[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1154":3,"related-1154":88},{"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":24,"tags":26,"primary_topic":33,"secondary_topics":38,"classification":44,"search_phrases":82,"slug":85,"view_count":86,"doi":8,"paper":8,"created_at":87},1154,"台风红霞来袭——农业生产紧急防范应对指南发布 水稻蔬菜渔业畜牧四方面全覆盖","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F564113.html","农业农村部发布台风红霞农业生产紧急防范应对指南,涵盖水稻生产、蔬菜生产、渔业、畜牧生产四大方面。水稻方面:疏通田间沟渠确保排水畅通,台风过境前24—48小时喷施保护性药剂+芸苔素内酯等免疫诱抗剂,过境后抢收早稻、抢排积水、查苗补肥、加密巡查纹枯病稻纵卷叶螟稻飞虱二化螟等病虫害。蔬菜方面:检修加固大棚骨架收好遮阳网防虫网,灾后清理残株杂物、补施速效肥、防治霜霉病疫病软腐病,绝收田块抢种小白菜空心菜生菜。渔业方面:提前降低池塘水位防止漫堤跑鱼、检查电气线路加固看护棚,灾后及时捞除死亡个体无害化处理。畜牧方面:加固畜禽圈舍防止漏电,提前转运畜禽及饲料,灾后全面消毒、强化紧急免疫。",null,"[![Image 5: 榜言堂](https:\u002F\u002Fcss.bim99.cn\u002Frcgz\u002Flogo.jpg)![Image 6: 榜言堂](https:\u002F\u002Fcss.bim99.cn\u002Frcgz\u002Flogo.jpg)](https:\u002F\u002Fh.mffb.com.cn\u002F)\n\n*    农业信息 \n    *   [农业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F64 \"农业\")\n    *   [畜牧业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F65 \"畜牧业\")\n    *   [林业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F66 \"林业\")\n    *   [渔业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F67 \"渔业\")\n\n当前位置：[首页](https:\u002F\u002Fh.mffb.com.cn\u002F \"首页\")>[农业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F64 \"农业\")>台风“红霞”来袭——农业生产紧急防范应对指南 \n\n# [台风“红霞”来袭——农业生产紧急防范应对指南 * 2026-09-01 07:05:32 台风“红霞”来袭——农业生产紧急防范应对指南 受今年第12号台风“红霞”环流影响，25日晚～27日受热带系统影响，我县有一次明显降水过程，局部有大暴雨（过程累计雨量100～200毫米，局地250毫米以上，最大雨量出现在26日晚上至27日白天）。期间全县风力加大到3～5级，叠加雷雨大风影响，阵风可达7～9级。台风“红霞”将与西南季风结合，且丘陵山地对台风降水有明显增幅作用，我县累计雨量大，发生洪水风险高，发生地质灾害和城乡暴雨积涝可能性大。 台风“红霞”最新动态 今年第12号台风“红霞”（热带风暴级）的中心今天（24日）上午11点钟位于菲律宾马尼拉东北方向约450公里的洋面上，中心附近最大风力有8级（20米\u002F秒），中心最低气压为995百帕，七级风圈半径100-380公里。 ![Image 7](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-07-25\u002F5609_17849870217269.webp) 预计，“红霞”将以每小时30-35公里的速度向西偏北方向移动，强度逐渐加强，24日夜间至25日凌晨移入南海东北部海面，向粤闽沿海靠近，25日晚上至26日早晨在广东珠海到福建漳浦一带沿海登陆（33-40米\u002F秒，12-13级，台风级）。 ![Image 8](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-07-25\u002F5609_17849870586109.webp) 未来天气趋势 受今年第12号台风“红霞”环流影响，25日晚～27日受热带系统影响，我县有一次明显降水过程，局部有大暴雨（过程累计雨量100～200毫米，局地250毫米以上，最大雨量出现在26日晚上至27日白天）。期间全县风力加大到3～5级，叠加雷雨大风影响，阵风可达7～9级。 ![Image 9](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-07-25\u002F5609_17849870633490.webp) 具体预报如下： 今天白天到晚上（星期五）：晴天多云，偏南风1～2级，气温26～36℃； 7月25日（星期六）：多云，局部有阵雨或雷阵雨，偏南风1～2级，气温26～36℃； 7月26日（星期日）：阴有大雨，局部暴雨，偏北风3～4级，气温26～32℃； 7月27日（星期一）：阴有大到暴雨，偏南风3～4级，气温25～30℃； **台风来袭这份农业生产紧急防范应对指南** **请收下** **↓↓↓** 一、水稻生产方面 当前全县早稻正处于收割期，中稻正处于分蘖期至拔节期，台风带来的强风暴雨易造成早稻植株倒伏、低洼田块积水、中稻植株僵苗滞长、叶片泛黄，严重时可导致受淹死苗。 **台风来临前：** 切实抓好中稻田管。各地要根据苗情和天气情况，提前疏通田间沟渠、确保内外沟渠排水畅通，完善排涝体系，防止因强降雨造成秧苗长时间淹水。台风过境前24—48小时，喷施保护性药剂加芸苔素内酯等免疫诱抗剂，重点保护叶片和茎基部，提高植株抗涝抗风能力；密切关注台风动态，提前做好应急排涝设备检修调试。 **台风过后：** 各地要指导农户紧盯气象预警、抢抓晴好间隙，精准把握农时及时抢收早稻，重点是选用高性能收割机抢收倒伏的早稻，最大限度降低因灾损失，努力实现颗粒归仓。第一时间组织抢排田间积水，及早开好丰产沟，雨前清沟排水，尽可能减轻涝渍危害；及时查苗，对僵苗滞长、叶片泛黄的田块，亩施尿素3—5公斤加适量锌肥，养根保叶，促进恢复生长；涝后高温高湿，重点监控纹枯病、稻纵卷叶螟、稻飞虱、二化螟等病虫害发生发展，加密巡查监测，灾后选用三氟苯嘧啶、氯虫苯甲酰胺、春雷霉素、噻呋酰胺等药剂混配进行防治。 二、蔬菜生产方面 台风易造成设施大棚损毁、田间积水内涝、蔬菜植株倒伏受损，高湿环境极易诱发病害。 **台风来临前：** 全面检修加固大棚骨架，收好遮阳网、防虫网，极端大风天气时可揭膜保护骨架，防止塑料薄膜随风飘散；疏通田间沟渠，低洼菜地加挖围沟、腰沟，必要时配备抽水设备。 **台风过后：** 及时清理田间沟系和残株杂物，排除积水后适时中耕松土，改善土壤通透性；做好受损棚体检修，及时更换破损棚膜、骨架，扶正倒伏植株；补施速效肥促进恢复；涝后高温高湿，蔬菜极易暴发霜霉病、疫病、软腐病等病害，天晴后及时选用合适杀菌剂，加氨基寡糖等免疫诱抗剂喷雾防治；受灾严重、绝收田块，可根据季节和市场需求，抢种小白菜、空心菜、生菜等速生耐涝蔬菜，尽快恢复生产。 三、渔业方面 台风带来的强风暴雨易造成池塘水位暴涨，漫堤跑鱼，可能断电导致增氧停止引发泛塘，造成大量鱼类缺氧。 **台风来临前：** 全面排查疏通排涝系统、检修电力设备和增氧机，提前降低池塘水位，防止漫堤跑鱼;检查电气线路,加固看护棚，做好应急供电和增氧准备，提前储备应急物资，防范缺氧泛塘风险。 **台风过后：** 全面检修受损设施设备,加固存在隐患的塘埂,防范养殖动物逃逸;强化增氧管理,做好水质科学调控,防止灾后水质恶化;及时捞除台风过后死亡的养殖个体，迅速集中进行无害化处理,防止污染水质和疫病传播;科学恢复投喂,逐步恢复正常养殖生产。 四、畜牧生产方面 台风易造成畜禽圈舍倒塌损毁、电气线路漏电引发事故，灾后高温高湿易诱发疫病。 **台风来临前：** 全面检查圈舍排水设施，疏通养殖场区排水沟渠，低洼地带备足沙袋与抽水泵，防止场区积水；智能化养殖场要调试发电机运行情况，备足燃油，确保断电时应急供电；排查电路漏电隐患，防止大风暴雨导致线路短路引发安全事故，确保人员和畜禽安全；加强畜禽粪污处理、储存设施管理，加固畜禽圈舍、备足饲料饲草等生产物资。 **台风过后：** 全面检修受损圈舍设施，及时修复加固；定期开展巡查，发现异常畜禽立即隔离治疗，防止疫病扩散；及时规范做好病死畜禽无害化处理，防止随意丢弃造成疫病传播和环境污染；做好灾后消毒灭源工作，确保不发生区域性重大动物疫情。 ![Image 10](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-07-25\u002F5609_17849870739082.webp)编辑：曾翀鹏 一审：魏小丽 二审：廖成佩 三审：夏才潜](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F564113.html)\n\n 本文来自网友投稿或网络内容，如有侵犯您的权益请联系我们删除，联系邮箱：wyl860211@qq.com 。 \n\n### [2027农业硕士考研内训班招生简章](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F564112.html \"2027农业硕士考研内训班招生简章\")\n\n### [邹广深入一线调研安全生产、防风防汛、农业防灾减灾和学习教育情况](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F564114.html \"邹广深入一线调研安全生产、防风防汛、农业防灾减灾和学习教育情况\")\n\n### 最新文章\n\n*   [2027农业硕士考研内训班招生简章](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F564112.html \"2027农业硕士考研内训班招生简章\")\n*   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测产","花香优872水稻-5444","2026-10-21T00:10:01.936262Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":8,"content":254,"source_name":255,"source_url":8,"published_at":256,"category":99,"cover_url":8,"hotness":13,"is_selected":14,"score":257,"score_detail":258,"sources":260,"tags":262,"primary_topic":267,"secondary_topics":268,"classification":274,"search_phrases":296,"slug":299,"view_count":142,"doi":8,"paper":8,"created_at":300},5432,"广东农科院水稻所主办 2026 年再生稻技术培训和现场观摩会在始兴举行","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F573077.shtm","广东省农业科学院水稻研究所主办的 2026 年再生稻技术培训和现场观摩会在韶关市始兴县举行，面向粤北「一季有余、两季不足」光温受限区域挖掘周年粮食产能。始兴县澄江镇善亨村 750 亩再生稻示范基地在省农科院水稻所、省农技推广中心和省水稻产业技术体系技术支撑下实行标准化种植，集中展示多个优质稻、杂交稻品种并开展品种对比试验。近年来始兴再生稻已连续 3 年实现头季、再生季两季亩产突破「吨粮」。","广东省农业技术推广中心种植业技术与种业推广部部长罗学梅在总结中充分肯定再生稻项目取得的技术突破和示范成效，并就下一阶段强化技术指导、完善示范推广体系、推动再生稻扩大应用等工作作出部署。他指出，要进一步加强科研、推广、种业和种植主体协同联动，加快适宜品种、先进技术和标准模式落地，为稳定粮食生产提供有力支撑。\n\n随后，与会人员前往始兴县澄江镇善亨村750亩再生稻示范基地开展现场观摩。该基地在广东省农业科学院水稻研究所、广东省农业技术推广中心以及广东省水稻产业技术体系的技术支撑下实行标准化种植，集中展示多个优质稻、杂交稻品种，并开展品种对比试验。技术人员走进田间，结合不同品种长势，围绕品种筛选、栽培管理、促芽肥施用、病虫害防控和机械化收割等关键环节进行讲解，并面对面解答参会人员提出的生产问题。\n\n参训人员沿着田埂仔细察看苗情和穗型，认真听取田间讲解，踊跃交流技术要点，现场学习氛围浓厚。此次活动将室内授课与田间教学有机结合，进一步提升了基层农技人员和种植主体对再生稻技术的认识，增强了推广应用信心。与会人员表示，将结合本地生产实际，把学习成果转化为田间管理措施，积极提升再生稻种植水平。\n\n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。","科学网 2026-10-17","2026-10-17T00:00:00Z",58,{"impact":217,"substance":154,"depth":70,"authority":20,"freshness":89,"relevant":22,"comment":259},"省级科研机构主办的再生稻技术培训与田间观摩，有示范基地和品种对比等实质内容，但属常规推广活动，影响力限于区域。",[261],{"name":255,"url":252},[263,27,264,265,266],"广东","再生稻","农技推广","粮食生产",{"code":40,"slug":41,"name":27,"path":42,"parent_code":43},[269],{"code":174,"slug":270,"name":271,"path":272,"parent_code":273},"extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","G",{"status":45,"locked":14,"version":46,"method":47,"evidence":275},{"candidates":276,"agricultural_evidence":51},[277,282,287,292],{"code":40,"score":278,"supported":51,"title":279,"abstract":280,"tags":281},27,[27,264],[27,233,264],[264,27],{"code":174,"score":19,"supported":51,"title":283,"abstract":285,"tags":286},[284],"技术培训",[265,284],[265],{"code":129,"score":86,"supported":51,"title":288,"abstract":289,"tags":291},[],[290],"粮食产能",[266],{"code":43,"score":22,"supported":14,"title":293,"abstract":294,"tags":295},[],[],[266],[297,298],"广东农科院 再生稻 始兴","再生稻 技术培训 观摩会","广东农科院再生稻始兴-5432","2026-10-21T00:10:01.007510Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":8,"source_name":307,"source_url":304,"published_at":308,"category":309,"cover_url":8,"hotness":13,"is_selected":14,"score":310,"score_detail":311,"sources":314,"tags":316,"primary_topic":321,"secondary_topics":322,"classification":329,"search_phrases":363,"slug":366,"view_count":142,"doi":367,"paper":368,"created_at":383},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":18,"substance":312,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":313},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。",[315],{"name":307,"url":304},[317,318,27,319,320],"智慧农业","农业人工智能","产量预测","遥感",{"code":40,"slug":41,"name":27,"path":42,"parent_code":43},[323],{"code":324,"slug":325,"name":326,"path":327,"parent_code":328},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":330},{"candidates":331,"agricultural_evidence":51},[332,336,341,347,353,358],{"code":40,"score":19,"supported":51,"title":333,"abstract":334,"tags":335},[41],[27,41],[27],{"code":324,"score":21,"supported":51,"title":337,"abstract":338,"tags":340},[],[320,339],"remote sensing",[319,320],{"code":129,"score":89,"supported":51,"title":342,"abstract":343,"tags":346},[],[344,345],"粮食安全","food security",[],{"code":348,"score":77,"supported":51,"title":349,"abstract":350,"tags":352},"J1",[],[351],"农业规划",[],{"code":354,"score":22,"supported":14,"title":355,"abstract":356,"tags":357},"I1",[],[],[317],{"code":359,"score":22,"supported":14,"title":360,"abstract":361,"tags":362},"I2",[],[],[318],[364,365],"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","10.3390\u002Feng7100572",{"doi":367,"openalex_id":369,"authors":370,"venue":307,"cited_by_count":142,"oa_url":304,"card":376,"direction":380,"ingested_from":382},"W7222942064",[371,373],{"name":372,"orcid":8},"Nayana Venkatesh",{"name":374,"orcid":375},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":377,"method":378,"finding":379,"direction":380,"opportunity":381},"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":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":8,"source_name":390,"source_url":387,"published_at":391,"category":309,"cover_url":8,"hotness":13,"is_selected":14,"score":392,"score_detail":393,"sources":396,"tags":398,"primary_topic":401,"secondary_topics":402,"classification":403,"search_phrases":444,"slug":447,"view_count":142,"doi":448,"paper":449,"created_at":463},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":18,"substance":312,"depth":18,"authority":154,"freshness":394,"relevant":22,"comment":395},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。",[397],{"name":390,"url":387},[317,318,27,399,400],"数字孪生","病虫害预警",{"code":40,"slug":41,"name":27,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":404},{"candidates":405,"agricultural_evidence":51},[406,414,419,425,432,439],{"code":354,"score":407,"supported":51,"title":408,"abstract":410,"tags":413},19,[409],"digital twin",[411,399,412,409],"精准农业","precision agriculture",[399,317],{"code":40,"score":18,"supported":51,"title":415,"abstract":417,"tags":418},[416],"paddy",[27,171,41,416],[27],{"code":183,"score":89,"supported":51,"title":420,"abstract":421,"tags":424},[],[422,423],"农药","pesticide",[],{"code":426,"score":89,"supported":51,"title":427,"abstract":428,"tags":431},"H3",[],[429,430],"灌溉","irrigation",[],{"code":433,"score":89,"supported":51,"title":434,"abstract":435,"tags":438},"I5",[],[436,437],"物联网","IoT",[],{"code":359,"score":86,"supported":51,"title":440,"abstract":441,"tags":443},[],[442],"decision support",[318],[445,446],"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":448,"openalex_id":450,"authors":451,"venue":390,"cited_by_count":142,"oa_url":8,"card":456,"direction":462,"ingested_from":382},"W7222969403",[452,454],{"name":453,"orcid":8},"Jyoti Rani",{"name":455,"orcid":8},"Dr.  Narendra S.  Jadhav",{"tldr":457,"method":458,"finding":459,"direction":460,"opportunity":461},"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"]