立“**美好生活的种子**”展区**\n\n本届种子双交会首次单独开设政策解读专场首次设立“美好生活的种子”展区，集中展示“十四五”育种联合攻关成果、国家农作物优良品种推广目录推介品种及安徽地方特色品种。现场设置“好种好味好生活”食味品鉴活动，市民可现场体验优质稻米、鲜食玉米、特色蔬菜等品类，直观感受种业创新从田间到餐桌的变化。\n\n**443家单位参展，规模创新高**\n\n从参展规模看，本届种子双交会已确定参展单位443家，其中国家种业阵型企业29家、育繁推一体化企业80家。预计参会企业约3000家，观展人数达2万人，参展各类农作物新品种和新技术新装备将达4000多种（项）。室内展览面积2.3万平方米，折合标准展位700余个。\n\n**安徽种业强省建设取得阶段性成效**\n\n安徽省农业农村厅副厅长潘鑫介绍，全省现有国家农作物种业阵型企业5家、育繁推一体化企业11家，种子年销售额117亿元，均位居全国第一方阵。2025年全省水稻种子出口1900万公斤，连续7年居全国首位。荃银高科、国投丰乐、安徽袁粮3家企业进入全国商品种子销售总额20强。\n\n目前展会各项筹备工作正有序推进。组委会将坚持廉洁办展、节约办展、绿色办展理念，全力打造高质量行业盛会。","中安在线",80,{"impact":150,"substance":151,"depth":254,"authority":25,"freshness":152,"relevant":34,"comment":497},"全国性种业盛会，首次设立政策解读专场和AI育种展区，信息量大，对行业有指导意义。",[499],{"name":494,"url":491},[40,501,502,503,504],"AI育种","生物育种","合肥","政策解读",{"code":49,"slug":506,"name":507,"path":508,"parent_code":20},"inputs-equipment","农业投入品与装备","\u002Ftopic\u002Finputs-equipment",[],{"status":511,"locked":53,"version":54,"method":55,"evidence":512},"partial",{"candidates":513,"agricultural_evidence":26,"reason":531},[514,519,523,527],{"code":515,"score":59,"supported":26,"title":516,"abstract":517,"tags":518},"J1",[504],[504],[504],{"code":45,"score":66,"supported":26,"title":520,"abstract":521,"tags":522},[],[61],[40],{"code":85,"score":66,"supported":26,"title":524,"abstract":525,"tags":526},[],[502],[502],{"code":65,"score":73,"supported":26,"title":528,"abstract":529,"tags":530},[],[69],[],"一级领域明确，二级方向需要细分",[533,534],"政策解读生物育种种业振兴 AI育种","政策解读生物育种","政策解读生物育种种业振兴AI育种-1697","2026-09-05T00:04:39.145524Z",{"id":538,"title":539,"url":540,"summary":541,"summary_zh":20,"content":542,"source_name":543,"source_url":20,"published_at":544,"category":299,"cover_url":20,"hotness":25,"is_selected":53,"score":495,"score_detail":545,"sources":548,"tags":550,"primary_topic":20,"secondary_topics":554,"classification":555,"search_phrases":565,"slug":568,"view_count":86,"doi":20,"paper":20,"created_at":569},1663,"第 9 批拟批准登记农药产品公示：707 个农药产品 + 137 个登记变更产品","http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Fdetail\u002F33020","根据《行政许可法》《农药管理条例》有关规定，现将第十届全国农药登记评审委员会第 36 次执行委员会议审议通过的申请登记的 707 个农药产品、137 个登记变更农药产品相关信息予以公示。公示时间：2026 年 8 月 31 日至 9 月 6 日。如对公示内容有异议，请在公示期内以传真或电子邮件形式实名反馈。","[English](http:\u002F\u002Fwww.chinapesticide.org.cn\u002Feng\u002Findex)[办公平台](http:\u002F\u002Foa.icama.org.cn\u002Fseeyon\u002Findex.jsp)[政务邮箱![Image 2: 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web-logo](http:\u002F\u002Fw.icama.cn\u002Fnyupload\u002Fimage\u002F202301\u002Fc4656dee-8927-4c04-9f68-d0aa98bdb1ab.png)](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Fdetail\u002F33020#)\n\n搜索\n\n[中文版](http:\u002F\u002Fwww.chinapesticide.org.cn\u002Fzwb\u002Findex)[农药科学与管理](http:\u002F\u002Fwww.chinapesticide.org.cn\u002Fkgls\u002Findex)[English](http:\u002F\u002Fwww.chinapesticide.org.cn\u002Feng\u002Findex)\n\n[首页](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Findex)[药检系统](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FdrugTest)[新闻动态](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Fnews)[信息公开](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FinfoPublic)[服务大厅](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FserviceHall)[专题栏目](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Ftopic)[数据中心](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FdataCenter)[数字平台](https:\u002F\u002Fwww.icama.cn\u002F)[农药科学与管理](http:\u002F\u002Fw.icama.cn\u002Fkgls\u002Findex)\n\n[首页](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Findex)[药检系统](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FdrugTest)[新闻动态](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Fnews)[信息公开](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FinfoPublic)[服务大厅](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FserviceHall)[专题栏目](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Ftopic)[数据中心](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FdataCenter)[服务平台](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FservicePlatform)[数字平台](https:\u002F\u002Fwww.icama.cn\u002F)[农药科学与管理](http:\u002F\u002Fw.icama.cn\u002Fkgls\u002Findex)\n\n![Image 4](http:\u002F\u002Fw.icama.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Fm-nav-icon.png)\n\n搜索\n\n当前位置：[信息公开](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FnewsType\u002F5)[公示公开](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FnewsType\u002F61)\n\n# 2026年第9批拟批准登记农药产品公示\n\n发布时间：2026-08-31 来源：农业农村部农药检定所作者：综合审评处浏览次数：4197\n\n农业农村部农药检定所**_2026-08-31_****_![Image 5](http:\u002F\u002Fw.icama.cn\u002Fresources\u002Fweb\u002Fimages\u002Fdetail\u002Ficon-gkl.png)_ _4197_**\n\n根据《行政许可法》《农药管理条例》有关规定，现将第十届全国农药登记评审委员会第 36次执行委员会议审议通过的申请登记的 707 个农药产品、137 个登记变更农药产品相关信息予以公示。如对公示内容有异议，请在公示期内以传真或电子邮件形式实名反馈（包含反馈人姓名、所在单位及联系方式）。\n\n公示时间：2026 年 8 月 31 日至 2026 年 9 月 6 日。\n\n联系单位：农业农村部农药检定所综审处（北京市朝阳区麦子店街 22号楼，邮编：100125）。\n\n传真：010－59194063\n\n电子信箱：icamags＠agri.gov.cn。\n\n附件：\n\n1. 拟批准登记农药产品名单\n\n2. 拟批准登记变更农药产品名单\n\n农业农村部农药检定所\n\n2026 年 8 月 31 日\n\n附件:\n* [拟批准登记农药产品名单](http:\u002F\u002Fw.icama.cn\u002Fnyupload\u002Ffile\u002F202608\u002F7357ae5d-62af-4fc5-bd67-51b776faebf3.xlsx)\n\n* [拟批准登记变更农药产品名单](http:\u002F\u002Fw.icama.cn\u002Fnyupload\u002Ffile\u002F202608\u002F5398a0ba-3415-42b6-9ec5-f466a76f45b1.xlsx)\n\n8\n\n#### [微信扫一扫：分享 ![Image 6](blob:http:\u002F\u002Flocalhost\u002F6f849addb8525a48a71f67f791931ed0) 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中国政府网](http:\u002F\u002Fw.icama.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002F%E4%B8%AD%E5%9B%BD%E6%94%BF%E5%BA%9C%E7%BD%91.png)](http:\u002F\u002Fwww.gov.cn\u002F)[![Image 8: 中华人民共和国农业农村部](http:\u002F\u002Fw.icama.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Fpng_mar.png)](http:\u002F\u002Fwww.moa.gov.cn\u002F)\n\n[网站声明](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002Fwebsite_statement)|[关于我们](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FdrugTest)|[联系我们](http:\u002F\u002Fw.icama.cn\u002Fzwb\u002FdrugTest?contact=1)\n\n[![Image 9: 事业单位](http:\u002F\u002Fw.icama.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Ficon_sydy.png)](javascript:void(0))\n* 主办单位：农业农村部农药检定所\n* 网站管理：农业农村部农药检定所药情信息处 [备案序号：京ICP备18011029号-1](javascript:void(0))\n* 地址：北京市朝阳区麦子店22号邮编：100125 电话：010-59194018\n* [京公网安备 11010502043033号![Image 10: 京公网安备 11010502043033号](http:\u002F\u002Fw.icama.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Ficon_beiab.png)](javascript:void(0))\n* 网站保留所有权, 未经允许不得复制, 镜像\n\n* ![Image 11: 中国农药查询(安卓版)](http:\u002F\u002Fw.icama.cn\u002Fnyupload\u002Fimage\u002F202212\u002Fb82d3d6d-5398-42e8-a70d-16fb23414579.png)中国农药查询(安卓版)\n* ![Image 12: 微语农药(公众号)](http:\u002F\u002Fw.icama.cn\u002Fnyupload\u002Fimage\u002F202212\u002F0828b460-066d-4348-808b-53859ccf02b8.jpg)微语农药(公众号)\n\n![Image 13: 回到顶部](http:\u002F\u002Fw.icama.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Ftop.png)回到顶部","中国农药信息网·农业农村部农药检定所","2026-08-31T00:00:00Z",{"impact":150,"substance":301,"depth":104,"authority":104,"freshness":546,"relevant":34,"comment":547},5,"全国性农药登记公示，涉及707个产品，对农资行业影响广泛，信息权威且具体。",[549],{"name":543,"url":540},[551,552,553],"农药登记","产品公示","行业监管",[],{"status":556,"locked":53,"version":54,"method":55,"evidence":557},"needs_review",{"candidates":558,"agricultural_evidence":53,"reason":564},[559],{"code":460,"score":59,"supported":26,"title":560,"abstract":562,"tags":563},[561],"农药",[561],[551],"农业相关性需要复核",[566,567],"产品公示农药登记行业监管","产品公示农药登记","产品公示农药登记行业监管-1663","2026-09-05T00:04:35.626149Z",{"id":571,"title":572,"url":573,"summary":574,"summary_zh":20,"content":20,"source_name":575,"source_url":20,"published_at":576,"category":24,"cover_url":20,"hotness":25,"is_selected":53,"score":495,"score_detail":577,"sources":579,"tags":581,"primary_topic":20,"secondary_topics":585,"classification":586,"search_phrases":599,"slug":602,"view_count":86,"doi":20,"paper":20,"created_at":603},1660,"农民日报 9 月 5 日头版：数智兴村护家底——浙江 2025 年 918 个村集体总收入达 9.42 亿元","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260905\u002F20260905_1\u002Fnmrb_20260905_13397_1_2095987668286541841.html","农民日报头版报道浙江乡村治理数字化探索。2025 年全市 918 个村集体总收入达 9.42 亿元，303 个村依靠混合所有制模式新增经营性收入超 6000 万元，实现严监管与促发展双向发力。从\"墙上贴公告\"到\"云端晒家底\"，从\"跑腿签字审批\"到\"指尖办理事务\"，大数据正在为乡村治理注入澎湃动能。报道强调乡村治理现代化既要善用数字工具，也要筑牢制度根基，充分激活群众主体力量。","农民日报头版","2026-09-04T22:00:00Z",{"impact":151,"substance":301,"depth":254,"authority":274,"freshness":255,"relevant":34,"comment":578},"央媒头版报道浙江数字乡村治理成效，数据详实，具有省级示范意义。",[580],{"name":575,"url":573},[582,583,584],"数字乡村","乡村治理","集体经济",[],{"status":556,"locked":53,"version":54,"method":55,"evidence":587},{"candidates":588,"agricultural_evidence":53,"reason":564},[589,594],{"code":590,"score":66,"supported":26,"title":591,"abstract":592,"tags":593},"G5",[],[583],[583],{"code":595,"score":34,"supported":53,"title":596,"abstract":597,"tags":598},"I6",[],[],[582],[600,601],"乡村治理数字乡村集体经济","乡村治理数字乡村","乡村治理数字乡村集体经济-1660","2026-09-05T00:04:35.435736Z",{"id":605,"title":606,"url":607,"summary":608,"summary_zh":609,"content":20,"source_name":610,"source_url":607,"published_at":341,"category":342,"cover_url":20,"hotness":25,"is_selected":53,"score":495,"score_detail":611,"sources":613,"tags":615,"primary_topic":619,"secondary_topics":620,"classification":621,"search_phrases":640,"slug":643,"view_count":86,"doi":644,"paper":645,"created_at":664},1640,"A Biome-Specific Light Use Efficiency Model for Spatiotemporal Dynamics of GPP Across Europe Using PROBA-V and Sentinel-3 FAPAR","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18172981","Estimates of gross primary production (GPP) derived from satellite remote sensing are crucial for assessing the terrestrial carbon dynamics. While model comparison is important, existing GPP products rely on a limited number of satellite sensors. In this study, the contemporary fraction of absorbed photosynthetically active radiation product derived from PROBA-V and Sentinel-3 was used to develop a new GPP product (EU-GPP) based on a light use efficiency (LUE) model for the European continent. EU-GPP accounted for the distinct responses of biomes to temperature and water stresses by incorporating biome-specific environmental scalars. Evaluation against eddy covariance GPP and model comparison against other LUE-based GPP products demonstrated the high model accuracy of EU-GPP within Europe. The model also responded well to the drought-induced stress, indicating its ability to capture interannual variability and the impact of extreme weather events. EU-GPP highlighted increasing GPP trends in croplands during 2014–2023, underlining the recent advancements in agricultural management practices. The Mediterranean forest ecosystems exhibited weakening GPP, suggesting the strong adverse impact of summer droughts on these ecosystems. This study presents an LUE-based GPP product based on novel remote sensing data and provides an independent perspective for the monitoring of GPP across the European continent.","基于卫星遥感估算的总初级生产力（GPP）对于评估陆地碳动态至关重要。尽管模型对比具有重要意义，但现有GPP产品依赖于数量有限的卫星传感器。本研究利用由PROBA-V和Sentinel-3卫星获取的当代光合有效辐射吸收比例产品，基于光能利用率（LUE）模型，为欧洲大陆开发了一套新的GPP产品（EU-GPP）。EU-GPP通过纳入生物群落特定的环境标量，考虑了不同生物群落对温度和水胁迫的差异化响应。通过与涡度协方差GPP数据的验证以及与其他基于LUE的GPP产品的模型对比，证明了EU-GPP在欧洲范围内具有较高的模型精度。该模型对干旱引发的胁迫也表现出良好的响应，表明其能够捕捉年际变率及极端天气事件的影响。EU-GPP揭示了2014—2023年间农田GPP呈上升趋势，凸显了近年来农业管理实践的进步。地中海森林生态系统则表现出GPP减弱，表明夏季干旱对这些生态系统产生了强烈的不利影响。本研究基于新型遥感数据提出了一套基于LUE的GPP产品，为监测欧洲大陆GPP提供了独立视角。","Remote Sensing",{"impact":31,"substance":151,"depth":301,"authority":32,"freshness":152,"relevant":34,"comment":612},"基于新型卫星数据构建欧洲GPP产品，方法新颖且验证充分，对农业生态监测有参考价值。",[614],{"name":610,"url":607},[370,616,617,618],"农业生态","碳循环","欧洲",{"code":351,"slug":352,"name":353,"path":354,"parent_code":355},[],{"status":52,"locked":53,"version":54,"method":55,"evidence":622},{"candidates":623,"agricultural_evidence":26},[624,628,635],{"code":351,"score":152,"supported":26,"title":625,"abstract":626,"tags":627},[],[370,371],[370],{"code":629,"score":256,"supported":26,"title":630,"abstract":631,"tags":634},"H7",[],[632,633],"干旱","drought",[],{"code":636,"score":34,"supported":53,"title":637,"abstract":638,"tags":639},"H6",[],[],[616],[641,642],"农业生态碳循环欧洲遥感","农业生态碳循环","农业生态碳循环欧洲遥感-1640","10.3390\u002Frs18172981",{"doi":644,"openalex_id":646,"authors":647,"venue":610,"cited_by_count":414,"oa_url":607,"card":659,"direction":421,"ingested_from":422},"W7207753730",[648,650,653,656],{"name":649,"orcid":20},"Mingyuan Zhang",{"name":651,"orcid":652},"Lanhui Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4353-1739",{"name":654,"orcid":655},"Sadegh Jamali","https:\u002F\u002Forcid.org\u002F0000-0002-0961-9497",{"name":657,"orcid":658},"Torbern Tagesson","https:\u002F\u002Forcid.org\u002F0000-0003-3011-1775",{"tldr":660,"method":661,"finding":662,"direction":421,"opportunity":663},"利用PROBA-V和Sentinel-3的FAPAR数据，开发了欧洲生物群系特异的LUE模型EU-G","基于光能利用率模型，结合生物群系特异的环境标量，使用PROBA-V和Sentin","EU-GPP精度高，能捕捉干旱胁迫，显示农田GPP增加，地中海森林GPP减弱。","可延伸至中国区域，结合国产卫星数据，构建高分辨率GPP产品，并探索管理措施对农田生产力的影响。","2026-09-04T23:30:32.316178Z",{"id":666,"title":667,"url":668,"summary":669,"summary_zh":670,"content":20,"source_name":671,"source_url":668,"published_at":23,"category":342,"cover_url":20,"hotness":25,"is_selected":53,"score":495,"score_detail":672,"sources":674,"tags":676,"primary_topic":678,"secondary_topics":683,"classification":684,"search_phrases":703,"slug":706,"view_count":34,"doi":707,"paper":708,"created_at":725},1595,"Site-specific mechanical weeding robot with automatic self-adjustable individually controlled cultivator","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10445-3","Abstract Context Weeds significantly reduce crop productivity and profitability, particularly in sugar beet production where losses exceed $1.25 billion annually in the United States. Limitations of herbicide-based control and drawbacks of conventional tillage highlight the need for efficient, sustainable alternatives. Aims To develop and evaluate an autonomous site-specific mechanical weeding (SSMW) system with adaptive control for precise inter-row weed removal under field conditions. Methods An SSMW system was designed by integrating an inter-row tillage mechanism with a robotic platform using RTK-GPS for navigation and site-specific actuation. Site-specific weeding was performed in the field test and a strain-based feedback control system was implemented in computer simulation to enable ASIC tillage under uneven terrain. Simulation studies based on field data were conducted to validate control performance, followed by field experiments to assess system effectiveness. Key Results The SSMW system’s efficacy was found to be 96.5% by tillage length. Conclusion The SSMW system achieved precise site-specific weeding using GPS-based control of individual sweeps, with a weeding efficacy of 96.5% based on tillage length. The ASIC framework with sensor feedback simulation showed the feasibility of improved adaptability to uneven terrain, with future work focusing on enhanced sensing and faster actuation for improved field performance. Implications and Impacts This study contributes to precision agriculture by enabling targeted weed management through intelligent sensing and control, reducing energy use and soil disruption. The system offers a sustainable alternative for organic farming, improving operational efficiency and environmental stewardship.","摘要背景杂草显著降低作物产量和经济效益，尤其是在甜菜生产中，美国每年因此造成的损失超过12.5亿美元。基于除草剂的防治方法存在局限性，而传统耕作方式也有弊端，因此亟需高效、可持续的替代方案。目的开发并评估一种具有自适应控制能力的自主位点特异性机械除草（SSMW）系统，用于田间条件下精确的行间杂草清除。方法设计了一套SSMW系统，将行间耕作机构与机器人平台集成，利用RTK-GPS实现导航和位点特异性驱动。田间试验中实施了位点特异性除草，并在计算机仿真中引入基于应变的反馈控制系统，以实现不平整地形下的自适应智能位点特异性耕作（ASIC）。基于田间数据的仿真研究用于验证控制性能，随后开展田间试验评估系统有效性。关键结果 SSMW系统按耕作长度计算的除草效率为96.5%。结论 SSMW系统通过基于GPS的单个锄铲控制实现了精确的位点特异性除草，按耕作长度计算的除草效率为96.5%。带有传感器反馈仿真的ASIC框架展示了在不平整地形下提高适应性的可行性，未来工作将聚焦于增强感知能力和加快驱动响应，以提升田间表现。影响与意义本研究通过智能感知与控制实现靶向杂草管理，减少能源消耗和土壤扰动，为精准农业做出贡献。该系统为有机农业提供了可持续的替代方案，提高了作业效率并促进了环境可持续性。","Precision Agriculture",{"impact":31,"substance":151,"depth":31,"authority":274,"freshness":255,"relevant":34,"comment":673},"论文提出基于GPS与自适应控制的间作除草机器人，田间除草效率达96.5%，为精准除草提供可持续方案，具有较高专业价值。",[675],{"name":671,"url":668},[437,438,364,677],"除草技术",{"code":470,"slug":679,"name":680,"path":681,"parent_code":682},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":685},{"candidates":686,"agricultural_evidence":26},[687,691,695,699],{"code":361,"score":255,"supported":26,"title":688,"abstract":689,"tags":690},[],[364,365],[437,364],{"code":470,"score":256,"supported":26,"title":692,"abstract":693,"tags":694},[],[473,679],[],{"code":460,"score":73,"supported":26,"title":696,"abstract":697,"tags":698},[],[463],[],{"code":441,"score":34,"supported":53,"title":700,"abstract":701,"tags":702},[],[],[438],[704,705],"农业机器人智慧农业精准农业除草技术","农业机器人智慧农业","农业机器人智慧农业精准农业除草技术-1595","10.1007\u002Fs11119-026-10445-3",{"doi":707,"openalex_id":709,"authors":710,"venue":671,"cited_by_count":414,"oa_url":668,"card":720,"direction":419,"ingested_from":422},"W7207873251",[711,713,715,717],{"name":712,"orcid":20},"Shafi Md. Istiak",{"name":714,"orcid":20},"Mohammad Aftabi Talami",{"name":716,"orcid":20},"James Y. Kim",{"name":718,"orcid":719},"Sulaymon Eshkabilov","https:\u002F\u002Forcid.org\u002F0000-0002-7266-2201",{"tldr":721,"method":722,"finding":723,"direction":419,"opportunity":724},"开发并评估了基于RTK-GPS的自主位点特异性机械除草系统，除草效率达96.5%。","集成行间耕作机构与机器人平台，采用RTK-GPS导航和应变反馈控制模拟。","系统除草效率96.5%，自适应控制模拟显示适应不平地形的可行性。","未来可研究增强传感和更快执行器，以提升田间性能，并探索在有机农业中的实际应用。","2026-09-04T23:30:03.245281Z",{"id":727,"title":728,"url":729,"summary":730,"summary_zh":20,"content":20,"source_name":731,"source_url":20,"published_at":341,"category":342,"cover_url":20,"hotness":25,"is_selected":53,"score":732,"score_detail":733,"sources":735,"tags":737,"primary_topic":740,"secondary_topics":745,"classification":746,"search_phrases":765,"slug":768,"view_count":86,"doi":20,"paper":769,"created_at":777},1704,"AI Drones Unlock Hidden Avocado & Hazelnut Secrets（Information Processing in Agriculture）","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F03\u002Fai-drones-unlock-hidden-avocado-hazelnut-secrets","西蒙弗雷泽大学 Xizhi Xia（机电系统工程学校）发表于《Information Processing in Agriculture》。研究开发了基于 AI 的无人机系统，将原始航拍图像转化为可操作的洞察，无需昂贵的训练数据或复杂的数字地形模型（DTM）。研究将无人机映射与加权 K-means、DBSCAN 和聚类分离三种无监督算法相结合，创建逐步分割过程。在田间试验中，该系统在牛油果树木上实现了树高 R² = 0.967、冠层体积 R² = 0.91 的准确度，结果可与许多监督方法相媲美甚至超越。","Agritech Insights",79,{"impact":301,"substance":151,"depth":31,"authority":59,"freshness":152,"relevant":34,"comment":734},"研究提出无监督AI无人机分割方法，在果树表型测量上达到监督学习精度，为低成本精准农业提供新路径。",[736],{"name":731,"url":729},[738,39,364,370,739],"无人机","牛油果",{"code":741,"slug":742,"name":743,"path":744,"parent_code":117},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":747},{"candidates":748,"agricultural_evidence":26},[749,753,757,761],{"code":741,"score":66,"supported":26,"title":750,"abstract":751,"tags":752},[],[743],[739],{"code":361,"score":34,"supported":53,"title":754,"abstract":755,"tags":756},[],[],[364],{"code":65,"score":34,"supported":53,"title":758,"abstract":759,"tags":760},[],[],[39],{"code":351,"score":34,"supported":53,"title":762,"abstract":763,"tags":764},[],[],[370],[766,767],"农业人工智能精准农业无人机牛油果","农业人工智能精准农业","农业人工智能精准农业无人机牛油果-1704",{"doi":20,"openalex_id":20,"authors":770,"venue":20,"cited_by_count":414,"oa_url":20,"card":771,"direction":421,"ingested_from":776},[],{"tldr":772,"method":773,"finding":774,"direction":421,"opportunity":775},"开发基于AI的无人机系统，用无监督算法从航拍图像估算果树高度和冠层体积。","无人机航拍结合加权K-means、DBSCAN和聚类分离的无监督分割方法。","树高R²=0.967，冠层体积R²=0.91，媲美监督方法且无需昂贵训练数据。","无监督方法在多种作物和复杂地形下的泛化能力，以及结合时间序列监测果树生长动态。","agent","2026-09-05T00:04:40.788850Z",{"id":779,"title":780,"url":781,"summary":782,"summary_zh":783,"content":20,"source_name":784,"source_url":781,"published_at":341,"category":342,"cover_url":20,"hotness":25,"is_selected":53,"score":732,"score_detail":785,"sources":787,"tags":789,"primary_topic":793,"secondary_topics":798,"classification":799,"search_phrases":815,"slug":818,"view_count":86,"doi":819,"paper":820,"created_at":864},1630,"BioIMA: a one-click desktop tool for standardized extraction of phenotypic traits from biological images","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.08.30.747465","Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.","从图像中标准化提取定量表型在植物生物学中日益重要，涵盖从生态学和进化研究到遗传学、育种及功能基因组学等领域。然而，随着大型图像数据集越来越多地用于性状分析，许多生物学相关性状（包括大小、形状、颜色和空间模式）仍依赖人工测量或零散的半自动化流程进行。这些局限性降低了通量、可重复性和可及性，尤其对缺乏计算专长的研究人员而言更为突出。在此，我们介绍BioIMA，一款用于从生物图像中快速、标准化提取表型的开源桌面工具。BioIMA将基于基础模型的图像分割与自动化性状计算相结合，使用户能够通过直观的图形界面从图像中提取定量测量数据，且无需进行模型训练。为验证其性能，我们对两个杨属（Populus）物种的节部形态性状进行了定量分析，因为这些测量通常耗时费力。自动测量结果与基于ImageJ的人工测量结果高度一致（R² > 0.95），同时将单张图像的处理时间缩短约75%（从约15秒降至约4秒）。BioIMA进一步应用于多种植物数据集，包括形态和背景条件各异的向日葵属（Helianthus）和杜鹃花属（Rhododendron）图像。尽管BioIMA是为植物表型分析而开发，但也可推广至其他关注区域大小、形状或颜色性状的生物样本。通过将可及性与标准化整合于轻量级本地应用中，BioIMA为生态学和进化研究中的图像表型分析提供了实用的社区资源。","bioRxiv (Cold Spring Harbor Laboratory)",{"impact":31,"substance":151,"depth":31,"authority":274,"freshness":152,"relevant":34,"comment":786},"开源桌面工具BioIMA实现标准化表型提取，显著提升效率，对植物表型研究有实用价值。",[788],{"name":784,"url":781},[437,39,790,791,792],"表型提取","图像分析","开源工具",{"code":794,"slug":795,"name":796,"path":797,"parent_code":165},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[],{"status":52,"locked":53,"version":54,"method":55,"evidence":800},{"candidates":801,"agricultural_evidence":26},[802,807,811],{"code":794,"score":73,"supported":26,"title":803,"abstract":804,"tags":806},[],[805],"向日葵",[],{"code":65,"score":86,"supported":53,"title":808,"abstract":809,"tags":810},[],[],[39,791],{"code":361,"score":34,"supported":53,"title":812,"abstract":813,"tags":814},[],[],[437],[816,817],"农业人工智能图像分析开源工具智慧农业","农业人工智能图像分析","农业人工智能图像分析开源工具智慧农业-1630","10.64898\u002F2026.08.30.747465",{"doi":819,"openalex_id":821,"authors":822,"venue":784,"cited_by_count":414,"oa_url":857,"card":858,"direction":421,"ingested_from":422},"W7207542090",[823,825,828,830,832,834,837,840,843,845,847,849,851,854],{"name":824,"orcid":20},"Xiner Qumu",{"name":826,"orcid":827},"Xuming Dan","https:\u002F\u002Forcid.org\u002F0000-0002-2629-7506",{"name":829,"orcid":20},"Jianju Feng",{"name":831,"orcid":20},"Yangkai Cui",{"name":833,"orcid":20},"Yi Gong",{"name":835,"orcid":836},"Yukang Hou","https:\u002F\u002Forcid.org\u002F0000-0002-8939-5293",{"name":838,"orcid":839},"Qiang Lai","https:\u002F\u002Forcid.org\u002F0000-0002-8274-0111",{"name":841,"orcid":842},"Zeng Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-4296",{"name":844,"orcid":20},"Yulin Zhang",{"name":846,"orcid":20},"Yiman Zhu",{"name":848,"orcid":20},"Yan Yu",{"name":850,"orcid":20},"Feng Zhang",{"name":852,"orcid":853},"Marco Todesco","https:\u002F\u002Forcid.org\u002F0000-0002-6227-4096",{"name":855,"orcid":856},"Jing Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3793-3264","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F02\u002F2026.08.30.747465.full.pdf",{"tldr":859,"method":860,"finding":861,"direction":862,"opportunity":863},"开发了BioIMA桌面工具，实现生物图像表型标准化自动提取，无需训练模型。","集成基础模型分割与自动性状计算，提供图形界面，无需编程。","自动测量与手动测量高度一致（R²>0.95），处理时间减少约75%。","农业人工智能与决策模型","可扩展至更多物种和性状，探索不同分割模型对复杂背景的适应性，或集成深度学习提升精度。","2026-09-04T23:30:26.168022Z"]