[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-10-05":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-10-05","秋粮收获过三成，玉米高产刷新纪录",[7,8,9,10,11,12],"全国秋粮收获进度过三成、快于去年同期，新疆伊犁玉米密植高产精准调控技术万亩示范田亩产达1609.4公斤，攻关田实收亩产1799.1公斤刷新全国纪录。","黑龙江粮食产量连续16年居全国首位，水稻、玉米、大豆机械化率均近九成，北斗导航无人收割与智慧农场加速落地，端牢14亿人饭碗向科技要粮。","中国农科院破解飞蝗聚集产卵密码，锁定共生细菌释放的2,4,6-三甲基吡啶及两个必需嗅觉受体，为蝗灾绿色防控提供新靶点。","作科所解析野生稻耐冷抗病复合位点RBA1的结构变异与基因分工，实现水稻耐冷与稻瘟病抗性定向协同改良。","山东集中发布45个耐盐品种、71项技术、30个应用场景，广东28个主导品种、39项主推技术领跑全省，盐碱地与特色产业成果加速落地。","多篇论文聚焦数字乡村对粮食安全与农业碳循环的非对称影响、AI赋能质量农业、双机械臂协同采摘与农业碳市场等前沿议题。","本期聚焦秋粮大面积收获与玉米高产新纪录，汇集飞蝗产卵机制、野生稻耐冷抗病位点等前沿研究，并覆盖黑龙江智慧农场、山东盐碱地成果、广东主导品种发布及数字乡村、农业碳市场等论文动态。\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。*",[15,79,130,182,255,327,371,429,487,595,659,752,796,883,950],{"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":35,"tags":37,"primary_topic":43,"secondary_topics":49,"classification":50,"search_phrases":73,"slug":76,"view_count":77,"doi":20,"paper":20,"created_at":78},4186,"飞蝗\"抱团产卵\"密码被中国农科院科学家破解——共生细菌2,4,6-三甲基吡啶驱动聚集产卵+卵化学防御","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_4786ac21ce296452","中国农业科学院基因组所（大鹏湾实验室）常贺坦研究员、Bill S.Hansson院士与王桂荣研究员团队联合国内外合作单位，揭示飞蝗借助母传共生细菌实现聚集产卵与卵防御的分子机制。研究锁定关键挥发物2,4,6-三甲基吡啶（2,4,6-TMP），由群居飞蝗卵荚附着的共生细菌释放；该分子还能驱避及毒杀红火蚁、线蚓、步甲等多种土壤蝗卵天敌。团队从82个受体基因中鉴定出两个必需嗅觉受体；敲除任一受体，集群产卵行为随之被破坏。研究构建了\"飞蝗宿主—母传共生菌—挥发物—天敌\"的多级互作链条，为飞蝗两型分化、蝗灾世代延续与爆发成灾提供全新解释。",null,"飞蝗为何敢“抱团”产卵？我国科学家发现飞蝗成灾的“跨代密码”\n\n![Image 1: 科创中国](https:\u002F\u002Fqb-profile.image.myqcloud.com\u002Favatar_97f10ce1f1671e3fe2ff110b39b810ac.png)\n\n科创中国\n\n14小时前\n\n![Image 2](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-3334609562-EA2C218EF224F5698618C284115FB6D2\u002F0?fmt=png&size=5&h=1253&w=1471&ppv=1)\n\n飞蝗是全球重大农业害虫，聚集成群的飞蝗会把卵集中产在同一片土壤中，这种\"抱团\"产卵的方式是蝗灾世代延续的关键。然而，卵聚得越密越容易被天敌捕食，飞蝗为何敢\"抱团\"产卵？\n\n日前，中国农科院基因组所（大鹏湾实验室）常贺坦研究员、Bill S. Hansson院士与王桂荣研究员团队联合国内外合作单位，研究揭示了飞蝗借助母传共生细菌实现聚集产卵与卵防御的分子机制。\n\n![Image 3](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-162868261-D68955C79681CD510DF2C7F815DE9209\u002F0?fmt=png&size=7&h=29&w=1080&ppv=1)\n\n![Image 4](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-3390245336-BABF2223F5D7FE48C7C754306D473CCC\u002F0?fmt=png&size=1547&h=1175&w=1200&ppv=1)\n\n研究团队从蝗虫产卵行为入手。实验证实，群居飞蝗倾向聚集产卵，散居飞蝗的产卵位点则高度分散，而群居型的卵荚里藏着吸引同类雌蝗聚集产卵的气味密码。\n\n借助气相色谱-质谱联用技术层层分离，团队锁定了一种关键挥发物：2,4,6-三甲基吡啶（Trimethylpyridine，简称 2,4,6-TMP），该分子由群居飞蝗卵荚特异性释放，可吸引同类雌蝗集中产卵。\n\n借助果蝇空神经元异源表达体系与基因编辑技术，团队从82个受体基因中鉴定出飞蝗感知该信号的两个必需嗅觉受体；敲除任一受体，飞蝗即丧失对上述挥发物的嗅觉偏好，集群产卵行为随之被破坏。\n\n更关键的是，研究发现2,4,6-TMP并非飞蝗自身合成，而是源于卵荚附着的共生细菌。研究还发现，除介导聚集产卵外，2,4,6-TMP还能驱避以及毒杀红火蚁、线蚓、步甲等多种土壤蝗卵天敌。\n\n该研究完整构建了“飞蝗宿主—母传共生菌—挥发物—天敌”的多级互作链条，创新性阐明飞蝗借助母本垂直传递的共生细菌，依靠单一挥发性分子同时实现生殖同步化与后代化学防御，巧妙化解高密度产卵带来的高捕食风险。这一发现不仅拓展了昆虫—微生物共生互作的认知边界，也为理解飞蝗两型分化、蝗灾世代延续与爆发成灾提供了全新解释。\n\n来自：中国农业科学院深圳农业基因组研究所","中国农科院深圳农业基因组研究所·2026年10月4日","2026-10-04T08:00:00Z","报道",10,true,93,{"impact":29,"substance":30,"depth":31,"authority":32,"freshness":25,"relevant":33,"comment":34},24,25,19,15,1,"国家级科研机构在顶级期刊级成果，揭示飞蝗聚集产卵与卵防御的分子机制，为蝗灾绿色防控提供全新靶点，信息增量与权威性俱佳。",[36],{"name":22,"url":18},[38,39,40,41,42],"绿色防控","农业科技","虫害预警","共生细菌","飞蝗防治",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":55},"classified",false,"2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":26},[57,63,68],{"code":44,"score":58,"supported":26,"title":59,"abstract":60,"tags":62},3,[],[61],"土壤",[],{"code":64,"score":33,"supported":52,"title":65,"abstract":66,"tags":67},"F4",[],[],[38],{"code":69,"score":33,"supported":52,"title":70,"abstract":71,"tags":72},"H8",[],[],[38],[74,75],"中国农科院 飞蝗 共生细菌","飞蝗 聚集产卵 2 4 6-三甲基吡啶","中国农科院飞蝗共生细菌-4186",4,"2026-10-05T00:04:52.882299Z",{"id":80,"title":81,"url":82,"summary":83,"summary_zh":20,"content":84,"source_name":85,"source_url":20,"published_at":86,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":87,"score_detail":88,"sources":94,"tags":96,"primary_topic":20,"secondary_topics":102,"classification":103,"search_phrases":125,"slug":128,"view_count":58,"doi":20,"paper":20,"created_at":129},4185,"中国农科院作科所解析野生稻耐冷抗病\"黄金位点\"——RBA1复合遗传位点实现耐冷与抗病定向协同改良","https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?cat=T&id=572495&mobile=1&type=detail","中国农业科学院作物科学研究所水稻优异种质资源发掘与创新利用团队联合崖州湾国家实验室、农业基因组研究所等单位，解析了野生稻复合遗传位点RBA1的结构变异对水稻苗期耐冷性和稻瘟病抗性的遗传效应，揭示位点内部不同基因在耐冷与抗病调控中的功能分工，并成功实现水稻耐冷和抗病的定向协同改良。研究团队从广西普通野生稻中鉴定到苗期耐冷位点RBA1，发现该位点存在一段约25700个碱基对的复杂结构变异，包含3个结构特殊的抗病相关基因。该研究发表在《生命》(Vita)上。","近日，中国农业科学院作物科学研究所水稻优异种质资源发掘与创新利用团队联合崖州湾国家实验室、中国农业科学院农业基因组研究所等单位，解析了野生稻复合遗传位点RBA1的结构变异对水稻苗期耐冷性和稻瘟病抗性的遗传效应，揭示了该位点内部不同基因在耐冷与抗病调控中的功能分工，并成功实现水稻耐冷和抗病的定向协同改良。相关研究成果发表在《生命》（_**Vita**_）上。\n\n低温是影响水稻苗期生长和区域适应性的重要环境因素。在我国南方早稻和部分双季稻种植区，水稻苗期不仅可能遭遇阶段性低温，后续还面临稻瘟病等病害压力，两类逆境叠加直接影响苗期群体建成和水稻稳产。此前研究多聚焦耐冷或抗病单一性状，但不同抗逆性状之间的遗传关系尚不明确。\n\n研究团队从广西普通野生稻中鉴定到苗期耐冷位点RBA1。分析发现，该位点存在一段约25700个碱基对的复杂结构变异，其中包含3个结构特殊的抗病相关基因。进一步分析表明，3个基因均参与苗期低温响应，但在耐冷和抗病中的作用并不相同。其中一个基因具有增强稻瘟病抗性的作用，另外两个侧重调控水稻低温耐受能力，位点内部基因功能分工清晰、功能互补。这种一个复杂位点、多个功能组分的遗传结构，为耐冷和抗病的协同改良提供了新的思路。\n\n在此基础上，研究团队根据不同品种育种需求，对位点内部基因进行有针对性的选择和改造，并分别在“低温+病害”双逆境胁迫主栽品种和杂交稻亲本中进行验证，成功实现了耐冷性和稻瘟病抗性的定向优化。\n\n该研究为突破复杂位点连锁利用的限制，以及水稻耐冷、抗病等多性状协同改良提供了新的遗传资源和育种途径。该研究得到国家自然科学基金、国家重点研发计划等项目资助。\n\n相关论文信息：https:\u002F\u002Fdoi.org\u002F10.15302\u002Fvita.2026.09.0073","中国科学报·2026年10月4日","2026-10-04T14:10:00Z",91,{"impact":89,"substance":90,"depth":91,"authority":32,"freshness":92,"relevant":33,"comment":93},26,23,18,9,"国家级科研机构在野生稻复合位点上实现耐冷与抗病协同改良，属种业领域实质性突破，信息增量与权威度俱佳，值得进入每日精选。",[95],{"name":85,"url":82},[97,98,99,100,101],"种业振兴","抗逆育种","水稻育种","基因挖掘","野生稻",[],{"status":104,"locked":52,"version":53,"method":54,"evidence":105},"needs_review",{"candidates":106,"agricultural_evidence":26,"reason":124},[107,113,119],{"code":108,"score":77,"supported":26,"title":109,"abstract":110,"tags":112},"A1",[],[111],"水稻",[99],{"code":114,"score":77,"supported":26,"title":115,"abstract":116,"tags":118},"F1",[],[117],"种质资源",[97],{"code":120,"score":33,"supported":52,"title":121,"abstract":122,"tags":123},"F2",[],[],[100],"多个产业证据相当，需要确认主归属",[126,127],"中国农科院作科所 野生稻 RBA1","水稻 耐冷 稻瘟病 协同改良","中国农科院作科所野生稻RBA1-4185","2026-10-05T00:04:52.770006Z",{"id":131,"title":132,"url":133,"summary":134,"summary_zh":20,"content":135,"source_name":136,"source_url":20,"published_at":137,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":87,"score_detail":138,"sources":144,"tags":146,"primary_topic":151,"secondary_topics":157,"classification":158,"search_phrases":177,"slug":180,"view_count":33,"doi":20,"paper":20,"created_at":181},4183,"玉米密植高产精准调控技术刷新我国玉米高产纪录——新疆伊犁亩产达1799.1公斤","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042","央广网北京10月4日消息：玉米密植高产精准调控技术由李少昆团队研发推广，日前在新疆伊犁玉米攻关田现场实收再次刷新全国玉米高产纪录，亩产达1799.1公斤（较此前纪录高135.85公斤）。当前李少昆任农业农村部玉米单产提升工程专家指导组组长，该技术累计推广2.4亿亩、覆盖18个省区。河南新乡463万亩玉米已进入收获期，卫辉市杨大屯村种粮大户任书云种植200多亩玉米全部采用密植高产精准调控技术，每亩5500株（比去年多近500株），产量增加10%。","X\n\n[](http:\u002F\u002Fwww.sohu.com\u002F)\n\n[新闻](http:\u002F\u002Fnews.sohu.com\u002F)\n\n[体育](http:\u002F\u002Fsports.sohu.com\u002F)\n\n[汽车](http:\u002F\u002Fauto.sohu.com\u002F)\n\n[房产](http:\u002F\u002Fwww.focus.cn\u002F)\n\n[旅游](http:\u002F\u002Ftravel.sohu.com\u002F)\n\n[教育](http:\u002F\u002Flearning.sohu.com\u002F)\n\n[时尚](http:\u002F\u002Ffashion.sohu.com\u002F)\n\n[科技](http:\u002F\u002Fit.sohu.com\u002F)\n\n[财经](http:\u002F\u002Fbusiness.sohu.com\u002F)\n\n[娱乐](http:\u002F\u002Fyule.sohu.com\u002F)\n\n[母婴](http:\u002F\u002Fbaobao.sohu.com\u002F)\n\n[健康](https:\u002F\u002Fhealthnews.sohu.com\u002F)\n\n[历史](http:\u002F\u002Fhistory.sohu.com\u002F)\n\n[军事](http:\u002F\u002Fmil.sohu.com\u002F)\n\n[美食](http:\u002F\u002Fchihe.sohu.com\u002F)\n\n更多\n\n[文化](http:\u002F\u002Fcul.sohu.com\u002F)[星座](http:\u002F\u002Fastro.sohu.com\u002F)[专题](https:\u002F\u002Fwww.sohu.com\u002Fxchannel\u002FTURBd01EQXhPVGt5)[游戏](http:\u002F\u002Fgame.sohu.com\u002F)[搞笑](http:\u002F\u002Ffun.sohu.com\u002F)[动漫](http:\u002F\u002Facg.sohu.com\u002F)[宠物](http:\u002F\u002Fpets.sohu.com\u002F)\n\n[新闻](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[体育](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[汽车](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[房产](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[旅游](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[教育](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[时尚](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[科技](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[财经](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[娱乐](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[母婴](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[健康](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[历史](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[军事](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[美食](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[文化](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[星座](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[专题](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[游戏](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[搞笑](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[动漫](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n[宠物](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042)\n\n更多\n\n无障碍 关怀版\n\n登录\n\n# 玉米密植高产精准调控技术刷新我国玉米高产纪录\n\n2026-10-04 09:05 来源: [央广网](https:\u002F\u002Fwww.cnr.cn\u002Fchina\u002Fnews\u002F20261004\u002Ft20261004_527833712.shtml)\n\n![Image 1](https:\u002F\u002Fstatics.itc.cn\u002Fmptc-mpfe\u002Fimg\u002Farticle\u002Ficon_Wechat.png)\n\n[![Image 2](https:\u002F\u002Fstatics.itc.cn\u002Fmptc-mpfe\u002Fimg\u002Farticle\u002Ficon_Weibo.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https%3A%2F%2Fwww.sohu.com%2Fa%2F1083821308_362042&title=%E7%8E%89%E7%B1%B3%E5%AF%86%E6%A4%8D%E9%AB%98%E4%BA%A7%E7%B2%BE%E5%87%86%E8%B0%83%E6%8E%A7%E6%8A%80%E6%9C%AF%E5%88%B7%E6%96%B0%E6%88%91%E5%9B%BD%E7%8E%89%E7%B1%B3%E9%AB%98%E4%BA%A7%E7%BA%AA%E5%BD%95_%E5%8D%AB%E8%BE%89%E5%B8%82_%E5%8D%95%E4%BA%A7_%E6%9D%8E%E5%B0%91%E6%98%86&searchPic=true&pic=&appkey=2890110694)[![Image 3](https:\u002F\u002Fstatics.itc.cn\u002Fmptc-mpfe\u002Fimg\u002Farticle\u002Ficon_Space.png)](http:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https%3A%2F%2Fwww.sohu.com%2Fa%2F1083821308_362042)\n\n![Image 4](https:\u002F\u002Fstatics.itc.cn\u002Fmptc-mpfe\u002Fimg\u002Farticle\u002Ficon_Link.png)\n\n链接复制成功\n\n发布于：北京市\n\n央广网北京10月4日消息（记者朱敏）据中央广播电视总台中国之声《新闻和报纸摘要》报道，我国玉米密植高产精准调控技术正加快推广应用，助力玉米大面积单产提升，为全年粮食丰收打下坚实基础。\n\n当前，河南新乡市463万亩玉米已经进入收获期，卫辉市杨大屯村种粮大户任书云今年种植200多亩玉米，全部采用了密植高产精准调控技术，每亩达到了5500株，比去年多了近500株，产量增加10%。\n\n任书云：这几年长势非常好，每亩地产量在一千四五百斤以上。\n\n近几年，河南卫辉大力推广玉米密植高产精准调控技术，河南省卫辉市农业技术推广服务站站长刘广亮指出，增产明显。\n\n刘广亮：这几年，我们主要是玉米密度上合理增密，四年来平均每年密度增加150株左右。\n\n在玉米收获的同时，良种玉米筛选试验田里的工作人员也在开展田间评测和估产调查，河南省卫辉市农业技术推广中心农艺师姬东华正从众多候选材料中挑选出更加耐密的品种，为明年大面积推广做好储备。\n\n姬东华：我们正在品种的筛选区进行估产调查，目的是筛选出来耐密高产抗逆性强的品种，助力单产提升。\n\n玉米密植高产精准调控技术由中国农业科学院作物科学研究所研究员李少昆团队研发推广。日前，该技术在新疆伊犁的一块玉米攻关田，现场实收再次刷新全国玉米高产纪录，亩产达1799.1公斤，比此前的纪录高135.85公斤。当前，李少昆任农业农村部玉米单产提升工程专家指导组组长，该技术累计推广2.4亿亩，覆盖18个省区。\n\n李少昆：单产提升工程到今年是第四年，咱们的种植密度普遍提高，增产的幅度达到了168.1公斤，所以今年预计推广的面积应该超过1亿亩。[返回搜狐，查看更多](https:\u002F\u002Fwww.sohu.com\u002F?strategyid=00001&spm=smpc.content-abroad.content.2.1791158737673KKJBWmX \"点击进入搜狐首页\")\n\n_首赞_\n\n+1\n\n点赞失败\n\n阅读 (_362_)\n\n平台声明\n\n该文观点仅代表作者本人，搜狐号系信息发布平台，搜狐仅提供信息存储空间服务。\n\n[内容举报](https:\u002F\u002Fwww.sohu.com\u002Ffeedback?articleUrl=https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083821308_362042&pageType=report)\n\n[by Taboola](https:\u002F\u002Fpopup.taboola.com\u002Fen\u002F?template=colorbox&utm_source=sohuchina-sohucom&utm_medium=referral&utm_content=thumbnails-a:Below%20article%20thumbnails:)[by Taboola](https:\u002F\u002Fpopup.taboola.com\u002Fen\u002F?template=colorbox&utm_source=sohuchina-sohucom&utm_medium=referral&utm_content=thumbnails-a:Below%20article%20thumbnails:)\n\n[Sponsored Links](https:\u002F\u002Fpopup.taboola.com\u002Fen\u002F?template=colorbox&utm_source=sohuchina-sohucom&utm_medium=referral&utm_content=thumbnails-a:Below%20article%20thumbnails:)[Sponsored Links](https:\u002F\u002Fpopup.taboola.com\u002Fen\u002F?template=colorbox&utm_source=sohuchina-sohucom&utm_medium=referral&utm_content=thumbnails-a:Below%20article%20thumbnails:)\n\n[Promoted 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22:52[34](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083802551_121868748?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.3.1791158737673KKJBWmX#comment_area)\n\n[![Image 12: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083517838_122429467?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.4.1791158737673KKJBWmX)\n\n# [国庆高速堵疯了！今年的堵车，堵出新高度，彻底把网友笑不活了！](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083517838_122429467?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.4.1791158737673KKJBWmX)\n\n[![Image 13: 我是电影迷小雅头像](https:\u002F\u002Fq0.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20250517\u002Fe9bdbfea54c74d288d6852ec40af2794.png)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=NDY1NmMzODgtYzI4ZS00N2Y4LWE5MDgtZmI4ZWU5YWU4NDcy&spm=smpc.content-abroad.fd-d.4.1791158737673KKJBWmX)[我是电影迷小雅](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=NDY1NmMzODgtYzI4ZS00N2Y4LWE5MDgtZmI4ZWU5YWU4NDcy&spm=smpc.content-abroad.fd-d.4.1791158737673KKJBWmX)·10-02 20:47[3](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083517838_122429467?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.4.1791158737673KKJBWmX#comment_area)\n\n[![Image 14: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083975104_122496491?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.6.1791158737673KKJBWmX)\n\n# [1955年，四野旅长王化一仅获少校军衔，苦笑\"太丢人\"转身退伍，7年后一份密报让所有人...](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083975104_122496491?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.6.1791158737673KKJBWmX)\n\n[![Image 15: 离笙深夜谈头像](https:\u002F\u002Fq5.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20250808\u002Ffa7bc917de05468dafe0f8a155a7d155.png)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=MzY4MTBhNDktOThhOS00M2UxLTk5YTEtMzU3MmYwZGQwOGQx&spm=smpc.content-abroad.fd-d.6.1791158737673KKJBWmX)[离笙深夜谈](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=MzY4MTBhNDktOThhOS00M2UxLTk5YTEtMzU3MmYwZGQwOGQx&spm=smpc.content-abroad.fd-d.6.1791158737673KKJBWmX)·昨天 09:28[1](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083975104_122496491?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.6.1791158737673KKJBWmX#comment_area)\n\n[![Image 16: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083384689_121825481?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.7.1791158737673KKJBWmX)\n\n# [新型“出轨”太脏！不约会不开房，多少夫妻被“隐形绿”毁掉“性生活”](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083384689_121825481?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.7.1791158737673KKJBWmX)\n\n[![Image 17: 乔虎子头像](https:\u002F\u002Fq9.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20251215\u002F22071e1f7fb44865b5e9fab1b1a01c51.jpeg)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=OWY3ZmZjNzgtZDM2Yi00ZDhhLWFiMTUtYzE5NzZjZGEwNWUz&spm=smpc.content-abroad.fd-d.7.1791158737673KKJBWmX)[乔虎子](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=OWY3ZmZjNzgtZDM2Yi00ZDhhLWFiMTUtYzE5NzZjZGEwNWUz&spm=smpc.content-abroad.fd-d.7.1791158737673KKJBWmX)·10-02 21:23[4](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083384689_121825481?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.7.1791158737673KKJBWmX#comment_area)\n\n[![Image 18: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083068758_122543000?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.8.1791158737673KKJBWmX)\n\n# [“穿个内裤就出门了？”宝妈穿瑜伽短裤接孩子，到底得罪了谁](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083068758_122543000?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.8.1791158737673KKJBWmX)\n\n[![Image 19: 文静侃轶事头像](https:\u002F\u002Fq9.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20251017\u002F4482a2e8f01f4254ac6e750836387fc4.png)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=MzI2OTMzNDgtMTI1Zi00YjhjLTg1MDctOTJlOTU4N2EzYWNk&spm=smpc.content-abroad.fd-d.8.1791158737673KKJBWmX)[文静侃轶事](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=MzI2OTMzNDgtMTI1Zi00YjhjLTg1MDctOTJlOTU4N2EzYWNk&spm=smpc.content-abroad.fd-d.8.1791158737673KKJBWmX)·10-01 04:58[13](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083068758_122543000?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.8.1791158737673KKJBWmX#comment_area)\n\n[![Image 20: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1082313415_122952443?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.9.1791158737673KKJBWmX)\n\n# [唐朝，一个叫马周的书生找袁天罡看相，袁天罡说：你活不过一年！一年后，马周又来了](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1082313415_122952443?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.9.1791158737673KKJBWmX)\n\n[![Image 21: 稻述头像](https:\u002F\u002Fq1.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20260731\u002F5498ee55abc74c8b8f10428adb7b03cc.jpeg)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=YzliYWQyZTMtYTg0NS00M2Y0LWJmN2YtNDkxZjkzMzc3OWMy&spm=smpc.content-abroad.fd-d.9.1791158737673KKJBWmX)[稻述](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=YzliYWQyZTMtYTg0NS00M2Y0LWJmN2YtNDkxZjkzMzc3OWMy&spm=smpc.content-abroad.fd-d.9.1791158737673KKJBWmX)·10-03 10:19[1](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1082313415_122952443?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.9.1791158737673KKJBWmX#comment_area)\n\n深入探索\n\n体验极速搜索\n\n比较影音设备\n\n了解东亚文化\n\n预订体育场地\n\n发现精彩活动\n\n畅读历史书籍\n\n[![Image 22: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083666185_122769325?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.11.1791158737673KKJBWmX)\n\n# [丧尽天良！8 月 22 日辽宁沈阳，女子陪丈夫参加单位体检，顺手做了个胃肠镜。麻醉还没...](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083666185_122769325?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.11.1791158737673KKJBWmX)\n\n[![Image 23: 林鹿简科头像](https:\u002F\u002Fq3.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20260827\u002Fd3bfa0354a87416192dca81557218fc2.jpeg)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=Njk2Njk3ZDEtNTg1YS00N2RmLTlhYjctN2YyMmRiOGFkMzU3&spm=smpc.content-abroad.fd-d.11.1791158737673KKJBWmX)[林鹿简科](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=Njk2Njk3ZDEtNTg1YS00N2RmLTlhYjctN2YyMmRiOGFkMzU3&spm=smpc.content-abroad.fd-d.11.1791158737673KKJBWmX)·10-03 07:27[6](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083666185_122769325?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.11.1791158737673KKJBWmX#comment_area)\n\n[![Image 24: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083637033_121971598?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.12.1791158737673KKJBWmX)\n\n# [刘欢在上海因病离世，毛阿敏陪伴到最后，治疗地点为何选在上海](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083637033_121971598?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.12.1791158737673KKJBWmX)\n\n[![Image 25: 凌蝶聊汽车头像](https:\u002F\u002Fq5.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20240514\u002F79fcd01d4a144405823cbfabcf62f660.jpeg)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=MDgxNDE0NTctMGY1MS00MTRmLWE0OTEtM2I2MzE1YjE4NDZl&spm=smpc.content-abroad.fd-d.12.1791158737673KKJBWmX)[凌蝶聊汽车](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=MDgxNDE0NTctMGY1MS00MTRmLWE0OTEtM2I2MzE1YjE4NDZl&spm=smpc.content-abroad.fd-d.12.1791158737673KKJBWmX)·10-03 06:11[1](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083637033_121971598?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.12.1791158737673KKJBWmX#comment_area)\n\n[![Image 26: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083712405_122383536?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.13.1791158737673KKJBWmX)\n\n# [克林顿披露隐瞒近50年的数字：当年为了打赢朝鲜战争，美国共投入了不是30万，而是将近2...](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083712405_122383536?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.13.1791158737673KKJBWmX)\n\n[![Image 27: 刁纶唠游戏头像](https:\u002F\u002Fq8.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20250403\u002F66ee2578504a4ac3acc2260a415b4738.jpeg)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=YmZiNTA3OTAtMTA5Mi00NjVkLWE3YjAtNmEyNTMzNGZjMjEx&spm=smpc.content-abroad.fd-d.13.1791158737673KKJBWmX)[刁纶唠游戏](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=YmZiNTA3OTAtMTA5Mi00NjVkLWE3YjAtNmEyNTMzNGZjMjEx&spm=smpc.content-abroad.fd-d.13.1791158737673KKJBWmX)·10-03 10:11[1](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083712405_122383536?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.13.1791158737673KKJBWmX#comment_area)\n\n[![Image 28: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083433085_122407845?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.14.1791158737673KKJBWmX)\n\n# [世上没有后悔药！下半身贪婪的任素汐，终究为自己的行为付出代价](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083433085_122407845?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.14.1791158737673KKJBWmX)\n\n[![Image 29: 诺斯罗普的笔记头像](https:\u002F\u002Fq9.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20250422\u002F66da517b9fdf4392a6032b44b007abea.png)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=OWExMjJiMzktMzg3ZC00YTMyLWIyODgtYzg4YTU4ODUxMTBl&spm=smpc.content-abroad.fd-d.14.1791158737673KKJBWmX)[诺斯罗普的笔记](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=OWExMjJiMzktMzg3ZC00YTMyLWIyODgtYzg4YTU4ODUxMTBl&spm=smpc.content-abroad.fd-d.14.1791158737673KKJBWmX)·10-02 09:17[1](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1083433085_122407845?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.14.1791158737673KKJBWmX#comment_area)\n\n[![Image 30: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1082565796_121739944?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.16.1791158737673KKJBWmX)\n\n# [顾不上美国了！伊朗突发政变，总统下台已成定局？中俄万没料到，伊朗内部彻底乱成一锅粥](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1082565796_121739944?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.16.1791158737673KKJBWmX)\n\n[![Image 31: 科学知识点秀头像](https:\u002F\u002Fp6.itc.cn\u002Fc_lfill,w_40,h_40,g_face\u002Fimages03\u002F20230620\u002F6ec8e3f3171248c997dccb7019db7f5f.png)](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=N2I4NzVmNTYtYWVhYy00M2VhLWFkMDEtYzg4OWRhNWFjZTg3&spm=smpc.content-abroad.fd-d.16.1791158737673KKJBWmX)[科学知识点秀](https:\u002F\u002Fmp.sohu.com\u002Fprofile?xpt=N2I4NzVmNTYtYWVhYy00M2VhLWFkMDEtYzg4OWRhNWFjZTg3&spm=smpc.content-abroad.fd-d.16.1791158737673KKJBWmX)·10-03 16:41[2](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1082565796_121739944?scm=10008.1479_13-1479_13-68_68.4935001-5096002-0.0-1-0-0-0.0&spm=smpc.content-abroad.fd-d.16.1791158737673KKJBWmX#comment_area)\n\n[![Image 32: 文章配图-82](https:\u002F\u002Fstatics.itc.cn\u002Fweb\u002Fstatic\u002Fimages\u002Fpic\u002Fpreload.png)](https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1084006365_122655210?scm","央广网·2026年10月4日","2026-10-04T01:05:00Z",{"impact":139,"substance":140,"depth":141,"authority":142,"freshness":25,"relevant":33,"comment":143},28,22,17,14,"全国玉米高产纪录刷新，技术累计推广2.4亿亩，属产业级重大突破，时效性强，值得进入每日精选。",[145],{"name":136,"url":133},[97,147,148,149,150],"粮食安全","玉米单产提升","新疆伊犁","密植精准调控",{"code":152,"slug":153,"name":154,"path":155,"parent_code":156},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":159},{"candidates":160,"agricultural_evidence":26},[161,166,173],{"code":152,"score":162,"supported":26,"title":163,"abstract":164,"tags":165},12,[154],[154],[148],{"code":167,"score":168,"supported":26,"title":169,"abstract":170,"tags":172},"A8",5,[],[171],"单产提升",[148,147],{"code":114,"score":33,"supported":52,"title":174,"abstract":175,"tags":176},[],[],[97],[178,179],"玉米密植高产精准调控技术 1799.1公斤","李少昆 新疆伊犁 玉米高产纪录","玉米密植高产精准调控技术1799.1公斤-4183","2026-10-05T00:04:48.577330Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":20,"content":20,"source_name":187,"source_url":20,"published_at":188,"category":189,"cover_url":20,"hotness":25,"is_selected":26,"score":190,"score_detail":191,"sources":193,"tags":195,"primary_topic":200,"secondary_topics":204,"classification":216,"search_phrases":240,"slug":243,"view_count":77,"doi":20,"paper":244,"created_at":254},4194,"数字乡村建设的代价？数字乡村战略对粮食安全和农业碳循环的非对称影响——来自中国的证据","https:\u002F\u002Fideas.repec.org\u002Fa\u002Fspr\u002Fagfoec\u002Fv14y2026i1d10.1186_s40100-026-00503-9.html","Weilong Gao、Qian Zhang、Ying Peng（吉林大学国家发展与安全研究院、大连理工大学商学院、长春工业大学经济管理学院）。发表于Agricultural and Food Economics 2026, 14(1): 1-22。基于989个县和1835户农户的多层次数据，将县级数字乡村发展指数与家庭层面数字采纳指数链接，运用固定效应和两阶段最小二乘法模型。结果显示：农村数字化与粮食产量显著正相关，与农业碳排放显著负相关，但对作物生物量碳吸收或净碳足迹无显著改善。机制分析表明生产收益与土地利用集约化（特别是双季种植扩张）相关，减排与减少劳动力和农药使用一致。异质性分析显示数字基础设施驱动生产率增长，数字治理和数字生活与减排关联更密切。","Agricultural and Food Economics 2026, 14(1)","2026-10-01T00:00:00Z","论文",88,{"impact":140,"substance":29,"depth":31,"authority":142,"freshness":92,"relevant":33,"comment":192},"基于989县1835户的全国性实证研究，揭示数字乡村对粮食安全与农业碳循环的非对称影响，数据规模大、结论有政策参考价值，值得进入每日精选。",[194],{"name":187,"url":185},[196,197,147,198,199],"数字乡村","数字基础设施","农业碳排放","农户数字化",{"code":167,"slug":201,"name":202,"path":203,"parent_code":156},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",[205,210],{"code":206,"slug":207,"name":208,"path":209,"parent_code":48},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon",{"code":211,"slug":212,"name":213,"path":214,"parent_code":215},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",{"status":51,"locked":52,"version":53,"method":54,"evidence":217},{"candidates":218,"agricultural_evidence":26},[219,226,231,235],{"code":206,"score":91,"supported":26,"title":220,"abstract":222,"tags":225},[221],"农业碳",[223,221,224],"碳排放","碳足迹",[198],{"code":211,"score":227,"supported":26,"title":228,"abstract":229,"tags":230},13,[196],[196],[196,197],{"code":167,"score":92,"supported":26,"title":232,"abstract":233,"tags":234},[147],[],[147],{"code":64,"score":58,"supported":26,"title":236,"abstract":237,"tags":239},[],[238],"农药",[],[241,242],"数字乡村 粮食安全 农业碳循环","吉林大学 数字乡村 农户","数字乡村粮食安全农业碳循环-4194",{"doi":20,"openalex_id":20,"authors":245,"venue":20,"cited_by_count":246,"oa_url":20,"card":247,"direction":251,"ingested_from":253},[],0,{"tldr":248,"method":249,"finding":250,"direction":251,"opportunity":252},"基于中国县级与农户数据，评估数字乡村战略对粮食安全和农业碳循环的非对称影响。","989县与1835户多层次数据，固定效应和两阶段最小二乘法。","数字化提升粮食产量并减少碳排放，但未改善作物碳吸收或净碳足迹。","数字乡村与农业信息化","可探究数字乡村对碳吸收无显著影响的原因，并优化数字治理以协同减排与固碳。","agent","2026-10-05T00:04:55.880239Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":20,"content":260,"source_name":261,"source_url":20,"published_at":262,"category":24,"cover_url":20,"hotness":25,"is_selected":26,"score":190,"score_detail":263,"sources":265,"tags":267,"primary_topic":273,"secondary_topics":279,"classification":280,"search_phrases":322,"slug":325,"view_count":77,"doi":20,"paper":20,"created_at":326},4180,"特稿：端牢14亿人饭碗——黑龙江大粮仓向科技要粮","https:\u002F\u002Fwww.zaobao.com\u002Fnews\u002Fchina\u002Fstory20261004-9770795","10月4日，联合早报刊发特稿介绍黑龙江北大荒七星农场无人驾驶收割稻机（北斗导航+5G）+智慧农场演示。黑龙江省去年粮食产量8200.3万吨、连续16年居全国首位。水稻、玉米、大豆机械化率分别达89.18%、92.23%、89.42%。农业农村部部长张柱9-29在国新办发布会上表示，中国粮食产量已连续两年稳定在1.4万亿斤以上，但供需仍处\"紧平衡\"，\"口粮减、饲料粮增\"；2025\u002F26年度中国大豆进口量上调至1.08亿吨。一台玉米联合收获机售价近70万元、大马力混动拖拉机约100万元，连片土地是科技推广前提。","![Image 1: 位于中国黑龙江省佳木斯市富锦市的北大荒七星农场，无人驾驶收割机借助北斗卫星导航定位系统和5G技术，进行水稻收割、脱粒和清选。图摄于9月29日。 （沈泽玮摄）](https:\u002F\u002Fcassette.sphdigital.com.sg\u002Fimage\u002Fzaobao\u002F90f22e341ad0eff28ddf646189deaea84d9e5c174cd360d51367a917be0fbbb2?f=webp&o=zbimg&oloc=se)\n\n位于中国黑龙江省佳木斯市富锦市的北大荒七星农场，无人驾驶收割机借助北斗卫星导航定位系统和5G技术，进行水稻收割、脱粒和清选。图摄于9月29日。 （沈泽玮摄）\n\n“沙沙、嚓嚓、轰轰……”\n\n在记者们摆好器材后，工作人员手机一按，五台无人驾驶水稻收割机同时启动，在中国黑龙江北大荒七星农场的稻田上演示收割，现场传出阵阵响声。\n\n驾驶座空无一人，配备北斗导航系统的收割机按照预设路线自动行驶、转弯，在金黄稻浪中完成收割、脱粒和清选。“新农人”无需下田，通过手机屏幕便能实时掌控运行状态。城里人若第一次在黑夜中看到无人车自行穿梭农田，相信会产生些许超现实魔幻感，正如黑龙江副省长丛丽在新闻发布会上形容初见时：“既神奇，也很惊悚。”\n\n这份“惊悚”背后是中国农业从“看天”到“看屏”、从人工到机械再到智能的演变；从卷起裤管拿镰刀下田，到改革开放后逐步采用拖拉机和联合收割机，再到近年来的无人驾驶农机。机器替代的不只是汗水，更是在[农村年轻劳动力减少](https:\u002F\u002Fwww.zaobao.com\u002Fnews\u002Fchina\u002Fstory20251121-7854967?ref=article-inline)、老龄化加剧背景下降低对人工依赖。\n\n不过，对于拥有14亿多人口的大国而言，农业科技最终要解决的，不仅是机器能不能无人化，更是能否让有限土地产出更多、更稳定、更符合市场需求的粮食。\n\n工作人员手机一按，五台无人驾驶水稻收割机同时启动，在中国黑龙江北大荒七星农场的稻田上演示收割。图摄于9月29日。（沈泽玮摄）\n\n黑龙江北大荒七星农场使用的无人驾驶收割机。图摄于9月29日。（沈泽玮摄）\n\n## 从“吃得饱”到“吃得好”：消费升级下的紧平衡\n\n中国在1959年至1962年经历严重困难时期。长期以来，粮食安全已成中国治国理政的重要议题，守住18亿亩耕地红线更是坚持的刚性约束。\n\n中共总书记习近平2013年12月在中央经济工作会议上指出：“我国是人口众多的大国，解决好吃饭问题，始终是治国理政的头等大事。”会议提出，实施“以我为主、立足国内、确保产能、适度进口、科技支撑”的国家粮食安全战略，强调依靠自己保口粮，做到谷物基本自给、口粮绝对安全。\n\n10多年后，中国粮食生产能力明显提高。2024年，全国农作物耕种收综合机械化率达到75.64%，提前一年完成“十四五”（2021年至2025年）规划目标；水稻、小麦、玉米和大豆的机械化率分别达到89.18%、98%、92.23%和89.42%，植保无人驾驶航空器达到20万1000架。\n\n黑龙江北大荒七星农场在9月29日演示植保无人机飞行。植保无人机主要用于田间智能化精准施药、施肥和巡田作业。（沈泽玮摄）\n\n但粮食安全并不意味供需压力已消除。中国农业农村部部长张柱9月29日在国务院新闻办公室新闻发布会上说，中国粮食产量已连续两年稳定在1.4万亿斤以上，人均粮食占有量超过500公斤，粮食安全有充分保障；但与此同时，粮食供需仍处于“紧平衡”；粮食供给结构还需优化。\n\n主因是中国人的饮食结构正在发生变化。米面消费减少，肉、蛋、奶等副食品消费增加，而畜牧业又需要大量玉米、大豆等饲料粮。张柱将这种变化形容为“口粮减、饲料粮增”，粮食总需求仍会持续增加。他也提到农业自然灾害旱情影响较大，“旱是一大片，涝是一条线”；今年涝灾重于旱灾，但总体气象条件仍有利。\n\n粮食安全不仅涉及有没有粮，还包括种什么、怎么种、怎样提高单产，以及哪些粮食需要通过国际市场补足。黑龙江这个大粮仓，也是中国最大的大豆生产省所面对的问题更尖锐：在有限的耕地上，粮食要如何继续增产？又该种什么？\n\n## 大豆的结构性困境与国际市场\n\n2012年中共十八大以来，习近平四次赴黑龙江，并强调黑龙江要“当好国家粮食安全压舱石”。记者在“十一”长假前秋收时，参加由国新办组织安排的黑龙江省参访行程中，不止一次听到当地官员说，“全国每九碗饭中有一碗来自黑龙江”。\n\n黑龙江主要种植玉米、水稻和大豆；玉米产量最高，水稻其次，大豆则是中国最大的大豆生产省，约占全国43%。去年，黑龙江粮食产量达8200.3万吨，连续16年位居全国首位。水稻关系口粮安全，玉米和大豆则与饲料、食用油需求密切相关。\n\n[中美贸易摩擦加剧后](https:\u002F\u002Fwww.zaobao.com\u002Fnews\u002Fchina\u002Fstory20260914-9675365?ref=article-inline)，中国持续推动大豆扩种和增产。2022年国产大豆产量首破2000万吨，2025年达2091万吨，但仍不足以改变高度依赖进口的格局。中国农业农村部9月的最新分析，上调了2025\u002F26年度中国大豆进口量至1.08亿吨，据估算占消费量近九成，进口大豆主要用于饲料和榨油。2025年全年实际进口则达创纪录的1.12亿吨，其中约四分之三来自巴西，美国、阿根廷分列其后。\n\n位于黑龙江省哈尔滨市的和粮农业有限公司，其中一个角落展示世界各种杂粮，包括大豆。图摄于9月28日。（沈泽玮摄）\n\n中国近年来持续通过大豆生产者补贴、粮豆轮作和单产提升等政策，扩大大豆种植、提高单位面积产量，同时降低农户种植风险。\n\n但土地有限，水稻、玉米和大豆又各有需求，农业农村部种植业管理司农情信息处处长秦兴国将大豆问题形容为“结构性问题”，认为“手心手背都是肉”，不能简单为了扩大大豆种植而把其他作物挤掉。他认为，“价格是个风向”，农户也会比较不同作物的价格、成本和收益做决定。\n\n这种种植结构上的取舍，在黑龙江同样明显。北大荒集团建三江七星农场智慧农业农机中心主任孟庆山介绍，建三江耕地面积超过1200万亩，其中水稻约1000万亩，大豆和玉米合计约200多万亩，大豆和玉米实行轮作，以保护黑土地。\n\n因此，在有限耕地上提高国产大豆产量之外，中国仍需通过国际市场保障大豆供给，并降低进口来源过度集中的风险。\n\n## 精度重塑黑土地：一亩地还能多产多少？\n\n黑龙江要解决的另一问题是，依靠农业科技让一亩地多产一点。\n\n北大荒集团七星农场智慧农业农机中心高级主管刘建国介绍，无人收割机利用北斗导航，定位误差仅2.5厘米，一台手机即可控制五台收割机。自动驾驶能让农机行驶得更笔直，减少人为偏差，“合理地利用我们每一寸土地，产量最大化”。\n\n佳木斯骥驰拖拉机制造公司籽粒机项目总监杨圣斌指出，智能农机的关键不只是无人，而是机器能否根据作物含水量、环境湿度及不同地块适应调整作业参数。\n\n在建三江国家农业科技园区，科技从土壤源头就已介入。七星农场农业技术推广中心副主任金立军介绍，当地根据土壤检测结果精准施肥，“缺什么补什么，缺多少补多少”，以动态维持养分平衡，保护黑土地长期生产力。\n\n在北大荒建三江国家农业科技园区的黑土地综合保护实验室，科研人员正在进行土壤化验。据介绍，科研人员将按缺什么补什么、缺多少补多少的原理，指导种植户科学精准施肥。图摄于9月30日。（沈泽玮摄）\n\n黑龙江北大荒七星农场的黑土地被形容为“黑油油”，是因为这里拥有珍贵、肥沃的优质黑土资源。图摄于9月29日。（沈泽玮摄）\n\n在以“五常大米”闻名的五常市，乔府大院农业公司党委书记、董事长乔文志在记者见面会上介绍，企业与中科院遗传所合作10多年，对“稻花香二号”提纯复壮，并开展新品种选育以及抗寒、抗倒伏等方面技术攻关。\n\n面对极端天气风险，北大荒创业农场公司副总经理张少波提到，依靠气象预警提前防范。桦川县种粮大户李玉成的感受更直接：过去农民发现病虫害后再“治”，如今结合气象、土壤墒情等监测数据提前判断，变为“防”。他感叹：“科学让我从会种地变到不会种地，现在智慧农场又变成会种地了。”他又称，智慧农场让农户节省人力，“让我们在土地上解放出来了，也让我们农民有了自己的快乐”。\n\n## 从地头到千家万户：科技推广的现实门槛\n\n负责七星农场演示活动的潍柴雷沃易田智慧农业工作人员宋金星说，智慧农业正形成覆盖“耕种管收”的一体化解决方案，但目前仍难以实现农业生产全流程完全无人化。他也观察到，智能设备与数据化管理降低了门槛，让年轻人也能参与农业生产。\n\n大型农场有集中连片的土地让大型农机发挥效率，但对小农户而言，问题不仅在于设备价格，更在于没有足够大的土地规模摊薄成本。\n\n佳木斯骥驰拖拉机制造公司销售总监杨海燕透露，一台玉米联合收获机售价接近70万元人民币（13万4000新元），大马力混动拖拉机价格约100万元人民币；大马力农机需要连片土地，若地块过于零碎，频繁转弯转场会浪费时间和成本。杨圣斌则认为，土地流转和人口城镇化后，大型农机具越来越派得上用场。\n\n佳木斯骥驰拖拉机制造有限公司的玉米联合收获机。摄于9月29日。（沈泽玮摄）\n\n佳木斯骥驰拖拉机制造有限公司的拖拉机，具备300马力发动机、配置油电混动无极变速箱。摄于9月29日。（沈泽玮摄）\n\n土地流转不仅是一笔经济账，也涉及农民的情感黏性。按秦兴国观察，对一些年长农民来说，土地不仅是收入来源，也是心理依托：“对老百姓来讲，他有几亩地，他觉得他还有个事干。”黑龙江当地人士则透露，老农民不愿托管土地，还涉及“地留子孙、把地交出去不踏实”的老观念以及分成条件的拉锯。\n\n科技从示范田走向千家万户，仍存在技术与成本门槛。《经济日报》引述中国农业大学教授王玉斌指出，现代农业装备存在成本高、运维难等问题，小农户“不是买不起，就是用不好”；中国网信网9月也刊文提出，智慧农业要走“适宜化、低成本”路线，通过社会化服务解决小农户痛点。\n\n黑龙江的无人农机与智慧农场，直面农业生产如何提高效率的问题，但要让科技支撑起14亿多人的饭碗，还需将技术以更低成本覆盖普通农户。端牢饭碗，既要靠科技，也要通过种植结构调整和进口，补足大豆等国内难以满足的供给。\n\n饭碗在田里，也在田外。","联合早报·2026年10月4日","2026-10-04T00:00:00Z",{"impact":89,"substance":140,"depth":91,"authority":227,"freshness":92,"relevant":33,"comment":264},"联合早报特稿，以黑龙江北大荒秋收现场为切口，结合官方发布会数据与多方信源，系统呈现无人农机、大豆结构性困境与黑土地保护等粮食安全议题，信息增量与专业深度俱佳，值得进入每日精选。",[266],{"name":261,"url":258},[268,269,147,270,271,272],"智慧农业","北斗导航","黑土地保护","无人农机","大豆扩种",{"code":274,"slug":275,"name":276,"path":277,"parent_code":278},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":281},{"candidates":282,"agricultural_evidence":26},[283,288,292,296,302,308,312,317],{"code":274,"score":168,"supported":26,"title":284,"abstract":285,"tags":287},[],[286],"机械化",[269,271],{"code":108,"score":58,"supported":26,"title":289,"abstract":290,"tags":291},[],[111],[],{"code":152,"score":58,"supported":26,"title":293,"abstract":294,"tags":295},[],[154],[],{"code":297,"score":58,"supported":26,"title":298,"abstract":299,"tags":301},"A4",[],[300],"大豆",[],{"code":303,"score":58,"supported":26,"title":304,"abstract":305,"tags":307},"C4",[],[306],"饲料",[],{"code":167,"score":33,"supported":52,"title":309,"abstract":310,"tags":311},[],[],[147],{"code":313,"score":33,"supported":52,"title":314,"abstract":315,"tags":316},"H1",[],[],[270],{"code":318,"score":33,"supported":52,"title":319,"abstract":320,"tags":321},"I1",[],[],[268],[323,324],"北大荒 七星农场 无人收割机","黑龙江 大豆 进口 扩种","北大荒七星农场无人收割机-4180","2026-10-05T00:04:48.399082Z",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":20,"content":332,"source_name":333,"source_url":20,"published_at":334,"category":24,"cover_url":20,"hotness":25,"is_selected":52,"score":335,"score_detail":336,"sources":340,"tags":342,"primary_topic":348,"secondary_topics":349,"classification":350,"search_phrases":366,"slug":369,"view_count":168,"doi":20,"paper":20,"created_at":370},4181,"我国秋粮迎来大面积收获——进度过三成，进度快于去年同期","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7692734410965254696","央视网新闻联播10月3日报道：秋粮是全年粮食生产的大头，约占总产量3\u002F4。金秋时节，国庆假期迎来大面积收获。农业农村部最新农情调度显示，全国秋粮收获已过三成，进度快于去年。新疆伊犁察布查尔县玉米密植高产精准调控技术万亩示范田亩产从去年1542.2公斤增至1609.4公斤；黑龙江北大荒嫩北农场930亩大豆高产攻关田单产突破540斤、同比+17%；江苏稻飞虱防控面积超8000万亩次。中央财政今年安排13.31亿元支持秋粮抗逆防灾等喷施作业。","## 【在希望的田野上】我国秋粮迎来大面积收获 进度过三成\n\n2026-10-04 16:58·[央广网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAiR6RuL6xARCa1a88UYsGDkFIn-3CItRPyJJaR4Ym4C8\u002F?source=tuwen_detail)\n\n来源：央视网\n\n**央视网消息**（新闻联播）：秋粮是全年粮食生产的大头，约占总产量的3\u002F4。金秋时节，我国秋粮陆续成熟，国庆假期迎来大面积收获。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Fb3171c9d999c4acc917848f14c05956a~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791763514&x-signature=dCd2qTc7FkaOXjOIbQw%2BQzJ9HIg%3D)\n我国秋粮从南到北分布广、品种多，主要包括中稻、晚稻、玉米、大豆和薯类等。农业农村部最新农情调度显示，全国秋粮收获已过三成，进度快于去年同期。\n\n在新疆伊犁察布查尔县，当地重点推广玉米密植高产精准调控技术，万亩示范田亩产从去年的1542.2公斤增加到1609.4公斤，刷新当地万亩高产纪录。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fc206f2a9cc6ea86c77721adc08c48197~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791763514&x-signature=tJ5cAXfmICGBRusZEiyuC%2B%2FJGRk%3D)\n这几天，东北地区大豆开始收获，长势良好。在黑龙江北大荒嫩北农场930亩大豆高产攻关田，得益于集成增产技术落实到位，大豆成功抗住多雨渍涝影响，实现逆势增产，单产突破540斤，同比增长17%。\n\n眼下，长江流域中稻正陆续成熟。受前期台风影响，再加上近期气温偏高，对水稻产量影响较大的稻飞虱发生风险增加。在江苏，当地加密巡田频次，加紧统防统治，目前防控面积超过8000万亩次。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F5f9d21e3934c4cf22463ec6b84545024~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791763514&x-signature=GDXRDMUqxN%2FqLrqEbRZOQC8rfBg%3D)\n今年，中央财政安排13.31亿元资金重点支持秋粮抗逆防灾等喷施作业，促灌浆。\n\n全国秋粮集中收获期预计持续到10月下旬。农业农村部提醒，要分区分类加强后期田管，提早组织农机供需对接，抢抓晴好天气推进秋粮收获。\n\n**更多精彩资讯请在应用市场下载“央广网”客户端。欢迎提供新闻线索，24小时报料热线400-800-0088；消费者也可通过央广网“啄木鸟消费者投诉平台”线上投诉。版权声明：本文章版权归属央广网所有，未经授权不得转载。转载请联系：cnrbanquan@cnr.cn，不尊重原创的行为我们将追究责任。**","央视网新闻联播·2026年10月3日","2026-10-03T11:56:00Z",83,{"impact":89,"substance":337,"depth":32,"authority":32,"freshness":338,"relevant":33,"comment":339},20,7,"全国秋粮收获进度过三成的权威农情调度，含高产纪录与防灾资金等实质数据，时效性稍弱但值得精选。",[341],{"name":333,"url":330},[343,344,345,346,347],"抗逆防灾","大豆单产","秋粮收获","玉米密植","稻飞虱防控",{"code":152,"slug":153,"name":154,"path":155,"parent_code":156},[],{"status":51,"locked":52,"version":53,"method":54,"evidence":351},{"candidates":352,"agricultural_evidence":26},[353,357,361],{"code":152,"score":77,"supported":26,"title":354,"abstract":355,"tags":356},[],[154],[346],{"code":297,"score":58,"supported":26,"title":358,"abstract":359,"tags":360},[],[300],[],{"code":362,"score":33,"supported":52,"title":363,"abstract":364,"tags":365},"J4",[],[],[345],[367,368],"嫩北农场 大豆 高产攻关","察布查尔 玉米密植 万亩示范田","嫩北农场大豆高产攻关-4181","2026-10-05T00:04:48.451878Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":20,"content":20,"source_name":376,"source_url":20,"published_at":188,"category":189,"cover_url":20,"hotness":25,"is_selected":52,"score":377,"score_detail":378,"sources":381,"tags":383,"primary_topic":389,"secondary_topics":394,"classification":395,"search_phrases":416,"slug":419,"view_count":58,"doi":20,"paper":420,"created_at":428},4196,"中国农业大学学报2026年第10期：人工智能赋能质量农业发展等6篇论文集中刊发","https:\u002F\u002Fcjournal.hep.com.cn\u002F1007-4333\u002FCN\u002F2026\u002F31\u002F10","中国农业大学学报2026年第31卷第10期集中刊发多篇论文：(1)徐雷《人工智能赋能质量农业发展：农产品智能检测与质量保障研究进展》（1-13）；(2)马勇等《基于\"AI+半导体光催化\"的特种鱼养殖水体绿色原位治理与智能调控方案研究》（14-26）；(3)丁晓颖等《基于机器学习与多维度气象特征的湖北省稻瘟病预测研究》（27-42）；(4)刘宁等《低空技术赋能智慧林业的关键经济学问题与展望》（43-58）；(5)王珏等《智能经济对绿色全要素生产率的影响》（59-75）；(6)蔡雪玲《中国农业农村数字化绿色化协同发展的时空格局及其增收效应》（76-93）。","中国农业大学学报2026, 31(10)",81,{"impact":91,"substance":140,"depth":31,"authority":142,"freshness":379,"relevant":33,"comment":380},8,"核心期刊集中刊发6篇AI赋能农业论文，方法新颖、信息增量足，但属学术成果汇编，产业级影响有限。",[382],{"name":376,"url":374},[268,384,385,386,387,388],"农业人工智能","农业数字化","智慧林业","绿色低碳","稻瘟病预测",{"code":390,"slug":391,"name":392,"path":393,"parent_code":215},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":396},{"candidates":397,"agricultural_evidence":26},[398,403,408,412],{"code":390,"score":77,"supported":26,"title":399,"abstract":400,"tags":402},[],[401],"机器学习",[384],{"code":318,"score":404,"supported":52,"title":405,"abstract":406,"tags":407},2,[],[],[385,268],{"code":206,"score":33,"supported":52,"title":409,"abstract":410,"tags":411},[],[],[387],{"code":215,"score":33,"supported":52,"title":413,"abstract":414,"tags":415},[],[],[385],[417,418],"中国农业大学学报 人工智能 质量农业","机器学习 稻瘟病预测 湖北","中国农业大学学报人工智能质量农业-4196",{"doi":20,"openalex_id":20,"authors":421,"venue":20,"cited_by_count":246,"oa_url":20,"card":422,"direction":426,"ingested_from":253},[],{"tldr":423,"method":424,"finding":425,"direction":426,"opportunity":427},"中国农业大学学报2026年第10期集中刊发6篇论文，主题涵盖人工智能赋能质量农业、智慧林业、绿色发展","论文合集，涉及AI检测、机器学习、半导体光催化、低空技术、智能经济与数字化绿色化","AI、低空技术、机器学习等正赋能农业质量检测、病害预测、水体治理及林业经济，并影响绿色全要素生产率与","农业人工智能与决策模型","可探索AI与半导体光催化、低空技术在种养业中的集成应用，以及数字化绿色化协同的农户增收机制。","2026-10-05T00:04:56.013263Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":20,"content":20,"source_name":434,"source_url":20,"published_at":188,"category":189,"cover_url":20,"hotness":25,"is_selected":52,"score":435,"score_detail":436,"sources":438,"tags":440,"primary_topic":444,"secondary_topics":445,"classification":446,"search_phrases":474,"slug":477,"view_count":478,"doi":20,"paper":479,"created_at":486},4197,"智慧农业(中英文)2026年多篇论文集中上线","https:\u002F\u002Fwww.ncpssd.cn\u002Fjournal\u002Fsecure\u002Fdetails?params=Q0Z3MVcrNHQxM3JCazJqWnVka3BVYXFWdUtYZS9xSit2elNpRXp6NThqRWt2dUZ1dTFtTFJ4Rk0wTHNYQ1hyUQ","智慧农业(中英文)2026年网络首发多篇：(1)杨积忠等《基于特征优选与优化算法的干热河谷区柑橘蒸腾模拟》；(2)吴明清等《多尺度异构特征协同的棉花叶片病害检测模型》；(3)沈学利等《LiteFocus-Net：面向玉米叶片病虫害小目标检测的三点式轻量化增强框架》；(4)杨玉等《农业产业链可信数据空间：理论框架、运行机制与实施路径》；(5)张新等《数字技术赋能下生态农产品产业链脆弱性的基模分析与韧性治理》；(6)王翠霞等《数字技术赋能下生态农产品产业链脆弱性的基模分析与韧性治理》。","智慧农业(中英文)·2026年",80,{"impact":91,"substance":140,"depth":91,"authority":142,"freshness":379,"relevant":33,"comment":437},"核心期刊多篇论文集中网络首发，涵盖病害检测、蒸腾模拟与产业链数据空间等前沿方向，专业增量充足，值得精选。",[439],{"name":434,"url":432},[268,384,441,442,443],"产业链韧性","农业数据空间","病虫害检测",{"code":390,"slug":391,"name":392,"path":393,"parent_code":215},[],{"status":51,"locked":52,"version":53,"method":54,"evidence":447},{"candidates":448,"agricultural_evidence":26},[449,453,458,462,468],{"code":318,"score":162,"supported":26,"title":450,"abstract":451,"tags":452},[268],[268],[268],{"code":390,"score":168,"supported":26,"title":454,"abstract":455,"tags":457},[],[456],"目标检测",[384,443],{"code":152,"score":58,"supported":26,"title":459,"abstract":460,"tags":461},[],[154],[],{"code":463,"score":58,"supported":26,"title":464,"abstract":465,"tags":467},"A6",[],[466],"棉花",[],{"code":469,"score":58,"supported":26,"title":470,"abstract":471,"tags":473},"B2",[],[472],"柑橘",[],[475,476],"智慧农业 中英文 网络首发","棉花叶片病害 玉米病虫害 检测模型","智慧农业中英文网络首发-4197",6,{"doi":20,"openalex_id":20,"authors":480,"venue":20,"cited_by_count":246,"oa_url":20,"card":481,"direction":426,"ingested_from":253},[],{"tldr":482,"method":483,"finding":484,"direction":426,"opportunity":485},"汇总《智慧农业》2026年多篇论文，涵盖蒸腾模拟、病害检测与产业链数字化。","特征优选与优化算法、多尺度异构特征、轻量化检测框架、可信数据空间。","研究聚焦作物表型智能检测与农业产业链数字治理两大方向。","可探索轻量化病害检测模型在边缘设备部署与产业链数据可信共享的交叉研究。","2026-10-05T00:04:56.092764Z",{"id":488,"title":489,"url":490,"summary":491,"summary_zh":492,"content":20,"source_name":493,"source_url":490,"published_at":494,"category":189,"cover_url":20,"hotness":25,"is_selected":52,"score":435,"score_detail":495,"sources":497,"tags":499,"primary_topic":20,"secondary_topics":504,"classification":505,"search_phrases":564,"slug":567,"view_count":33,"doi":568,"paper":569,"created_at":594},4178,"From agronomic practices to carbon credits: A systematic review of agricultural carbon markets in India","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16451","Carbon markets are becoming an important tool for reducing greenhouse gas (GHG) emissions from agriculture and they are particularly relevant for India, which is both the world’s third-largest GHG emitter and a major agricultural economy. This systematic review synthesises current evidence on the development, mechanisms and emerging trends of carbon markets in Indian agriculture, situating them within the wider global context. Following a Preferred reporting items for systematic reviews and meta-analyses (PRISMA)-guided search of five databases (247 records identified, 62 studies retained), the review examines five closely connected areas: global and India-specific carbon market mechanisms; agronomic practices that enable market participation; major agricultural GHG sources and mitigation opportunities; soil health and carbon sequestration; and methodological, policy and inclusion gaps. The synthesis demonstrates that sustainable practices such as conservation agriculture, alternate wetting and drying (AWD) in rice systems, biochar application, precision nutrient management and agroforestry can reduce agricultural emissions while also improving soil fertility and carbon storage. Furthermore, emerging tools, including blockchain-based transparency systems and AI-enabled precision agriculture, can strengthen market credibility and make participation more accessible for smallholder farmers. However, agricultural carbon markets in India remain at an early stage and their growth is constrained by weak measurement, reporting and verification (MRV) systems, inadequate smallholder inclusion, concerns around additionality and the lack of a comprehensive domestic regulatory framework. Uncertainties around carbon permanence, leakage, transaction costs and market volatility further temper these prospects and warrant cautious interpretation of reported gains. This review identifies four priority areas for future work: standardising robust MRV systems, developing integrated impact models, designing inclusive market mechanisms and aligning carbon market policies with India’s broader agricultural, climate and livelihood commitments. Addressing these gaps will be essential for realising agriculture’s mitigation potential and supporting a low-carbon, climate-resilient agricultural future in India.","碳市场正成为减少农业温室气体（GHG）排放的重要工具，对印度而言尤其具有现实意义，因为印度既是全球第三大温室气体排放国，也是重要的农业经济体。本系统综述综合了当前关于印度农业碳市场发展、机制及新兴趋势的证据，并将其置于更广泛的全球背景中加以审视。遵循系统综述和荟萃分析优先报告条目（PRISMA）指南，对五个数据库进行检索（识别出247条记录，保留62项研究）后，本综述考察了五个密切关联的领域：全球及印度特有的碳市场机制；能够实现市场参与的农艺实践；主要农业温室气体来源与减排机会；土壤健康与碳固存；以及方法学、政策和包容性方面的差距。综合结果表明，保护性农业、水稻系统中的干湿交替灌溉（AWD）、生物炭施用、精准养分管理和农林业等可持续实践，能够在减少农业排放的同时改善土壤肥力和碳储存。此外，包括基于区块链的透明系统和人工智能驱动的精准农业在内的新兴工具，可以增强市场可信度，并使小农户更容易参与。然而，印度的农业碳市场仍处于早期阶段，其增长受到测量、报告与核查（MRV）体系薄弱、小农户包容性不足、额外性方面的担忧以及缺乏全面的国内监管框架等因素的制约。碳永久性、泄漏、交易成本和市场波动等方面的不确定性进一步削弱了这些前景，因此需要对所报告的收益持谨慎解读。本综述确定了未来工作的四个优先领域：标准化稳健的MRV体系、开发综合影响模型、设计包容性市场机制，以及使碳市场政策与印度更广泛的农业、气候和生计承诺相协调。解决这些差距对于实现农业的减排潜力、支持印度低碳且气候适应型的农业未来至关重要。","Plant Science Today","2026-10-03T00:00:00Z",{"impact":91,"substance":140,"depth":91,"authority":227,"freshness":92,"relevant":33,"comment":496},"系统综述印度农业碳市场机制、减排实践与MRV短板，对国内农业碳汇与数字监测议题有参考价值。",[498],{"name":493,"url":490},[268,500,501,502,503],"小农户","碳汇","农业碳市场","MRV",[],{"status":104,"locked":52,"version":53,"method":54,"evidence":506},{"candidates":507,"agricultural_evidence":26,"reason":124},[508,514,519,525,530,537,541,547,553,559],{"code":509,"score":338,"supported":26,"title":510,"abstract":511,"tags":513},"G2",[],[500,512],"smallholder",[500],{"code":206,"score":338,"supported":26,"title":515,"abstract":516,"tags":518},[],[221,517],"carbon sequestration",[501],{"code":318,"score":338,"supported":26,"title":520,"abstract":521,"tags":524},[],[522,523],"精准农业","precision agriculture",[268],{"code":108,"score":478,"supported":26,"title":526,"abstract":527,"tags":529},[],[111,528],"rice",[],{"code":531,"score":478,"supported":26,"title":532,"abstract":533,"tags":536},"B7",[],[534,535],"温室","greenhouse",[],{"code":44,"score":478,"supported":26,"title":538,"abstract":539,"tags":540},[],[61,45],[],{"code":542,"score":58,"supported":26,"title":543,"abstract":544,"tags":546},"F3",[],[545],"养分管理",[],{"code":548,"score":58,"supported":26,"title":549,"abstract":550,"tags":552},"H3",[],[551],"灌溉",[],{"code":554,"score":58,"supported":26,"title":555,"abstract":556,"tags":558},"H7",[],[557],"气候适应",[],{"code":69,"score":58,"supported":26,"title":560,"abstract":561,"tags":563},[],[562],"conservation agriculture",[],[565,566],"印度 农业碳市场","农业碳信用 MRV","印度农业碳市场-4178","10.14719\u002Fpst.16451",{"doi":568,"openalex_id":570,"authors":571,"venue":493,"cited_by_count":246,"oa_url":490,"card":587,"direction":591,"ingested_from":593},"W7219112839",[572,575,578,581,584],{"name":573,"orcid":574},"R Sneha","https:\u002F\u002Forcid.org\u002F0009-0007-7779-2885",{"name":576,"orcid":577},"N. Kiruthika","https:\u002F\u002Forcid.org\u002F0000-0002-9332-5220",{"name":579,"orcid":580},"S P Sethuraman","https:\u002F\u002Forcid.org\u002F0000-0003-3283-1629",{"name":582,"orcid":583},"Kandaswamy Mahendran","https:\u002F\u002Forcid.org\u002F0000-0001-5146-6052",{"name":585,"orcid":586},"Samiappan Senthilnathan","https:\u002F\u002Forcid.org\u002F0000-0003-2924-3690",{"tldr":588,"method":589,"finding":590,"direction":591,"opportunity":592},"系统综述印度农业碳市场机制、减排实践与挑战，提出四大优先研究方向。","PRISMA系统综述，检索5个数据库，纳入62项研究。","印度农业碳市场尚处早期，受MRV薄弱、小农参与不足和监管缺失制约。","农业绿色发展与碳","可探索区块链与AI赋能MRV、降低小农交易成本及设计包容性碳市场机制。","openalex","2026-10-04T23:30:54.952066Z",{"id":596,"title":597,"url":598,"summary":599,"summary_zh":600,"content":20,"source_name":601,"source_url":598,"published_at":188,"category":189,"cover_url":20,"hotness":25,"is_selected":52,"score":435,"score_detail":602,"sources":604,"tags":606,"primary_topic":20,"secondary_topics":610,"classification":611,"search_phrases":636,"slug":639,"view_count":168,"doi":640,"paper":641,"created_at":658},4174,"Deep learning-based multi-view perception and adaptive grasp pose correction for cooperative dual-arm fruit harvesting under occlusion","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.09.004","This study presents a failure-recovery cooperative harvesting pipeline for a dual-arm agricultural robot, where each arm harvests independently and switches to cooperative manipulation only when a single-view grasp on an occluded fruit fails. Such cooperation is necessary because accurate grasp pose inference degrades substantially when target fruits are heavily occluded by surrounding foliage or branches; existing approaches, including explicit 6D pose estimation and shape-completion-based methods, depend on reconstructing geometric information that is often unavailable under heavy occlusion. To address this limitation, the proposed framework, referred to as the Cooperative Active Harvesting Robot Scheme (Co-AHRS), integrates direct grasp pose inference from incomplete point clouds, active multi-view perception, and closed-loop visual servoing. Instead of reconstructing hidden geometry, the framework leverages Contact-GraspNet to generate feasible grasp candidates directly from partial observations. When the initial grasp attempt fails—due to either inaccurate pose inference under heavy occlusion or physical displacement of the target fruit during approach—the opposite arm is reassigned as an auxiliary observer whose upward-facing viewpoint enables closed-loop visual servoing, continuously estimating the 3D positional error between the gripper and the target fruit for real-time pose correction. The Co-AHRS was evaluated through a laboratory implementation and achieved harvest success rates of 89.2% and 78.5% under unoccluded and occluded conditions, respectively. The proposed framework also substantially outperformed frustum-based localization (75.0% and 55.2%) and explicit 6D pose estimation (57.1% and 7.7%). A further evaluation on a second configuration built from two same-model manipulators verified that the two arms harvest in parallel and quantified the occluded condition by a visible surface ratio of 0.549 ± 0.101. These results demonstrate that direct grasp inference combined with cooperative multi-view visual feedback is an effective strategy for autonomous fruit harvesting in occlusion-prone orchard environments.","本研究提出了一种面向双臂农业机器人的失败恢复协同采收流水线：每条机械臂独立执行采收，仅当对遮挡果实的单视角抓取失败时，才切换至协同操作。这种协同是必要的，因为当目标果实被周围枝叶严重遮挡时，准确抓取位姿推断会显著退化；现有方法，包括显式6D位姿估计和基于形状补全的方法，依赖于重建几何信息，而这些信息在严重遮挡下往往无法获得。为解决这一局限，所提出的框架——称为协同主动采收机器人方案（Cooperative Active Harvesting Robot Scheme，Co-AHRS）——集成了从不完整点云直接进行抓取位姿推断、主动多视角感知以及闭环视觉伺服。该框架不重建隐藏几何结构，而是利用Contact-GraspNet直接从部分观测中生成可行抓取候选。当初始抓取尝试失败时——原因可能是严重遮挡下的位姿推断不准确，或目标果实在接近过程中发生物理位移——对侧机械臂被重新分配为辅助观察者，其朝上的视角可实现闭环视觉伺服，持续估计夹爪与目标果实之间的三维位置误差，以进行实时位姿校正。Co-AHRS通过实验室实现进行了评估，在无遮挡和有遮挡条件下分别取得了89.2%和78.5%的采收成功率。所提出的框架也显著优于基于视锥的定位（75.0%和55.2%）以及显式6D位姿估计（57.1%和7.7%）。在由两台同型号机械臂构成的第二种配置上进行的进一步评估验证了两条机械臂可并行采收，并以0.549 ± 0.101的可见表面比例量化了遮挡条件。这些结果表明，直接抓取推断结合协同多视角视觉反馈是在易遮挡果园环境中实现自主果实采收的有效策略。","Artificial Intelligence in Agriculture",{"impact":91,"substance":140,"depth":91,"authority":142,"freshness":379,"relevant":33,"comment":603},"提出双臂协同主动多视角感知与闭环视觉伺服抓取修正框架，遮挡条件下采摘成功率达78.5%，方法新颖、对比充分，对果园自动化采摘有实质参考价值。",[605],{"name":601,"url":598},[268,384,607,608,609],"采摘机器人","机器视觉","双臂协同",[],{"status":104,"locked":52,"version":53,"method":54,"evidence":612},{"candidates":613,"agricultural_evidence":26,"reason":124},[614,622,627,632],{"code":615,"score":25,"supported":26,"title":616,"abstract":617,"tags":621},"F6",[],[618,619,620],"农业机器人","agricultural robot","harvesting robot",[607],{"code":390,"score":25,"supported":26,"title":623,"abstract":625,"tags":626},[624],"deep learning",[],[384,608],{"code":469,"score":58,"supported":26,"title":628,"abstract":629,"tags":631},[],[630],"orchard",[],{"code":318,"score":33,"supported":52,"title":633,"abstract":634,"tags":635},[],[],[268],[637,638],"双臂采摘机器人 遮挡 抓取","Co-AHRS 多视角感知","双臂采摘机器人遮挡抓取-4174","10.1016\u002Fj.aiia.2026.09.004",{"doi":640,"openalex_id":642,"authors":643,"venue":601,"cited_by_count":246,"oa_url":651,"card":652,"direction":657,"ingested_from":593},"W7215102066",[644,646,648],{"name":645,"orcid":20},"Jeongil Seo",{"name":647,"orcid":20},"Han Ul Yoon",{"name":649,"orcid":650},"Kwang-Eun Ko","https:\u002F\u002Forcid.org\u002F0000-0001-6225-8080","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2589721726001133\u002Fpdf",{"tldr":653,"method":654,"finding":655,"direction":426,"opportunity":656},"提出双臂协作采摘框架，单臂失败时另一臂辅助多视角感知并闭环修正抓取位姿。","Contact-GraspNet直接从残缺点云推理抓取，配合主动多视角与视觉伺服","无遮挡与遮挡下采摘成功率89.2%和78.5%，显著优于6D位姿估计等方法。","可探索多臂任务分配与失败恢复策略学习，提升密集遮挡果园的自主采摘鲁棒性。","农业遥感与作物表型","2026-10-04T23:30:27.274509Z",{"id":660,"title":661,"url":662,"summary":663,"summary_zh":20,"content":664,"source_name":665,"source_url":20,"published_at":666,"category":24,"cover_url":20,"hotness":25,"is_selected":52,"score":667,"score_detail":668,"sources":671,"tags":673,"primary_topic":677,"secondary_topics":682,"classification":683,"search_phrases":747,"slug":750,"view_count":58,"doi":20,"paper":20,"created_at":751},4189,"广东省农科院2026年广东省农业主导品种、主推技术入选总数持续领跑全省——28个品种+39项技术","https:\u002F\u002Fwww.gdaas.cn\u002Ftpxw\u002Fcontent\u002Fpost_1314297.html","广东省农业科学院公布2026年广东省农业主导品种（28个）和主推技术（39项）入选名单，水稻、玉米、甘薯、花生、蔬菜、果树、桑树、茶叶、花卉、禽畜等品类全覆盖，主导品种入选数量与主推技术入选数量持续领跑全省。其中水稻吉丰优1002、南晶香占、19香、青香优19香、粤禾丝苗等为代表性品种，玉米粤甜41号、蔬菜铁柱2号冬瓜、果树仙进奉荔枝、岭南黄鸡I号配套系等为传统优势品种。","### 我院2026年广东省农业主导品种、主推技术入选总数持续领跑全省\n\n时间：2025-12-10 17:32 来源：科技合作部【字体：大 中 小】\n\n近日，广东省农业农村厅发布2026年广东省农业主导品种、主推技术及重大引领性技术。全省共遴选推介发布50个农业主导品种、60项主推技术和5项重大引领性技术。我院选育吉丰优1002等28个品种、研发水稻“三控”施肥技术等39项技术和抗枯萎病特色粉蕉新品种的选育及配套技术等4项重大引领性技术成功入选，在入选全省主导品种、主推技术和重大引领性技术中分别占比为56%、65%和80%，主导品种和主推技术的入选总数持续保持全省领先，进一步彰显我院农业科技主力军的重要地位。\n\n![Image 1: 图片2.png](http:\u002F\u002Fwww.gdaas.cn\u002Fimg\u002F0\u002F836\u002F836086\u002F1314297.png)\n\n在发布的2026年度全省5项重大引领性技术中，我院牵头研发的4项技术覆盖畜禽疫病防控、智能化养殖与特色果树育种等关键领域。其中，动卫所研发的“鸡球虫病及相关疫病绿色协同防控关键技术”与“规模化养猪重要疫病智能化防控技术”，为畜牧业安全生产提供了系统解决方案；果树所的“抗枯萎病特色粉蕉新品种的选育及配套技术”精准破解产业瓶颈；加工所联合推广的“热带亚热带热敏性水果低温高效加工关键技术”显著提升农产品加工附加值。4项技术的集中入选，凸显了我院在产业技术重大难题攻关方面的核心动能。\n\n在农业主导品种与主推技术方面，我院表现突出。茶叶、桑树、蚕、马铃薯等品类品种与技术覆盖率均达100%；水稻类品种与技术的占比均超过70%，持续巩固在主粮产业的应用主导地位。此外，在蔬菜、果树、畜禽等多个重要农业领域，我院选育的品种与研发的技术也广泛入选，展现出扎实全面的科技支撑能力。\n\n近年来，我院紧紧围绕广东省“百县千镇万村高质量发展工程”工作部署，持续强化科技创新与成果转化。通过遍布全省的地方分院、专家工作站和试验示范基地等院地合作平台，大力示范推广高产优质品种与先进适用技术，为保障粮食安全与重要农产品供给、全面推进乡村振兴提供了强有力的科技支撑。特别是面对新形势下，在2026年度全省主导品种、主推技术的遴选中，我院仍能取得这一成绩，充分体现了我院科研成果的先进性与产业应用的广泛接受度。成绩来之不易，全院科技人员仍须继续保持科研热情，持续努力为产业提供强力支撑，为广东农业高质量发展与农业强省建设注入科技动能。\n\n入选名单\n\n**主导品种（28个）**\n序号 品类 品种名称 申报单位\n1 水稻 吉丰优1002 广东省农业科学院水稻研究所\n2 水稻 南晶香占 广东省农业科学院水稻研究所、广东省金稻种业有限公司、中国种子集团有限公司\n3 水稻 19香 广东省农业科学院水稻研究所\n4 水稻 青香优19香 广东鲜美种苗股份有限公司；广东省农业科学院水稻研究所；稻道隆（广东）生物科技有限公司\n5 水稻 粤禾丝苗 广东省农业科学院水稻研究所\n6 水稻 吉丰优866 广东华茂高科种业有限公司、广东省农业科学院水稻研究所\n7 水稻 胜优19香 广东鲜美种苗股份有限公司、广东省农业科学院水稻研究所\n8 玉米 粤甜41号 广东省农业科学院作物研究所\n9 甘薯 广薯87 广东省农业科学院作物研究所\n10 花生 航花2号 广东省农业科学院作物研究所\n11 马铃薯 云薯306 广东省农业科学院作物研究所\n12 蔬菜 铁柱2号冬瓜 广东省农业科学院蔬菜研究所\n13 蔬菜 雅绿8号 广东省农业科学院蔬菜研究所\n14 蔬菜 粤薹5号菜薹 广东省农业科学院蔬菜研究所\n15 蔬菜 汇丰二号 广东省农业科学院蔬菜研究所\n16 果树 仙进奉 广东省农业科学院果树研究所，增城区农业技术推广中心，增城区新塘镇农业办公室\n17 果树 紫晖番木瓜 广东省农业科学院果树研究所\n18 果树 粉杂1号 广东省农业科学院果树研究所\n19 果树 粤甜 广东省农业科学院果树研究所\n20 桑树 粤椹大10 广东省农业科学院蚕业与农产品加工研究所\n21 茶叶 丹霞1号 广东省农业科学院茶叶研究所、广东省仁化县农业局\n22 茶叶 鸿雁12号 广东省农业科学院茶叶研究所\n23 花卉 红运朱顶红 广东省农业科学院环境园艺研究所\n24 花卉 金如意大花蕙兰 广东省农业科学院环境园艺研究所\n25 花卉 云彩火焰蝶兰 广东省农业科学院环境园艺研究所\n26 花卉 轻舞飞扬花叶芋 广东省农业科学院环境园艺研究所\n27 禽畜 岭南黄鸡I号配套系 广东省农业科学院动物科学研究所（畜牧研究所）\n28 蚕 粤蚕6号 广东省农业科学院蚕业与农产品加工研究所\n**主推技术（39项）**\n序号 品类 技术名称 申报单位\n1 水稻 水稻“三控”施肥技术 广东省农业科学院水稻研究所\n2 水稻 广东丝苗米细菌性病害减灾防控技术 广东省农业科学院植物保护研究所；广东省农业技术推广中心；广东省农业有害生物预警防控中心；广东天禾农资股份有限公司，广州市从化区农业技术推广中心\n3 水稻 基于智能监测的稻区鼠害精准防控技术 广东省农业科学院植物保护研究所\n4 水稻 水稻节水减肥低碳高产栽培技术 广东省农业科学院水稻研究所\n5 水稻 水稻一次性施肥技术 广东省农业科学院农业资源与环境研究所\n6 水稻 种子活力提升关键技术 广东省农业科学院农业生物基因研究中心\n7 甘薯 甘薯健康种苗应用技术 广东省农业科学院作物研究所\n8 马铃薯 冬种马铃薯高效栽培技术 广东省农业科学院作物研究所\n9 蔬菜 冬瓜减肥高效与“三护”栽培关键技术 广东省农业科学院蔬菜研究所，广东省农业技术推广中心，德钾盐深圳农业科技有限公司，广东绿宝农业科技发展有限公司，广东省良种引进服务公司\n10 蔬菜 菜心生产全过程生物农药防控害虫技术 广东省农业科学院植物保护研究所\n11 蔬菜 蔬菜集约化育苗关键技术示范与推广 广东省农业科学院蔬菜研究所，广东省农业技术推广中心，广东省良种引进服务公司，广东绿宝农业科技发展有限公司\n12 蔬菜 贝贝南瓜标准化种植与储藏技术 广东省农业科学院设施农业研究所\n13 蔬菜 辣椒轻简化高效栽培关键技术 广东省农业科学院蔬菜研究所\n14 蔬菜 华南型优质高效樱桃番茄工厂化（水培）生产技术 广东省农业科学院设施农业研究所\n15 蔬菜 都市蔬菜景观栽培与科普应用技术 广东省农业科学院蔬菜研究所、广东省农业技术推广中心，东莞市绿装农业科技有限公司，广东省良种引进服务公司，广州骄阳农业有限公司\n16 食用菌 紫芝林下仿野生段木生态栽培及灵芝代用茶加工技术 梅州市农林科学院微生物研究所、广东省农业科学院蔬菜研究所\n17 果树 柑橘绿色高效无病容器大苗种植技术 广东省农业科学院果树研究所\n18 果树 荔枝园农药精准高效施药技术 广东省农业科学院植物保护研究所\n19 果树 香蕉枯萎病Foc TR4田间可视化分子检测技术 广东省农业科学院果树研究所、中科绿控科技有限公司\n20 果树 菠萝“一肥二防三减”优质高效种植模式 广东省农业科学院果树研究所\n21 果树 柚专用配方有机肥冬季一次性施肥技术 广东省农业科学院农业资源与环境研究所，广东省农业科学院果树研究所\n22 桑树 果桑高效种植管理技术 广东省农业科学院蚕业与农产品加工研究所\n23 茶叶 茶树-广金钱草间作生态栽培技术 广东省农业科学院茶叶研究所、广东省农业技术推广中心、广东省农业有害生物预警防控中心、潮州市农业科学技术研究中心、梅州市农林科学院茶叶研究所\n24 茶叶 生态茶园标准化建设与管理技术 广东省农业科学院茶叶研究所\n25 茶叶 金萱茶树生态化高效栽培技术 广东省农业科学院茶叶研究所、广东鸿雁茶业有限公司\n26 茶叶 蝉茶标准化生产技术 紫金县农业综合服务中心、广东省农业科学院茶叶研究所、河源市农业技术推广中心\n27 花卉 国兰高效栽培与花期调控技术 广东省农业科学院环境园艺研究所\n28 畜禽 黄羽肉种鸡高效繁殖营养调控关键技术 广东省农业科学院动物科学研究所\n29 畜禽 蛋鸭精准营养与旱养技术 广东省农业科学院动物科学研究所\n30 畜禽 猪流行性腹泻综合防控关键技术 广东省农业科学院动物卫生研究所\n31 畜禽 改善仔猪肠道微生态的无抗饲粮配制技 广东省农业科学院动物科学研究所\n32 畜禽 畜禽饲料质量安全检测关键技术 广东省农业科学院农业质量标准与监测技术研究所\n33 水产 立体吸附桩养殖尾水处理技术 广东省农业科学院动物科学研究所（水产研究所）、广东益康生环保科技有限公司\n34 蚕 蚕蛹呈味基料制备技术 广东省农业科学院蚕业与农产品加工研究所\n35 其它 农业废弃物生物高效处理及资源化再生利用成套技术 广东省农业科学院农业资源与环境研究所；广东省农业环境与耕地质量保护中心（广东省农业农村投资项目中心）\n36 其它 荔枝超低温冻眠锁鲜和产地高效加工关键技术 广东省农业科学院蚕业与农产品加工研究所\n37 其它 高通量重金属检测及农用地质量安全监控技术 广东省农业科学院农业质量标准与监测技术研究所\n38 其它 南方镉铅污染农田生物炭基改良技术 广东省农业科学院农业资源与环境研究所\n39 其它 华南特色果蔬电商物流保鲜技术 广东省农业科学院蚕业与农产品加工研究所\n**重大引领性技术（4项）**\n序号 品类 技术名称 申报单位\n1 果树 抗枯萎病特色粉蕉新品种的选育及配套技术 广东省农业科学院果树研究所\n2 畜禽 鸡球虫病及相关疫病绿色协同防控关键技术 广东省农业科学院动物卫生研究所\n3 畜禽 规模化养猪重要疫病智能化防控技术 广东省农业科学院动物卫生研究所\n4 其它 热带亚热带热敏性水果低温高效加工关键技术 广东省农业科学院蚕业与农产品加工研究所，广东省农业技术推广中心","广东省农业科学院·2026年10月","2026-10-02T01:00:00Z",79,{"impact":140,"substance":140,"depth":669,"authority":227,"freshness":478,"relevant":33,"comment":670},16,"省级主导品种与主推技术遴选结果，数据详实、名单完整，具行业参考价值，但属机构成果通报，时效稍滞后。",[672],{"name":665,"url":662},[97,674,675,99,676],"成果转化","主推技术","畜禽养殖",{"code":469,"slug":678,"name":679,"path":680,"parent_code":681},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":684},{"candidates":685,"agricultural_evidence":26},[686,691,695,699,704,710,716,722,728,732,737,742],{"code":469,"score":478,"supported":26,"title":687,"abstract":688,"tags":690},[],[679,689],"荔枝",[],{"code":108,"score":77,"supported":26,"title":692,"abstract":693,"tags":694},[],[111],[99],{"code":152,"score":58,"supported":26,"title":696,"abstract":697,"tags":698},[],[154],[],{"code":297,"score":58,"supported":26,"title":700,"abstract":701,"tags":703},[],[702],"花生",[],{"code":705,"score":58,"supported":26,"title":706,"abstract":707,"tags":709},"A5",[],[708],"甘薯",[],{"code":711,"score":58,"supported":26,"title":712,"abstract":713,"tags":715},"B1",[],[714],"蔬菜",[],{"code":717,"score":58,"supported":26,"title":718,"abstract":719,"tags":721},"B3",[],[720],"茶叶",[],{"code":723,"score":58,"supported":26,"title":724,"abstract":725,"tags":727},"B4",[],[726],"花卉",[],{"code":114,"score":404,"supported":52,"title":729,"abstract":730,"tags":731},[],[],[675,97],{"code":733,"score":33,"supported":52,"title":734,"abstract":735,"tags":736},"C",[],[],[676],{"code":738,"score":33,"supported":52,"title":739,"abstract":740,"tags":741},"C3",[],[],[676],{"code":743,"score":33,"supported":52,"title":744,"abstract":745,"tags":746},"G1",[],[],[674],[748,749],"广东省农科院 主导品种 主推技术","吉丰优1002 岭南黄鸡","广东省农科院主导品种主推技术-4189","2026-10-05T00:04:53.111254Z",{"id":753,"title":754,"url":755,"summary":756,"summary_zh":20,"content":757,"source_name":758,"source_url":20,"published_at":759,"category":24,"cover_url":20,"hotness":25,"is_selected":52,"score":667,"score_detail":760,"sources":762,"tags":764,"primary_topic":768,"secondary_topics":769,"classification":770,"search_phrases":791,"slug":794,"view_count":404,"doi":20,"paper":20,"created_at":795},4188,"黄三角盐碱地里长出120多项好成果——山东省农科院45个耐盐品种+71项技术+30个场景集中亮相","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F03\u002Fcontent_591574.html","9月30日，山东省农业科学院在东营黄河三角洲农业高新技术产业示范区举行盐碱地科技成果观摩会，集中发布45个耐盐品种、71项技术产品、30个应用场景。山东现有盐碱地近900万亩，其中黄河三角洲地区近700万亩。8家企业在会议期间与院方签署项目合作协议。省农科院党委书记梁金光介绍，全院40余支科研团队常年扎根盐碱地生产一线，累计选育耐盐碱新品种120余个，示范推广新品种、新技术、新模式超30万亩。耐中轻度盐碱优质樱桃番茄新品种\"美小红\"由蔬菜所选育。","![Image 2](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 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[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F03\u002Fcontent_591574.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 黄三角盐碱地里长出120多项好成果\n\n 2026-10-03 14:44:03 来源: 科技日报 点击数：\n\n![Image 5](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n科技日报记者 王延斌 通讯员 李才林 杨可欣\n\n适合黄河口盐碱地栽培的优质脆藕品种“济莲5号”、耐盐碱葡萄新品种“锦华”、高产高油抗旱耐盐碱花生新品种花育9307……9月30日，这些成果以实物展出的形式出现在山东省东营市黄河三角洲农业高新技术产业示范区（以下简称“黄三角农高区”）里。它们拥有一个共同的标签——源自黄三角，用于黄三角，研发者都来自山东省农科院。\n\n当天，山东省农科院盐碱地科技成果观摩会在这里举行。本次会议通过现场观摩、项目签约、成果专项发布等形式，检验盐碱地有组织科研阶段性成效，打通科技创新与产业转化通道，激活盐碱地耕地后备资源，筑牢国家粮食安全根基。\n\n![Image 6](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F03\u002F591574_8eed4dff-3865-4b5d-9583-88230c77d3abcopy.jpg)\n\n果型小巧、丰满圆整，口感好，适合鲜食，这是山东省农科院蔬菜研究所选育的耐中轻度盐碱优质樱桃番茄新品种“美小红”，成为参观者眼中“红色的樱桃番茄中特别的那一个”，也被东营市津沃农业发展有限公司总经理杨健青睐。在大会现场，该公司与山东省农科院蔬菜研究所签约，将“美小红”推向了转化的快车道。\n\n以此成果为代表，8家企业与山东省农科院签署项目合作协议，“科学家+企业家”组合将好成果、好技术、好场景在脚下这片盐碱地里推而广之。\n\n据了解，山东现有盐碱地近900万亩，其中黄河三角洲地区近700万亩，向盐碱地要粮大有可为。在这里，科研人员通过培育耐盐碱作物进行“以种适地”和通过治理盐碱地进行“以地适种”，大力发展盐碱地特色农业，综合利用成效显著。\n\n记者注意到，在本次会议上，来自山东省农科院的45个耐盐品种，71项技术产品，30个应用场景集中亮相。\n\n“多年来，山东省农科院围绕耐盐碱种质创新、盐碱地生态治理、高效种养模式、智慧农机装备等重点方向，系统布局全链条科研攻关，与国内高校、科研院所、地方政府及龙头企业深化协同创新，一批重大平台与重点项目落地见效，为黄三角盐碱地综合利用提供了坚实的科技支撑。”山东省农科院党委书记梁金光说，我们立足黄河三角洲资源禀赋，布局2200余亩核心试验基地，筹建黄三角农业研究中心，全院40余支科研团队常年扎根盐碱地生产一线，累计选育耐盐碱新品种120余个，示范推广新品种、新技术、新模式超30万亩，在持续攻关中探索出一条科技赋能、生态优先、因地制宜的盐碱地高效利用发展路径。\n\n（受访者供图）\n\n责任编辑：陈可轩\n\n相关稿件：\n\n![Image 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3.暂不升级，继续浏览\n\n[继续浏览](javascript:;)","科技日报·2026年10月3日","2026-10-03T02:00:00Z",{"impact":140,"substance":337,"depth":669,"authority":227,"freshness":379,"relevant":33,"comment":761},"省级农科院在黄三角集中发布45个耐盐品种、71项技术与30个场景并签约转化，数据具体、信源权威，对盐碱地综合利用与种业振兴有实质参考价值。",[763],{"name":758,"url":755},[97,674,765,766,767],"盐碱地治理","黄河三角洲","耐盐碱品种",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[],{"status":51,"locked":52,"version":53,"method":54,"evidence":771},{"candidates":772,"agricultural_evidence":26},[773,778,783,787],{"code":44,"score":162,"supported":26,"title":774,"abstract":776,"tags":777},[775],"盐碱地",[775],[765],{"code":711,"score":478,"supported":26,"title":779,"abstract":780,"tags":782},[],[714,781],"番茄",[],{"code":114,"score":33,"supported":52,"title":784,"abstract":785,"tags":786},[],[],[97],{"code":743,"score":33,"supported":52,"title":788,"abstract":789,"tags":790},[],[],[674],[792,793],"山东省农科院 耐盐碱品种 黄河三角洲","黄三角农高区 盐碱地 成果观摩会","山东省农科院耐盐碱品种黄河三角洲-4188","2026-10-05T00:04:53.031971Z",{"id":797,"title":798,"url":799,"summary":800,"summary_zh":801,"content":20,"source_name":802,"source_url":799,"published_at":494,"category":189,"cover_url":20,"hotness":25,"is_selected":52,"score":667,"score_detail":803,"sources":805,"tags":807,"primary_topic":812,"secondary_topics":813,"classification":819,"search_phrases":850,"slug":853,"view_count":404,"doi":854,"paper":855,"created_at":882},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management",{"impact":669,"substance":140,"depth":91,"authority":142,"freshness":92,"relevant":33,"comment":804},"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[806],{"name":802,"url":799},[401,808,809,810,811],"土壤有机碳","生态修复","遥感监测","干旱区农业",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[814,818],{"code":554,"slug":815,"name":816,"path":817,"parent_code":48},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":390,"slug":391,"name":392,"path":393,"parent_code":215},{"status":51,"locked":52,"version":53,"method":54,"evidence":820},{"candidates":821,"agricultural_evidence":26},[822,826,833,838,845],{"code":44,"score":32,"supported":26,"title":823,"abstract":824,"tags":825},[45],[61,45],[808],{"code":554,"score":25,"supported":26,"title":827,"abstract":828,"tags":832},[],[829,830,831],"气候变化","干旱","climate change",[811],{"code":390,"score":92,"supported":26,"title":834,"abstract":836,"tags":837},[835],"machine learning",[],[401],{"code":839,"score":338,"supported":26,"title":840,"abstract":841,"tags":844},"I3",[],[842,843],"遥感","remote sensing",[810],{"code":846,"score":33,"supported":52,"title":847,"abstract":848,"tags":849},"H6",[],[],[809],[851,852],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172","10.1186\u002Fs13021-026-00522-5",{"doi":854,"openalex_id":856,"authors":857,"venue":802,"cited_by_count":246,"oa_url":799,"card":877,"direction":657,"ingested_from":593},"W7219075005",[858,860,863,865,868,870,873,875],{"name":859,"orcid":20},"Y. Chen",{"name":861,"orcid":862},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":864,"orcid":20},"Meng Zhu",{"name":866,"orcid":867},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":869,"orcid":20},"Yulian Hao",{"name":871,"orcid":872},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":874,"orcid":20},"Zhehui Wang",{"name":876,"orcid":20},"Bin Wang",{"tldr":878,"method":879,"finding":880,"direction":657,"opportunity":881},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z",{"id":884,"title":885,"url":886,"summary":887,"summary_zh":888,"content":20,"source_name":889,"source_url":886,"published_at":188,"category":189,"cover_url":20,"hotness":25,"is_selected":52,"score":667,"score_detail":890,"sources":892,"tags":894,"primary_topic":898,"secondary_topics":902,"classification":903,"search_phrases":920,"slug":923,"view_count":58,"doi":924,"paper":925,"created_at":949},4163,"Severity Grading of Early Visible Symptoms of Sugarcane Pokkah Boeng Disease Based on Multi-Scale Dynamic Feature Fusion and Channel-Spatial Interaction: An On-Device Framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102607","Sugarcane pokkah boeng disease is a fungal disease caused by pathogens in the Fusarium fujikuroi species complex (FFSC). Early visible symptoms include chlorosis and yellowing at the base of young leaves, leaf wrinkling, and morphological abnormalities, which may differ only subtly from healthy leaves and make manual inspection and severity assessment difficult. Existing studies have paid limited attention to severity grading of these early visible symptoms, while some models remain too complex for mobile deployment. To address these issues, this study established a four-grade severity standard using leaf colour alteration, leaf morphological abnormalities, and visible abnormalities in the growing-point region as visual symptom anchors, and constructed an image dataset for sugarcane pokkah boeng disease. A lightweight grading framework based on MobileNetV2 was then developed for this task, incorporating multi-scale dynamic feature fusion with a fast channel-spatial interaction module to enhance multi-scale and channel-spatial representations of severity-related visual features. The proposed method achieved Accuracy, Precision, Recall, and Macro-F1 of 96.29% ± 0.73%, 95.32% ± 0.79%, 95.99% ± 0.78%, and 95.43% ± 0.77%, respectively, outperforming the main comparison models overall. On-device accuracy was 96.53% under both FP32 and INT8 precision. After INT8 quantisation, the ONNX model size decreased from 8.63 MB to 2.57 MB, and single-image inference latency from 30.819 ± 4.625 ms to 10.794 ± 0.999 ms. This study provides a lightweight solution for image-based visual severity grading and mobile-assisted assessment of early visible symptoms of sugarcane pokkah boeng disease, thereby supporting technical support for mobile-assisted severity assessment and disease management.","甘蔗凤梨病（pokkah boeng disease）是由藤仓镰刀菌复合种（Fusarium fujikuroi species complex，FFSC）病原菌引起的真菌病害。早期可见症状包括幼叶基部褪绿黄化、叶片皱缩及形态异常，这些症状与健康叶片差异细微，导致人工巡检和严重度评估困难。现有研究对上述早期可见症状的严重度分级关注有限，且部分模型过于复杂，难以在移动端部署。针对上述问题，本研究以叶片颜色改变、叶片形态异常及生长点区域可见异常作为视觉症状锚点，建立了四级严重度标准，并构建了甘蔗凤梨病图像数据集。随后，基于MobileNetV2开发了适用于该任务的轻量化分级框架，融合多尺度动态特征融合与快速通道-空间交互模块，以增强严重度相关视觉特征的多尺度和通道-空间表征能力。所提方法在准确率、精确率、召回率和Macro-F1上分别达到96.29% ± 0.73%、95.32% ± 0.79%、95.99% ± 0.78%和95.43% ± 0.77%，总体优于主要对比模型。在FP32和INT8精度下，端侧准确率均为96.53%。经INT8量化后，ONNX模型大小从8.63 MB降至2.57 MB，单张图像推理延迟从30.819 ± 4.625 ms降至10.794 ± 0.999 ms。本研究为甘蔗凤梨病早期可见症状的基于图像的视觉严重度分级与移动辅助评估提供了轻量化解决方案，从而为移动辅助严重度评估和病害管理提供技术支持。","Smart Agricultural Technology",{"impact":91,"substance":140,"depth":141,"authority":227,"freshness":92,"relevant":33,"comment":891},"面向甘蔗凤梨病早期症状的轻量化端侧分级框架，方法新颖、指标扎实，具备移动辅助防控的实用价值。",[893],{"name":889,"url":886},[268,384,895,896,897],"轻量化模型","甘蔗病害","病害分级",{"code":463,"slug":899,"name":900,"path":901,"parent_code":156},"cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":904},{"candidates":905,"agricultural_evidence":26},[906,912,916],{"code":463,"score":142,"supported":26,"title":907,"abstract":909,"tags":911},[908],"sugarcane",[910,908],"甘蔗",[],{"code":390,"score":404,"supported":52,"title":913,"abstract":914,"tags":915},[],[],[384,895],{"code":318,"score":33,"supported":52,"title":917,"abstract":918,"tags":919},[],[],[268],[921,922],"甘蔗 凤梨病 分级","MobileNetV2 病害识别","甘蔗凤梨病分级-4163","10.1016\u002Fj.atech.2026.102607",{"doi":924,"openalex_id":926,"authors":927,"venue":889,"cited_by_count":246,"oa_url":886,"card":944,"direction":426,"ingested_from":593},"W7218607722",[928,931,933,935,937,939,941],{"name":929,"orcid":930},"Cuimin Sun","https:\u002F\u002Forcid.org\u002F0000-0003-4174-1094",{"name":932,"orcid":20},"Xiaojie Qin",{"name":934,"orcid":20},"Junyang Dou",{"name":936,"orcid":20},"Huijing Chen",{"name":938,"orcid":20},"Yongping Liao",{"name":940,"orcid":20},"Songjie Yuan",{"name":942,"orcid":943},"Chengwu Zou","https:\u002F\u002Forcid.org\u002F0000-0002-2836-2930",{"tldr":945,"method":946,"finding":947,"direction":426,"opportunity":948},"提出轻量级框架，对甘蔗凤梨病早期可见症状进行四级严重度分级。","基于MobileNetV2，融合多尺度动态特征与通道-空间交互模块，构建图像数据","准确率96.29%，INT8量化后模型仅2.57MB，推理延迟10.79ms，适合移动端部署。","可扩展至其他作物早期病害分级，并探索田间复杂光照下的鲁棒性与多病害联合识别。","2026-10-04T23:30:03.798689Z",{"id":951,"title":952,"url":953,"summary":954,"summary_zh":955,"content":20,"source_name":956,"source_url":953,"published_at":494,"category":189,"cover_url":20,"hotness":25,"is_selected":52,"score":957,"score_detail":958,"sources":961,"tags":963,"primary_topic":965,"secondary_topics":966,"classification":967,"search_phrases":999,"slug":1002,"view_count":58,"doi":1003,"paper":1004,"created_at":1025},4177,"The Changing Landscape of Plant Disease Detection: The Role of Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjsrr\u002F2026\u002Fv32i104569","Plant diseases constrain agricultural productivity and food security, while conventional diagnosis depends heavily on visible symptoms and specialist expertise, limiting rapid and scalable assessment across heterogeneous agricultural environments. Artificial intelligence (AI), particularly machine learning, deep learning, and computer vision, offers complementary approaches for automated and non-invasive disease detection. This review examines AI-based methods for image classification, object detection, image segmentation, severity assessment, and early or pre-symptomatic disease detection. It also considers the integration of AI with hyperspectral, multispectral, and thermal imaging and unmanned aerial vehicles for crop-scale monitoring and precision management. Reported studies demonstrate high performance under many experimental conditions; however, reliable field deployment remains constrained by limited or imbalanced datasets, symptom similarity, environmental variability, domain shift, computational requirements, and insufficient independent validation. The review further considers explainable artificial intelligence, edge-based computing, multimodal sensing, foundation models, vision-language models, and federated learning as emerging directions. These approaches may improve robustness, interpretability, accessibility, and scalability, but their value depends on representative field data, biological validity, rigorous external validation, and integration with plant-pathology expertise across diverse crops, cultivars, locations, seasons, and imaging conditions. AI should therefore be regarded as a complementary decision-support tool whose practical contribution depends on combining computational methods with biological knowledge and real-world agricultural observations.","植物病害制约农业生产力和粮食安全，而传统诊断高度依赖可见症状和专家经验，限制了在异质性农业环境中进行快速、可扩展的评估。人工智能（AI），特别是机器学习、深度学习和计算机视觉，为自动化、非侵入式病害检测提供了互补性方法。本文综述了基于AI的图像分类、目标检测、图像分割、严重度评估以及早期或症状前病害检测方法，并探讨了AI与高光谱、多光谱和热成像及无人机相结合用于作物尺度监测和精准管理的集成应用。已报道的研究在许多实验条件下表现出高性能；然而，可靠的田间部署仍受限于数据集有限或不平衡、症状相似性、环境变异性、域偏移、计算需求以及独立验证不足等问题。本文进一步探讨了可解释人工智能、边缘计算、多模态传感、基础模型、视觉语言模型和联邦学习等新兴方向。这些方法有望提升鲁棒性、可解释性、可及性和可扩展性，但其价值取决于具有代表性的田间数据、生物学有效性、严格的外部验证，以及与植物病理学专业知识在多样化作物、品种、地点、季节和成像条件下的整合。因此，AI应被视为一种互补性决策支持工具，其实际贡献取决于将计算方法与生物学知识和真实农业观测相结合。","Journal of Scientific Research and Reports",78,{"impact":91,"substance":959,"depth":91,"authority":227,"freshness":379,"relevant":33,"comment":960},21,"系统综述AI植物病害检测的方法、瓶颈与前沿方向，学术价值高但属综述性论文，产业落地影响有限。",[962],{"name":956,"url":953},[268,384,522,810,964],"植物病害检测",{"code":390,"slug":391,"name":392,"path":393,"parent_code":215},[],{"status":51,"locked":52,"version":53,"method":54,"evidence":968},{"candidates":969,"agricultural_evidence":26},[970,978,984,989,995],{"code":390,"score":91,"supported":26,"title":971,"abstract":973,"tags":977},[972],"artificial intelligence",[456,974,401,975,835,624,972,976],"深度学习","计算机视觉","computer vision",[384],{"code":839,"score":338,"supported":26,"title":979,"abstract":980,"tags":983},[],[981,982],"multispectral","hyperspectral",[810],{"code":167,"score":478,"supported":26,"title":985,"abstract":986,"tags":988},[],[147,987],"food security",[],{"code":990,"score":58,"supported":26,"title":991,"abstract":992,"tags":994},"I5",[],[993],"边缘计算",[],{"code":318,"score":404,"supported":52,"title":996,"abstract":997,"tags":998},[],[],[268,522],[1000,1001],"AI 植物病害检测","无人机 高光谱 作物监测","AI植物病害检测-4177","10.9734\u002Fjsrr\u002F2026\u002Fv32i104569",{"doi":1003,"openalex_id":1005,"authors":1006,"venue":956,"cited_by_count":246,"oa_url":953,"card":1020,"direction":426,"ingested_from":593},"W7218426608",[1007,1009,1011,1014,1016,1018],{"name":1008,"orcid":20},"Aksa Anna Shajan",{"name":1010,"orcid":20},"Sumiya K. V.",{"name":1012,"orcid":1013},"Sainamole Kurian P.","https:\u002F\u002Forcid.org\u002F0009-0004-1992-3582",{"name":1015,"orcid":20},"Pooja Naduvalath",{"name":1017,"orcid":20},"Athira Suresh",{"name":1019,"orcid":20},"Aswathi P. R.",{"tldr":1021,"method":1022,"finding":1023,"direction":426,"opportunity":1024},"综述AI在植物病害检测中的应用，涵盖分类、检测、分割及早期诊断，并指出田间部署的瓶颈与新兴方向。","综述机器学习、深度学习、计算机视觉与高光谱\u002F多光谱\u002F热成像及无人机结合的研究。","AI实验表现优异，但受数据、环境变异和验证不足限制，田间可靠部署仍困难。","可探索多模态基础模型与联邦学习在跨域田间病害检测中的鲁棒性和可解释性验证。","2026-10-04T23:30:40.159648Z"]