[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3577":3,"related-3577":37},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":8,"created_at":36},3577,"浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目中标公告——浙江移动数智科技中标，总金额398.6万元","https:\u002F\u002Fwww.ccgp.gov.cn\u002Fcggg\u002Fdfgg\u002Fzbgg\u002F202609\u002Ft20260925_27403210.htm","浙江省政府采购中心9月25日发布浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目中标公告，浙江移动数智科技有限公司以398.6万元中标，评审总得分85.1。评审专家包括龚建新、孙才俊（第1标项采购人代表）、高夫迅、岳丹、智庆芳。采购单位浙江省农业科学院位于杭州市石桥路198号。",null,"##### 公告概要：\n\n**公告信息：**\n采购项目名称 浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目\n品目\n采购单位 浙江省农业科学院（本级）\n行政区域 浙江省 公告时间 2026年09月25日 14:52\n评审专家名单 龚建新，孙才俊（第1标项采购人代表），高夫迅，岳丹，智庆芳\n总中标金额￥398.600000 万元（人民币）\n**联系人及联系方式：**\n项目联系人 吴皓哲\n项目联系电话 0571-88907791\n采购单位 浙江省农业科学院（本级）\n采购单位地址 杭州市石桥路198号\n采购单位联系方式 13121117700\n代理机构名称 浙江省政府采购中心\n代理机构地址 浙江省杭州市西湖区宝石一路3号\n代理机构联系方式 0571-88907791\n\n一、项目编号：330000263560010000421-ZZCG2026E-GK-147\n\n二、项目名称：浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目\n\n三、中标（成交）信息\n\n1.中标结果：\n\n**序号****中标（成交）金额(元)****中标供应商名称****中标供应商地址****评审总得分**\n1 投标总价：3986000（元）浙江移动数智科技有限公司 杭州市体育场路406号 85.1\n\n四、主要标的信息\n\n货物类主要标的信息：\n\n序号 标项名称 标的名称 品牌 规格型号 数量 单价(元)\n1 浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目 详见附件 详见附件 详见附件 详见附件 3986000\n\n五、评审专家（单一来源采购人员）名单\n\n龚建新，孙才俊（第1标项采购人代表），高夫迅，岳丹，智庆芳\n\n六、代理服务收费标准及金额：\n\n1.代理服务收费标准：无\n\n2.代理服务收费金额（元）：0\n\n七、公告期限\n\n自本公告发布之日起1个工作日。\n\n八、其他补充事宜\n\n1.各参加政府采购活动的供应商认为该中标\u002F成交结果和采购过程等使自己的权益受到损害的，可以自本公告期限届满之日（本公告发布之日后第2个工作日）起7个工作日内，以书面形式向采购人或受其委托的采购代理机构提出质疑。质疑供应商对采购人、采购代理机构的答复不满意或者采购人、采购代理机构未在规定的时间内作出答复的，可以在答复期满后十五个工作日内向同级政府采购监督管理部门投诉。质疑函范本、投诉书范本请到浙江政府采购网下载专区下载。\n\n2.其他事项：无\n\n九、对本次公告内容提出询问、质疑、投诉，请按以下方式联系\n\n1.采购人信息\n\n名 称：浙江省农业科学院（本级）\n\n地 址：杭州市石桥路198号\n\n传 真：\u002F\n\n项目联系人（询问）：孙才俊\n\n项目联系方式（询问）：13121117700\n\n质疑联系人：叶自然\n\n质疑联系方式：0571-88105537\n\n2.采购代理机构信息\n\n名 称：浙江省政府采购中心\n\n地 址：浙江省杭州市西湖区宝石一路3号\n\n传 真：\u002F\n\n项目联系人（询问）：吴皓哲\n\n项目联系方式（询问）：0571-88907791\n\n质疑联系人：张哲\n\n质疑联系方式：0571-88907707\n\n3.同级政府采购监督管理部门\n\n名 称：浙江省财政厅政府采购监管处、浙江省政府采购行政裁决服务中心（杭州）\n\n地 址：杭州市上城区清泰街549号城建综合大楼11楼\n\n传 真：\u002F\n\n联 系 人：匡老师\n\n监督投诉电话：0571-87227986（投诉专用电话）\n\n附件信息：\n\n*   [报价文件.pdf](https:\u002F\u002Fzcy-gov-open-doc.oss-cn-north-2-gov-1.aliyuncs.com\u002F1024FPA\u002F339900\u002F10016441811\u002F20269\u002Ff2a75c21-da03-4d50-8a08-1b6adce07406.pdf)\n\n351.2K\n\n*   [浙江省农业科学院农业人工智能创新中心算力服务平台建设（二期）项目采购文件.docx](https:\u002F\u002Fzcy-gov-open-doc.oss-cn-north-2-gov-1.aliyuncs.com\u002F1024FPA\u002F339900\u002F10013504574\u002F20268\u002Fb9d6d0aa-8538-406b-a100-7d56fa5799d9.docx)\n\n339.0K\n\n*   [关于符合本国产品标准的声明函](https:\u002F\u002Fzcy-gov-open-doc.oss-cn-north-2-gov-1.aliyuncs.com\u002F1024FPA\u002F20269\u002F769d3bc9-b2ea-433d-bf6e-a9de9f825f43.pdf)\n\n439.1K","中国政府采购网 2026-09-25","2026-09-25T06:52:00Z","报道",10,false,55,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":19,"relevant":21,"comment":22},12,14,8,13,1,"省级农科院AI算力平台二期采购中标公告，属农业信息化基础设施建设的实质性进展，但为常规政府采购结果公示，信息增量与专业深度有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","浙江","政府采购","算力服务",[32,33],"浙江省农业科学院 算力服务平台","浙江移动数智科技 中标","浙江省农业科学院算力服务平台-3577",0,"2026-09-27T00:05:15.284021Z",{"total":38,"page":21,"page_size":38,"items":39},6,[40,65,87,110,152,195],{"id":41,"title":42,"url":43,"summary":44,"summary_zh":8,"content":45,"source_name":46,"source_url":8,"published_at":47,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":48,"score_detail":49,"sources":54,"tags":56,"search_phrases":60,"slug":63,"view_count":35,"doi":8,"paper":8,"created_at":64},3597,"中国农科院农业信息研究所提出层次知识引导的枸杞害虫识别框架——Engineering Applications of Artificial Intelligence在线发表","https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571988.shtm","中国农业科学院农业信息研究所科学数据研究室科研团队近日提出一种层次知识引导的枸杞害虫识别框架，有效解决真实农业场景下样本稀缺与类间混淆的双重挑战，相关研究成果发表在《人工智能的工程应用》（Engineering Applications of Artificial Intelligence）上。研究团队依托长文本-图像检索模型，把专家撰写的长文本描述解析为粗、中、细三个语义层次，并与多尺度视觉特征实现精准跨模态对齐。同时设计知识引导的去混淆模块，通过构建文本和视觉双模态相似度图，明显降低容易混淆类别之间的预测误差。该研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。","[![Image 2: 科学网新闻频道](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨 来源：中国科学报 发布时间：2026\u002F9\u002F23 11:21:15 选择字号：小 中 大 新方法可智能识别枸杞害虫 近日，中国农业科学院农业信息研究所科学数据研究室科研团队，提出一种层次知识引导的枸杞害虫识别框架，有效解决真实农业场景下样本稀缺与类间混淆的双重挑战。相关研究成果发表在《人工智能的工程应用》（_**Engineering Applications of Artificial Intelligence**_）上。 ![Image 3](https:\u002F\u002Frmtzx.sciencenet.cn\u002Fkxwsprint\u002F6ab2a040e4b078fce44b0f9a.jpg)层次知识引导的枸杞害虫识别流程示意图。中国农科院供图 枸杞是高价值药用植物，虫害会对它的果实品质和药效造成很大影响。传统识别方法效率低且难以实现规模化应用。现有深度学习模型，面对外观高度相似的害虫种类时，常因标注样本不够，而识别效果不理想。 针对上述难题，科研团队依托长文本-图像检索模型，把专家撰写的长文本描述解析为粗、中、细三个语义层次，并与多尺度视觉特征实现精准跨模态对齐。同时，设计知识引导的去混淆模块，通过构建文本和视觉双模态相似度图，明显降低容易混淆类别之间的预测误差。该研究为枸杞等特色作物的虫害智能监测与精准防控提供新的技术路径，也为将农业专家知识融入视觉—语言模型提供参考。 该研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。 相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.115342 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n[![Image 4](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=571988)\n\n相关新闻 相关论文\n*   1\n*   [陈文新：用后半生走遍中国“寻根”](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571931.shtm)\n\n*   2\n*   [上海市自然科学基金（启明星计划）项目申报指南发布](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571928.shtm)\n\n*   3\n*   [从实验室到生产线 高校以应用型科研对接产业升级](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571927.shtm)\n\n*   4\n*   [重点新材料研发及应用国家科技重大专项项目启动申报](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571926.shtm)\n\n*   5\n*   [一线治疗失败！晚期胃癌二线治疗有了“新标杆”](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571925.shtm)\n\n*   6\n*   [新策略助力有机室温磷光寿命大幅提升](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571909.shtm)\n\n*   7\n*   [AI绘制固态储氢材料全景导航 | 专家访谈](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571894.shtm)\n\n*   8\n*   [基金委发布1个国家自然科学基金专项项目申请指南](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571893.shtm)\n\n*   1\n*   [智能柔性传感器可原位实时监测植物体内激素](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F2026923105644661155910.shtm)\n\n*   2\n*   [浙江大学刘明团队Angew.：利用“受挫堆积”调控多孔有机笼孔道结构](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F202692310372413155909.shtm)\n\n*   3\n*   [GCB | 华南植物园揭示长期高氮沉降驱动热带森林根系分泌物加速磷活化的机制](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F2026923103553943155908.shtm)\n\n*   4\n*   [FEC | 土壤氮磷比调控高寒草地物种共存](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F2026923103451833155907.shtm)\n\n*   5\n*   [前沿聚焦：为什么烤羊肉串香？合肥工大与渤海大学联合揭示“香气密码”](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F2026923103345287155906.shtm)\n\n*   6\n*   [前沿聚焦：福州大学程树英-王伟煌联合河北大学高青：沉淀物回收策略助力 CBD 法 Sb2S3太阳电池提效](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F2026923103218818155905.shtm)\n\n*   7\n*   [Angew. Chem.：本征近红外二区荧光纳米酶用于高分辨活体成像与肿瘤催化免疫治疗](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F2026923102125755155904.shtm)\n\n*   8\n*   [2型糖尿病不同临床亚型的分子异质性被揭示](https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F20269239501868155903.shtm)\n\n[图片新闻](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimgnews.aspx)\n\n[![Image 5: “鹏城首发星”成功入轨](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fupload\u002Fnews\u002Fimages\u002F2026\u002F9\u002F20269231515219900.png)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhttps:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571953.shtm)[![Image 6: 研究揭示甲烷部分氧化制合成气驱动因素](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fupload\u002Fnews\u002Fimages\u002F2026\u002F9\u002F2026917125225680.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571610.shtm)\n[![Image 7: 科学家揭示火星大气物质运输规律](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fupload\u002Fnews\u002Fimages\u002F2026\u002F9\u002F2026915174236470.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571503.shtm)[![Image 8: 超导双层镍氧化物薄膜的三维电子结构](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fupload\u002Fnews\u002Fimages\u002F2026\u002F9\u002F20269151415154110.png)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhttps:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571484.shtm)\n[>>更多](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimgnews.aspx)\n\n[一周新闻排行](https:\u002F\u002Fpaper.sciencenet.cn\u002Fzphnews.aspx)\n*   1\n*   [诺贝尔基金会宣布增加2026年诺奖奖金](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571724.shtm)\n\n*   2\n*   [直播回放｜新型铁基催化剂，为海水制氢提速](https:\u002F\u002Fnews.sciencenet.cn\u002Fhttps:\u002F\u002Fweibo.com\u002Fl\u002Fwblive\u002Fp\u002Fshow\u002F1022:2321325343837114990618)\n\n*   3\n*   [汪品先院士：年轻人应跳出人类中心论](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571776.shtm)\n\n*   4\n*   [从清华毕业做科普10年，我如何对抗AI](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571671.shtm)\n\n*   5\n*   [袁慧书：脊柱“一扫多查”，让AI成为影像医生得力助手](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571771.shtm)\n\n*   6\n*   [直播回放|马丁院士：共同追寻碳原子的“旅行路线”](https:\u002F\u002Fnews.sciencenet.cn\u002Fhttps:\u002F\u002Fweibo.com\u002Fl\u002Fwblive\u002Fp\u002Fshow\u002F1022:2321325344508505620608)\n\n*   7\n*   [新模型引爆AI圈：不生成文本，专为机器做快速决策](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571794.shtm)\n\n*   8\n*   [AI绘制固态储氢材料全景导航 | 专家访谈](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571894.shtm)\n\n*   9\n*   [AI正自主制造下一代AI？](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571716.shtm)\n\n*   10\n*   [一周热闻回顾（2026年9月20日）](https:\u002F\u002Fnews.sciencenet.cn\u002F\u002Fhtmlnews\u002F2026\u002F9\u002F571820.shtm)\n\n[编辑部推荐博文](http:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)\n*   ![Image 9](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [封面文章 | 效应蛋白TseMt的毒性机制及递送通路](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1553810.html)\n\n*   ![Image 10](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [科学网2026年8月十佳博文榜单公布！](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1551789.html)\n\n*   ![Image 11](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [我爱路亚](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-1185605-1554044.html)\n\n*   ![Image 12](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [学者的中秋家国](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-575926-1554043.html)\n\n*   ![Image 13](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [CD1 如何\"看见\"脂质](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-41174-1554027.html)\n\n*   ![Image 14](https:\u002F\u002Fpaper.sciencenet.cn\u002Fimages\u002Ft11.gif)\n*   [师傅领进门，修行在个人](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-729911-1553761.html)\n\n*   [更多>>](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)\n\n[关于我们](http:\u002F\u002Fwww.sciencenet.cn\u002Faboutus.aspx) | [网站声明](http:\u002F\u002Fwww.sciencenet.cn\u002Fshengming.aspx) | [服务条款](http:\u002F\u002Fwww.sciencenet.cn\u002Ftiaokuang.aspx) | [联系方式](http:\u002F\u002Fwww.sciencenet.cn\u002Fcontact.aspx) | [举报](https:\u002F\u002Fwww.sciencenet.cn\u002Fjubao.aspx) | [中国科学报社](https:\u002F\u002Fstimes.sciencenet.cn\u002F)  \n[京ICP备07017567号-12](http:\u002F\u002Fwww.beian.miit.gov.cn\u002F)互联网新闻信息服务许可证10120230008 京公网安备 11010802032783  \nCopyright @ 2007-2026 中国科学报社 All Rights Reserved  \n地址：北京市海淀区中关村南一条乙三号 电话：010-62580783","科学网（中国科学报）2026-09-23","2026-09-23T00:00:00Z",77,{"impact":50,"substance":51,"depth":52,"authority":18,"freshness":19,"relevant":21,"comment":53},18,20,17,"国家级科研机构在农业AI顶刊发表的技术突破，方法新颖且直击样本稀缺与类间混淆痛点，对特色作物虫害智能监测有示范价值，值得进入每日精选。",[55],{"name":46,"url":43},[26,27,57,58,59],"病虫害识别","视觉语言模型","枸杞",[61,62],"中国农科院 农业信息研究所 枸杞害虫","层次知识引导 害虫识别 视觉语言模型","中国农科院农业信息研究所枸杞害虫-3597","2026-09-27T00:05:17.078840Z",{"id":66,"title":67,"url":68,"summary":69,"summary_zh":8,"content":8,"source_name":70,"source_url":8,"published_at":47,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":76,"tags":78,"search_phrases":82,"slug":85,"view_count":35,"doi":8,"paper":8,"created_at":86},3595,"中国农科院第八届农科开放日启动，560余项农科成果集中亮相——野生稻智能表型巡检机器狗、'植物CT'、'全球动物疾病风险大模型'三大成果引关注","http:\u002F\u002Fwww.east-regis.com\u002Fcontent\u002F64305125.shtml","中国农业科学院党组书记杨振海指出，中国农科院作为农业科研国家队，深入贯彻落实习近平总书记关于科学普及工作的重要指示精神。本届农科开放日以'农业科技赋能 点亮美好生活'为主题，汇聚全院优势科技资源，通过农业科技成果展示、科普互动体验、优良品种品鉴、青少年科学启蒙等系列活动，全方位、多角度展示中国农业科学院的科技硬实力与创新活力。活动现场展示了中国农科院野生作物种质资源保护与利用团队研发的野生稻智能表型巡检机器狗。京内外36个院属单位结合自身特点，在分会场同步举办丰富多彩的科普活动，560余项最新农业科技成果集中亮相。农科开放日自2019年举办以来，累计吸引4400万公众通过线上线下参与。","东方瑞吉 2026-09-23",69,{"impact":73,"substance":74,"depth":17,"authority":20,"freshness":38,"relevant":21,"comment":75},22,16,"国家级农科开放日集中展示560余项成果，机器狗、植物CT等亮点具科普与传播价值，但属活动通稿、时效稍旧，可入精选但非必选。",[77],{"name":70,"url":68},[26,27,79,80,81],"农业科普","种质资源","农科开放日",[83,84],"中国农科院 农科开放日","野生稻 智能表型 机器狗","中国农科院农科开放日-3595","2026-09-27T00:05:16.916613Z",{"id":88,"title":89,"url":90,"summary":91,"summary_zh":8,"content":8,"source_name":92,"source_url":8,"published_at":93,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":99,"tags":101,"search_phrases":105,"slug":108,"view_count":35,"doi":8,"paper":8,"created_at":109},3574,"'智慧'点亮丰收图——甘肃'12316'智慧农业AI服务系统亮相2026年中国农民丰收节甘肃主场","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260925\u002F20260925_4\u002Fnmrb_20260925_13414_4_2103265943006318653.html","9月23日天水市麦积区南山花牛苹果基地举行的2026年中国农民丰收节甘肃省主场活动上，甘肃'12316'智慧农业AI服务系统展示'知识推荐、话术推荐、关键词锁定、专家转接、疑问解答'全流程：农户手机一点即可视频连线省级专家远程问诊。北京市农林科学院农业机器人团队成员郭鑫介绍，双臂采摘机器人依托柔性末端采摘机构与高精度深度视觉系统，可应对光照变化、枝叶遮挡、果实分布不均等复杂工况，实现苹果全自动识别、定位与采摘，作业覆盖高度0.5至3米。","农民日报 2026-09-25","2026-09-25T00:00:00Z",72,{"impact":50,"substance":51,"depth":96,"authority":17,"freshness":97,"relevant":21,"comment":98},15,7,"省级丰收节主场亮相的AI农技服务与采摘机器人，兼具服务模式与装备技术信息增量，值得进入每日精选。",[100],{"name":92,"url":90},[26,27,102,103,104],"采摘机器人","农业社会化服务","苹果产业",[106,107],"甘肃 智慧农业 AI服务系统","北京农林科学院 双臂采摘机器人 苹果","甘肃智慧农业AI服务系统-3574","2026-09-27T00:05:13.939515Z",{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":8,"source_name":116,"source_url":113,"published_at":93,"category":117,"cover_url":8,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":121,"tags":123,"search_phrases":127,"slug":130,"view_count":35,"doi":131,"paper":132,"created_at":151},3569,"Efficient and explainable attention-based multitask learning for fruit type, ripeness, and disease classification","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2108","Precision agriculture increasingly relies on automated crop monitoring to support disease prevention and harvest decisions. However, most existing systems treat fruit type, ripeness, and disease classification as separate tasks, leading to high computational costs in real-world deployments. This paper proposes an efficient and explainable multitask learning (MTL) model for fruit monitoring that performs all three tasks simultaneously. The MTL model uses a shared lightweight MobileNetV2 backbone with a convolutional block attention module (CBAM) to extract both shared and discriminative features. Dedicated classification heads then utilize these features to generate predictions for each task. To address the scarcity of fully annotated agricultural datasets, we combine multiple public datasets and apply a partial label-masking strategy to handle missing annotations. In addition, the proposed model is evaluated using several backbone architectures, including ResNet-50, EfficientNetV2-M, and ViT-B\u002F16, under both single optimizer and separate optimizers. Experimental results demonstrate that the proposed MTL model achieves 99.39% (fruit type), 96.35% (ripeness), and 99.7% (disease) accuracy, while using only 5.5M parameters, outperforming single-task solutions. Furthermore, gradient-based explainability analysis provides transparency into the model's decision-making process.","精准农业日益依赖自动化作物监测，以支持病害预防和收获决策。然而，现有大多数系统将水果类型、成熟度和病害分类视为独立任务，导致实际部署中的计算成本较高。本文提出了一种高效且可解释的多任务学习（MTL）模型用于水果监测，可同时执行上述三项任务。该MTL模型采用共享的轻量级MobileNetV2主干网络，并结合卷积块注意力模块（CBAM）以提取共享特征和判别特征。随后，专用分类头利用这些特征为每个任务生成预测。为应对完全标注农业数据集稀缺的问题，我们整合了多个公开数据集，并采用部分标签掩码策略来处理缺失标注。此外，我们在单一优化器和独立优化器两种设置下，使用包括ResNet-50、EfficientNetV2-M和ViT-B\u002F16在内的多种主干架构对所提模型进行了评估。实验结果表明，所提MTL模型在仅使用5.5M参数的情况下，分别达到了99.39%（水果类型）、96.35%（成熟度）和99.7%（病害）的准确率，优于单任务方案。此外，基于梯度的可解释性分析为模型的决策过程提供了透明度。","Journal of Agricultural Engineering","论文",80,{"impact":50,"substance":73,"depth":50,"authority":18,"freshness":19,"relevant":21,"comment":120},"提出轻量可解释多任务模型，同时识别水果种类、成熟度与病害，精度高、参数少，对智慧果园部署有实用价值。",[122],{"name":116,"url":113},[26,27,124,125,126],"病害识别","多任务学习","水果检测",[128,129],"MobileNetV2 CBAM 水果分类","多任务学习 果实成熟度 病害","MobileNetV2CBAM水果分类-3569","10.4081\u002Fjae.2026.2108",{"doi":131,"openalex_id":133,"authors":134,"venue":116,"cited_by_count":35,"oa_url":113,"card":144,"direction":148,"ingested_from":150},"W7214312987",[135,138,141],{"name":136,"orcid":137},"Rida El Chall","https:\u002F\u002Forcid.org\u002F0000-0002-7620-7767",{"name":139,"orcid":140},"Sarah Alayan","https:\u002F\u002Forcid.org\u002F0009-0007-5706-4688",{"name":142,"orcid":143},"Abed Ellatif Samhat","https:\u002F\u002Forcid.org\u002F0000-0002-1137-621X",{"tldr":145,"method":146,"finding":147,"direction":148,"opportunity":149},"提出轻量多任务模型，同时分类水果种类、成熟度和病害，并具可解释性。","MobileNetV2+CBAM共享主干，多数据集合并与部分标签掩码。","三任务准确率达99.39%、96.35%、99.7%，仅5.5M参数，优于单任务。","农业人工智能与决策模型","可探索多任务模型在田间实时部署与跨作物泛化，结合弱监督降低标注成本。","openalex","2026-09-26T23:30:57.555191Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":8,"content":8,"source_name":157,"source_url":155,"published_at":93,"category":117,"cover_url":8,"hotness":158,"is_selected":14,"score":159,"score_detail":160,"sources":163,"tags":168,"search_phrases":172,"slug":175,"view_count":35,"doi":176,"paper":177,"created_at":194},3565,"An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability","https:\u002F\u002Fdoi.org\u002F10.7759\u002Fs44389-026-00295-5","An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability。Cureus Journal of Computer Science.","Cureus Journal of Computer Science.",25,39,{"impact":19,"substance":38,"depth":13,"authority":38,"freshness":161,"relevant":21,"comment":162},9,"主题契合智慧农业与农业AI，但摘要仅重复标题、无方法与数据细节，信息增量有限，暂不建议进入每日精选。",[164,165],{"name":157,"url":155},{"name":166,"url":167},"Cureus Journal of Computer Science 2026-09-25","https:\u002F\u002Fwww.cureusjournals.com\u002Farticles\u002F20543",[26,27,169,170,171],"可解释AI","精准农业","气候适应",[173,174],"气候适应 精准农业 可解释AI","农业人工智能 智慧农业 气候适应 精准农业","气候适应精准农业可解释AI-3565","10.7759\u002Fs44389-026-00295-5",{"doi":176,"openalex_id":178,"authors":179,"venue":157,"cited_by_count":35,"oa_url":155,"card":8,"direction":148,"ingested_from":150},"W7214363342",[180,183,186,188,190,192],{"name":181,"orcid":182},"Mritunjay Kr. Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-0240-4909",{"name":184,"orcid":185},"Rohit Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-4436-8275",{"name":187,"orcid":8},"Nitin  D Mali",{"name":189,"orcid":8},"Ansh  A Rajore",{"name":191,"orcid":8},"Gaurav Narendra Patil",{"name":193,"orcid":8},"Ankita  N Patil","2026-09-26T23:30:47.893666Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":8,"source_name":201,"source_url":198,"published_at":93,"category":117,"cover_url":8,"hotness":13,"is_selected":202,"score":203,"score_detail":204,"sources":207,"tags":209,"search_phrases":212,"slug":215,"view_count":35,"doi":216,"paper":217,"created_at":240},3564,"Research Progress on Intelligent Seeding Technology and Equipment: The Development of Seeders from Multi-Functional Integration to Agricultural Intelligent Agents","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191884","Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems.","播种是决定种子空间分布、作物群体结构和潜在产量形成的关键作物生产环节，也是精准、高效、绿色农业的基础。然而，田间土壤特性、区域气候和作物农艺要求具有强烈的时空异质性。基于人工经验和固定预设参数的传统播种作业无法满足大规模精准农业的需求。在精准农业、智能感知、人工智能和自主机械等领域的进步推动下，现代智能播种系统集成了精密排种、高精度环境感知和闭环动态自适应调节。此类系统可在标准露地条件下提高株距均匀性并实现精量播种深度控制，但在全球导航卫星系统（GNSS）拒止的复杂环境中，包括密植作物冠层和温室，仍面临明显的性能局限。本文综述了播种机械的演进轨迹，总结了精量播种、多功能装备集成、多源信息感知和智能决策方面的研究进展。阐述了涵盖机械优化、电子控制和感知驱动决策系统的集成设计原则。识别出播种装备的四个发展阶段：机械精量作业、电子智能调控、多功能模块集成和智能认知集成。当前智能播种技术受限于对复杂农田条件的适应性不足、多源数据融合不稳定、长期运行可靠性不够以及多场景部署成本高昂，制约了其在田间的广泛规模化应用。未来研究应将农艺知识与人工智能相结合，提升环境感知和自主决策能力，开发低成本、高可靠性的集成播种装备，支撑智能农机体系建设。","Agronomy",true,82,{"impact":73,"substance":205,"depth":50,"authority":20,"freshness":19,"relevant":21,"comment":206},21,"系统梳理智能播种装备从机械化到智能体四阶段演进，指出GNSS受限环境与多源数据融合瓶颈，对智慧农业装备研发有参考价值。",[208],{"name":201,"url":198},[26,27,210,170,211],"智能农机","智能播种",[213,214],"智能播种 装备","Agronomy 智能播种 装备","智能播种装备-3564","10.3390\u002Fagronomy16191884",{"doi":216,"openalex_id":218,"authors":219,"venue":201,"cited_by_count":35,"oa_url":198,"card":234,"direction":148,"ingested_from":150},"W7214297818",[220,222,224,226,228,231],{"name":221,"orcid":8},"Yuting Dong",{"name":223,"orcid":8},"Yapeng Wu",{"name":225,"orcid":8},"Shiguo Wang",{"name":227,"orcid":8},"Xiaohu Guo",{"name":229,"orcid":230},"Xin Lu","https:\u002F\u002Forcid.org\u002F0000-0003-4462-3472",{"name":232,"orcid":233},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"tldr":235,"method":236,"finding":237,"direction":238,"opportunity":239},"综述智能播种技术装备从多功能集成到农业智能体的四阶段演进及瓶颈。","文献综述，梳理精量播种、多源感知与智能决策的集成设计。","智能播种在开阔农田表现良好，但复杂环境下适应性、数据融合与成本仍受限。","智慧农业 \u002F 农业物联网","GNSS拒止的冠层与温室环境下低成本高可靠感知与自主决策播种装备是研究空白。","2026-09-26T23:30:47.821171Z"]