[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3229":3,"related-3229":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":72,"slug":75,"view_count":57,"doi":8,"paper":8,"created_at":76},3229,"乔府大院第十一届开镰节系列活动启幕——黑龙江省农科院为乔府大院授牌\"科技包联示范基地\"","https:\u002F\u002Fwww.hlj.news.cn\u002F20260922\u002F30b3409efc004627ab756efa3334249e\u002Fc.html","9-22 新华网黑龙江频道报道：9-22 由黑龙江省农科院、哈尔滨市农业农村局、五常市农业农村局、亿林学堂联合主办，乔府大院承办的黑龙江省农科院科技包联哈尔滨市现场观摩会、首届亿林学堂世界商业武道大赛、第十一届乔府大院开镰节系列活动，在五常市乔府大院现代农业产业园启幕。依托黑龙江省\"科技包联、赋能增粮\"工作部署，黑龙江省农业科学院专家团队长期扎根乔府大院田间一线，围绕精准施肥、富硒稻米种植、良种选育研发、智慧农业应用四大领域持续技术赋能。活动现场，黑龙江省农业科学院正式授予乔府大院\"科技包联示范基地\"称号。最新数据显示，截至2026年上半年，黑龙江省定制农业主体数量发展到445家，种植定制基地面积达到179.9万亩，实现签约销售额55.5亿元，同比增长85%。",null,"新华网黑龙江频道","2026-09-22T00:00:00Z","报道",10,false,74,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},18,20,14,12,1,"省级科技包联授牌叠加定制农业最新数据，信息增量足、时效性强，但属企业承办活动，影响层级限于省级与细分行业。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","科技包联","五常大米","定制农业","富硒稻米",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,55,62,67],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},4,true,[],[48],"稻米",[27],{"code":51,"score":44,"supported":45,"title":52,"abstract":53,"tags":54},"I1",[],[25],[25],{"code":56,"score":57,"supported":45,"title":58,"abstract":59,"tags":61},"F3",3,[],[60],"施肥",[],{"code":63,"score":20,"supported":13,"title":64,"abstract":65,"tags":66},"E5",[],[],[28],{"code":68,"score":20,"supported":13,"title":69,"abstract":70,"tags":71},"G1",[],[],[26],[73,74],"黑龙江省农科院 乔府大院 科技包联示范基地","五常大米 开镰节 定制农业","黑龙江省农科院乔府大院科技包联示范基地-3229","2026-09-23T00:04:30.450309Z",{"total":78,"page":20,"page_size":78,"items":79},6,[80,135,188,269,351,427],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":8,"content":85,"source_name":86,"source_url":8,"published_at":87,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":89,"sources":94,"tags":96,"primary_topic":99,"secondary_topics":100,"classification":101,"search_phrases":130,"slug":133,"view_count":20,"doi":8,"paper":8,"created_at":134},3732,"科技包联赋能定制农业有了智慧芯","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fhlj\u002F202609\u002Ft20260924_8873815.htm","黑龙江省农科院科技包联服务团运用水稻品种筛选、基质育苗、智慧农业变量施肥等多项技术，兼顾稻米品质优化、生产投入减量与环境污染最小化多重目标。优质稻品种松粳22亩产达到557.5公斤，验证了良种良法配套对于定制农业的支撑效力。","金秋九月，黑土大地稻浪翻涌，丰收气息扑面而来。9月22日，黑龙江省农业科学院科技包联哈尔滨市现场观摩会在五常乔府大院现代农业产业园举行，农业科研专家、种植大户、涉农企业代表齐聚一堂，聚焦科技包联助力定制农业发展，共探黑土地粮食增产、产业提质新路径。\n\n站在乔府大院核心定制基地的稻田之中，湖南厨王粮油食品有限公司总经理何瞳瞳深有感触。此次企业与基地达成2000亩核心基地订单农业合作。“不来不知道，来了之后确实很震撼。”何瞳瞳说，定制农业一方面通过定制米卡面向家庭消费市场，另一方面对接全国中高端餐饮渠道。这套模式能够保障稻米全年品质稳定，实现全流程可溯源可追溯，契合当下食品安全消费趋势。\n\n在何瞳瞳的行业观察中，如今，不少湖南中高端湘菜餐饮偏好选用黑龙江优质大米，乔府大院产品成为海底捞、徐记海鲜等知名餐饮企业的优质供应商，同时拓展商超流通渠道。何瞳瞳表示，这次实地走访过后，企业更有信心、有底气把龙江优质稻米产品引入湖南长沙，送到更多消费者的餐桌。\n\n市场端对黑龙江定制农产品的高度认可，离不开省农科院科技包联服务团的技术赋能。省农科院哈尔滨市科技包联服务团团长、耕作栽培研究所所长李柱刚介绍，哈尔滨市科技包联服务团由省农科院耕作栽培研究所、玉米研究所、畜牧研究所、园艺分院等共同组成，重点围绕单产提升、特色产业和定制农业三大方向开展技术攻关与落地服务。\n\n在乔府大院的产业实践当中，科技包联服务团运用水稻品种筛选、基质育苗、智慧农业变量施肥等多项技术，兼顾稻米品质优化、生产投入减量与环境污染最小化多重目标。实测数据显示，优质稻品种“松粳22”亩产达到557.5公斤，验证了良种良法配套对于定制农业的支撑效力，为订单化、标准化稻米生产夯实技术基础。\n\n依托院地协同合作，当地企业打造科技包联示范基地，将科研成果转化为定制农业的现实生产力。通过新品种筛选、智慧田间管理、标准化种植等一系列技术落地，为企业订单生产、餐饮渠道定制、家庭消费产品供给输出稳定可靠的优质粮源。\n\n哈尔滨市农业农村局种植业管理处处长杜树林介绍，近年来，哈尔滨市持续深化与省农科院的院地合作、科技包联工作。聚焦粮食生产，推进优良品种试验示范推广，普及先进栽培模式，推进绿色高产高效行动示范县创建，保障粮食稳产增产；面向畜牧产业、特色经济作物开展定制化技术服务，探索畜禽粪污综合利用路径，提升特色作物种植水平，有效带动定制基地农民增收；发挥专家传帮带作用，组织市、县两级农技人员1000余人次参与科技包联，锤炼基层农技队伍服务能力。\n\n“省农科院科技包联工作实施四年来，通过组建13个科技包联服务团，覆盖全省13个地市，重点推进主要粮食作物大面积单产提升工作。在品种示范推广和技术应用方面，建立数百个高产攻关示范田，创新形成‘1+1+N’科技包联服务模式，同步发展特色产业和高端定制农业。”省农科院副院长、党组成员焦少杰表示，省农科院将持续深化院地协同，强化科技包联，推广定制农业，抓实单产提升，让科技之种在黑土地结出更多丰硕果实。（黑龙江日报记者：梁金池）","中国农业农村信息网","2026-09-24T00:00:00Z",65,{"impact":16,"substance":90,"depth":91,"authority":12,"freshness":92,"relevant":20,"comment":93},17,15,5,"省级科技包联赋能定制农业的现场观摩报道，有品种产量数据与多方信源，但属区域动态、时效稍旧，可作主题聚合素材而非头条精选。",[95],{"name":86,"url":83},[25,97,98,26,28],"单产提升","院地合作",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":102},{"candidates":103,"agricultural_evidence":45},[104,108,112,116,121,126],{"code":31,"score":78,"supported":45,"title":105,"abstract":106,"tags":107},[],[33,48],[],{"code":51,"score":44,"supported":45,"title":109,"abstract":110,"tags":111},[],[25],[25],{"code":56,"score":57,"supported":45,"title":113,"abstract":114,"tags":115},[],[60],[],{"code":68,"score":117,"supported":13,"title":118,"abstract":119,"tags":120},2,[],[],[26,98],{"code":122,"score":20,"supported":13,"title":123,"abstract":124,"tags":125},"A8",[],[],[97],{"code":63,"score":20,"supported":13,"title":127,"abstract":128,"tags":129},[],[],[28],[131,132],"黑龙江农科院 科技包联 定制农业","松粳22 五常 乔府大院","黑龙江农科院科技包联定制农业-3732","2026-09-29T00:08:24.152129Z",{"id":136,"title":137,"url":138,"summary":139,"summary_zh":8,"content":140,"source_name":141,"source_url":8,"published_at":142,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":143,"score_detail":144,"sources":149,"tags":151,"primary_topic":156,"secondary_topics":157,"classification":158,"search_phrases":183,"slug":186,"view_count":57,"doi":8,"paper":8,"created_at":187},3586,"黑龙江省农科院科技包联现场观摩会举办——'松粳22'优质食味水稻新品种示范田实测亩产557.5公斤","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html","9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，试验田新品种展示区块18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。黑龙江省农科院生物技术研究所（五常水稻研究所）副所长、研究员闫平介绍，新品种'松粳22'150亩示范田按照14.5%标准含水率折算，平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平，抗倒伏能力显著优于传统优质稻。秸秆基质育苗示范点位展示秸秆基质育苗技术，今年在哈尔滨18个点位示范。","![Image 1](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 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[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 3](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 乡村行 看振兴丨科技赋能丰产 定制深耕良田——黑龙江省农科院科技包联现场观摩会举办\n\n 2026-09-25 12:39:56 来源: 科技日报 点击数：\n\n![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n**科技日报记者 朱虹**\n\n金色稻海漫过黑龙江五常的黑土地，稻浪深处，开镰号令响起。随着乔府大院董事长乔文志一声“开镰”，嘉宾们手持镰刀踏入金黄稻田，割下了今秋第一束稻穗。\n\n9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，传统农耕与现代科技撞个满怀，一场丰收开镰，也是一次看得见摸得着的农业科技大考。\n\n在试验田新品种展示区块，18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。该院生物技术研究所（五常水稻研究所）副所长、研究员闫平站在田垄之间，伸手托起沉甸甸的“松粳22”稻穗，招呼围观嘉宾观察茎秆与籽粒状态。现场发布前，实收测产数据显示，150亩示范田按照14.5%标准含水率折算，松粳22平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平。\n\n“传统稻花香大米口感出众，可茎秆偏软，遇上风雨天气极易大面积倒伏。”闫平指着成片挺立的稻株讲解道，“今天展示的新品种，抗倒伏能力显著优于传统优质稻。”\n\n一句“抗倒伏”，点破了好米难产的旧账。传统优质稻往往“娇贵”——品质好却秆软易倒、产量不稳，种高端米像押宝。新品种把“好吃”和“抗倒”拧在一起：抗倒伏，意味着风雨天不趴窝、机械收割进得去、规模种植稳得住，高端米供给才算有了“压舱石”。从“靠天吃饭”到“凭种稳产”，良种攻关为高端稻米产业蹚出一条新路。\n\n米食品鉴区，新米出锅，香气四溢，米粒莹润油亮。定制客户捧着一碗米饭感慨：“一碗好米的根基就在种子，科研选育的新品种，让老百姓既吃得饱，更吃得好。”\n\n观摩队伍在试验田里走走停停。秸秆基质育苗示范点位旁，黑龙江省农科院耕作栽培研究所作物栽培与装备研究室主任董文军被团团围住，他弯腰捧起一把棕褐色基质，讲起秸秆基质育苗的门道。\n\n“原料就是经多年沤熟的秸秆有机肥，配上三成秸秆打包回收时附带的表层土，育苗完全不用再挖黑土。”董文军介绍，杀菌剂、控旺剂都已预配好，农户拿到手直接铺苗床，取土、晒土、混拌这些累活全省了。“今年在哈尔滨18个点位示范，秧苗长势不比传统育苗土差，产量持平就可以得到推广，前期试验发现不同的配方基质比当地基质和黑土能增产5%到15%。”董文军说。秸秆从田间收走、发酵成基质，秧苗育成再回归水田。这条闭环，把曾经的“田间负担”变成“黑土养料”，围观的种植大户连连点头，当场追问推广条件。\n\n智慧农业贯穿作物生长每一环。黑龙江省农科院农业遥感与信息研究所副所长张有智介绍：“无人机图像结合人工智能模型算法，实现作物的长势情况和杂草分布反演识别，自动生成施肥处方图和施药处方图，直接推送到无人机飞手账号进行作业。作业精度能到厘米级。”今年试验，这套技术实现减肥6%、减药17%左右。\n\n黑龙江省农科院耕作栽培研究所所长李柱刚告诉科技日报记者，2023年黑龙江省农科院建立科技包联服务机制以来，哈尔滨已建成粮食单产提升示范田、科技服务田28个，示范推广面积25800亩。\n\n“为龙头企业提供高端定制服务，是科技包联的重头戏。”黑龙江省农科院副院长焦少杰站在田埂上说，“从品种定向选育、栽培技术集成，到植保方案、加工品鉴，全程‘一对一’定制，让高端米从‘卖原粮’升级为‘卖标准、卖品质’。”他介绍，黑龙江省农科院已深化科技包联13个市（地）服务机制，推动“百项技术、千个基地、万名人才”增粮示范落地。\n\n责任编辑：王倩\n\n相关稿件：\n\n![Image 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21](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202408\u002F19\u002F345\u002Fimages\u002Ffirefox.jpg)](http:\u002F\u002Fwww.firefox.com.cn\u002Fdownload\u002F)[![Image 22](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202408\u002F19\u002F345\u002Fimages\u002Fchrome.jpg)](http:\u002F\u002Fwww.chromeliulanqi.com\u002F)\n\n## 3.暂不升级，继续浏览\n\n[继续浏览](javascript:;)","科技日报 2026-09-25","2026-09-25T00:00:00Z",75,{"impact":16,"substance":17,"depth":145,"authority":146,"freshness":147,"relevant":20,"comment":148},16,13,8,"省级科研机构科技包联现场会，含新品种实测产量、秸秆基质育苗与遥感处方图等实质数据，对种业与智慧农业主题有聚合价值。",[150],{"name":141,"url":138},[25,152,153,154,26,155],"种业振兴","遥感监测","水稻新品种","秸秆基质育苗",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":159},{"candidates":160,"agricultural_evidence":45},[161,165,170,174,178],{"code":31,"score":19,"supported":45,"title":162,"abstract":163,"tags":164},[33],[33],[154],{"code":166,"score":20,"supported":13,"title":167,"abstract":168,"tags":169},"F1",[],[],[152],{"code":68,"score":20,"supported":13,"title":171,"abstract":172,"tags":173},[],[],[26],{"code":51,"score":20,"supported":13,"title":175,"abstract":176,"tags":177},[],[],[25],{"code":179,"score":20,"supported":13,"title":180,"abstract":181,"tags":182},"I3",[],[],[153],[184,185],"黑龙江省农科院 松粳22 水稻","五常 科技包联 观摩会","黑龙江省农科院松粳22水稻-3586","2026-09-27T00:05:16.125922Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":8,"source_name":194,"source_url":191,"published_at":195,"category":196,"cover_url":8,"hotness":12,"is_selected":13,"score":197,"score_detail":198,"sources":201,"tags":203,"primary_topic":207,"secondary_topics":208,"classification":214,"search_phrases":247,"slug":250,"view_count":251,"doi":252,"paper":253,"created_at":268},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z","论文",78,{"impact":16,"substance":199,"depth":16,"authority":19,"freshness":147,"relevant":20,"comment":200},22,"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[202],{"name":194,"url":191},[25,204,33,205,206],"农业人工智能","产量预测","遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[209],{"code":179,"slug":210,"name":211,"path":212,"parent_code":213},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":38,"locked":13,"version":39,"method":40,"evidence":215},{"candidates":216,"agricultural_evidence":45},[217,221,226,232,238,242],{"code":31,"score":91,"supported":45,"title":218,"abstract":219,"tags":220},[32],[33,32],[33],{"code":179,"score":147,"supported":45,"title":222,"abstract":223,"tags":225},[],[206,224],"remote sensing",[205,206],{"code":122,"score":78,"supported":45,"title":227,"abstract":228,"tags":231},[],[229,230],"粮食安全","food security",[],{"code":233,"score":57,"supported":45,"title":234,"abstract":235,"tags":237},"J1",[],[236],"农业规划",[],{"code":51,"score":20,"supported":13,"title":239,"abstract":240,"tags":241},[],[],[25],{"code":243,"score":20,"supported":13,"title":244,"abstract":245,"tags":246},"I2",[],[],[204],[248,249],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394",0,"10.3390\u002Feng7100572",{"doi":252,"openalex_id":254,"authors":255,"venue":194,"cited_by_count":251,"oa_url":191,"card":261,"direction":265,"ingested_from":267},"W7222942064",[256,258],{"name":257,"orcid":8},"Nayana Venkatesh",{"name":259,"orcid":260},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":262,"method":263,"finding":264,"direction":265,"opportunity":266},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","农业遥感与作物表型","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","openalex","2026-10-20T23:30:19.292634Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":8,"source_name":275,"source_url":272,"published_at":276,"category":196,"cover_url":8,"hotness":12,"is_selected":13,"score":277,"score_detail":278,"sources":281,"tags":283,"primary_topic":286,"secondary_topics":287,"classification":288,"search_phrases":331,"slug":334,"view_count":251,"doi":335,"paper":336,"created_at":350},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development","2026-10-19T00:00:00Z",80,{"impact":16,"substance":199,"depth":16,"authority":146,"freshness":279,"relevant":20,"comment":280},9,"方法新颖、数据规模明确且量化效果显著，属智慧农业细分领域实质进展，值得进入每日精选。",[282],{"name":275,"url":272},[25,204,33,284,285],"数字孪生","病虫害预警",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":289},{"candidates":290,"agricultural_evidence":45},[291,299,305,312,319,326],{"code":51,"score":292,"supported":45,"title":293,"abstract":295,"tags":298},19,[294],"digital twin",[296,284,297,294],"精准农业","precision agriculture",[284,25],{"code":31,"score":16,"supported":45,"title":300,"abstract":302,"tags":304},[301],"paddy",[33,303,32,301],"稻田",[33],{"code":306,"score":78,"supported":45,"title":307,"abstract":308,"tags":311},"F4",[],[309,310],"农药","pesticide",[],{"code":313,"score":78,"supported":45,"title":314,"abstract":315,"tags":318},"H3",[],[316,317],"灌溉","irrigation",[],{"code":320,"score":78,"supported":45,"title":321,"abstract":322,"tags":325},"I5",[],[323,324],"物联网","IoT",[],{"code":243,"score":44,"supported":45,"title":327,"abstract":328,"tags":330},[],[329],"decision support",[204],[332,333],"水稻 病害预测 数字孪生","ST-GNN 水稻 病虫害","水稻病害预测数字孪生-5388","10.1080\u002F20421338.2026.2707951",{"doi":335,"openalex_id":337,"authors":338,"venue":275,"cited_by_count":251,"oa_url":8,"card":343,"direction":349,"ingested_from":267},"W7222969403",[339,341],{"name":340,"orcid":8},"Jyoti Rani",{"name":342,"orcid":8},"Dr.  Narendra S.  Jadhav",{"tldr":344,"method":345,"finding":346,"direction":347,"opportunity":348},"构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。","融合物联网微气候、物候与管理数据，用GCN+LSTM的ST-GNN建模。","预测准确率87.3%，农药减23.7%、损失减31.2%，优于RF与SVM。","农业人工智能与决策模型","可探索多尺度时空图建模与数字孪生闭环决策，并迁移至其他作物与区域。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:12.636299Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":8,"source_name":357,"source_url":354,"published_at":358,"category":196,"cover_url":8,"hotness":12,"is_selected":13,"score":359,"score_detail":360,"sources":362,"tags":364,"primary_topic":368,"secondary_topics":369,"classification":374,"search_phrases":396,"slug":399,"view_count":20,"doi":400,"paper":401,"created_at":426},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","Scientific Reports","2026-10-13T00:00:00Z",72,{"impact":19,"substance":17,"depth":16,"authority":18,"freshness":147,"relevant":20,"comment":361},"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[363],{"name":357,"url":354},[25,204,365,366,367],"可解释AI","农业气象","水稻产量预测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[370],{"code":243,"slug":371,"name":372,"path":373,"parent_code":213},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":13,"version":39,"method":40,"evidence":375},{"candidates":376,"agricultural_evidence":45},[377,383,387,392],{"code":243,"score":292,"supported":45,"title":378,"abstract":380,"tags":382},[379],"machine learning",[381,379,329],"机器学习",[204,365],{"code":31,"score":18,"supported":45,"title":384,"abstract":385,"tags":386},[32],[33,32],[],{"code":388,"score":20,"supported":13,"title":389,"abstract":390,"tags":391},"H7",[],[],[366],{"code":51,"score":20,"supported":13,"title":393,"abstract":394,"tags":395},[],[],[25],[397,398],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":400,"openalex_id":402,"authors":403,"venue":357,"cited_by_count":251,"oa_url":420,"card":421,"direction":349,"ingested_from":267},"W7221657053",[404,406,408,410,412,414,417],{"name":405,"orcid":8},"Noh-Hyun Lee",{"name":407,"orcid":8},"Wonjae Jeong",{"name":409,"orcid":8},"Hyeon-Ji Yang",{"name":411,"orcid":8},"Hana Cho",{"name":413,"orcid":8},"Hyeon-Yeong Ju",{"name":415,"orcid":416},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":418,"orcid":419},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":422,"method":423,"finding":424,"direction":347,"opportunity":425},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","2026-10-15T23:30:10.483881Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":8,"content":432,"source_name":433,"source_url":8,"published_at":358,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":434,"score_detail":435,"sources":439,"tags":441,"primary_topic":444,"secondary_topics":445,"classification":446,"search_phrases":470,"slug":473,"view_count":251,"doi":8,"paper":8,"created_at":474},4950,"'袁稻思源'杂交水稻智能育种平台发布 业界首个智能育种平台落户湖南","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696096616441774642\u002F","10月11日,'袁稻思源'杂交水稻智能育种平台发布会在岳麓山实验室举行,由中国科学院自动化研究所联合湖南省农业科学院、北京中科闻歌科技股份有限公司共同研发。平台集成'问答助手''育种资源库''知识图谱''文献库'四大功能模块,收纳35570个水稻品种、689项性状、28648条基因数据和624757篇中英文文献。中国科学院院士李家洋、钱前、谢道昕,中国工程院院士柏连阳等共同见证。","来源：环球网\n\n水稻育种专家过去最头疼的是什么？翻资料。查一个品种亲本、找一个性状基因，动辄耗费大量时间。近期，中科闻歌带着决策智能入场了。10月11日，由湖南省农科院、中科院自动化所与中科闻歌联合打造的“袁稻思源”杂交水稻智能育种平台正式发布，把海量育种数据和AI决策能力送进育种一线。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F2397644cc26144379c7e432950a2deec~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792627757&x-signature=o8pNXOWnEqlIl4mnK12LZ8rHkeA%3D)\n**水稻育种：一个“数据富矿”**\n\n杂交水稻育种是一个典型的“数据富矿、决策洼地”领域。全球积累了海量品种、性状、基因和文献数据，但长期分散、难以检索，育种更多依赖科研人员的经验与记忆。为破解这一难题，今年7月2日，中国科学院自动化研究所、北京中科闻歌科技股份有限公司与湖南省农业科学院正式签约，围绕“人工智能+农业科研”开展联合攻关。“袁稻思源”正是这一合作的阶段性成果，平台名称取自袁隆平杂交水稻创新团队，寓意“饮水思源”，感怀袁隆平院士对中国水稻杂交育种作出的突出贡献。\n\n湖南省农科院院长余应弘介绍，“袁稻思源”是湖南落实国家种业振兴行动、推进育种数字化智能化转型的关键举措，旨在传承袁隆平院士科学精神，把数据资源和智能技术转化为育种创新的现实生产力。在中科闻歌的战略版图里，这次入局农业科研，是其“决策大模型”“AI国家队”能力向产业纵深的又一次延伸。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F328723c57ddce965a8efda71ad02f21a~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792627757&x-signature=0csP6GpZya22ien7EebcU64%2FxRw%3D)\n**“袁稻思源”四大核心功能**\n\n“袁稻思源”平台构建四大核心功能模块，形成完整的智能育种知识和能力。\n\n数据资源库，将散落的育种数据统一“上牌”。目前平台收纳35570个水稻品种、689项性状、28648条基因数据和624757篇中英文文献。不育系按光敏、温敏、光温敏等维度精细分类，支持育种专种家像检索商品一样，快速定位目标种质。该数据资源库是“袁稻思源”平台的核心底座。\n\n基于资源库建立图谱，让品种、基因、文献等彼此“建联”。品种图谱勾画出每个品种的选育谱系和材料关系，基因图谱把基因、性状、品种与文献证据编织成一张可推理的网络。例如输入Y58S，系统可直接呈现该品种的千粒重、抗病性及相关基因。\n\n智能问答“小稻”，作为育种专家的对话式检索入口。专业科研人员只需自然语言，也可向平台提出专业问题，系统从文献和真实数据中生成答案，每条结论都标明来源，不凭空编造。在发布会现场，专家提问“第三代杂交水稻产业化有哪些障碍”，“小稻”当即从监管、生物安全、成本、知识产权等维度给出专业分析，并提示品种审定前需补充哪些数据。\n\n最后一个能力模块是育种智能体。品种审定智能体可根据品种性状，帮助科研人员预判审定通过率，杂交配组智能体可基于基因型数据预测候选组合的表现。育种家从“翻资料猜结果”变成“让AI算概率”，大幅提升工作效率。\n\n湖南省农科院副院长李莉现场演示了平台在育种资源查询、品种系谱呈现、基因关联展示等方面的能力，一套组合下来，育种专家从“盲试”变成“有据可依”。目前，平台已在第三代杂交水稻育种中开展应用示范，取得良好成效。\n\n**专家视角：这场变革会改变什么**\n\n平台发布当天，中国工程院院士、湖南省农科院党委书记柏连阳直言，“拥抱人工智能，是今天科研人员的必答题”。平台目前虽为初级版本，但已迈出AI赋能杂交水稻育种关键而可喜的一步。\n\n中国科学院院士、崖州湾国家实验室主任李家洋的判断更为前瞻：当前全球种业竞争加剧，育种正从“经验驱动”转向“数据驱动、设计育种”，“谁率先突破智能育种，谁就赢得下一轮农业产业革命的主动”。李家洋建议，平台可强化智能决策、深化协同创新、坚持服务产业，最终建设成为面向全国乃至全球的水稻智能育种公共产品。\n\n据中科闻歌方面表示，“袁稻思源”是决策智能第一次系统嵌入国家粮食安全的核心环节。从金融到政务，再到农业科研，AI行业正在把“数据决策”能力，嵌入更多关乎国计民生的领域。对育种行业而言，当AI助力科研人员更快、更准地“排雷”和“选路”，中国水稻育种的效率天花板或许才刚刚被打开。（心月）","环球网 今日头条 2026 年 10 月 13 日",88,{"impact":436,"substance":437,"depth":16,"authority":146,"freshness":147,"relevant":20,"comment":438},26,23,"业界首个杂交水稻智能育种平台落地湖南，数据规模与AI决策功能具体，属种业数字化标志性进展，值得入选每日精选。",[440],{"name":433,"url":430},[25,204,152,442,443],"智能育种","杂交水稻",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":447},{"candidates":448,"agricultural_evidence":45},[449,453,457,462,466],{"code":31,"score":19,"supported":45,"title":450,"abstract":451,"tags":452},[33],[33],[443],{"code":166,"score":20,"supported":13,"title":454,"abstract":455,"tags":456},[],[],[152],{"code":458,"score":20,"supported":13,"title":459,"abstract":460,"tags":461},"F2",[],[],[442],{"code":51,"score":20,"supported":13,"title":463,"abstract":464,"tags":465},[],[],[25],{"code":243,"score":20,"supported":13,"title":467,"abstract":468,"tags":469},[],[],[204],[471,472],"袁稻思源 杂交水稻 智能育种平台","湖南省农科院 中科闻歌 智能育种","袁稻思源杂交水稻智能育种平台-4950","2026-10-15T00:06:02.857637Z"]