[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4929":3,"related-4929":66},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":60,"slug":63,"view_count":64,"doi":8,"paper":8,"created_at":65},4929,"一人能管千亩田!外媒记者惊叹中国农业现代化——'一带一路'记者走进南通","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-13\u002Fdoc-inivcatz7974292.shtml","10月13日,2026'一带一路'记者组织论坛暨'全球南方国家记者看中国'主题采风活动走进南通市崇川区都市现代农业产业园区,赞比亚等南部非洲记者惊叹中国农业现代化水平。江苏沿江地区农业科学研究所示范基地288亩开展玉米、豆类、稻麦育种攻关,万顷良田高光谱无人机实时传送数据,'一人管千亩田',智慧烘干线16台低温循环烘干机日烘干上千吨。",null,"*   [新浪首页](http:\u002F\u002Fwww.sina.com.cn\u002F)\n*   [新闻](http:\u002F\u002Fnews.sina.com.cn\u002F)\n*   [体育](http:\u002F\u002Fsports.sina.com.cn\u002F)\n*   [财经](http:\u002F\u002Ffinance.sina.com.cn\u002F)\n*   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\"分享到QQ\")[QQ空间](http:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=http%3A%2F%2Fcj.sina.com.cn%2Farticle%2Fnormal_detail%3Furl%3Dhttps%253A%252F%252Ffinance.sina.com.cn%252Fjjxw%252F2026-10-13%252Fdoc-inivcatz7974292.shtml&title=%E4%B8%80%E4%BA%BA%E8%83%BD%E7%AE%A1%E5%8D%83%E4%BA%A9%E7%94%B0%EF%BC%9F%EF%BC%81%E5%A4%96%E5%AA%92%E8%AE%B0%E8%80%85%E6%83%8A%E5%8F%B9%E4%B8%AD%E5%9B%BD%E5%86%9C%E4%B8%9A%E7%8E%B0%E4%BB%A3%E5%8C%96&desc=&summary=&pics=https%3A%2F%2Fn.sinaimg.cn%2Fsinakd20261013s%2F200%2Fw640h360%2F20261013%2F3000-3f619ef48de5701a8502ed08ccc3228f.jpg \"分享到QQ空间\")\n\n> 10月20-22日上海黄浦江畔，2026可持续全球领导者大会再度来袭。诺奖、图灵奖得主现场讲述科技未来与趋势，更有“解救”焦虑、内卷时代的网红教授——麦克法兰与项飙现场对话！ 名额有限，手慢无。让我们共赴可持续之约！[**【点击报名】**](https:\u002F\u002Ffinance.sina.com.cn\u002F2026esg\u002Findex.shtml)\n\n转自：扬子晚报\n\n交汇点讯 “你们种地会使用人工降雨吗？”“每公顷土地产量是多少？”“1公顷地大概多久收获完？”……10月13日，2026“一带一路”记者组织论坛暨“全球南方国家记者看中国”主题采风活动走进南通市崇川区都市现代农业产业园区，国际嘉宾们看到种业科研基地、无人农场、智能烘干一体化设施等农业现代化新场景惊叹不已，接连抛出关心的问题。\n\n![Image 15](https:\u002F\u002Fn.sinaimg.cn\u002Fsinakd20261013s\u002F200\u002Fw640h360\u002F20261013\u002F3000-3f619ef48de5701a8502ed08ccc3228f.jpg)\n\n眼前的江苏沿江地区农业科学研究所现代农业示范基地总面积288亩，是集试验示范、科技推广、教育培训于一体的农业综合平台。这里，科研人员正在开展玉米、豆类、稻麦等农作物的育种攻关。“种子是农业的‘芯片’，种源安全是粮食安全的基础。”江苏沿江地区农业科学研究所所长王顺祥介绍。\n\n![Image 16](https:\u002F\u002Fn.sinaimg.cn\u002Fsinakd20261013s\u002F267\u002Fw640h427\u002F20261013\u002F19e1-943976c37c797eb1193da40712ed68c2.jpg)\n\n不远处的万顷良田产区，一架高光谱无人机唰地一下飞向农田，将视频监控、田间监测站、气象、土壤和作物生长数据等田间信息实时传送至智慧屏幕。放眼望去，高标准农田里不见人影，这套看不见的“[智慧农业](https:\u002F\u002Ffinance.sina.com.cn\u002Frealstock\u002Fcompany\u002Fsz000816\u002Fnc.shtml)(2.740, -0.06, -2.14%)大脑”正是幕后真正的种田能手。\n\n![Image 17](https:\u002F\u002Fn.sinaimg.cn\u002Fsinakd20261013s\u002F319\u002Fw640h479\u002F20261013\u002F3d3d-3661d0181d140a0031d26c096c1df81f.jpg)\n\n“我们这里一个人就能管1000亩田。”南通粮食集团经营发展部副部长梅德州介绍，整套体系从监测研判到落地执行形成完整链条，真正做到“数据可追溯、作业可管控、风险可预警”。\n\n南部非洲媒体协会赞比亚分会、赞比亚科技媒体联盟主席洛兰·姆万扎提出一个有趣的问题：“传统农业和新兴智慧农业种出来的粮食口味有区别吗？”\n\n“粮食口味一致性会更好，食品安全更有保证，农民也会更愿意种田，更重要的是当电脑、无人机成为新型农具，会有更多年轻人爱上农业。”梅德州回答。\n\n![Image 18](https:\u002F\u002Fn.sinaimg.cn\u002Fsinakd20261013s\u002F408\u002Fw640h568\u002F20261013\u002F6c6f-547c3601ad61ae865a9b9c7e5fdab4e7.jpg)\n\n智慧种田，颗粒归仓。走进顺捷农业烘干车间，机器轰鸣，16台低温循环烘干机日烘干产能可达上千吨，是全省体量最大的粮食烘干线。“刚收割的粮食有20%的水分，不及时烘干极易霉变。”工作人员介绍，粮食烘干后通过密闭输送直接进入自动化仓储，稻谷全程“不落地”，大米从清理、砻谷到色选、灌包，加工生产线实现全智能无人操作。\n\n![Image 19](https:\u002F\u002Fn.sinaimg.cn\u002Fsinakd20261013s\u002F200\u002Fw640h360\u002F20261013\u002F0556-29a5885b840937660fe3dcd7c05f65c3.jpg)\n\n[![Image 20: 广告](https:\u002F\u002Fn.sinaimg.cn\u002Fnews\u002F42f7389d\u002F20190523\u002FJiJin300x250.jpg)](http:\u002F\u002Fsaxn.sina.com.cn\u002Fclick?type=bottom&t=UERQUzAwMDAwMDA0NDA5OQ%3D%3D&url=https%3A%2F%2Ffinance.sina.com.cn%2Fmobile%2Fcomfinanceweb.shtml%3Fsource%3DPDPS000000044099&sign=e32b776cb8255330)\n\n在沿江农科所，良种从试验田源源不断孕育；在通粮智慧农场，一粒种子在“智慧大脑”守护下生根拔节、灌浆成熟；在顺捷农业，一粒粒稻谷经由智能烘干、仓储、加工全链系统变成晶莹的大米。科研育种、数字种植、加工保供首尾相接，实现从一粒种、一根苗到一袋米的粮食生产加工完整闭环，令在场国际嘉宾零距离感受到中国以数字技术改造传统农业的生动实践。\n\n来源：新华日报·交汇点记者 贲腾 华钰 丁威程\u002F文 陈越 余勤雍\u002F摄\n\n![Image 21](http:\u002F\u002Fqr.weibo.cn\u002Finf\u002Fgen?api_key=bdccdb65d34320c10b65cdf6c3503a53&data=https%3A%2F%2Ffinance.sina.cn%2Fapp%2FQRtoSFA.shtml%3Fsudatrackvalue%3Dcaitou_inQRcode_PcBottom%26schema%3Dtype%253D100%26docid%3Dnivcatz7974292%26sinaglobal%3D34.96.49.5_1792022876.884428&datetime=nocheck&level=H&output_type=img&redirect=0&size=100&type=url&sign=e9e40d09aaad721789de943c6a43fc85)\n\n海量资讯、精准解读，尽在新浪财经APP\n\n新浪财经声明：此消息系转载自合作媒体，新浪财经登载此文出于传递更多信息之目的，文章内容仅供参考，不构成投资建议。\n\n[![Image 22](https:\u002F\u002Fn.sinaimg.cn\u002Ffinance\u002Ftransform\u002F260\u002Fw880h180\u002F20180326\u002Fzi4T-fysqfnf9377213.png)](http:\u002F\u002Ftousu.sina.com.cn\u002F)[![Image 23](https:\u002F\u002Fn.sinaimg.cn\u002Ffinance\u002F520\u002Fw1760h360\u002F20260812\u002Fd61b-1813d0b7ccb639b0e4b014d6d7d60a11.png)](https:\u002F\u002Ffinance.sina.com.cn\u002Fstock\u002Froll\u002F2026-08-11\u002Fdoc-inimxvum4228316.shtml)\n\n[![Image 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28](https:\u002F\u002Fn.sinaimg.cn\u002Ftech\u002F413\u002Fw264h149\u002F20260106\u002Fbe52-606bfeb05efb28e38e5261c96f531302.png)](https:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n[大题材的引爆点](https:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n\n[![Image 29](https:\u002F\u002Fn.sinaimg.cn\u002Ftech\u002F413\u002Fw264h149\u002F20260106\u002Fc06d-e91f515c0275244acd99f11c653a4819.png)](https:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2060480585)\n[科技仍是26年主线](https:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2060480585)\n\n[![Image 30](https:\u002F\u002Fn.sinaimg.cn\u002Ftech\u002F413\u002Fw264h149\u002F20241214\u002F1287-013a827d7798ba4e16c253c1b78ad48a.png)](https:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2342117515)\n[26年继续牛市格局](https:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2342117515)\n\n## [新浪直播](http:\u002F\u002Flive.finance.sina.com.cn\u002F)\n\n[百位牛人在线解读股市热点，带你挖掘板块龙头](http:\u002F\u002Flive.finance.sina.com.cn\u002F)_收起_\n\n[![Image 31: @@title@@](https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-13\u002F@@pic1@@) ### @@title@@](https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-13\u002F@@program_url@@)\n\n*   @@status_text@@[@@program_title@@](https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-13\u002F@@program_url@@ \"@@program_title@@\")\n\n## APP专享直播\n\n![Image 32](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Faffc1249cc66f240fec9539e96853f95.png)开播时间 | 10-14 16:00\n\n##### 光大证券：物价、外贸数据发布，这些行业景气度较高\n\n![Image 33](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F526732049a6c9f7378b60a7fb95cc5c6.png)开播时间 | 10-14 15:30\n\n##### 华安基金：指数大学堂：动力筑基，储能放量 捕捉电池景气新周期\n\n![Image 34](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F6d4c5e729ee216567684735ce1b80cac.png)开播时间 | 10-14 15:30\n\n##### 招商基金：权益波动加大，债市配置价值如何看？\n\n![Image 35](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F8ffb546912edf3a3d3819a2aa99dc018.png)开播时间 | 10-14 15:00\n\n##### 中信期货：悦见周期 季度大宗前瞻\n\n![Image 36](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Ffd4b17b83685f875b36b160ca5908f63.png)开播时间 | 10-14 15:00\n\n##### 富国基金：关于创新药，你想知道的都在这里\n\n![Image 37](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F648f76295cfd111d7a184a0a68aa9066.png)开播时间 | 10-14 14:00\n\n##### 国投瑞银：药知道 | 四季度机会落在哪里？\n\n![Image 38](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Fab6a60ba68e01b06ecfda798c4192fe1.png)开播时间 | 10-14 13:29\n\n##### 平行论坛一：油脂油料论坛\n\n![Image 39](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F7001ad11f70e4cda553f1e74ecf45290.png)开播时间 | 10-14 13:29\n\n##### 平行论坛二：饲料养殖论坛\n\n![Image 40](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Fb9d269bf2634a96f556d6421ba4b4b11.png)开播时间 | 10-14 10:00\n\n##### 国投瑞银：AI知多少 | AI的效率革命\n\n![Image 41](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Fe088e31c33efc4aabfb67628d2a34a17.png)开播时间 | 10-14 09:00\n\n##### 易方达基金 盘前早知道\n\n![Image 42](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Faffc1249cc66f240fec9539e96853f95.png)开播时间 | 10-14 16:00\n\n##### 光大证券：物价、外贸数据发布，这些行业景气度较高\n\n![Image 43](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F526732049a6c9f7378b60a7fb95cc5c6.png)开播时间 | 10-14 15:30\n\n##### 华安基金：指数大学堂：动力筑基，储能放量 捕捉电池景气新周期\n\n![Image 44](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F6d4c5e729ee216567684735ce1b80cac.png)开播时间 | 10-14 15:30\n\n##### 招商基金：权益波动加大，债市配置价值如何看？\n\n![Image 45](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F8ffb546912edf3a3d3819a2aa99dc018.png)开播时间 | 10-14 15:00\n\n##### 中信期货：悦见周期 季度大宗前瞻\n\n![Image 46](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Ffd4b17b83685f875b36b160ca5908f63.png)开播时间 | 10-14 15:00\n\n##### 富国基金：关于创新药，你想知道的都在这里\n\n![Image 47](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F648f76295cfd111d7a184a0a68aa9066.png)开播时间 | 10-14 14:00\n\n##### 国投瑞银：药知道 | 四季度机会落在哪里？\n\n![Image 48](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Fab6a60ba68e01b06ecfda798c4192fe1.png)开播时间 | 10-14 13:29\n\n##### 平行论坛一：油脂油料论坛\n\n![Image 49](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002F7001ad11f70e4cda553f1e74ecf45290.png)开播时间 | 10-14 13:29\n\n##### 平行论坛二：饲料养殖论坛\n\n![Image 50](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Fb9d269bf2634a96f556d6421ba4b4b11.png)开播时间 | 10-14 10:00\n\n##### 国投瑞银：AI知多少 | AI的效率革命\n\n![Image 51](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202610\u002Fe088e31c33efc4aabfb67628d2a34a17.png)开播时间 | 10-14 09:00\n\n##### 易方达基金 盘前早知道\n\n[上一页](javascript:;)[下一页](javascript:;)\n\n1\u002F10\n\n## 热门推荐\n\n香港周生生发生珠宝大劫案 _收起_\n\n![Image 52: 10岁女童被男老师摸脸，家长索赔20万，法院判了](https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-13\u002Fdoc-inivcatz7974292.shtml)\n\n#### 香港周生生发生珠宝大劫案\n\n*   2026年10月14日\n*   09:21\n*   APP专享\n*   扒圈小记\n*   ![Image 53](https:\u002F\u002Fn.sinaimg.cn\u002Ffinance\u002Fpctianyi_wdg\u002Fsrc\u002Fimages\u002Ficon-cmnt.png)3,190\n\nAPP专享\n\n![Image 54: 10岁女童被男老师摸脸，家长索赔20万，法院判了](https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-13\u002Fdoc-inivcatz7974292.shtml)\n\n#### “黑客杀死了我的一缸鱼？”\n\n*   2026年10月14日\n*   02:29\n*   APP专享\n*   扒圈小记\n*   ![Image 55](https:\u002F\u002Fn.sinaimg.cn\u002Ffinance\u002Fpctianyi_wdg\u002Fsrc\u002Fimages\u002Ficon-cmnt.png)1,921\n\nAPP专享\n\n![Image 56: 10岁女童被男老师摸脸，家长索赔20万，法院判了](https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-13\u002Fdoc-inivcatz7974292.shtml)\n\n#### 10岁女童被男老师摸脸，家长索赔20万，法院判了\n\n*   2026年10月14日\n*   04:23\n*   APP专享\n*   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[创新药国内销售快速增长，海外将密集进入商业化阶段！创新转型，ZSP1601潜在空间大+RAY1225获顶线数据，减重达标率数值上优于替尔泊肽](https:\u002F\u002Fdaxue.sina.cn\u002Fdetail\u002Fgraphics\u002F1a3971a466489e1796dc092794f59956\u002F7664345d3a2d)10-14 11:35[火线电报会员专享](https:\u002F\u002Fdaxue.sina.cn\u002Fcolumn\u002F1a3971a466489e1796dc092794f59956)\n*   [看好核心龙头Q4反弹机会，弹性在外贸、大飞机&商发等！航空发动机和燃气轮机零部件核心企业；智能物流仓储厂商，烟草、军工具有准入优势](https:\u002F\u002Fdaxue.sina.cn\u002Fdetail\u002Fgraphics\u002Ff5443bd63a8b4e858b9225ffa008a6c6\u002Fb35488632dce)10-14 11:02[会选股会员专享](https:\u002F\u002Fdaxue.sina.cn\u002Fcolumn\u002Ff5443bd63a8b4e858b9225ffa008a6c6)\n\n## [](http:\u002F\u002Flive.finance.sina.com.cn\u002F)[股市直播](http:\u002F\u002Flive.finance.sina.com.cn\u002F)\n\n*   图文直播间\n*   视频直播间\n\n*   [![Image 59: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-10-14 23:23:37 一、央行公布经济数据，大家注意注意两点：1、1-9月债券和股票融资占社融增量49.48%，超过贷款约7个百分点，说明资本市场正在取代银行贷款。翻译一下：资本市场正在取代银行贷款，现在的市场本质就是融资市场。如果不能融资？郭嘉队必须要救！2、钱没有流入股市，反而在跑路。 9月住户存款大增约2.4万亿，而非银行业金融机构存款骤降约1.91万亿，同比多减 8500 亿，已连续第 5 个月同比少增。非银金融就是股市、基金、理财等资金。这也是本季度大盘走弱，量能大幅萎缩的原因，也是昨天下午大盘跳水的主要原因之一，因为有资金先知先觉了。二、美国数据超预期美国9月CPI同比增长3.7% 高于市场预期的3.6%；美国9月核心CPI同比增长2.6%，高于预估的2.5%。数据出来以后，美债直线拉升，黄金有色跳水，美联储10月份加息概率由21.6%上升到43.7%。重点是预计到2027年底美联储还将加息三次。说明美联储正式进入到加息周期，这是王炸了！三、美股大跌、科技股领跌美股三大指数集体收跌，道指跌0.09%，纳指跌1.88%，标普500指数跌1.1%。费城半导体指数收跌4.3%，芯片股指数六连跌，创2025年5月以来最长下跌纪录。SK海力士跌超6%，闪迪跌超5%，英伟达跌超3%。油气、减肥药、食品饮料涨幅靠前。美联储加息利空最大的就是黄金有色和科技股。而科技股占比A股权重比较大，今天大盘跌破3741点没有任何疑问，大家还是重点关注沪深300吧，只要沪深300能够放量，大盘就有望探底回升。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 60: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-10-14 23:23:12 晚间三大利空 今天怎么走昨天又是腥风血雨的一天，两市大盘低开低走，上午还象征性地挣扎了几下，像溺水的人扑腾两把；下午直接躺平，连呼救都省了。两市上涨家数不足千只，超过4300只个股绿盘，个股中位数-1.93%，亏钱效应直接拉满，这哪里是股市，分明是一片绿色菜地！这种行情，巴菲特来了都得摇头：“我原以为价值投资是等待，没想到A股教我的是忍耐。”说真的，这个时候还能淡定的人，要么是空仓，看戏不怕台高的；要么是家里有矿，亏完还有下顿的！最让人心疼的是高位追涨科技股的朋友，每天打开账户就像拆盲盒——别人拆开的是惊喜，自己拆开的是悲伤。割肉？舍不得，补仓？不敢动，心里只能自我安慰：我是长期投资者。其实，夜深人静的时候，自己心里比谁都清楚，当初追的那个高点，可能这辈子都很难见到了！我们要说的是，弹簧压的越久，弹起的力度就越大，这个时候还是再等等吧。A股最擅长的，就是在你绝望的时候，突然给你一记惊喜。虽然惊喜来得比渣男的回心转意还晚，但它最终会来！毕竟A股从来不会让你白等，它只是让你等到怀疑人生！近期，每次大盘到3750点附近都有郭嘉队出手，所以，这附近就是政策底，现在要走的是市场底，市场底主要在两点：一是消息面；二是外围股市的走势。如果两头都不给力，这个底就不太好看了。而昨晚，市场一口气迎来了三大利空。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 61: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-10-14 23:22:52 大家早上好！趋势为王，做股海的领航者，新的一天，新的战斗，欢迎你来到本直播室！新进的朋友请注意点赞，收藏本直播室，以方便你下次观看，谢谢大家的支持！【更多独家重磅股市观点请点击】](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 62: 东方红老陈](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F5d2cbaa7\u002F20260918\u002FChenChen1.png\u002Fw120h120f1q80liv.png)### 东方红老陈 2026-10-14 23:06:26 【情绪热点短线策略】（情绪+趋势+风口）1、AI短剧：近期最强的短线之一板块，具有中期成长逻辑，有可能变成中线板块，分歧大跌后低吸策略，不追涨。2、AI硬件：主要是光通信和PCB，超跌，耐心等待超跌反弹机会。3、半导体：超跌，耐心等待超跌反弹机会。4、近端次新科技：需要看到科技反弹，才能做情绪套利，先重点盯着。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1877471117)\n*   [![Image 63: 东方红老陈](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F5d2cbaa7\u002F20260918\u002FChenChen1.png\u002Fw120h120f1q80liv.png)### 东方红老陈 2026-10-14 23:06:12 2026.10.15【中线波段持股策略】（业绩+趋势+风口）1、科技成长方向：中长期继续看好硬科技赛道，AI算力硬件、半导体、人形机器人（sz300024）、商业航天。2、中线标准持仓50%，保守40%，激进60%，盘中加仓1-2成，不放量大阳，收盘再减掉1-2成仓。3、美股科技大跌，形势危急，警惕A股继续杀跌，今日谨慎加仓做T。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1877471117)\n*   [![Image 64: 东方红老陈](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F5d2cbaa7\u002F20260918\u002FChenChen1.png\u002Fw120h120f1q80liv.png)### 东方红老陈 2026-10-14 23:00:16 【沪电股份（sz002463）：预计前三季度净利同比增长101%-110% 受益于高速交换机、AI服务器等领域结构性需求】财联社10月14日电，沪电股份（sz002463）(002463.SZ)公告称，预计2026年前三季度归属于上市公司股东的净利润为54.50亿元-57.00亿元，同比增长100.54%-109.74%。业绩变动主要系受益于高速交换机、AI服务器、高性能计算及智能汽车等应用领域的结构性需求，叠加公司产品结构持续优化，营业收入和净利润较快增长；泰国子公司已从产能爬坡期迈入规模化运营期，实现扭亏为盈。小财注：公司Q3净利润预计25.27亿-27.77亿，Q2净利润16.81亿，据此计算，Q3净利润预计环比增长50%-65%。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1877471117)\n*   [![Image 65: 北京红竹](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F4ffadfe8\u002F20170407\u002FBeiJingHongZhuTouXiang.jpg\u002Fw120h120f1q80liv.jpg)### 北京红竹 2026-10-14 23:00:02 三、昨晚直播总结1、缠论客观上还是碎碎念，依然没有给止跌信号，大概率还要破低之后等新的平台下移，格局外围走势今天铁定了。2、客观的大家已经不愿意听了，因为始终不给信号，昨晚主要聊的是主观的资金行为，主观上我认为这里若出现反弹也顶多是30分钟级别的，不是是日线的级别的，跌破3741点之后，还要在向下回落一些才能有望产生日线的上涨。3、目前直播间很多新朋友7月初没有逃顶，现在都是重仓被套，根据大家需求，红竹答应大家，在我的能力范围之内，出现反弹之后给大家找位置，分享波段T。4、贵金属今天若再次下跌破低，日线123就出现了，后面只差一个信号就是突破平台。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2910812187)\n*   [![Image 66: 北京红竹](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F4ffadfe8\u002F20170407\u002FBeiJingHongZhuTouXiang.jpg\u002Fw120h120f1q80liv.jpg)### 北京红竹 2026-10-14 22:59:52 二、加银行减科技截至2026年三季度末，北向资金共持有3974只个股，持仓总规模回落至2.58万亿元，较上季度末的3.13万亿元大幅下降17.57%，同期上证指数下跌6.16%。行业层面，三季度银行持股市值增长居首，大增674亿元，环比增长47%，同期行业指数涨幅14.56%。而上季度持股市值增长最多的电子、电力设备、通信行业垫底，分别大降2844亿元、1984亿元和771亿元，同期行业指数分别跌32.65%、23.73%和28.72%。还得是聪明资金，直接重仓了银行，同时也降低了其他行业的仓位。值得咱们学习，这些聪明资金的仓位配置。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2910812187)\n*   [![Image 67: 北京红竹](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F4ffadfe8\u002F20170407\u002FBeiJingHongZhuTouXiang.jpg\u002Fw120h120f1q80liv.jpg)### 北京红竹 2026-10-14 22:59:39 一、美股迎来致命一击美国9月消费者价格指数（CPI）同比增长3.7%，预估为3.6%，前值为3.4%。9月CPI环比增长0.6%，预估0.6%。数据公布后，交易员增加对10月美联储加息的押注纳指下挫近2%，热门科技股普跌，芯片股指数六连跌，创2025年5月以来最长下跌纪录。从结构上来看，美股日线上涨段走完之后，隔夜跌破平台，宣告至少30分钟的调整，后面要看是否进入日线级别调整。但人家有任性，怎么跌下来的还能怎么涨回去，如果要配置的，等日线回落段出现可以考虑。回到A股今天开盘要受影响了，沪指3741点怕是守不住了，压力给到了东家这里。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2910812187)\n*   [![Image 68: 东方红老陈](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F5d2cbaa7\u002F20260918\u002FChenChen1.png\u002Fw120h120f1q80liv.png)### 东方红老陈 2026-10-14 22:42:47 【崇达技术（sz002815）：预计前三季度净利同比增长90%-118% 高端产品出货占比提升明显】财联社10月14日电，崇达技术（sz002815）(002815.SZ)公告称，预计2026年","新华日报 交汇点 新浪财经 2026 年 10 月 13 日","2026-10-13T00:00:00Z","报道",10,false,55,{"impact":17,"substance":18,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},14,12,9,8,1,"外媒视角展示南通智慧农业装备应用，有传播亮点但信息增量有限，属可选的国际传播类报道。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","一带一路","农业现代化","高光谱无人机","国际传播",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,55],{"code":45,"score":18,"supported":46,"title":47,"abstract":48,"tags":49},"J2",true,[28],[28],[28],{"code":32,"score":51,"supported":46,"title":52,"abstract":53,"tags":54},3,[],[34],[],{"code":56,"score":21,"supported":14,"title":57,"abstract":58,"tags":59},"I1",[],[],[26],[61,62],"南通 智慧农业 无人机","一带一路 外媒记者 南通","南通智慧农业无人机-4929",0,"2026-10-15T00:05:53.382527Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,119,173,263,321,398],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":8,"content":74,"source_name":75,"source_url":8,"published_at":76,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":77,"score_detail":78,"sources":83,"tags":85,"primary_topic":90,"secondary_topics":91,"classification":92,"search_phrases":114,"slug":117,"view_count":64,"doi":8,"paper":8,"created_at":118},5518,"河北农林科学院玉米抗病性鉴定开放日 AR智能眼镜助力分级定级","https:\u002F\u002Fwww.cfsn.cn\u002Fnews\u002Fdetail\u002F345\u002F371985.html","河北省农林科学院植物保护研究所主办2026年黄淮海玉米抗病性鉴定田间开放日。作为全国玉米抗病性鉴定牵头单位，该所推进智能化技术嫁接鉴定工作，引入AR智能眼镜拍照自动标注叶片病斑率并判定严重度等级，支持语音控制与后台批量导出，另探索无人机悬停拍照系统开展规模化快速调查。","金秋时节，冀中平原玉米长势喜人、丰收在望。近日，由河北省农林科学院植物保护研究所主办的“2026年黄淮海玉米抗病性鉴定田间开放日”活动在河北省保定市顺利举行。来自玉米育种、植保、种业企业等领域的领导、专家和同仁齐聚一堂，围绕玉米抗病性鉴定标准化、智能化与精准化展开深入交流与现场观摩。\n\n河北省农林科学院植物保护研究所副所长吴玉星表示，玉米抗病性鉴定是育种升级和品种审定推广的必要环节，对保障粮食安全具有重要意义。河北省农林科学院植物保护研究所作为全国玉米抗病性鉴定的牵头单位，严格按照国家标准规范布局、统一试验，全程实行标准化操作程序，确保每一项数据真实、准确、公正。他强调，团队将继续恪守公平、公正、科学、严谨的工作原则，不断优化试验方案，为各试验单位提供优质高效的抗病性鉴定服务，助力优良玉米品种替代升级和高质量发展。\n\n活动期间，河北省农林科学院植物保护研究所研究员石洁结合多年田间鉴定实践，介绍了当前玉米抗病性鉴定面临的现实挑战。她指出，黄淮海地区玉米病害种类多、调查面积大、工作强度高，仅靠人工逐品种、逐病害调查，骨干人员长期在田间超负荷工作，难以满足规模化、精准化鉴定需求。为此，团队积极推动智能化技术嫁接到抗病性鉴定工作中，着力实现数据实时采集、结果直接反馈和精准分级。\n\n北京金禾天成科技有限公司有关负责人介绍了通过AR智能眼镜识别玉米病害及判定严重度等级技术。该技术通过拍照识别，自动标注每片叶片的病斑率和整张图片的病斑率，并给出严重度等级判定，拍照后自动保存识别结果和原图，支持现场快速呈现结果和后台批量导出。系统还可按品种编号自动对应品种名称，支持人工校正和后续复核，有效减轻调查人员劳动强度，提升鉴定效率和标准化水平。\n\n石洁告诉记者，团队正在积极协助研发和应用AR智能眼镜技术，用于玉米叶斑病、穗腐病等病害的精准分级。该设备支持语音控制，可解放双手，在田间行走过程中即可完成“所见即所得”的病情定级，并实时反馈数据。相比传统人眼判断，智能眼镜能够提供更精细的数量化级别，满足抗性基因定位等研究对定量数据的需求。此外，团队还在积极探索无人机高空悬停拍照系统，用于茎腐病、矮花叶病等病害的规模化快速调查，进一步提升鉴定效率。\n\n活动现场还展示了快速检测技术在玉米病害防控中的应用。石洁介绍，该技术可在病害尚未发病的叶片上，通过棉签擦拭、裂解液处理和便携设备检测，30分钟内常温下判断叶片是否携带孢子或已被病原菌侵入。目前可检测病理上几乎所有病害（病毒病除外），包括南方锈病、穗腐病、弯孢叶斑病、瘤黑粉病等。荧光定量PCR版本一次可同时检测8种病原菌，帮助种植大户和种业企业据检测结果用药，实现“检防联动”，从普惠性预防用药转向按需防控、对靶施药、早防早治，大幅节省农药和飞防成本。\n\n本次开放日活动全面开放观摩现场，与会代表近距离了解了玉米抗病性鉴定从试验设计、田间管控、症状跟踪到数据调查的全流程。大家一致认为，智能化鉴定技术和快速检测技术的推广应用，将有力推动黄淮海玉米抗病性鉴定工作向精准化、标准化、高效化迈进，为玉米产业高质量发展保驾护航。（王田）","中国食品安全网 2026-10-21","2026-10-21T00:00:00Z",73,{"impact":79,"substance":80,"depth":81,"authority":13,"freshness":19,"relevant":21,"comment":82},18,20,16,"省级科研机构开放日，AR智能眼镜与快速检测技术有实质信息增量，属细分领域智能化进展，值得精选。",[84],{"name":75,"url":72},[26,86,87,88,89],"农业人工智能","智能装备","玉米病害","抗病性鉴定",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":93},{"candidates":94,"agricultural_evidence":46},[95,100,105,109],{"code":32,"score":96,"supported":46,"title":97,"abstract":98,"tags":99},11,[34],[34],[],{"code":101,"score":21,"supported":14,"title":102,"abstract":103,"tags":104},"F5",[],[],[87],{"code":56,"score":21,"supported":14,"title":106,"abstract":107,"tags":108},[],[],[26],{"code":110,"score":21,"supported":14,"title":111,"abstract":112,"tags":113},"I2",[],[],[86],[115,116],"河北农科院 玉米抗病性鉴定 AR智能眼镜","黄淮海 玉米 病害 快速检测","河北农科院玉米抗病性鉴定AR智能眼镜-5518","2026-10-22T00:07:04.176689Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":8,"content":124,"source_name":125,"source_url":8,"published_at":126,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":131,"tags":133,"primary_topic":138,"secondary_topics":139,"classification":140,"search_phrases":168,"slug":171,"view_count":64,"doi":8,"paper":8,"created_at":172},5359,"千金方变量处方图技术在北大荒鹤山农场测产:玉米增产 6.9% 尿素减 5.7%","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696358451626181183\u002F","依托智慧农场技术与系统全国重点实验室平台,由东北农业大学与北大荒集团鹤山农场有限公司联合主办的智慧农作千金方变量处方图技术测产评估会在鹤山农场举办。千金方变量处方图技术在玉米、大豆两种作物上均实现中低产田的增产节本:玉米变量处方区较定量作业区增产 6.9%、尿素投入降低 5.7%,每公顷收益增加约 470 元;大豆变量处方区较对照区增产 2.3%,每公顷收益最高增加约 350 元。技术由东北农业大学智慧农业团队研发。","## 田间开出“千金方” 黑土算准“增收账”\n\n日前，依托智慧农场技术与系统全国重点实验室平台，由东北农业大学与北大荒集团鹤山农场有限公司联合主办的智慧农作“千金方”变量处方图技术测产评估会在鹤山农场举办。经第三方专家取点测产和实收测产，“千金方”变量处方图技术在玉米、大豆两种作物上均实现“中低产田”的增产节本：玉米变量处方区较定量作业区增产6.9%、尿素投入降低5.7%，每公顷收益增加约470元；大豆变量处方区较对照区增产2.3%，每公顷收益最高增加约350元。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F29c175dfbe394a34af43ada958310b56~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1793069548&x-signature=wosebaJveF3ksDGq7EEr4PTRctk%3D)\n活动现场\n\n测产环节，邀请嫩江市农业技术推广中心刘月辉研究员担任测产组组长，联合各专家团队，严格按照测产标准科学选点取样，通过人工点测、机械实收相结合的方式进行测定，检验玉米精准变量处方技术的应用成效。专家团现场报告实测结果：点测结果显示，变量处方图技术区较定量作业区平均增产5.7%；机械实收结果显示，变量处方图技术区产量达12.4吨\u002F公顷，定量区为11.6吨\u002F公顷，增产6.9%，同时玉米尿素投入降低5.7%，每公顷收益增加约470元。\n\n此前9月26日，东北农业大学智慧农业团队组织黑龙江省农垦科学院、嫩江农业技术推广中心和鹤山农场专家对“大豆种植全流程‘三变’处方技术”执行地块进行田间实收测产。该田块为中低产田，其中变量作业区10.18公顷，对照区14.23公顷，测产结果显示变量技术作业区平均公顷产量较对照区增产2.3%，机收产量3.11吨\u002F公顷，每公顷收益增加约350元。\n\n东北农业大学副校长陈庆山表示，东北农业大学与九三分公司长期保持协同联动合作，鹤山农场为科研团队提供了良好的田间试验用地和高中低产地块场景，为各项科研技术攻关、成果验证提供了有力支持。双方将扎根生产一线破解实际难题，以大数据、智能技术实现精准变量作业，最大限度挖掘土地增产潜力。学校将持续发挥科研、人才、技术优势，深化产学研融合，推动智慧农业技术落地生根、迭代完善，助力农业向信息化、数字化、智能化更深层次发展。\n\n据介绍，“千金方”技术由东北农业大学智慧农业团队研发，以气象和农田信息数据为基础，以作物信息技术和遥感技术为核心，融合GIS技术对地块间和地块内实现差异化对齐，通过“三看”到“三变”的处方图技术完成耕地水肥资源分配不均衡下的信息诊断和精准化、智能化的农事作业管理。历经3年研发和1年示范，在鹤山农场实现种肥精准调控，在优化肥料投入前提下实现大豆、玉米单产提升，验证了变量处方播种、变量追肥一体化技术在生产田上的增产增效效果，为黑土丘陵区精准变量作业趟出了一条可复制的路子。\n\n下一步，各方将持续深化产学研用协同合作，立足北大荒黑土地资源禀赋，不断优化完善精准变量处方图技术体系，补齐技术落地推广中的现实问题，把科研方案转化为大田可复制、易推广的实操模式。面向现代农业发展需求，进一步凝聚智慧农场技术与系统全国重点实验室科技力量，打造北方寒地智慧农场建设模式，加快智慧农业技术成果转化应用，持续提升粮食综合生产能力，为北大荒集团现代农业高质量发展、筑牢国家粮食安全“压舱石”贡献科技力量。\n\n（图片由东北农业大学提供）\n\n记者 赵一诺","黑龙江新闻网(今日头条) 2026-10-14","2026-10-14T00:00:00Z",68,{"impact":79,"substance":129,"depth":81,"authority":19,"freshness":51,"relevant":21,"comment":130},22,"第三方实收测产数据扎实，变量处方技术增产节本效果明确，具可复制推广价值，但时效偏旧、属区域示范进展。",[132],{"name":125,"url":122},[26,134,135,136,137],"精准施肥","黑土地保护","玉米大豆","变量处方图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":141},{"candidates":142,"agricultural_evidence":46},[143,147,152,158,163],{"code":32,"score":18,"supported":46,"title":144,"abstract":145,"tags":146},[34],[34],[136],{"code":56,"score":148,"supported":46,"title":149,"abstract":150,"tags":151},4,[],[26],[26],{"code":153,"score":51,"supported":46,"title":154,"abstract":155,"tags":157},"A4",[],[156],"大豆",[],{"code":159,"score":21,"supported":14,"title":160,"abstract":161,"tags":162},"F3",[],[],[134],{"code":164,"score":21,"supported":14,"title":165,"abstract":166,"tags":167},"H1",[],[],[135],[169,170],"东北农业大学 千金方 变量处方图","鹤山农场 玉米 测产","东北农业大学千金方变量处方图-5359","2026-10-20T02:47:45.290758Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":8,"source_name":179,"source_url":176,"published_at":180,"category":181,"cover_url":8,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":185,"tags":187,"primary_topic":190,"secondary_topics":191,"classification":197,"search_phrases":221,"slug":224,"view_count":21,"doi":225,"paper":226,"created_at":262},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","论文",69,{"impact":18,"substance":80,"depth":81,"authority":18,"freshness":19,"relevant":21,"comment":184},"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。",[186],{"name":179,"url":176},[26,86,188,189,88],"边缘计算","深度学习",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[192],{"code":110,"slug":193,"name":194,"path":195,"parent_code":196},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":198},{"candidates":199,"agricultural_evidence":46},[200,204,209,216],{"code":32,"score":17,"supported":46,"title":201,"abstract":202,"tags":203},[33],[34,33],[],{"code":110,"score":20,"supported":46,"title":205,"abstract":206,"tags":208},[],[189,207],"deep learning",[86,189],{"code":56,"score":210,"supported":46,"title":211,"abstract":212,"tags":215},7,[],[213,214],"精准农业","precision agriculture",[26],{"code":217,"score":21,"supported":14,"title":218,"abstract":219,"tags":220},"I5",[],[],[188],[222,223],"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":225,"openalex_id":227,"authors":228,"venue":179,"cited_by_count":64,"oa_url":176,"card":254,"direction":260,"ingested_from":261},"W7222602850",[229,232,235,237,239,242,244,246,249,251],{"name":230,"orcid":231},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":233,"orcid":234},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":236,"orcid":8},"Bhavani K",{"name":238,"orcid":8},"Sailee N. Panchbhai",{"name":240,"orcid":241},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":243,"orcid":8},"Manoj Kumar S B",{"name":245,"orcid":8},"Dharamvir",{"name":247,"orcid":248},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":250,"orcid":8},"Rani Chhagan Dalvi",{"name":252,"orcid":253},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",{"tldr":255,"method":256,"finding":257,"direction":258,"opportunity":259},"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。","农业人工智能与决策模型","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","openalex","2026-10-17T23:30:28.248539Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":8,"source_name":269,"source_url":266,"published_at":270,"category":181,"cover_url":8,"hotness":13,"is_selected":14,"score":271,"score_detail":272,"sources":274,"tags":276,"primary_topic":279,"secondary_topics":280,"classification":282,"search_phrases":301,"slug":304,"view_count":64,"doi":305,"paper":306,"created_at":320},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":20,"substance":79,"depth":81,"authority":18,"freshness":19,"relevant":21,"comment":273},"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。",[275],{"name":269,"url":266},[26,86,277,34,278],"机器学习","耐盐育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[281],{"code":110,"slug":193,"name":194,"path":195,"parent_code":196},{"status":39,"locked":14,"version":40,"method":41,"evidence":283},{"candidates":284,"agricultural_evidence":46},[285,292,297],{"code":110,"score":286,"supported":46,"title":287,"abstract":289,"tags":291},19,[288],"machine learning",[277,288,290],"decision support",[86,277],{"code":32,"score":293,"supported":46,"title":294,"abstract":295,"tags":296},15,[33],[34,33],[34],{"code":56,"score":210,"supported":46,"title":298,"abstract":299,"tags":300},[],[213,214],[26],[302,303],"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":305,"openalex_id":307,"authors":308,"venue":269,"cited_by_count":64,"oa_url":266,"card":315,"direction":258,"ingested_from":261},"W7221462188",[309,312],{"name":310,"orcid":311},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":313,"orcid":314},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":316,"method":317,"finding":318,"direction":258,"opportunity":319},"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":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":8,"source_name":327,"source_url":324,"published_at":328,"category":181,"cover_url":8,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":334,"tags":336,"primary_topic":340,"secondary_topics":341,"classification":347,"search_phrases":380,"slug":383,"view_count":21,"doi":384,"paper":385,"created_at":397},4694,"Precision Agriculture Approaches for Nutrient Management in Maize: A Comprehensive Review of Productivity, Efficiency and Economic Benefits","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104559","Maize nutrient management is an unusually demanding target for precision agriculture because crop demand, soil nutrient supply, weather, rooting conditions and management history vary simultaneously across space and time. Uniform fertiliser programmes can therefore be efficient in one part of a field and inefficient in another, while a recommendation that is appropriate before planting may become suboptimal after rainfall, mineralisation and crop establishment have altered nutrient availability. This critical narrative review evaluates precision approaches for nutrient management in maize, with emphasis on productivity, nutrient-use efficiency and farm-level economic performance. Literature published from 1 January 1990 to 2 August 2026 was examined using agricultural and multidisciplinary scholarly sources, supplemented by citation searching and bibliographic verification. The evidence was organised around the decision chain linking measurement, diagnosis, prescription and nutrient application. Strongest empirical support exists for site-specific and in-season nitrogen management because nitrogen is mobile, strongly coupled to weather and readily expressed in canopy signals. Chlorophyll meters, active optical sensors, aerial and unmanned aerial vehicle imagery, management zones, crop models and machine-learning approaches can improve the timing or spatial targeting of nitrogen, but diagnostic accuracy does not automatically translate into an economically superior fertiliser prescription. Multi-nutrient decision-support systems broaden precision management beyond nitrogen and can improve the balance of nitrogen, phosphorus and potassium, particularly where farmer practice is poorly aligned with crop demand. Across the evidence base, yield gains are often modest when conventional fertilisation is already adequate; benefits more consistently arise through reduced excess nutrient use, improved nutrient-use efficiency, better risk management or correction of under-fertilisation. Profitability is highly conditional on field heterogeneity, fertiliser and grain prices, baseline management, technology cost, scale and weather. Future progress requires multi-year on-farm validation of complete decision chains, stronger treatment of uncertainty and multi-stress confounding, improved multi-nutrient diagnostics, and economic evaluations that include fixed, transaction and information costs. Precision nutrient management is best understood as an adaptive decision framework rather than a single sensor or variable-rate technology.","玉米养分管理对精准农业而言是一个异常苛刻的目标，因为作物需求、土壤养分供应、天气、根系条件和管理历史在空间和时间上同时变化。因此，均匀施肥方案在田块某一部分可能高效，而在另一部分则可能低效；而播种前适宜的推荐方案，在降雨、矿化和作物建成改变养分有效性之后，也可能变得次优。这篇批判性叙述综述评估了玉米养分管理的精准方法，重点关注生产力、养分利用效率和农场层面经济表现。利用农业与多学科学术来源，检索了1990年1月1日至2026年8月2日发表的文献，并以引文检索和书目核验作为补充。证据围绕连接测量、诊断、处方和养分施用的决策链进行组织。最强的实证支持存在于位点特异性和季内氮管理，因为氮具有移动性、与天气强耦合，并易于在冠层信号中表达。叶绿素仪、主动光学传感器、航空和无人机影像、管理分区、作物模型和机器学习方法可以改善氮的施用时机或空间靶向，但诊断准确性并不会自动转化为经济上更优的施肥处方。多养分决策支持系统将精准管理扩展到氮之外，并能改善氮、磷、钾的平衡，尤其是在农户实践与作物需求严重不匹配的情况下。在整个证据基础中，当常规施肥已经充足时，产量增益往往有限；效益更一致地来自减少过量养分使用、提高养分利用效率、改善风险管理或纠正施肥不足。盈利能力高度取决于田块异质性、肥料和谷物价格、基线管理、技术成本、规模和天气。未来进展需要对完整决策链进行多年农场验证，更有力地处理不确定性和多胁迫混杂，改进多养分诊断，以及纳入固定成本、交易成本和信息成本的经济评价。精准养分管理最好被理解为一个适应性决策框架","Journal of Experimental Agriculture International","2026-10-10T00:00:00Z",71,{"impact":18,"substance":80,"depth":331,"authority":332,"freshness":19,"relevant":21,"comment":333},17,13,"系统综述精准养分管理在玉米上的产量、效率与经济性证据，方法学扎实但属学术综述，产业落地价值需长期验证。",[335],{"name":327,"url":324},[26,337,34,213,338,339],"变量施肥","遥感监测","养分管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[342],{"code":159,"slug":343,"name":344,"path":345,"parent_code":346},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":39,"locked":14,"version":40,"method":41,"evidence":348},{"candidates":349,"agricultural_evidence":46},[350,354,358,364,371,375],{"code":56,"score":81,"supported":46,"title":351,"abstract":352,"tags":353},[214],[213,214],[26,213],{"code":32,"score":293,"supported":46,"title":355,"abstract":356,"tags":357},[33],[34,33],[34],{"code":159,"score":96,"supported":46,"title":359,"abstract":360,"tags":363},[],[361,362,339],"施肥","肥料",[339,337],{"code":365,"score":67,"supported":46,"title":366,"abstract":367,"tags":370},"H2",[],[368,369],"土壤","soil",[],{"code":110,"score":51,"supported":46,"title":372,"abstract":373,"tags":374},[],[277],[],{"code":376,"score":21,"supported":14,"title":377,"abstract":378,"tags":379},"I3",[],[],[338],[381,382],"玉米 精准施肥 氮肥管理","遥感 变量施肥 养分利用效率","玉米精准施肥氮肥管理-4694","10.9734\u002Fjeai\u002F2026\u002Fv48i104559",{"doi":384,"openalex_id":386,"authors":387,"venue":327,"cited_by_count":64,"oa_url":324,"card":392,"direction":258,"ingested_from":261},"W7221366291",[388,390],{"name":389,"orcid":8},"Piyali Pal",{"name":391,"orcid":8},"Disha Chattopadhyay",{"tldr":393,"method":394,"finding":395,"direction":258,"opportunity":396},"综述玉米精准养分管理在生产力、效率与经济效益上的证据与局限。","系统检索1990-2026年文献，按测量-诊断-处方-施用决策链组织证据。","氮素精准管理证据最强，但诊断精度不必然带来经济更优的施肥处方。","亟需多年多点田间验证完整决策链，并纳入不确定性、多养分诊断与信息成本的经济评价。","2026-10-11T23:30:51.450082Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":8,"content":8,"source_name":403,"source_url":401,"published_at":270,"category":181,"cover_url":8,"hotness":13,"is_selected":14,"score":404,"score_detail":405,"sources":408,"tags":410,"primary_topic":415,"secondary_topics":416,"classification":423,"search_phrases":443,"slug":446,"view_count":447,"doi":448,"paper":449,"created_at":467},4650,"A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105011","A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact。Agricultural Systems","Agricultural Systems",81,{"impact":79,"substance":406,"depth":79,"authority":17,"freshness":13,"relevant":21,"comment":407},21,"核心期刊提出的面向过程的夏玉米干旱逐日动态监测框架，方法新颖、面向农业信息化应用，时效性强，值得进入每日精选。",[409],{"name":403,"url":401},[26,411,412,413,414],"农业遥感","夏玉米","农业气象","干旱监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[417],{"code":418,"slug":419,"name":420,"path":421,"parent_code":422},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":424},{"candidates":425,"agricultural_evidence":46},[426,431,435,439],{"code":418,"score":332,"supported":46,"title":427,"abstract":429,"tags":430},[428],"drought",[428],[413,414],{"code":32,"score":18,"supported":46,"title":432,"abstract":433,"tags":434},[33],[33],[412],{"code":56,"score":21,"supported":14,"title":436,"abstract":437,"tags":438},[],[],[26],{"code":376,"score":21,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[411],[444,445],"夏玉米 干旱 动态监测","气象胁迫 农业影响 监测框架","夏玉米干旱动态监测-4650",2,"10.1016\u002Fj.agsy.2026.105011",{"doi":448,"openalex_id":450,"authors":451,"venue":403,"cited_by_count":64,"oa_url":401,"card":8,"direction":260,"ingested_from":261},"W7221308782",[452,454,456,458,461,463,465],{"name":453,"orcid":8},"Yuanda Zhang",{"name":455,"orcid":8},"Peijuan Wang",{"name":457,"orcid":8},"Jun Zhu",{"name":459,"orcid":460},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0001-9021-0201",{"name":462,"orcid":8},"Dianchen Han",{"name":464,"orcid":8},"Dingrong Wu",{"name":466,"orcid":8},"Jianping Guo","2026-10-11T23:30:03.734429Z"]