[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5220":3,"related-5220":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":71,"slug":74,"view_count":75,"doi":8,"paper":8,"created_at":76},5220,"省农科院水稻高粱所与平昌县十年协同:酿酒专用粮全产业链加速成型","https:\u002F\u002Fwww.sctv.com\u002Fnews\u002F2110606011947991042","四川省农业科学院水稻高粱所深度融入平昌县白酒产业\"融圈建链\"战略,以科技成果转化示范基地为平台,携手小角楼酒业探索\"专家驻点+品种示范+技术集成+订单回收\"模式,推动科研成果从实验室走向田间,为秦巴山区乡村振兴提供科技动能。",null,"秦巴山区，秋风送爽，平昌大地高粱红遍。从土兴镇的示范田到白衣镇的产业园区，从种植大户的丰收笑脸到小角楼酒业的原粮基地，一条以科技为引领、以院地企协同为支撑的酿酒专用粮全产业链正加速成型。近年来，四川省农业科学院水稻高粱所深度融入平昌县白酒产业“融圈建链”战略，以科技成果转化示范基地为平台，以驻村工作队为纽带，以良种良法为核心，携手小角楼酒业十年深耕，探索出“专家驻点+品种示范+技术集成+订单回收”的协同发展模式，将科技优势转化为产业优势，为秦巴山区乡村振兴和川东北白酒产业高质量发展注入强劲动能。","四川网络广播电视台(2026-10)","报道",10,false,57,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,14,12,9,6,1,"省级科研机构与县域、企业十年协同的产业链报道，有模式提炼但属地方宣传性通稿，信息增量有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"乡村振兴","高粱","科技成果转化","酿酒专用粮","院地企协同",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":50},[49,54,59,65],{"code":32,"score":18,"supported":50,"title":51,"abstract":52,"tags":53},true,[27],[27],[27],{"code":39,"score":55,"supported":50,"title":56,"abstract":57,"tags":58},11,[41],[41],[],{"code":60,"score":61,"supported":50,"title":62,"abstract":63,"tags":64},"G",4,[],[26],[26],{"code":66,"score":61,"supported":50,"title":67,"abstract":68,"tags":70},"G1",[],[69],"成果转化",[28],[72,73],"四川省农科院 水稻高粱所 平昌县","平昌 酿酒专用粮 小角楼","四川省农科院水稻高粱所平昌县-5220",0,"2026-10-18T00:05:23.650956Z",{"total":20,"page":21,"page_size":20,"items":78},[79,132,184,250,319,433],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":8,"content":84,"source_name":85,"source_url":8,"published_at":86,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":87,"score_detail":88,"sources":92,"tags":94,"primary_topic":99,"secondary_topics":100,"classification":102,"search_phrases":127,"slug":130,"view_count":75,"doi":8,"paper":8,"created_at":131},5280,"省农科院水稻高粱所泸县召开国家重点研发计划子课题中稻-再生稻现场培训会","https:\u002F\u002Fwww.chinawestagr.com\u002Fzwgk\u002Fshowcontent.asp?id=67457","10月9日—10日\"2026年国家重点研发项目课题五(2024YFD2300405)子课题—川南地区中稻-再生稻大面积单产提升技术模式集成示范现场培训会\"在泸县召开,四川省农技推广总站、宜宾市农科院、犍为县农业农村局等70余人参会;徐富贤研究员、卢代华研究员分别作\"气候变暖背景下川渝再生稻生产布局与高产栽培技术\"等专题培训。","λԺˮ            ʱ䣺2026\u002F10\u002F13245\n\n109—10գΪõƶϵе-ƹӦãԺˮ“2026صзĿ壨2024YFD2300405ӿ—ϵе-ģʽʾֳѵ”سɹٿĴʡũҵƹվ˱ũҵѧԺΪũҵũ֡˳ũҵũ֡¡ũҵũ֡ũҵũ֡ũҵũ֡Ժˮ쵼רҡظũԱ;Ӫ70˲λᡣӿ⸺˹оԱ֡","四川省农业科学院(2026-10-09)","2026-10-09T00:00:00Z",56,{"impact":16,"substance":89,"depth":19,"authority":89,"freshness":90,"relevant":21,"comment":91},13,5,"省级科研机构牵头的国家重点研发计划子课题现场培训会，属细分领域技术推广动态，有实质内容但影响面限于区域，时效性一般，可作常规资讯而非每日精选。",[93],{"name":85,"url":82},[41,95,96,97,98],"国家重点研发计划","泸县","再生稻","农技培训",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[101],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":103},{"candidates":104,"agricultural_evidence":50},[105,110,115,120],{"code":39,"score":106,"supported":50,"title":107,"abstract":108,"tags":109},21,[41,97],[97],[97,41],{"code":32,"score":111,"supported":50,"title":112,"abstract":113,"tags":114},8,[27],[],[],{"code":66,"score":61,"supported":50,"title":116,"abstract":117,"tags":119},[],[118],"农技推广",[98],{"code":121,"score":122,"supported":50,"title":123,"abstract":124,"tags":126},"A8",3,[],[125],"单产提升",[],[128,129],"泸县 中稻再生稻 现场培训","四川省农科院 水稻高粱所 再生稻","泸县中稻再生稻现场培训-5280","2026-10-19T00:07:41.316232Z",{"id":133,"title":134,"url":135,"summary":136,"summary_zh":8,"content":137,"source_name":138,"source_url":8,"published_at":139,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":140,"score_detail":141,"sources":144,"tags":146,"primary_topic":152,"secondary_topics":153,"classification":155,"search_phrases":179,"slug":182,"view_count":75,"doi":8,"paper":8,"created_at":183},5279,"四川院水稻高粱所节水抗旱稻德阳旌阳区示范片测产亩产672.79公斤","https:\u002F\u002Fwww.chinawestagr.com\u002Fzwgk\u002Fshowcontent.asp?id=67451","10月8日德阳市旌阳区农业农村局组织专家对节水抗旱稻旱优73及配套丰产高效栽培技术科技示范片进行现场测产验收,平均亩产量为672.79公斤;该项目由四川省农科院水稻高粱所郭晓艺研究员主持,采用\"智能旱条播、旱管与关键期补水、基蘖穗平衡施肥、科学除草、病虫害绿色防控\"为核心的节水抗旱稻旱直播旱管栽培技术体系。","λԺˮ            ʱ䣺2026\u002F10\u002F13224\n\n108գũҵũ֯ʡũԺСоԱũԺоԱרңԽˮ73׷Ч༼ƼʾƬֳա\n\nʾƬǰΪС󣬲˻еֱ“ܺؼڲˮƽʩʡѧݡ溦ɫ”ΪĵĽˮֱ༼ϵˮƾ⣬ʵʸߡרʾƬȡֳղƽĶΪ672.79רһΪüȽʵãˮڱЧ","四川省农业科学院(2026-10-08)","2026-10-08T00:00:00Z",63,{"impact":16,"substance":142,"depth":18,"authority":89,"freshness":61,"relevant":21,"comment":143},18,"省级科研机构发布的节水抗旱稻示范片实测产量数据，具备实质信息与专业参考价值，但属区域性田间测产进展，时效偏旧，适合作为主题聚合素材而非每日精选头条。",[145],{"name":138,"url":135},[147,148,149,150,151],"水稻育种","节水抗旱稻","四川水稻","粮食单产","旱优73",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[154],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":156},{"candidates":157,"agricultural_evidence":50},[158,162,166,173],{"code":39,"score":89,"supported":50,"title":159,"abstract":160,"tags":161},[41],[41],[147,148],{"code":32,"score":55,"supported":50,"title":163,"abstract":164,"tags":165},[27],[27],[],{"code":167,"score":20,"supported":50,"title":168,"abstract":169,"tags":172},"F4",[],[170,171],"绿色防控","除草",[],{"code":174,"score":122,"supported":50,"title":175,"abstract":176,"tags":178},"F3",[],[177],"施肥",[],[180,181],"四川省农科院 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亩产672.79公斤","四川省农科院节水抗旱稻德阳旌阳区-5279","2026-10-19T00:07:41.265327Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":8,"content":189,"source_name":190,"source_url":8,"published_at":191,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":192,"score_detail":193,"sources":196,"tags":198,"primary_topic":204,"secondary_topics":205,"classification":207,"search_phrases":245,"slug":248,"view_count":21,"doi":8,"paper":8,"created_at":249},4538,"福建省农科院水稻所送选\"福恢342\"及高粱、马铃薯、紫山药等4个特色作物品种入选\"十四五\"国家农作物育种联合攻关90项代表性成果","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7694255477954085422","第十八届中国国际种业博览会暨第二十三届全国种子信息交流与产品交易会在安徽合肥举办，其间发布\"十四五\"国家农作物育种联合攻关90项代表性成果。福建自主选育的4个农作物新品种入选突破性品种选育类成果，包括：福建省农业科学院食用菌研究所选育的工厂化专用双孢蘑菇新品种\"福蘑78\"，重点解决我国双孢蘑菇三次发酵工厂化生产自主选育品种缺乏问题，目前已面向全国推广，在20多家企业实现对进口品种不同比例的替代；福建金品农业科技股份有限公司选育的优质耐湿热高产苗青梗菜新品种\"金品绿青风\"；厦门中厦蔬菜种籽有限公司选育的广适高产优质晚熟花椰菜新品种\"中厦松花100\"；福建省农业科学院茶叶研究所选育的天然茉莉香型茶树新品种\"春闺\"，重点解决特殊香型茶树品种选育问题。此次发布包含突破性品种选育类73项、育种技术创新类11项、骨干亲本创制类6项。","[](https:\u002F\u002Fwww.toutiao.com\u002F)\n\n*   关注 \n*   推荐 \n*   北京   \n*   视频 \n*   财经 \n*   科技 \n*   热点 \n*   国际 \n*   更多 \n\n    *   军事 \n    *   体育 \n    *   娱乐 \n    *   历史 \n    *   美食 \n    *   [直播](https:\u002F\u002Flive.ixigua.com\u002F) \n    *   旅游 \n    *   [懂车帝](https:\u002F\u002Fwww.dongchedi.com\u002F?zt=tt_pc_home_channel) \n\n搜索\n\n[消息](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7694255477954085422)\n\n[发布](https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7694255477954085422)\n\n[登录](https:\u002F\u002Fsso.toutiao.com\u002Flogin\u002F?service=https%3A%2F%2Fwww.toutiao.com%2Farticle%2F7694255477954085422%2F%3Fwid%3D1791591309240)\n\n1\n\n评论\n\n收藏\n\n分享\n\n*   转发到头条 \n*   复制链接 \n*   微信  微信扫码分享  \n*   新浪微博 \n*   QQ空间 \n\n# 福建4个品种入选！“十四五”国家农作物育种联合攻关成果发布\n\n2026-10-08 19:21·[福建日报](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAvjz3oLPyLlxOgGOCwk2WsiyBB5mi8nskKXeDqZ5ubyg\u002F?source=tuwen_detail)\n\n作品声明：内容取材于网络\n\n近日，第十八届中国国际种业博览会暨第二十三届全国种子信息交流与产品交易会在安徽合肥举办，其间发布“十四五”国家农作物育种联合攻关90项代表性成果。福建自主选育的4个农作物新品种，入选突破性品种选育类成果。\n\n其中，由福建省农业科学院食用菌研究所选育的工厂化专用双孢蘑菇新品种“福蘑78”，重点解决我国双孢蘑菇三次发酵工厂化生产自主选育品种缺乏问题，目前已面向全国推广，在20多家企业实现对进口品种不同比例的替代；由福建金品农业科技股份有限公司选育的优质耐湿热高产苗青梗菜新品种“金品绿青风”，重点解决耐湿热鸡毛菜品种品质差问题；由厦门中厦蔬菜种籽有限公司选育的广适高产优质晚熟花椰菜新品种“中厦松花100”，重点解决花椰菜秋冬两季晚熟品种缺乏问题；由福建省农业科学院茶叶研究所选育的天然茉莉香型茶树新品种“春闺”，重点解决特殊香型茶树品种选育问题。\n\n据悉，2022年农业农村部印发《国家育种联合攻关总体方案》，旨在构建以十大优势企业自主攻关为塔尖、以十大主要粮食和重要畜禽联合攻关为塔身、以64个重要特色物种联合攻关为塔基的“金字塔”攻关阵型。此次发布的成果包含突破性品种选育类73项、育种技术创新类11项、骨干亲本创制类6项。\n\n> 来源：福建日报·新福建客户端\n> \n> \n> 记者：张辉\n> \n> \n> 通讯员：曾辉\n\n举报\n\n评论 0\n\n请先 登录 后发表评论～\n\n加载失败，请重新刷新\n\n热门：\n*   [宜城红娘同城热](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7694155074570492345 \"宜城红娘同城热搜抖音推荐附近人\")\n*   [白毛绝对是最有](https:\u002F\u002Fm.ixigua.com\u002Fdx\u002F7694155057412188643 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转福建省农业科学院","2026-10-08T11:21:00Z",73,{"impact":194,"substance":142,"depth":17,"authority":55,"freshness":111,"relevant":21,"comment":195},22,"省级农科院多项品种入选国家级育种攻关代表性成果，具产业参考价值，但正文与标题品种不一致、信息略转载化。",[197],{"name":190,"url":187},[199,200,201,202,203],"种业振兴","育种联合攻关","福建省农科院","水稻品种","特色作物",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[206],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":208},{"candidates":209,"agricultural_evidence":50},[210,215,219,226,233,239],{"code":32,"score":16,"supported":50,"title":211,"abstract":213,"tags":214},[212,27],"马铃薯",[],[],{"code":39,"score":19,"supported":50,"title":216,"abstract":217,"tags":218},[41],[],[202],{"code":220,"score":20,"supported":50,"title":221,"abstract":222,"tags":225},"B3",[],[223,224],"茶叶","茶树",[],{"code":227,"score":20,"supported":50,"title":228,"abstract":229,"tags":232},"B5",[],[230,231],"食用菌","蘑菇",[],{"code":234,"score":61,"supported":50,"title":235,"abstract":236,"tags":238},"F1",[],[237],"种业",[199],{"code":240,"score":122,"supported":50,"title":241,"abstract":242,"tags":244},"B1",[],[243],"蔬菜",[],[246,247],"福建省农科院 福恢342","国家育种联合攻关 代表性成果","福建省农科院福恢342-4538","2026-10-10T00:14:57.872589Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":8,"content":255,"source_name":256,"source_url":8,"published_at":257,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":258,"score_detail":259,"sources":263,"tags":265,"primary_topic":269,"secondary_topics":270,"classification":280,"search_phrases":313,"slug":316,"view_count":317,"doi":8,"paper":8,"created_at":318},4960,"四川省农科院油菜-水稻大面积单产提升技术模式在德阳中江落地","https:\u002F\u002Fwww.deyang.gov.cn\u002Fxwdt\u002Fgxdt\u002F1974184.htm","2026年,四川省农科院在德阳中江县永太镇多宝村落地实施国家重点研发计划课题'西南地区油菜—水稻大面积单产提升技术模式集成示范',示范田水稻平均亩产达694.29公斤,较中江全县平均水平增产20%以上。多宝村采取'机收为主、人工补收'模式,金穗源粮食合作社投入8台收割机同步作业,每日收割超400亩,通过稻田间隙、田埂套种黄豆,实现一地多收,村民人均月增收2000元以上,累计带动500余户农户增收。","金秋时节，稻浪铺金。中江县永太镇多宝村千亩稻田里，收割机往来穿梭，收割、脱粒、清杂一气呵成，农户抢抓晴好天气，成为该县秋收有序推进的生动缩影。\n\n“今年稻子穗大粒满、收成稳。我们闲时在基地务工，顾家增收两不误。”一位正在田间抢收的村民说。依托良好的水土气候和精细化管护，多宝村今年水稻长势均衡、品质优良。当地采取“机收为主、人工补收”模式，连片稻田由收割机一次性作业，边角零散地块人工兜底，全力应收尽收、颗粒归仓。村民分工开展收割、转运、晾晒、入仓，田间一派繁忙景象。\n\n稳产丰产的背后，是农业科技的持续下沉。2026年，四川省农科院在该县永太镇落地实施国家重点研发计划课题“西南地区油菜—水稻大面积单产提升技术模式集成示范”。项目围绕盆地丘陵区油菜—水稻周年轮作，集中推广高产良种、土壤改良、壮根育秧、密植丰产等先进种植技术，全方位升级稻田种植标准化水平与产出效益。\n\n经省、县两级农业专家及合作社负责人按照机械化收割、称重去杂、水分折算等标准化流程田间实收测产，示范田水稻平均亩产达694.29公斤，较中江全县平均水平增产20%以上。“油菜水稻周年轮作技术适配川中丘陵地块，依靠良种良法与精细管护改良土壤、挖掘增产潜力，今年示范效果突出。”四川省农科院专家李其勇、杨泽鹏及团队全程驻田，跟踪水稻长势、落地配套技术，为高产稳产提供技术支撑。\n\n据金穗源粮食合作社负责人常滔介绍，示范田亩产最高可达800公斤，合作社投入8台收割机同步作业，每日收割超400亩。依托良种与科学种植，新米品质优良，每斤售价3元至8.8元，产业收益较去年稳步增长。在稳产丰产之外，当地还盘活土地资源、延伸产业链，通过稻田间隙、田埂套种黄豆，实现一地多收，开展稻谷深加工、打造精品大米，借助电商、社群团购、连锁餐饮拓宽销路，让优质稻米走出乡村。\n\n科技水稻产业正为多宝村乡村振兴、群众增收筑牢支撑。村里落实绿色防控、科学管护稳住粮食产能，吸纳本地及周边村民就近务工，参与田间管护、收割、分拣、包装等环节，村民人均月增收2000元以上，累计带动500余户农户增收，千亩良田变成群众的“致富田、幸福田”。“今年气候适宜、管护到位、技术给力，是实打实的丰收年。乡亲们在‘家门口’就能务工增收，种地有收益、务工有薪金，日子越过越红火。”多宝村党支部书记王玲说。\n\n数据显示，今年中江全县水稻种植面积达41.4万亩，预计总产量约24万吨，粮食丰产形势喜人。目前该县秋收工作正稳步推进，金色稻田绘就和美乡村的秋日画卷，也为县域乡村振兴筑牢了坚实的粮食产业根基。（记者 徐皓 通讯员 薛涛）","德阳市人民政府 2026 年 10 月 13 日","2026-10-13T00:00:00Z",71,{"impact":142,"substance":260,"depth":261,"authority":12,"freshness":111,"relevant":21,"comment":262},20,15,"省级科研机构在县域落地国家重点研发计划，实测亩产694.29公斤、增产超20%，数据与多方信源扎实，具行业示范价值。",[264],{"name":256,"url":253},[26,125,266,267,268],"农业科技示范","水稻测产","油菜水稻轮作",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[271,275],{"code":121,"slug":272,"name":273,"path":274,"parent_code":36},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",{"code":276,"slug":277,"name":278,"path":279,"parent_code":36},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",{"status":44,"locked":13,"version":45,"method":46,"evidence":281},{"candidates":282,"agricultural_evidence":50},[283,288,292,297,303,309],{"code":39,"score":261,"supported":50,"title":284,"abstract":285,"tags":287},[41],[41,286],"稻田",[267],{"code":121,"score":18,"supported":50,"title":289,"abstract":290,"tags":291},[125],[125],[125],{"code":276,"score":55,"supported":50,"title":293,"abstract":295,"tags":296},[294],"油菜",[294],[],{"code":298,"score":122,"supported":50,"title":299,"abstract":300,"tags":302},"A7",[],[301],"套种",[],{"code":304,"score":122,"supported":50,"title":305,"abstract":306,"tags":308},"F5",[],[307],"收割机",[],{"code":60,"score":21,"supported":13,"title":310,"abstract":311,"tags":312},[],[],[26],[314,315],"四川省农科院 油菜水稻 中江","中江 水稻 亩产 694.29公斤","四川省农科院油菜水稻中江-4960",2,"2026-10-15T00:06:03.551910Z",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":8,"source_name":325,"source_url":322,"published_at":326,"category":327,"cover_url":8,"hotness":12,"is_selected":13,"score":328,"score_detail":329,"sources":331,"tags":333,"primary_topic":338,"secondary_topics":339,"classification":351,"search_phrases":402,"slug":405,"view_count":61,"doi":406,"paper":407,"created_at":432},3918,"UAV-based RGB imagery and supervised–unsupervised machine learning for estimating sorghum biomass and grain yield under water and nitrogen stress","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1928610","Precision agriculture emerged as a key strategy for rational management of water and nutrients amid growing food demand driven by global population expansion. Therefore, timely monitoring of field crops is crucial in maintaining adequate nutrient and water levels in the crop fields. Vegetation indices are increasingly being studied for their use in plant nutrient management and in guiding fertilizer recommendations. However, these indices were not adequately evaluated for nutrient management of crops grown with different water and nitrogen application levels. This study investigated the potential application of RGB imagery from low-cost Unmanned Aerial Vehicles (UAVs), combined with machine learning techniques, for quantifying water and nutrient stress in sorghum [ Sorghum bicolor (L.) Moench] crop performance. The field experimental setup at Prairie View A&M University, located in Southeast Texas, applied four irrigation levels (0, 75, 100, and 125% of irrigation; the 100% irrigation level is a fully irrigated treatment with no water stress) and four nitrogen application rates (0, 90, 180, and 360 kg N ha −1 ). The RGB images were captured before harvesting the grain sorghum. Image segmentation for filtering sorghum crops from soil and weeds was performed using K-means clustering and a Convolutional Neural Network (CNN). The vegetation indices, including the Green-Red Vegetation Index (GRVI), Green Leaf Index (GLI), and Excess Green Index (ExG), derived from the segmented images were used to predict sorghum grain and biomass yields using multiple linear regression, Partial least squares regression, Ridge regression, and XGBoost. The results indicated that no single model consistently outperformed the others across both segmentation approaches and response variables, suggesting that the predictive information in the RGB indices is meaningful with selected ML approaches. For example, biomass prediction using ridge regression yielded a Pearson correlation coefficient (r) of 0.65 and an RMSE of 0.72 for K-means clustering-derived indices. The models exhibited moderate, stage-specific predictive performance, with no single regression method consistently outperforming the others across both segmentation approaches and response variables. Because imagery was acquired only once during the late reproductive period, the RGB indices primarily reflected canopy condition, maturity, and senescence at that time. The findings demonstrate the feasibility of a low-cost, single-date UAV RGB workflow for late-season sorghum assessment, while emphasizing the need for multi-temporal imagery and independent validation before application to season-long stress diagnosis or operational yield forecasting.","精准农业已成为在水资源与养分合理管理中的关键策略，以应对全球人口增长所驱动的日益增长的粮食需求。因此，及时监测田间作物对于维持作物田块中适宜的养分和水分水平至关重要。植被指数在植物养分管理和指导施肥推荐中的应用正日益受到研究。然而，这些指数尚未在不同水分和氮素施用水平下对作物养分管理进行充分评估。本研究探讨了来自低成本无人机（UAV）的RGB影像结合机器学习技术在量化高粱[Sorghum bicolor (L.) Moench]作物表现中水分和养分胁迫方面的潜在应用。位于德克萨斯州东南部的Prairie View A&M大学的田间试验设置了四个灌溉水平（灌溉量的0%、75%、100%和125%；100%灌溉水平为充分灌溉处理，无水分胁迫）和四个氮素施用量（0、90、180和360 kg N ha⁻¹）。RGB图像在收获籽粒高粱前采集。使用K-means聚类和卷积神经网络（CNN）进行图像分割，以将高粱作物与土壤和杂草分离。从分割图像中提取的植被指数，包括绿红植被指数（GRVI）、绿叶指数（GLI）和过量绿指数（ExG），被用于通过多元线性回归、偏最小二乘回归、岭回归和XGBoost预测高粱籽粒和生物量产量。结果表明，在两种分割方法和响应变量下，没有单一模型始终优于其他模型，这表明RGB指数中的预测信息在所选机器学习方法下具有意义。例如，使用岭回归进行生物量预测时，基于K-means聚类所得指数的Pearson相关系数（r）为0.65，RMSE为0.72。这些模型表现出中等、特定阶段的预测性能，在两种分割方法和响应变量下，没有单一回归方法始终优于其他方法。由于影像仅在生殖后期采集一次，RGB指数主要反映当时的冠层状况、成熟度和衰老程度。研究结果证明了低成本、","Frontiers in Environmental Science","2026-09-30T00:00:00Z","论文",69,{"impact":18,"substance":260,"depth":16,"authority":89,"freshness":111,"relevant":21,"comment":330},"低成本无人机RGB影像结合机器学习估算高粱生物量与产量的方法学研究，结论审慎，对精准农业养分水分管理有参考价值，但属单点单期试验，影响范围有限。",[332],{"name":325,"url":322},[334,335,336,337,27],"智慧农业","农业人工智能","精准农业","遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[340,346],{"code":341,"slug":342,"name":343,"path":344,"parent_code":345},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":174,"slug":347,"name":348,"path":349,"parent_code":350},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":44,"locked":13,"version":45,"method":46,"evidence":352},{"candidates":353,"agricultural_evidence":50},[354,359,365,371,379,385,392,397],{"code":32,"score":261,"supported":50,"title":355,"abstract":357,"tags":358},[356],"sorghum",[27,356],[27],{"code":341,"score":261,"supported":50,"title":360,"abstract":362,"tags":364},[361],"machine learning",[363,361],"机器学习",[335],{"code":174,"score":19,"supported":50,"title":366,"abstract":367,"tags":370},[],[177,368,369],"养分管理","fertilizer",[],{"code":372,"score":19,"supported":50,"title":373,"abstract":374,"tags":378},"H3",[],[375,376,377],"灌溉","水资源","irrigation",[],{"code":380,"score":111,"supported":50,"title":381,"abstract":382,"tags":384},"I1",[],[336,383],"precision agriculture",[334,336],{"code":386,"score":20,"supported":50,"title":387,"abstract":388,"tags":391},"H2",[],[389,390],"土壤","soil",[],{"code":298,"score":122,"supported":50,"title":393,"abstract":394,"tags":396},[],[395],"间作",[],{"code":398,"score":21,"supported":13,"title":399,"abstract":400,"tags":401},"I3",[],[],[337],[403,404],"Prairie View A&M University 高粱 无人机","高粱 生物量 产量 无人机 RGB","PrairieViewAMUniversity高粱无人机-3918","10.3389\u002Ffenvs.2026.1928610",{"doi":406,"openalex_id":408,"authors":409,"venue":325,"cited_by_count":75,"oa_url":322,"card":424,"direction":430,"ingested_from":431},"W7214994063",[410,412,415,417,420,422],{"name":411,"orcid":8},"Anoop Valiya Veettil",{"name":413,"orcid":414},"Ripendra Awal","https:\u002F\u002Forcid.org\u002F0000-0002-2453-2592",{"name":416,"orcid":8},"Ali Fares",{"name":418,"orcid":419},"Binita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-7093-4915",{"name":421,"orcid":8},"Almoutaz Elhassan",{"name":423,"orcid":8},"Atikur Rahman",{"tldr":425,"method":426,"finding":427,"direction":428,"opportunity":429},"用无人机RGB影像与机器学习估算水分氮胁迫下高粱生物量和产量。","低成本无人机RGB影像，K-means与CNN分割，多种回归模型预测。","单一模型未持续最优，RGB指数在特定时期具中等预测能力。","农业遥感与作物表型","可研究多时相RGB影像与深度学习融合，提升胁迫下作物产量预测精度。","农业人工智能与决策模型","openalex","2026-10-01T23:30:54.379150Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":438,"content":8,"source_name":439,"source_url":436,"published_at":326,"category":327,"cover_url":8,"hotness":440,"is_selected":13,"score":87,"score_detail":441,"sources":443,"tags":451,"primary_topic":455,"secondary_topics":456,"classification":458,"search_phrases":481,"slug":484,"view_count":122,"doi":485,"paper":486,"created_at":496},3912,"An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054047","An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction presents a technology-driven approach for automated crop health monitoring and yield estimation in sorghum cultivation. The study integrates computer vision techniques and machine learning algorithms to identify and quantify disease severity from crop images and to predict potential yield based on relevant visual and agronomic characteristics. The framework aims to reduce the limitations of conventional manual disease assessment, which can be time-consuming and subject to human variability. Image processing and feature extraction techniques are employed to capture disease-related characteristics, while machine learning models support classification, severity estimation, and yield prediction. By combining disease assessment with yield forecasting, the proposed framework provides a comprehensive approach to precision agriculture and data-driven crop management. The work demonstrates the potential of artificial intelligence and computer vision to support timely decision-making, improve crop monitoring, and contribute to sustainable and efficient sorghum production.","一个集成的计算机视觉与机器学习框架用于高粱病害严重度评估与产量预测，提出了一种技术驱动的方法，用于高粱种植中的自动化作物健康监测与产量估算。该研究整合了计算机视觉技术和机器学习算法，从作物图像中识别和量化病害严重度，并基于相关视觉和农艺特征预测潜在产量。该框架旨在减少传统人工病害评估的局限性，传统方法可能耗时且受人为变异影响。研究采用图像处理和特征提取技术捕捉病害相关特征，同时机器学习模型支持分类、严重度估计和产量预测。通过将病害评估与产量预测相结合，所提出的框架为精准农业和数据驱动的作物管理提供了一种综合方法。该工作展示了人工智能和计算机视觉在支持及时决策、改善作物监测以及促进可持续和高效高粱生产方面的潜力。","International Journal of Emerging Technologies and Innovative Research",55,{"impact":18,"substance":16,"depth":17,"authority":20,"freshness":111,"relevant":21,"comment":442},"论文提出计算机视觉与机器学习结合的高粱病害严重度评估与产量预测框架，方法有一定新意但期刊权威性一般，属细分领域技术进展。",[444,445,447,449],{"name":439,"url":436},{"name":439,"url":446},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054048",{"name":439,"url":448},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053752",{"name":439,"url":450},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053753",[334,335,452,453,27,454],"计算机视觉","产量预测","病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[457],{"code":341,"slug":342,"name":343,"path":344,"parent_code":345},{"status":44,"locked":13,"version":45,"method":46,"evidence":459},{"candidates":460,"agricultural_evidence":50},[461,468,472,477],{"code":341,"score":462,"supported":50,"title":463,"abstract":465,"tags":467},28,[361,464],"computer vision",[363,452,361,466,464],"artificial intelligence",[335,454,452],{"code":32,"score":261,"supported":50,"title":469,"abstract":470,"tags":471},[356],[27,356],[27],{"code":380,"score":473,"supported":50,"title":474,"abstract":475,"tags":476},7,[],[336,383],[334],{"code":398,"score":21,"supported":13,"title":478,"abstract":479,"tags":480},[],[],[453],[482,483],"高粱 病害识别 产量预测","计算机视觉 作物病害评估","高粱病害识别产量预测-3912","10.5281\u002Fzenodo.23054047",{"doi":485,"openalex_id":487,"authors":488,"venue":439,"cited_by_count":75,"oa_url":436,"card":491,"direction":430,"ingested_from":431},"W7214900332",[489],{"name":490,"orcid":8},"Anantjit Publication",{"tldr":492,"method":493,"finding":494,"direction":430,"opportunity":495},"融合计算机视觉与机器学习，实现高粱病害严重度评估与产量预测。","图像处理与特征提取结合机器学习分类回归模型。","该框架可自动量化病害严重度并预测产量，减少人工评估偏差。","可探索多作物迁移学习与田间实时轻量化部署，提升泛化性。","2026-10-01T23:30:48.742698Z"]