[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3396":3,"related-3396":104},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":50,"search_phrases":98,"slug":101,"view_count":102,"doi":8,"paper":8,"created_at":103},3396,"从吃到用——中国农科院第八届农科开放日现场直击：彩色胡萝卜、丙醇二酸黄瓜、甜糯同粒玉米、转运机器人、动物疾病风险大模型","https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml","9月19日中国农科院36个研究所的560多项最新科研成果集中亮相：彩色胡萝卜、橘红心白菜、特色彩椒等全新果蔬品种轮番展示；富含丙醇二酸的晶莹剔透黄瓜能阻止糖类转化为脂肪；低豆腥味、高异黄酮、适合榨汁的大豆及'甜糯同粒'玉米；农业转运机器人能扛100公斤以上作物在丘陵山区跑、具备原地掉头能力；正在研发的全球动物疾病风险大模型将对动物疫病实现实时预报预警。",null,"[首页](https:\u002F\u002Fwww.cctv.cn\u002F)\n\n[时政](javascript:;)\n\n[新闻](https:\u002F\u002Fnews.cctv.cn\u002F)\n\n[评论](https:\u002F\u002Fopinion.cctv.cn\u002F)\n\n[视频](https:\u002F\u002Fv.cctv.cn\u002F)\n\n[财经](https:\u002F\u002Ffinance.cctv.cn\u002Findex.shtml)\n\n[体育](https:\u002F\u002Fsports.cctv.cn\u002F)\n\n[教育](https:\u002F\u002Fedu.cctv.cn\u002F)\n\n[熊猫](https:\u002F\u002Fwww.ipanda.cn\u002F)\n\n[直播中国](https:\u002F\u002Flivechina.cctv.cn\u002F)\n\n[4K](https:\u002F\u002Ftv.cctv.cn\u002F4K\u002Findex.shtml)\n\n[CCTV.直播](https:\u002F\u002Ftv.cctv.cn\u002Findex.shtml)\n\n[中](https:\u002F\u002Fwww.cctv.cn\u002Findex.shtml)\n\n[](https:\u002F\u002Faigc.cctv.cn\u002Findex.shtml 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43](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)](javascript:;)\n\n图形验证码错误\n\n短信验证码\n\n_获取验证码_\n\n短信验证码错误\n\n验证码已发送，可能会有延后请耐心等待\n\n[登 录](javascript:;)\n\n请阅读并勾选下列协议\n\n同意[《央视网用户服务协议》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002Ffuwuxieyi\u002Findex.shtml)[《央视网隐私政策》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002FPAGE1381887126760260\u002Findex.shtml)[《儿童隐私保护指引》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002F20200605\u002Findex.shtml)\n\n免费注册\n\n手机号\u002F邮箱\n\n输入格式有误，请使用邮箱或手机号码登录 \n\n密码长度为 8-16 个字符\n\n图形验证码\n\n[![Image 44](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)](javascript:;)\n\n图形验证码错误\n\n记住我\n\n[忘记密码](https:\u002F\u002Fuser.cctv.cn\u002FfindPassword\u002Findex.shtml)\n\n[登 录](javascript:;)\n\n请阅读并勾选下列协议\n\n同意[《央视网用户服务协议》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002Ffuwuxieyi\u002Findex.shtml)[《央视网隐私政策》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002FPAGE1381887126760260\u002Findex.shtml)[《儿童隐私保护指引》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002F20200605\u002Findex.shtml)\n\n免费注册\n\n[](javascript:;)[](javascript:;)[](javascript:;)[](javascript:;)\n\n#### 打开 央视影音手机客户端\n\n#### 扫描二维码登录\n\n二维码已失效\n\n点击刷新\n\n扫码成功\n\n请在手机上确认 [登录]\n\n登录即代表同意[《央视网用户服务协议》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002Ffuwuxieyi\u002Findex.shtml)[《央视网隐私政策》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002FPAGE1381887126760260\u002Findex.shtml)[《儿童隐私保护指引》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002F20200605\u002Findex.shtml)\n\n[下载央视影音客户端](https:\u002F\u002Fapp.cctv.cn\u002F)\n\n手机注册 邮箱注册\n\n +86 | \n手机号\n\n手机号格式错误\n\n图形验证码\n\n[![Image 45](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)](javascript:;)\n\n图形验证码错误\n\n短信验证码\n\n_获取验证码_\n\n短信验证码错误\n\n验证码已发送，可能会有延后请耐心等待\n\n密码与确认密码不一致\n\n请再次输入密码\n\n[注 册](javascript:;)\n\n请阅读并勾选下列协议\n\n同意[《央视网用户服务协议》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002Ffuwuxieyi\u002Findex.shtml)[《央视网隐私政策》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002FPAGE1381887126760260\u002Findex.shtml)[《儿童隐私保护指引》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002F20200605\u002Findex.shtml)\n\n返回登录\n\n邮箱\n\n邮箱格式错误\n\n图形验证码\n\n[![Image 46](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)](javascript:;)\n\n图形验证码错误\n\n密码与确认密码不一致\n\n请再次输入密码\n\n[注 册](javascript:;)\n\n请阅读并勾选下列协议\n\n同意[《央视网用户服务协议》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002Ffuwuxieyi\u002Findex.shtml)[《央视网隐私政策》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002FPAGE1381887126760260\u002Findex.shtml)[《儿童隐私保护指引》](https:\u002F\u002Fwww.cctv.cn\u002Fspecial\u002Fguanyunew\u002F20200605\u002Findex.shtml)\n\n返回登录\n\n扫码成功\n\n请绑定手机号\n\n +86 | \n手机号\n\n手机号格式错误\n\n图形验证码\n\n[![Image 47](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)](javascript:;)\n\n图形验证码错误\n\n短信验证码\n\n_获取验证码_\n\n短信验证码错误\n\n验证码已发送，可能会有延后请耐心等待\n\n[提 交](javascript:;)\n温馨提示：若您的手机号已在央视网注册，绑定后， 仅保留手机账号内的信息。\n\n验证邮件发送成功  \n立即[进入邮箱查看](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n24h同一个用户请求邮件验证码超过10次\n\n邮箱验证码\n\n已发送,可能会有延后请耐心等待\n\n[确定](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n安全验证\n\n图形验证码\n\n[![Image 48](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)](javascript:;)\n\n图形验证码错误\n\n确 定\n\n您的密码存在安全风险，请 [立即修改](https:\u002F\u002Fuser.cctv.cn\u002FfindPassword\u002Findex.shtml)\n\n激活成功\n\n3s 后自动跳转到登录页\n\n返回登录\n\n信息已提交  \n请点击邮箱中的链接激活账号\n\n立即访问邮箱\n\n系统维护中，请稍后再试\n\n请完成安全验证\n\n![Image 49](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n向右滑动完成验证\n\n![Image 50](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n[](http:\u002F\u002Fm.cctv.cn\u002F)\n\n[新闻](http:\u002F\u002Fnews.cctv.cn\u002F)\n\n[](http:\u002F\u002Fm.cctv.cn\u002Fquanzhannav2019\u002Findex.shtml)\n\n[](https:\u002F\u002Fsearch.cctv.cn\u002Fm\u002F)\n\n[新闻频道](https:\u002F\u002Fnews.cctv.cn\u002F)[>](javascript:void(0);)[中国新闻](https:\u002F\u002Fnews.cctv.cn\u002Fzgxwyc\u002F)\n\n# 从吃到用 实验室与餐桌距离被看得见、尝得到、用得上的“新鲜事”拉近\n\n来源：央视网 | 2026年09月20日 13:58:17\n\n央视网 | 2026年09月20日 13:58:17\n\n###### 原标题：\n\n正在加载\n\n[Video 3](https:\u002F\u002Fdh5.cntv.cdn20.com\u002Fasp\u002Fh5e\u002Fhls\u002Fmain\u002F0303000a\u002F3\u002Fdefault\u002F8d26c0f8c36e4e1e8ed02d6406007686\u002Fmain.m3u8?maxbr=2048&contentid=15120519184043)\n\n静音(m)全屏(f)\n\nADCountdown Time | 广告\n\n![Image 51](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)关闭广告\n\n![Image 52](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n尊贵的用户，已为您跳过广告\n\n0:00\n\n正在加载 视频播放器。\n\n播放(p)播放视频\n\n播放(p)\n\n当前时间 0:00\n\n\u002F\n\n时长 2:12\n\n媒体流类型 直播\n\n高清\n\n画质\n\n*   超清\n*   高清, 选择\n*   标清\n*   流畅\n\n静音(m)\n\n全屏(f)\n\n加载完成: 0%\n\n-\n\n88:88\n\n[](javascript:void(0);)\n\n*   core:undefined.undefined\n*   wrap:3.8.2.260723\n\n画中画\n\n**央视网消息：**9月19日，中国农业科学院36个研究所的560多项最新科研成果集中面向社会公众亮相，把农业实验室里的“新鲜事”带到公众眼前。\n\n![Image 53](https:\u002F\u002Fp3.img.cntv.cn\u002Fphotoworkspace\u002Fcontentimg\u002F2026\u002F09\u002F20\u002F2026092013212476326.png)\n\n在果蔬成果展区，最受欢迎的不是常见的香甜水果，而是一杯胡萝卜汁。  \n  \n 科研人员通过调节不同品种胡萝卜的收获时间和栽培模式，培育出口感更甜更细腻的品种以及减糖版胡萝卜。  \n  \n 中国农业科学院蔬菜花卉研究所大宗蔬菜产业技术体系岗位专家庄飞云介绍，胡萝卜口感整体受环境影响特别大，通过调节里面的一些组分，很多让人不愉快的组分把它尽量减少到很低，当然它的营养还能保持原有的营养。\n\n![Image 54](https:\u002F\u002Fp5.img.cntv.cn\u002Fphotoworkspace\u002Fcontentimg\u002F2026\u002F09\u002F20\u002F2026092013213274797.png)\n\n另一边的展区里，这种晶莹剔透的黄瓜不仅口感脆嫩，还富含一种能有效阻止糖类转化为脂肪的功能小分子——“丙醇二酸”。吃根黄瓜还能顺带帮着管理体重。  \n  \n 作物展区里，作物科学研究所带来了新的攻关成果。低豆腥味、高异黄酮、适合榨汁的大豆，还有一款新研发的“甜糯同粒”玉米。\n\n![Image 55](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n中国农业科学院作物科学研究所育种专家肖永贵介绍，原来的玉米要么是甜，要么是糯，要么就是7粒糯1粒甜，要么3粒糯1粒甜，把甜和糯放到一个粒里按照不同比例调配口感会更好。  \n  \n 机械展区，各种新型农用机器人集体亮相。这台农业转运机器人不仅能扛100公斤以上的作物在丘陵山区跑，还会“凌波微步”。\n\n![Image 56](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n农业农村部南京农业机械化研究所助理研究员宫庆硕介绍，可以进行左右横移，在狭窄的田间环境中可以实现快速换行，也可以实现原地掉头能力。  \n  \n 正在研发的全球动物疾病风险大模型，未来将对动物疫病实现实时预报预警，相当于给全球畜牧业装上一张“天气图”，让餐桌上的肉蛋奶更安全。从吃到用，实验室与餐桌的距离正在被这些“看得见、尝得到、用得上”的成果不断拉近。\n\n编辑：杨书杰 责任编辑：刘亮\n\n*   [从吃到用](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E4%BB%8E%E5%90%83%E5%88%B0%E7%94%A8)\n*   [实验室](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%AE%9E%E9%AA%8C%E5%AE%A4)\n*   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2026-09-20","2026-09-20T00:00:00Z","报道",10,false,65,{"impact":17,"substance":18,"depth":19,"authority":18,"freshness":20,"relevant":21,"comment":22},18,15,12,5,1,"央媒对中国农科院开放日的综合报道，集中展示彩色胡萝卜、丙醇二酸黄瓜、甜糯同粒玉米及转运机器人、动物疾病风险大模型等成果，信息量足但属活动报道，时效已过一周，可作主题聚合素材。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业机器人","种业振兴","动物疫病防控","农业科技","功能型农产品",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,64,69,74,81,88,93],{"code":32,"score":57,"supported":58,"title":59,"abstract":62,"tags":63},22,true,[60,61],"黄瓜","萝卜",[60,61],[],{"code":39,"score":65,"supported":58,"title":66,"abstract":67,"tags":68},11,[41],[41],[],{"code":45,"score":65,"supported":58,"title":70,"abstract":72,"tags":73},[71],"大模型",[71],[],{"code":75,"score":76,"supported":58,"title":77,"abstract":78,"tags":80},"C5",4,[],[79],"动物疫病",[28],{"code":82,"score":83,"supported":58,"title":84,"abstract":85,"tags":87},"A4",3,[],[86],"大豆",[],{"code":89,"score":21,"supported":14,"title":90,"abstract":91,"tags":92},"F1",[],[],[27],{"code":94,"score":21,"supported":14,"title":95,"abstract":96,"tags":97},"F6",[],[],[26],[99,100],"中国农科院 农科开放日","彩色胡萝卜 丙醇二酸黄瓜","中国农科院农科开放日-3396",2,"2026-09-25T00:09:29.650043Z",{"total":105,"page":21,"page_size":105,"items":106},6,[107,177,225,288,370,427],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":8,"content":8,"source_name":112,"source_url":8,"published_at":113,"category":114,"cover_url":8,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":123,"tags":125,"primary_topic":129,"secondary_topics":130,"classification":132,"search_phrases":161,"slug":164,"view_count":102,"doi":165,"paper":166,"created_at":176},2492,"BiFormer-Enhanced YOLOv11n for Accurate Maize Ear Detection in Seed Production Fields(玉米穗识别 BiF-YOLO)","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181780","青岛农业大学机电工程学院 2026 年 9 月 10 日在《Agronomy》(Section Precision and Digital Agriculture)发表。提出 BiF-YOLO 模型基于 YOLOv11n 和 BiFormer 架构,三项目标性增强策略:①在特征提取网络嵌入 C3k2_AdditiveBlock 模块以增强田间被遮挡和小型玉米穗的识别能力;②构建 C2BRA 双层路由注意力机制(Cross-stage partial 2 Bottleneck with Residual Attention)以抑制背景干扰;③采用 ShapeIoU 损失函数。从胶州和张掖种子生产田采集玉米穗图像。BiF-YOLO 模型有效适应复杂田间作业场景,为制种玉米收获机的实时识别与产量估计提供技术支撑。","MDPI Agronomy 2026-09-10","2026-09-09T16:00:00Z","论文",74,{"impact":117,"substance":118,"depth":119,"authority":120,"freshness":121,"relevant":21,"comment":122},16,21,17,13,7,"面向制种玉米收获的轻量化检测模型改进，方法有针对性增强且数据来自真实制种田，对智能农机与产量估计有实用价值，但属细分技术进展，影响面有限。",[124],{"name":112,"url":110},[126,127,27,41,128],"智慧农业","农业人工智能","目标检测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[131],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":133},{"candidates":134,"agricultural_evidence":58},[135,140,145,151,157],{"code":39,"score":136,"supported":58,"title":137,"abstract":138,"tags":139},20,[41,40],[41],[41],{"code":45,"score":120,"supported":58,"title":141,"abstract":143,"tags":144},[142],"YOLO",[142],[127,128],{"code":146,"score":76,"supported":58,"title":147,"abstract":148,"tags":150},"I1",[],[149],"digital 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[舜耕论坛](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch16829\u002F)\n*   [文献资源](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10761\u002F)\n*   [查新检索](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10763\u002F)\n\n![Image 20](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Fwgty.png) 您现在的位置： [首页](http:\u002F\u002Fwww.saas.ac.cn\u002F)→ [科技创新](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10737\u002F)→ [科技动态](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10721\u002F)\n\n山东省重点研发计划（农业良种工程）十字花科蔬菜智能育种大模型研发及应用项目启动会在济南召开\n\n 发布日期：2026-09-02 浏览次数： 154 文章来源：蔬菜研究所\n\n8月31日，山东省重点研发计划（农业良种工程）十字花科蔬菜智能育种大模型研发及应用项目（2026LZGC006）启动会在济南召开。会议由蔬菜所党委书记、所长王文亮主持，山东省科技厅农村科技处处长冯兴石、山东省农业科学院科技管理处处长张文君出席会议。哈尔滨工业大学胡杨教授、山东省种子管理总站刘存辉研究员、南京农业大学柳李旺教授、华北理工大学宋小明教授及山东正华会计师事务所门枫叶注册会计师应邀担任项目咨询专家。项目主持人、各参与单位课题负责人、科研骨干及科技管理处相关负责同志等30余人参加会议。\n\n![Image 21](http:\u002F\u002Fwww.saas.ac.cn\u002Fresource\u002Fsdnky\u002Fimage\u002F202609\u002F2bb431d1-949a-4584-8e74-235c22832679m.png)\n\n会上，冯兴石对项目后续推进工作提出明确要求：一是聚焦任务目标，强化协同创新。细化任务指标，强化节点调度与全程监管，产出十字花科蔬菜育种标志性成果，助力农业强省建设，保障种业安全。二是规范财务管理，筑牢风险防线。落实专项资金专款专用、单独核算，依托科技云平台动态监管，健全监督追责机制，严控资金风险。三是深化制度落地，抓实过程管理。强化合同“军令状”约束，依规履行报批备案程序，规范项目全流程管理。四是创新宣传机制，放大示范效应。规范标注项目资助信息，推动成果省内落地转化，完善多层次报送宣传机制，展现山东种业科技创新实力。张文君对省科技厅给予院科技创新工作的支持表示感谢，并强调项目实施要树立“验收思维”：聚焦“一个目标”，即项目任务指标；做好“两个强化”，强化项目全过程管理，强化各课题协同攻关力度；严守“三条红线”，坚决守住任务指标红线、项目经费红线、科研诚信红线；明确“四个边界”，严格划清时间边界、资金边界、成果边界、责任边界；最终实现“五个产出”，出品种、出技术、出人才、出场景、出效益。\n\n随后，会议宣布成立项目咨询专家组，由胡杨教授担任组长。项目主持人及各课题负责人依次对任务目标、责任分工、实施方案、前期基础等内容进行了汇报。咨询专家组围绕数据标准化采集、多组学数据库搭建、智能育种大模型构建等关键环节提出了指导性建议并形成了论证意见。此次启动会的召开，进一步明确了任务目标、凝聚了协同合力，为项目高质量实施奠定了坚实基础。\n\n（撰稿：刘辰 核稿：王文亮）\n\n分享\n\n分享到\n\n[微信](http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10721\u002F202609\u002Fe2cb63ee-efcf-414c-a53f-c4494c368428.shtml \"分享到微信\")\n\n政府机构\n\n[山东省人民政府](http:\u002F\u002Fwww.shandong.gov.cn\u002F 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25](http:\u002F\u002Fwww.saas.ac.cn\u002Fresource\u002Fsdnky01\u002Fimage\u002F202604\u002F3fcb1043-85f2-4dca-958d-b207876c780b.png)\n\n![Image 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十字花科蔬菜 智慧农业 智能育种","农业人工智能 十字花科蔬菜","农业人工智能十字花科蔬菜智慧农业智能育种-1794","2026-09-07T00:04:39.072833Z",{"id":226,"title":227,"url":228,"summary":229,"summary_zh":230,"content":8,"source_name":231,"source_url":228,"published_at":232,"category":114,"cover_url":8,"hotness":13,"is_selected":14,"score":233,"score_detail":234,"sources":237,"tags":239,"primary_topic":243,"secondary_topics":244,"classification":246,"search_phrases":266,"slug":269,"view_count":168,"doi":270,"paper":271,"created_at":287},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":19,"substance":17,"depth":117,"authority":13,"freshness":235,"relevant":21,"comment":236},8,"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[238],{"name":231,"url":228},[126,127,240,241,242],"深度学习","番茄病害","图像识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[245],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":247},{"candidates":248,"agricultural_evidence":58},[249,255,261],{"code":32,"score":18,"supported":58,"title":250,"abstract":252,"tags":254},[251],"tomato",[253,251],"番茄",[241],{"code":45,"score":256,"supported":58,"title":257,"abstract":258,"tags":260},9,[],[240,259],"deep learning",[127,242,240],{"code":146,"score":121,"supported":58,"title":262,"abstract":263,"tags":265},[],[126,264],"smart agriculture",[126],[267,268],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":270,"openalex_id":272,"authors":273,"venue":231,"cited_by_count":168,"oa_url":279,"card":280,"direction":285,"ingested_from":286},"W7222977034",[274,276],{"name":275,"orcid":8},"Mrs. Vijetalaxmi A Hegde",{"name":277,"orcid":278},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":281,"method":282,"finding":283,"direction":173,"opportunity":284},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","智慧农业 \u002F 农业物联网","openalex","2026-10-20T23:30:10.424511Z",{"id":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":8,"source_name":294,"source_url":291,"published_at":295,"category":114,"cover_url":8,"hotness":13,"is_selected":14,"score":296,"score_detail":297,"sources":299,"tags":301,"primary_topic":304,"secondary_topics":305,"classification":307,"search_phrases":330,"slug":333,"view_count":21,"doi":334,"paper":335,"created_at":369},5164,"Lightweight Hybrid CNN-Transformer Network for Robust Maize Leaf Disease Detection and Edge Deployment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.50","Early and accurate detection of maize leaf diseases is essential for improving crop productivity and supporting precision agriculture.However, deep learning models trained on controlled leaf-image datasets often experience performance degradation under real-world agricultural conditions because of illumination variation, background clutter, occlusion, leaf orientation, and imaging noise.To address these challenges, this study develops a lightweight deep learning framework for robust maize leaf disease classification and edge-oriented deployment.The framework employs MobileNetV3-Small for efficient extraction of disease-discriminative visual features and investigates lightweight feature-learning strategies for improving classification under challenging field conditions.Experiments were conducted using PlantVillage-Maize and Field-Maize datasets comprising 6,188 images from four categories: Maize Leaf Blight (MLB), Common Rust, Healthy, and Leaf Spot.The image-only MobileNetV3-Small model achieved 97.8% accuracy and a 97.4% F1-score on the controlled PlantVillage-Maize dataset, while its performance decreased to 86.1% accuracy and an 83.9% F1-score on Field-Maize, highlighting the domain gap between controlled and real-field imagery.Robustness was further evaluated under Gaussian noise, illumination variation, and partial occlusion, together with computational-efficiency and edge-deployment analyses.The findings demonstrate the importance of lightweight and robust visual feature learning for practical maize disease diagnosis under real-world agricultural imaging conditions.","早期准确检测玉米叶片病害对于提高作物生产力和支持精准农业至关重要。然而，在受控叶片图像数据集上训练的深度学习模型，在真实农业条件下往往会出现性能下降，原因包括光照变化、背景杂乱、遮挡、叶片朝向以及成像噪声。为应对这些挑战，本研究开发了一个轻量级深度学习框架，用于稳健的玉米叶片病害分类和面向边缘的部署。该框架采用MobileNetV3-Small高效提取具有病害判别力的视觉特征，并研究轻量级特征学习策略，以提升在复杂田间条件下的分类性能。实验使用PlantVillage-Maize和Field-Maize数据集进行，共包含来自四个类别的6，188张图像：玉米大斑病（Maize Leaf Blight，MLB）、普通锈病（Common Rust）、健康（Healthy）和叶斑病（Leaf Spot）。仅使用图像的MobileNetV3-Small模型在受控的PlantVillage-Maize数据集上达到了97.8%的准确率和97.4%的F1分数，而在Field-Maize上的性能下降至86.1%的准确率和83.9%的F1分数，凸显了受控图像与真实田间图像之间的域差距。研究进一步在高斯噪声、光照变化和部分遮挡条件下评估了稳健性，并进行了计算效率和边缘部署分析。研究结果表明，轻量级且稳健的视觉特征学习对于真实农业成像条件下的实际玉米病害诊断具有重要意义。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z",69,{"impact":19,"substance":136,"depth":117,"authority":19,"freshness":256,"relevant":21,"comment":298},"该研究提出轻量级玉米叶病检测框架，并揭示受控数据集与田间数据之间的性能差距，方法扎实、结论有参考价值，但属细分技术论文，公共影响有限。",[300],{"name":294,"url":291},[126,127,302,240,303],"边缘计算","玉米病害",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[306],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":308},{"candidates":309,"agricultural_evidence":58},[310,315,319,325],{"code":39,"score":311,"supported":58,"title":312,"abstract":313,"tags":314},14,[40],[41,40],[],{"code":45,"score":235,"supported":58,"title":316,"abstract":317,"tags":318},[],[240,259],[127,240],{"code":146,"score":121,"supported":58,"title":320,"abstract":321,"tags":324},[],[322,323],"精准农业","precision agriculture",[126],{"code":326,"score":21,"supported":14,"title":327,"abstract":328,"tags":329},"I5",[],[],[302],[331,332],"MobileNetV3 玉米叶部病害","玉米叶片病害 边缘部署","MobileNetV3玉米叶部病害-5164","10.22266\u002Fijies2026.1130.50",{"doi":334,"openalex_id":336,"authors":337,"venue":294,"cited_by_count":168,"oa_url":291,"card":363,"direction":368,"ingested_from":286},"W7222602850",[338,341,344,346,348,351,353,355,358,360],{"name":339,"orcid":340},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":342,"orcid":343},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":345,"orcid":8},"Bhavani K",{"name":347,"orcid":8},"Sailee N. Panchbhai",{"name":349,"orcid":350},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":352,"orcid":8},"Manoj Kumar S B",{"name":354,"orcid":8},"Dharamvir",{"name":356,"orcid":357},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":359,"orcid":8},"Rani Chhagan Dalvi",{"name":361,"orcid":362},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",{"tldr":364,"method":365,"finding":366,"direction":173,"opportunity":367},"构建轻量MobileNetV3-Small框架实现玉米叶片病害分类并评估边缘部署。","MobileNetV3-Small，PlantVillage-Maize与Fie","受控数据准确率97.8%，田间降至86.1%，揭示明显域差距。","可研究跨域自适应与田间鲁棒特征学习，缩小受控与真实场景性能差距。","农业遥感与作物表型","2026-10-17T23:30:28.248539Z",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":8,"source_name":376,"source_url":373,"published_at":377,"category":114,"cover_url":8,"hotness":13,"is_selected":14,"score":378,"score_detail":379,"sources":381,"tags":383,"primary_topic":386,"secondary_topics":387,"classification":389,"search_phrases":407,"slug":410,"view_count":168,"doi":411,"paper":412,"created_at":426},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":235,"substance":17,"depth":117,"authority":19,"freshness":256,"relevant":21,"comment":380},"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[382],{"name":376,"url":373},[126,127,384,41,385],"机器学习","耐盐育种",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[388],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":390},{"candidates":391,"agricultural_evidence":58},[392,399,403],{"code":45,"score":393,"supported":58,"title":394,"abstract":396,"tags":398},19,[395],"machine learning",[384,395,397],"decision support",[127,384],{"code":39,"score":18,"supported":58,"title":400,"abstract":401,"tags":402},[40],[41,40],[41],{"code":146,"score":121,"supported":58,"title":404,"abstract":405,"tags":406},[],[322,323],[126],[408,409],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":411,"openalex_id":413,"authors":414,"venue":376,"cited_by_count":168,"oa_url":373,"card":421,"direction":173,"ingested_from":286},"W7221462188",[415,418],{"name":416,"orcid":417},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":419,"orcid":420},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":422,"method":423,"finding":424,"direction":173,"opportunity":425},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":8,"source_name":433,"source_url":430,"published_at":434,"category":114,"cover_url":8,"hotness":13,"is_selected":14,"score":435,"score_detail":436,"sources":438,"tags":440,"primary_topic":442,"secondary_topics":443,"classification":445,"search_phrases":462,"slug":465,"view_count":21,"doi":466,"paper":467,"created_at":490},4494,"Tomato ripeness classification with weight-based and texture-adaptive pooling in CNNs","https:\u002F\u002Fdoi.org\u002F10.64960\u002Feasr.2026.266223","This research introduces a weight-based mixed pooling and adaptive fuzzy pooling mechanism based on feature map texture analysis for attention mechanism Squeeze-and-Excitation Networks (SE) and Convolutional Neural Networks (CNNs) in tomato ripeness classification. Traditional CNNs and SE networks rely on deterministic pooling strategies that can be suboptimal for capturing diverse visual features such as texture and color gradients in fruit. A novel fuzzy pooling system is proposed that dynamically adjusts the fusion ratio of min and max pooling based on real-time texture analysis of feature maps via a Laplacian filter. This mechanism was integrated into Darknet53, ResNet34, and ResNet101 architectures, both directly and within the SE attention block. The method was evaluated on a five-class tomato ripeness dataset using 10-fold cross-validation against traditional and fixed-weight pooling methods. The fuzzy and mixed pooling approach demonstrated superior performance on certain models which include ESEnet: Darknet53 + SE (Fuzzy pool) achieved an average F1 score across folds of 0.8429, outperforming Darknet53 + baseline SE (Average pool) at 0.8350; Extended Net: ResNet34 + Fuzzy pool achieved an average F1 score across folds of 0.8305, outperforming baseline ResNet34 at 0.8130 and ResNet101 + Fuzzy pool achieved an average F1 score across folds of 0.8284, outperforming baseline ResNet101 at 0.8267. This improvement was realized with minimal increase in computational overhead. Specifically, measurements of model parameters (M) and FLOPs (GMACs) showed only a slight increase across all architectures after integrating SEnet into the system, and zero increase after integrating the Laplacian filter and fuzzy logic. The study concludes that logic-based adaptive pooling is a robust strategy that improves feature representation in CNNs, achieving a balance between classification accuracy and computational cost. These findings offer practical value for developing more reliable automated harvesting systems and provide a generalizable enhancement for a wide range of computer vision classification tasks.","本研究提出了一种基于特征图纹理分析的权重混合池化与自适应模糊池化机制，用于番茄成熟度分类中的注意力机制挤压激励网络（Squeeze-and-Excitation Networks, SE）和卷积神经网络（Convolutional Neural Networks, CNNs）。传统的CNN和SE网络依赖确定性池化策略，在捕捉果实纹理和颜色梯度等多样化视觉特征时可能并非最优。本文提出了一种新颖的模糊池化系统，通过拉普拉斯滤波器对特征图进行实时纹理分析，动态调整最小池化和最大池化的融合比例。该机制被集成到Darknet53、ResNet34和ResNet101架构中，既可直接集成，也可嵌入SE注意力模块内。该方法在五类番茄成熟度数据集上采用10折交叉验证，与传统池化及固定权重池化方法进行了对比评估。模糊池化和混合池化方法在部分模型上表现出优越性能，包括：ESEnet：Darknet53 + SE（模糊池化）在各折上的平均F1得分为0.8429，优于Darknet53 + 基线SE（平均池化）的0.8350；扩展网络：ResNet34 + 模糊池化在各折上的平均F1得分为0.8305，优于基线ResNet34的0.8130；ResNet101 + 模糊池化在各折上的平均F1得分为0.8284，优于基线ResNet101的0.8267。这一改进仅带来了极小的计算开销增加。具体而言，模型参数量（M）和浮点运算次数（GMACs）的测量结果表明，将SEnet集成到系统后所有架构仅有轻微增加，而集成拉普拉斯滤波器和模糊逻辑后则无任何增加。研究结论认为，基于逻辑的自适应池化是一种鲁棒策略，能够改善CNN中的特征表示，在分类精度和计算成本之间实现平衡。这些发现为开发更可靠的自动化收获系统提供了实用价值，并为广泛的计算机视觉分类任务提供了一种可推广的增强方法。","Engineering and Applied Science Research","2026-10-07T00:00:00Z",66,{"impact":235,"substance":136,"depth":119,"authority":19,"freshness":256,"relevant":21,"comment":437},"提出基于纹理自适应模糊池化的CNN改进方法，在番茄成熟度分类上取得小幅但稳定的F1提升且算力开销几乎不增，对自动采摘与农业视觉识别有参考价值，但属细分方法类论文，影响面有限。",[439],{"name":433,"url":430},[126,127,253,242,441],"自动采摘",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[444],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":446},{"candidates":447,"agricultural_evidence":58},[448,452,458],{"code":32,"score":18,"supported":58,"title":449,"abstract":450,"tags":451},[251],[253,251],[253],{"code":45,"score":235,"supported":58,"title":453,"abstract":454,"tags":457},[],[455,456],"计算机视觉","computer vision",[127,242],{"code":146,"score":21,"supported":14,"title":459,"abstract":460,"tags":461},[],[],[126],[463,464],"番茄 成熟度 分类 卷积神经网络","Darknet53 ResNet 番茄 成熟度","番茄成熟度分类卷积神经网络-4494","10.64960\u002Feasr.2026.266223",{"doi":466,"openalex_id":468,"authors":469,"venue":433,"cited_by_count":168,"oa_url":484,"card":485,"direction":285,"ingested_from":286},"W7220840158",[470,472,474,476,479,481],{"name":471,"orcid":8},"Rut Yatigul",{"name":473,"orcid":8},"Wisanu Jitviriya",{"name":475,"orcid":8},"Eiji Hayashi",{"name":477,"orcid":478},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":480,"orcid":8},"Watcharin Tangsuksant",{"name":482,"orcid":483},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":486,"method":487,"finding":488,"direction":173,"opportunity":489},"提出基于纹理自适应模糊池化机制，提升CNN番茄成熟度分类性能。","用Laplacian滤波分析特征图纹理，动态调整最大最小池化融合比，集成到Dar","模糊池化在多个模型上F1提升，且计算开销几乎不增加。","可探索自适应池化在其它果蔬成熟度或田间复杂光照下的泛化能力。","2026-10-09T23:30:09.993787Z"]