[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5522":3,"related-5522":72},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":66,"slug":69,"view_count":70,"doi":8,"paper":8,"created_at":71},5522,"安徽农科院蚕桑所研讨「十五五」发展规划 审议全国主推技术推荐工作","https:\u002F\u002Fcss.aaas.org.cn\u002F4311487\u002F71576609.html","10月14日蚕桑所召开2026年第六次学术委员会会议，审议2027年度全国农业主导品种主推技术和重大引领性技术暨全省农业主推技术推荐工作，并专题研讨《安徽省农业科学院蚕桑研究所「十五五」发展规划》。委员们围绕家蚕种质资源保护与创制、突破性新品种选育、绿色高效种养技术与蚕桑资源综合利用等方向建言。",null,"为充分发挥学术委员会学术评议和咨询职能，把好高层次人才引进学术关，谋划推进重大科技任务和“十五五”发展等工作，10月14日上午，安徽省农业科学院蚕桑研究所召开2026年第六次学术委员会会议。会议由所学术委员会主任李冰主持，所学术委员会全体委员参加会议。\n\n会议开展了2026年新进博士面试预答辩。新进博士依次围绕个人基本情况、学习和研究经历、代表性成果以及入职后的工作设想作PPT汇报。与会委员结合学科发展需求，从专业基础、科研能力、学科契合度、发展潜力等方面进行提问和评议，并对提出意见建议，为高层次人才引进把好学术关。\n\n会议审议2027年度全国农业主导品种主推技术和重大引领性技术暨全省农业主推技术推荐工作事宜。牵头科室就推荐工作的政策依据、推荐条件、指标分配和前期组织申报情况作说明。委员们立足蚕桑、食药用菌等学科优势，围绕拟推荐品种和技术的先进性、适用性、示范推广基础和产业带动能力进行审议，提出推荐意见，着力遴选优质成果、促进科技成果转化应用。\n\n会议专题研讨《安徽省农业科学院蚕桑研究所“十五五”发展规划》。编制组就规划编制过程、总体思路、主要目标和重点任务作说明。委员们围绕发展定位、指标设置、重点任务和保障措施提出修改建议，重点就家蚕种质资源保护与创制、突破性新品种选育、绿色高效种养技术、蚕桑资源综合利用等方向建言献策。\n\n本次会议议题聚焦、讨论充分，委员们的意见建议务实中肯，体现了学术委员会的专业水准和责任担当。会议强调，学术委员会要持续发挥专家智库作用，做强蚕桑优势学科，拓展特色经济作物研究，强化科技创新与成果服务，为安徽蚕桑和特色农业产业高质量发展提供科技支撑。","安徽省农业科学院蚕桑研究所 2026-10-19","2026-10-19T00:00:00Z","报道",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},16,14,12,6,1,"省级科研院所学术委员会会议通稿，涉及蚕桑学科规划与全国主推技术推荐，有行业参考价值但属常规会议动态。",[24],{"name":10,"url":6},[26,27,28,29,30],"十五五规划","成果转化","种质资源","主推技术","蚕桑",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,56,61],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},11,true,[30],[30],[],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":55},"F1",5,[],[28],[29,28],{"code":57,"score":21,"supported":14,"title":58,"abstract":59,"tags":60},"G1",[],[],[27],{"code":62,"score":21,"supported":14,"title":63,"abstract":64,"tags":65},"J1",[],[],[26],[67,68],"安徽农科院 蚕桑所 十五五规划","蚕桑 主推技术 推荐","安徽农科院蚕桑所十五五规划-5522",0,"2026-10-22T00:07:04.530022Z",{"total":20,"page":21,"page_size":20,"items":73},[74,112,160,230,279,337],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":8,"content":79,"source_name":80,"source_url":8,"published_at":81,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":82,"score_detail":83,"sources":86,"tags":88,"primary_topic":93,"secondary_topics":94,"classification":95,"search_phrases":106,"slug":109,"view_count":110,"doi":8,"paper":8,"created_at":111},3240,"加工所赴广西蚕业技术推广站开展蚕桑科技合作交流——粤桂两广蚕桑科技合作续写新篇章","https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1339422.html","9-21 广东省农业科学院发布：9-21 加工所首席科学家廖森泰、副所长邹宇晓率蚕桑学科科技骨干前往广西蚕业技术推广站开展合作交流。广西蚕业技术推广站党委书记、站长卿军，副站长黄红燕、汤庆坤及相关技术骨干参会交流。廖森泰应邀作专题报告，回顾了两广蚕桑科技合作取得的丰硕成果，介绍了加工所近期蚕桑科技新进展。双方围绕蚕桑产业科技创新、核心技术联合攻关、创新平台搭建、科技成果落地转化等关键议题展开深入交流，达成多项合作共识。双方将聚焦产业发展痛点难点，联合申报、共建国家级科技创新平台，协同开展蚕桑优质新品种、高效新技术科研攻关。","[![Image 2](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002FIPv6.png)](https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1339422.html)\n\n[简体](https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1339422.html) | [繁体](https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1339422.html) | [EN](http:\u002F\u002Fwww.gdaas.cn\u002Fenglish\u002Findex.html)\n\n![Image 3](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fphone.png)手机版 \n\n![Image 4: 手机版二维码](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fmoblie_ewm.png)\n\n![Image 5](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fwetchat.png)微信 \n\n![Image 6: 手机版二维码](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fwx_ewm.png)\n\n![Image 7](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Flogo.png)\n\n![Image 8](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fsearch_top.png)\n\n![Image 9](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fsearch.png)\n\n*   [首页](https:\u002F\u002Fwww.gdaas.cn\u002F)\n|*   [本院概况](http:\u002F\u002Fwww.gdaas.cn\u002Fnkygk\u002Findex.html)\n    *   [本院简介](http:\u002F\u002Fwww.gdaas.cn\u002Fnkygk\u002Fbyjj\u002Findex.html)\n    *   [发展历程](http:\u002F\u002Fwww.gdaas.cn\u002Fnkygk\u002Flsyg\u002Findex.html)\n    *   [文化标识](http:\u002F\u002Fwww.gdaas.cn\u002Fnkygk\u002Fbyjj\u002Fwhbz\u002Findex.html)\n    *   [现任领导](http:\u002F\u002Fwww.gdaas.cn\u002Fzzjg\u002Fxrld\u002Findex.html)\n    *   [院属单位](http:\u002F\u002Fwww.gdaas.cn\u002Fzzjg\u002Fysdw\u002Findex.html)\n    *   [管理部门](http:\u002F\u002Fwww.gdaas.cn\u002Fzzjg\u002Fglbm\u002Findex.html)\n\n|*   [人才队伍](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Findex.html)\n    *   [知名专家](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Fzmzj\u002Findex.html)\n    *   [学科团队](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Fxktd\u002Findex.html)\n    *   [博士后工作站](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Fbshgzz\u002Findex.html)\n    *   [研究生培养](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Fyjspy\u002Findex.html)\n\n|*   [科学研究](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Findex.html)\n    *   [科研进展](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fkyjz\u002Findex.html)\n    *   [学术活动](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fxshd\u002Findex.html)\n    *   [科技成果](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fkjcg\u002Findex.html)\n    *   [科研条件](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fkytj\u002Findex.html)\n    *   [国际合作](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fgjhz\u002Findex.html)\n\n|*   [服务三农](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Findex.html)\n    *   [科技服务](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Findex.html)\n    *   [院地合作](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fydhz\u002Findex.html)\n    *   [院企合作](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fyqhz\u002Findex.html)\n    *   [主导品种](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fzdpz\u002Findex.html)\n    *   [主推技术](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fztjs\u002Findex.html)\n    *   [成果转化（孵化器）](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fcgzhfhq\u002Findex.html)\n\n|*   [党建群团](http:\u002F\u002Fwww.gdaas.cn\u002Fdjqt\u002Findex.html)\n\n![Image 10](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fpos.png)[首页](http:\u002F\u002Fwww.gdaas.cn\u002F)>[服务三农](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002F)>[科技服务](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002F)\n\n### 加工所赴广西蚕业技术推广站开展蚕桑科技合作交流\n\n时间：2026-09-22 17:49 来源：加工所【字体：大 中 小】\n\n 分享到： \n\n为加强粤桂两地蚕桑科技交流，推动蚕桑产学研协同发展，9月21日，加工所首席科学家廖森泰、副所长邹宇晓率蚕桑学科科技骨干前往广西蚕业技术推广站开展合作交流。广西蚕业技术推广站党委书记、站长卿军，副站长黄红燕、汤庆坤及相关技术骨干参会交流。\n\n会上，廖森泰应邀作专题报告，回顾了两广蚕桑科技合作取得的丰硕成果，介绍了加工所近期蚕桑科技新进展，并结合产业发展趋势，展望了双方未来重点合作领域与发展方向。\n\n座谈研讨环节中，双方围绕蚕桑产业科技创新、核心技术联合攻关、创新平台搭建、科技成果落地转化等关键议题展开深入交流，达成多项合作共识。双方表示，后续将聚焦产业发展痛点难点，联合申报、共建国家级科技创新平台，协同开展蚕桑优质新品种、高效新技术科研攻关，加速优质科研成果转化和产业化应用。\n\n![Image 11: 图片3.png](http:\u002F\u002Fwww.gdaas.cn\u002Fimg\u002F0\u002F889\u002F889577\u002F1339422.png)\n\n院属单位\n\n*   [水稻研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdrice\u002F)\n*   [果树研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdfruit\u002F)\n*   [蔬菜研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdvri\u002F)\n*   [作物研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fcrigdaas\u002F)\n*   [植物保护研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdppri\u002F)\n*   [农业质量标准与监测技术研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fnkyjczx\u002F)\n*   [设施农业研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdaasifa\u002F)\n*   [动物科学研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdias\u002F)\n*   [蚕业与农产品加工研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fsrigaas\u002F)\n*   [农业资源与环境研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdiare\u002F)\n*   [动物卫生研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fvmi\u002F)\n*   [农业经济与信息研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fiard\u002F)\n*   [茶叶研究所](https:\u002F\u002Fwww.gdaas.cn\u002Ftea\u002F)\n*   [环境园艺研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdflower\u002F)\n*   [农业生物基因研究中心](https:\u002F\u002Fwww.gdaas.cn\u002Fagrogene\u002F)\n*   [《广东农业科学》](https:\u002F\u002Fgdnykx.gdaas.cn\u002F)\n\n政府机构\n\n*   [中国政府网](http:\u002F\u002Fwww.gov.cn\u002F)\n*   [科技部](http:\u002F\u002Fwww.most.gov.cn\u002Findex.htm)\n*   [农业农村部](http:\u002F\u002Fwww.moa.gov.cn\u002F)\n*   [中国科学院](http:\u002F\u002Fwww.cas.cn\u002F)\n*   [中国工程院](http:\u002F\u002Fwww.cae.cn\u002F)\n*   [国家自然科学基金委员会](https:\u002F\u002Fwww.nsfc.gov.cn\u002F)\n*   [广东省人民政府](http:\u002F\u002Fwww.gd.gov.cn\u002F)\n*   [省发展改革委](http:\u002F\u002Fdrc.gd.gov.cn\u002F)\n*   [省教育厅](https:\u002F\u002Fedu.gd.gov.cn\u002F)\n*   [省科技厅](http:\u002F\u002Fgdstc.gd.gov.cn\u002F)\n*   [省财政厅](http:\u002F\u002Fczt.gd.gov.cn\u002F)\n*   [省人力资源社会保障厅](http:\u002F\u002Fhrss.gd.gov.cn\u002F)\n*   [省生态环境厅](https:\u002F\u002Fgdee.gd.gov.cn\u002F)\n*   [省水利厅](http:\u002F\u002Fslt.gd.gov.cn\u002F)\n*   [省农业农村厅](http:\u002F\u002Fdara.gd.gov.cn\u002F)\n*   [省政府外事办公室](http:\u002F\u002Fwww.gdfao.gov.cn\u002F)\n*   [省国资委](http:\u002F\u002Fgzw.gd.gov.cn\u002F)\n*   [省市场监督管理局](http:\u002F\u002Famr.gd.gov.cn\u002F)\n*   [省林业局](http:\u002F\u002Flyj.gd.gov.cn\u002F)\n*   [广东省直机关党建](http:\u002F\u002Fwww.gdszjgdj.org\u002F)\n*   [广州市科学技术局](https:\u002F\u002Fkjj.gz.gov.cn\u002F)\n*   [广州市农业农村局](https:\u002F\u002Fnyncj.gz.gov.cn\u002F)\n\n科研机构\n\n*   [中国农业科学院](https:\u002F\u002Fwww.caas.cn\u002F)\n*   [中国热带农业科学院](http:\u002F\u002Fwww.catas.cn\u002F)\n*   [北京市农林科学院](http:\u002F\u002Fwww.baafs.net.cn\u002F)\n*   [河北省农林科学院](http:\u002F\u002Fwww.hebnky.com\u002F)\n*   [山西省农业科学院](http:\u002F\u002Fwww.sxau.edu.cn\u002F)\n*   [内蒙古农牧业科学院](http:\u002F\u002Fwww.imaaahs.ac.cn\u002F)\n*   [辽宁省农业科学院](https:\u002F\u002Fwww.laas.cn\u002F)\n*   [吉林省农业科学院](http:\u002F\u002Fwww.jaas.com.cn\u002F)\n*   [黑龙江省农业科学院](https:\u002F\u002Fwww.haas.cn\u002F)\n*   [上海市农业科学院](http:\u002F\u002Fwww.saas.sh.cn\u002F)\n*   [江苏省农业科学院](http:\u002F\u002Fwww.jaas.ac.cn\u002F)\n*   [浙江省农业科学院](http:\u002F\u002Fwww.zaas.ac.cn\u002F)\n*   [安徽省农业科学院](http:\u002F\u002Fwww.ahas.org.cn\u002F)\n*   [福建省农业科学院](http:\u002F\u002Fwww.faas.cn\u002F)\n*   [江西省农业科学院](https:\u002F\u002Fwww.jxaas.cn\u002F)\n*   [山东省农业科学院](http:\u002F\u002Fwww.saas.ac.cn\u002F)\n*   [河南省农业科学院](http:\u002F\u002Fwww.hnagri.org.cn\u002F)\n*   [湖北省农业科学院](http:\u002F\u002Fwww.hbaas.ac.cn\u002F)\n*   [湖南省农业科学院](http:\u002F\u002Fwww.hunaas.cn\u002F)\n*   [广西农业科学院](http:\u002F\u002Fwww.gxaas.net\u002F)\n*   [海南省农业科学院](http:\u002F\u002Fwww.hnaas.org.cn\u002F)\n*   [重庆市农业科学院](http:\u002F\u002Fwww.cqaas.cn\u002F)\n*   [四川省农业科学院](http:\u002F\u002Fwww.chinawestagr.com\u002F)\n*   [贵州省农业科学院](http:\u002F\u002Faas.guizhou.gov.cn\u002F)\n*   [云南省农业科学院](http:\u002F\u002Fwww.yaas.org.cn\u002F)\n*   [西藏自治区农牧科学院](https:\u002F\u002Fwww.xzaas.cn\u002F)\n*   [甘肃省农业科学院](https:\u002F\u002Fwww.gsagr.cn\u002F)\n*   [青海省畜牧兽医科学院](https:\u002F\u002Fmky.qhu.edu.cn\u002F)\n*   [宁夏农林科学院](http:\u002F\u002Fwww.nxaas.com.cn\u002F)\n*   [新疆农业科学院](http:\u002F\u002Fwww.xaas.ac.cn\u002F)\n*   [中国农业大学](http:\u002F\u002Fwww.cau.edu.cn\u002F)\n*   [华南农业大学](http:\u002F\u002Fwww.scau.edu.cn\u002F)\n*   [华中农业大学](http:\u002F\u002Fwww.hzau.edu.cn\u002F)\n*   [东北农业大学](https:\u002F\u002Fwww.neau.edu.cn\u002F)\n*   [南京农业大学](http:\u002F\u002Fwww.njau.edu.cn\u002F)\n*   [中山大学](http:\u002F\u002Fwww.sysu.edu.cn\u002F)\n*   [广东省科学院](http:\u002F\u002Fwww.gdas.gd.cn\u002F)\n*   [中国农业新闻网](http:\u002F\u002Fwww.farmer.com.cn\u002F)\n\n[](https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1339422.html)\n\n版权所有：广东省农业科学院\n\n地址：广州市天河区金颖路29号 邮编：510640\n\n[![Image 12: 粤公网安备44010602013764号](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_ga.png)粤公网安备44010602013764号](https:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=44010602013764)[粤ICP备16101361号](https:\u002F\u002Fbeian.miit.gov.cn\u002F)\n\n[![Image 13](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_wzdt.png)](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_986930.html)\n\n[网站地图](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_986930.html)\n\n[![Image 14](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_lxwm.png)](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_986932.html)\n\n[联系我们](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_986932.html)\n\n[![Image 15](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_wzjc.png)](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_1029428.html)\n\n[网站纠错](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_1029428.html)","广东省农业科学院","2026-09-21T00:00:00Z",58,{"impact":19,"substance":18,"depth":19,"authority":19,"freshness":84,"relevant":21,"comment":85},8,"粤桂两地蚕桑科技合作交流报道，有具体合作方向与共识，但属常规院所交流动态，公共影响有限。",[87],{"name":80,"url":77},[27,89,90,91,92],"产学研协同","粤桂合作","蚕桑科技","科技创新平台",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":96},{"candidates":97,"agricultural_evidence":46},[98,102],{"code":32,"score":45,"supported":46,"title":99,"abstract":100,"tags":101},[30],[30],[],{"code":57,"score":21,"supported":14,"title":103,"abstract":104,"tags":105},[],[],[27],[107,108],"广东农科院 加工所 蚕桑","广西蚕业技术推广站 粤桂合作","广东农科院加工所蚕桑-3240",2,"2026-09-23T00:04:31.518992Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":8,"content":117,"source_name":118,"source_url":8,"published_at":119,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":123,"tags":125,"primary_topic":130,"secondary_topics":131,"classification":137,"search_phrases":155,"slug":158,"view_count":21,"doi":8,"paper":8,"created_at":159},2598,"云南农科院甘蔗所 国家甘蔗种质资源圃开远 科普开放日9月23日","https:\u002F\u002Fwww.yngzs.net\u002FOrganization_1\u002F203.html","2026年中国农民丰收节·国家甘蔗种质资源圃(开远)种质资源科普开放日活动邀请。活动主题蔗里良种出好糖—资源助丰收 实干促振兴；2026年9月23日 上午场8:30-10:30 下午场14:30-17:00。","为热烈庆祝**2026年中国农民丰收节**，深入落实“资源助丰收 实干促振兴”主题活动部署，扎实推进种业振兴行动，全面展示国家甘蔗种质资源圃（开远）在甘蔗种质资源保护、鉴定评价、育种创新及深加工利用领域的丰硕成果，普及农作物种质资源与生物多样性科学知识，提升全社会农业种质资源保护意识，搭建科研科普交流、优异种质推介共享平台，云南省农业科学院甘蔗研究所特举办**2026年中国农民丰收节·国家甘蔗种质资源圃（开远）种质资源科普开放日**活动，诚邀各界人士莅临参与、交流观摩！\n\n## **一、活动主题**\n\n“蔗”里良种出好糖—资源助丰收 实干促振兴\n\n## **二、活动时间**\n\n2026年9月23日\n\n上午场：8:30-10:30\n\n下午场：14:30-17:00\n\n## **三、活动地点**\n\n云南省开远市灵泉东路 363号 云南省农业科学院甘蔗研究所\n\n核心体验区域：专题科普讲座会议室、国家甘蔗种质资源圃、育种创新基地、分子细胞实验室、蔗糖深加工及副产物综合利用实验室\n\n## **四、邀约对象**\n\n1.青少年群体：开远及周边中小学师生（重点邀约）；\n\n2.社会群体：市民家庭、农业科普爱好者；\n\n3.行业群体：种业企事业单位技术人员、甘蔗育种科研工作者、基层农技人员。\n\n## **五、活动内容**\n\n本次活动采用**专题讲座+导览参观+实物展示+科研演示+互动品鉴**的沉浸式科普模式，全程由一线科研人员现场讲解答疑。\n\n**1. 分层专题科普讲座**\n\n面向青少年开设趣味科普课堂，讲解甘蔗起源传播、种质资源保护、育种基础知识、良种赋能产业丰收等内容，以通俗语言解锁甘蔗种业科技奥秘；面向行业同仁开展专项交流，重点推介抗逆、抗病、高糖等优异甘蔗种质资源，分享种质性状数据与育种应用方向，搭建种质资源共享合作桥梁。\n\n**2. 全场景科研观摩体验**\n\n实地参观国家甘蔗种质资源圃，直观感受甘蔗种质资源多样性；走进育种创新基地，观摩实生苗群体、优质新品系及突破性主栽品种田间表现；走进科研实验室，近距离观看种质 DNA提取、分子细胞鉴定等科研实操过程；体验蔗糖深加工产品及副产物综合利用成果，参与产品品鉴互动。\n\n**3. 专属导览答疑服务**\n\n由研究所核心科研团队全程带队导览，针对种质保护、育种创新、产业应用等问题一对一答疑，普及种业科学知识，传播现代农业科技理念。\n\n## **六、参与须知**\n\n1. **公益免费参与**：本次活动全程免费，无任何参观、体验、品鉴费用；\n\n2. **实名预约报名**：活动实行实名制线上预约（微信扫描下发二维码），名额有限，报满即止，报名时间截止 2026年9月18日22:00，欢迎大家积极报名参与；\n\n![Image 1](https:\u002F\u002Fomo-oss-image.thefastimg.com\u002Fportal-saas\u002Fpg2024011918250367977\u002Fcms\u002Fimage\u002F1f6121a8-5f51-4d70-977f-cb5cf3854f67.png)\n\n3. **安全管理要求**：活动单场人数严控 50人以内，全天总人数不超过100人；学生团体须由教师全程带队，未成年人需监护人或带队老师全程监护；\n\n4. **现场纪律规范**：参观需遵从指定路线，严禁擅自采摘甘蔗植株、触碰科研设备及实验样品，遵守场地安全管理规定。\n\n## **七、****温馨提示**\n\n**1.关于活动：**本次活动为公益科普开放活动，采取自愿报名方式，报名截止后不再接受增补报名，请意向参与者提前完成报名登记。\n\n**2.关于安全：**活动期间请自觉服从工作人员安排，文明参与，切勿随意采摘花草、攀折果枝。\n\n**3.关于出行：**活动现场不提供停车位，请选择绿色出行。\n\n诚挚欢迎各界朋友踊跃预约、莅临参与，共赏蔗园丰收之美、共探种业创新之路！\n\n云南省农业科学院甘蔗研究所\n\n2026年9月11日","云南农科院甘蔗所 2026-09","2026-09-15T01:00:00Z",46,{"impact":84,"substance":13,"depth":84,"authority":19,"freshness":84,"relevant":21,"comment":122},"省级科研机构举办的公益科普开放日活动，内容以参观体验为主，缺乏政策条款或科研数据等实质增量，公共价值有限，适合作为地方活动简讯而非每日精选。",[124],{"name":118,"url":115},[126,127,128,28,129],"种业振兴","甘蔗","农业科普","丰收节",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[132],{"code":51,"slug":133,"name":134,"path":135,"parent_code":136},"seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",{"status":39,"locked":14,"version":40,"method":41,"evidence":138},{"candidates":139,"agricultural_evidence":46},[140,145,149],{"code":51,"score":141,"supported":46,"title":142,"abstract":143,"tags":144},13,[28],[28],[126,28],{"code":32,"score":19,"supported":46,"title":146,"abstract":147,"tags":148},[127],[127],[127],{"code":150,"score":151,"supported":46,"title":152,"abstract":153,"tags":154},"J4",4,[],[129],[129],[156,157],"农业科普 种业振兴 种质资源 丰收节","农业科普 种业振兴","农业科普种业振兴种质资源丰收节-2598","2026-09-16T00:03:49.576151Z",{"id":161,"title":162,"url":163,"summary":164,"summary_zh":8,"content":8,"source_name":165,"source_url":8,"published_at":166,"category":167,"cover_url":8,"hotness":13,"is_selected":14,"score":168,"score_detail":169,"sources":174,"tags":176,"primary_topic":182,"secondary_topics":183,"classification":190,"search_phrases":216,"slug":219,"view_count":70,"doi":8,"paper":220,"created_at":229},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z","论文",69,{"impact":17,"substance":170,"depth":171,"authority":172,"freshness":20,"relevant":21,"comment":173},21,17,9,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[175],{"name":165,"url":163},[177,178,179,180,181],"智慧农业","农业人工智能","目标检测","无人机遥感","棉花病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[184],{"code":185,"slug":186,"name":187,"path":188,"parent_code":189},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":191},{"candidates":192,"agricultural_evidence":46},[193,200,206,211],{"code":185,"score":194,"supported":46,"title":195,"abstract":198,"tags":199},18,[196,197],"深度学习","YOLO",[],[178,179],{"code":32,"score":45,"supported":46,"title":201,"abstract":203,"tags":205},[202],"棉花",[204],"cotton",[],{"code":207,"score":21,"supported":14,"title":208,"abstract":209,"tags":210},"I1",[],[],[177],{"code":212,"score":21,"supported":14,"title":213,"abstract":214,"tags":215},"I3",[],[],[180],[217,218],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":8,"openalex_id":8,"authors":221,"venue":8,"cited_by_count":70,"oa_url":8,"card":222,"direction":226,"ingested_from":228},[],{"tldr":223,"method":224,"finding":225,"direction":226,"opportunity":227},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","农业遥感与作物表型","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","agent","2026-10-22T00:07:09.816790Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":8,"content":8,"source_name":235,"source_url":8,"published_at":236,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":237,"sources":239,"tags":241,"primary_topic":247,"secondary_topics":248,"classification":249,"search_phrases":274,"slug":277,"view_count":70,"doi":8,"paper":8,"created_at":278},5350,"中原电气实验室一行赴河南农科院烟草所考察 达成烟草烘烤节能减排合作意向","https:\u002F\u002Fhnagri.org.cn\u002Fycs\u002Farticle-114211.html","10 月 14 日,中原电气实验室能源研究所所长马志豪、电气研究所所长贾霖、顾问梁东杰等一行 5 人莅临河南农科院烟草所开展调研交流,实地参观了密集烤房设施、天敌昆虫繁育车间以及各学科实验室。双方就烟叶烘烤过程中新能源利用、储能技术应用、热能高效利用、纳米技术在农药、肥料研发等关键环节进行了深入探讨,并达成初步合作意向。","河南省农业科学院烟草研究所 2026-10-14","2026-10-14T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":238},"省级科研机构与实验室的跨领域合作，涉及烟叶烘烤新能源与储能应用，有实质技术方向但尚处意向阶段，时效一般。",[240],{"name":235,"url":233},[242,243,244,245,246],"院企合作","节能减排","烟草烘烤","新能源利用","储能技术",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":250},{"candidates":251,"agricultural_evidence":46},[252,257,264,270],{"code":32,"score":45,"supported":46,"title":253,"abstract":255,"tags":256},[254],"烟草",[254],[],{"code":258,"score":259,"supported":46,"title":260,"abstract":261,"tags":263},"F3",3,[],[262],"肥料",[],{"code":265,"score":259,"supported":46,"title":266,"abstract":267,"tags":269},"F4",[],[268],"农药",[],{"code":57,"score":21,"supported":14,"title":271,"abstract":272,"tags":273},[],[],[242],[275,276],"中原电气实验室 河南农科院烟草所","烟草烘烤 节能减排","中原电气实验室河南农科院烟草所-5350","2026-10-20T02:47:44.469410Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":8,"source_name":285,"source_url":282,"published_at":286,"category":167,"cover_url":8,"hotness":13,"is_selected":14,"score":168,"score_detail":287,"sources":290,"tags":292,"primary_topic":294,"secondary_topics":295,"classification":296,"search_phrases":314,"slug":317,"view_count":21,"doi":318,"paper":319,"created_at":336},5234,"A smart agriculture-oriented cross-attention multiscale CNN for sugarcane disease diagnosis","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23080477.2026.2749739","Sugarcane leaf diseases can adversely affect crop health and yield, making timely disease identification important for smart agriculture. Manual inspection of leaf diseases is time-consuming and requires expert knowledge, particularly when large cultivation areas need to be monitored. To tackle this problem, this study proposes a Cross-Attention Multiscale Parallel Convolutional Neural Network for automatic classification of sugarcane leaf diseases. This framework comprises a Parallel Feature Extraction Stem, cross-attention mechanism, Dense Connection Blocks, and Inception Blocks to learn disease features at various spatial scales. The PFES extracts fine and broad disease patterns using parallel convolutional branches, while the cross-attention mechanism refines the extracted features by emphasizing disease-related information. The DCB supports feature propagation and reuse, and the IB further captures multi-scale feature representations. The proposed model was tested on two benchmark datasets of leaf diseases for sugarcane plants. The proposed framework obtained an accuracy (98.79%), precision (98.31%), recall (97.81%), specificity (98.60%), and F1-Score (98.05%). For assessing the classification accuracy and consistency of the framework, the comparative study, ablation studies, and five-fold cross-validation are carried out. The results show that CA-MPCNN provides accurate sugarcane leaf disease classification and can support automated disease diagnosis in smart agriculture applications.","甘蔗叶片病害会对作物健康与产量造成不利影响，因此及时识别病害对智慧农业具有重要意义。人工检查叶片病害耗时且需要专业知识，尤其是在需要监测大面积种植区域时。为解决该问题，本研究提出一种用于甘蔗叶片病害自动分类的交叉注意力多尺度并行卷积神经网络（Cross-Attention Multiscale Parallel Convolutional Neural Network）。该框架由并行特征提取主干（Parallel Feature Extraction Stem）、交叉注意力机制、密集连接块（Dense Connection Blocks）和Inception块组成，用于学习不同空间尺度上的病害特征。PFES通过并行卷积分支提取细粒度与广域病害模式，而交叉注意力机制通过强调病害相关信息对提取的特征进行细化。DCB支持特征传播与复用，IB进一步捕获多尺度特征表示。所提出的模型在两个甘蔗植株叶片病害基准数据集上进行了测试。该框架取得了准确率（98.79%）、精确率（98.31%）、召回率（97.81%）、特异性（98.60%）和F1分数（98.05%）。为评估该框架的分类准确性和一致性，开展了对比研究、消融研究和五折交叉验证。结果表明，CA-MPCNN能够实现准确的甘蔗叶片病害分类，并可支持智慧农业应用中的自动化病害诊断。","Smart Science","2026-10-17T00:00:00Z",{"impact":19,"substance":288,"depth":17,"authority":19,"freshness":172,"relevant":21,"comment":289},20,"该研究提出一种面向甘蔗叶部病害自动分类的交叉注意力多尺度CNN，在两个基准数据集上准确率达98.79%，方法新颖、实验充分，但属于细分作物应用研究，产业影响有限，时效性强，值得进入每日精选。",[291],{"name":285,"url":282},[177,178,127,196,293],"病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":297},{"candidates":298,"agricultural_evidence":46},[299,305,310],{"code":32,"score":300,"supported":46,"title":301,"abstract":303,"tags":304},15,[302],"sugarcane",[127,302],[127],{"code":207,"score":300,"supported":46,"title":306,"abstract":308,"tags":309},[307],"smart agriculture",[177,307],[177],{"code":185,"score":110,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[178,196],[315,316],"甘蔗 叶部病害 智能诊断","CA-MPCNN 甘蔗 病害","甘蔗叶部病害智能诊断-5234","10.1080\u002F23080477.2026.2749739",{"doi":318,"openalex_id":320,"authors":321,"venue":285,"cited_by_count":70,"oa_url":8,"card":328,"direction":334,"ingested_from":335},"W7222832245",[322,324,326],{"name":323,"orcid":8},"Bala Subramanian Chokkalingam",{"name":325,"orcid":8},"Uvarani Meenrajan",{"name":327,"orcid":8},"Shanmugapriya Sankaran",{"tldr":329,"method":330,"finding":331,"direction":332,"opportunity":333},"提出交叉注意力多尺度并行CNN，实现甘蔗叶病自动分类。","并行卷积分支、交叉注意力、密集连接与Inception块，两个基准数据集验证。","模型准确率达98.79%，消融与五折交叉验证证明其有效稳定。","农业人工智能与决策模型","可探索田间复杂背景与轻量化部署，并扩展到多作物病害实时诊断。","智慧农业 \u002F 农业物联网","openalex","2026-10-18T23:30:07.777036Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":8,"source_name":343,"source_url":340,"published_at":286,"category":167,"cover_url":8,"hotness":13,"is_selected":14,"score":344,"score_detail":345,"sources":347,"tags":349,"primary_topic":353,"secondary_topics":354,"classification":355,"search_phrases":379,"slug":382,"view_count":21,"doi":383,"paper":384,"created_at":392},5233,"Cotton pest and disease multi-class classification with weather and temporal predictors features across Indian agroclimatic zones","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.73s.45","The Pink Bollworm (Pectinophora gossypiella) remains one of the most destructive pests affecting cotton productivity, with infestation levels strongly driven by climatic fluctuations and crop phenology.This study presents a machine learning-based framework for early multi-classification and prediction of Pink Bollworm incidence using agro-meteorological variables.The dataset includes maximum and minimum air temperature, relative humidity, rainfall, sunshine hours, evaporation, and wind speed, all of which influence Pink Bollworm development, migration, and reproductive cycles.After preprocessing, applying cyclic encoding to temporal variables, and generating lagged climatic features, multiple machine learning algorithms-including SVM, Neural Networks, KNN, Regression models, Ensemble methods, and Naïve Bayes-were trained and systematically compared.SVM model demonstrated the best test accuracy, effectively capturing dynamic climate-pest interactions.The resulting MATLAB-based classification tool enables rapid assessment and supports climate-informed pest surveillance.A stratified 80:20 train-test split was employed, with 5-fold cross-validation on the training set for hyperparameter tuning, ensuring no data leakage between partitions.The validation and training accuracies of these models are 89.4% and 96 % respectively.Overall, the model offers a practical and interpretable solution for early detection of Pink Bollworm risk, thereby contributing to informed decision-making in integrated pest management (IPM) and precision agriculture.","棉红铃虫（Pectinophora gossypiella）仍是影响棉花产量最具破坏性的害虫之一，其发生程度受气候波动和作物物候的强烈驱动。本研究提出了一种基于机器学习的框架，利用农业气象变量对棉红铃虫的发生进行早期多分类与预测。数据集包括最高和最低气温、相对湿度、降雨量、日照时数、蒸发量和风速，这些因素均影响棉红铃虫的发育、迁移和繁殖周期。经过预处理、对时间变量应用循环编码并生成滞后气候特征后，训练并系统比较了多种机器学习算法，包括支持向量机（SVM）、神经网络、K近邻（KNN）、回归模型、集成方法和朴素贝叶斯。SVM模型表现出最佳的测试准确率，有效捕捉了气候与害虫之间的动态相互作用。所构建的基于MATLAB的分类工具可实现快速评估，并支持气候信息驱动的害虫监测。采用分层80：20的训练-测试划分，并在训练集上进行5折交叉验证以调优超参数，确保各分区之间无数据泄漏。这些模型的验证准确率和训练准确率分别为89.4%和96%。总体而言，该模型为棉红铃虫风险的早期检测提供了一种实用且可解释的解决方案，从而有助于综合害虫管理（IPM）和精准农业中的科学决策。","International Journal of Drug Delivery Technology",55,{"impact":84,"substance":194,"depth":18,"authority":20,"freshness":172,"relevant":21,"comment":346},"研究用多类机器学习模型结合气象与时序特征预测棉铃虫风险，方法扎实但属细分领域应用研究，信源期刊权威性一般，公共影响有限。",[348],{"name":343,"url":340},[177,350,202,351,352],"机器学习","农业气象","病虫害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":356},{"candidates":357,"agricultural_evidence":46},[358,362,369,374],{"code":32,"score":300,"supported":46,"title":359,"abstract":360,"tags":361},[204],[202,204],[202],{"code":207,"score":363,"supported":46,"title":364,"abstract":365,"tags":368},7,[],[366,367],"精准农业","precision agriculture",[177],{"code":185,"score":363,"supported":46,"title":370,"abstract":371,"tags":373},[],[350,372],"machine learning",[350],{"code":375,"score":151,"supported":46,"title":376,"abstract":377,"tags":378},"H7",[],[351],[351],[380,381],"棉铃虫 机器学习 预测","棉花 病虫害 气象因子","棉铃虫机器学习预测-5233","10.25258\u002Fijddt.16.73s.45",{"doi":383,"openalex_id":385,"authors":386,"venue":343,"cited_by_count":70,"oa_url":340,"card":387,"direction":334,"ingested_from":335},"W7222826491",[],{"tldr":388,"method":389,"finding":390,"direction":332,"opportunity":391},"用农业气象变量与时间特征构建机器学习模型，实现印度棉区棉铃虫多级发生预测。","气象数据加周期编码与滞后特征，比较SVM等模型，5折交叉验证。","SVM测试精度最高达89.4%，能有效刻画气候与虫害动态关系。","可引入遥感植被指数与实时气象物联网数据，构建跨区域可迁移的病虫害预警系统。","2026-10-18T23:30:07.725427Z"]