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“最后一公里”。\n\n潘集区农技推广中心主任杨中保表示：“下一步，我们农技部门要锚定农业高质量发展总目标，紧扣稳粮、提质、增效、绿色核心任务，持续深耕良种良法推广、科学施肥示范工作，推动农业生产提质增效、提档升级，为稳定粮食生产、壮大特色农业、助力乡村产业振兴提供有力支撑。”","淮南市潘集区人民政府 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保护视力色：\n\n2026年10月12日，我院的安徽省财政农业科技成果转化项目“粮油农文旅融合集成技术转化应用”，在黄山市休宁县商山镇荪田村实施的水稻新品种“银月丝苗”百亩示范片进行现场测产验收。该项目由农业机械装备与工程所牵头、联合水稻研究所共同实施。\n\n专家组听取了实施情况汇报，实地考察了项目示范片并机收测产。示范片面积331亩，种植了水稻新品种“银月丝苗”，配套应用了水稻机直播、机插同步侧深施肥、稻田杂草绿色防控、优质稻病虫害绿色防控等绿色高产优质栽培技术。示范片处于黄熟期，专家组随机选取代表性田块，实收折算干谷亩产达605.2公斤。示范片单产比去年黄山市统计的水稻平均亩产460.0公斤高出145.2公斤，增产幅度达31.6%，远超项目增产50公斤的预期目标。\n\n示范片田间生长整齐平衡，熟相好，未发现明显病虫草害，充分展现了该品种高产、优质、抗倒伏能力较强的综合优势，项目区生态环境优良，稻米产量高、品质优，同时展现了稻丰田园的大地景观，为游客初秋乡村旅游和美食体验提供了良好去处。","安徽省农业科学院 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银月丝苗 水稻","休宁 水稻 测产 605.2公斤","安徽省农科院银月丝苗水稻-5447","2026-10-21T00:10:02.221618Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":8,"content":232,"source_name":233,"source_url":8,"published_at":118,"category":12,"cover_url":8,"hotness":19,"is_selected":14,"score":234,"score_detail":235,"sources":238,"tags":240,"primary_topic":244,"secondary_topics":245,"classification":246,"search_phrases":262,"slug":265,"view_count":162,"doi":8,"paper":8,"created_at":266},5444,"四川省农科院生核所「花香优872」百亩连片实收亩产 635 公斤","https:\u002F\u002Fwww.chinawestagr.com\u002Fzwgk\u002Fshowcontent.asp?id=67505","四川省农业科学院生物核与农业工程研究所在成都郫都区举办郫都粮经复合综合试验站杂交水稻新品种百亩连片现场观摩与验收会，重点展示该所选育的优质一级香型杂交稻「花香优872」百亩连片示范方。副所长王平研究员就该品种特征特性、配套栽培技术要点及粮经复合茬口衔接模式作现场讲解。种植大户反馈该品种在迟播条件下百亩片现场实收平均亩产 635 公斤，机械化效率提升 2.1%，抗病性与机收适应性表现良好。","λԺ                ʱ䣺2026\u002F10\u002F16171\n\nΪӿӽˮƷƹӦãƶ֡أ1011գԺڳɶۯٰۯۺվӽˮƷְĶƬֳĦջᡣʡũԺйרҡҵҵֲ󻧲μӡ\n\nĿصչʾԺѡһӽ“872”ĶƬʾԺƽоԱ͸Ʒԡ༼Ҫ㼰ϲνģʽֳ⡣λ﹡ʵز쿴ƷֵҶ̬ṹͿ֣ΧɹӦúͻеֲЧʵȷ˽ֲ󻧽ֲ飬϶ƷڿԺͻӦԷ֣ڳٲ“872”ĶƬֳʵƽĶ635еЧ2.1%ҵҵֲ󻧾͸ƷֵĽһƹ˶ԽǢ̸\n\n˴λͨ“—ҵ—ֲ”ƽ̨ѡֱָۡѧһԺΧˮֲЧӿ߲ʡ࿹˻Ʒֵɸѡչʾ׼ɣֱչʾ“”ںϵЧDZӶƹӦá","四川省农业科学院 2026-10-16",64,{"impact":174,"substance":236,"depth":85,"authority":175,"freshness":124,"relevant":21,"comment":237},16,"省级科研机构水稻新品种百亩连片实收测产，数据具体、有示范推广价值，但属区域性进展，时效稍旧。",[239],{"name":233,"url":230},[29,241,242,243],"种业振兴","粮食单产","新品种示范",{"code":35,"slug":36,"name":29,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":247},{"candidates":248,"agricultural_evidence":47},[249,253,258],{"code":35,"score":86,"supported":47,"title":250,"abstract":251,"tags":252},[],[29,50],[29],{"code":208,"score":54,"supported":47,"title":254,"abstract":255,"tags":257},[],[256],"机械化",[],{"code":53,"score":21,"supported":14,"title":259,"abstract":260,"tags":261},[],[],[241],[263,264],"花香优872 水稻","四川省农科院 花香优872 测产","花香优872水稻-5444","2026-10-21T00:10:01.936262Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":8,"content":272,"source_name":273,"source_url":8,"published_at":274,"category":12,"cover_url":8,"hotness":19,"is_selected":14,"score":275,"score_detail":276,"sources":278,"tags":280,"primary_topic":284,"secondary_topics":285,"classification":291,"search_phrases":312,"slug":315,"view_count":162,"doi":8,"paper":8,"created_at":316},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":236,"substance":175,"depth":122,"authority":85,"freshness":72,"relevant":21,"comment":277},"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。",[279],{"name":273,"url":270},[281,29,91,282,283],"广东","农技推广","粮食生产",{"code":35,"slug":36,"name":29,"path":37,"parent_code":38},[286],{"code":60,"slug":287,"name":288,"path":289,"parent_code":290},"extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","G",{"status":41,"locked":14,"version":42,"method":43,"evidence":292},{"candidates":293,"agricultural_evidence":47},[294,298,303,308],{"code":35,"score":101,"supported":47,"title":295,"abstract":296,"tags":297},[29,91],[29,50,91],[91,29],{"code":60,"score":18,"supported":47,"title":299,"abstract":301,"tags":302},[300],"技术培训",[282,300],[282],{"code":150,"score":69,"supported":47,"title":304,"abstract":305,"tags":307},[],[306],"粮食产能",[283],{"code":38,"score":21,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[283],[313,314],"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":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":8,"source_name":323,"source_url":320,"published_at":324,"category":325,"cover_url":8,"hotness":19,"is_selected":14,"score":326,"score_detail":327,"sources":329,"tags":331,"primary_topic":336,"secondary_topics":337,"classification":344,"search_phrases":378,"slug":381,"view_count":162,"doi":382,"paper":383,"created_at":398},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":174,"substance":84,"depth":174,"authority":85,"freshness":123,"relevant":21,"comment":328},"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。",[330],{"name":323,"url":320},[332,333,29,334,335],"智慧农业","农业人工智能","产量预测","遥感",{"code":35,"slug":36,"name":29,"path":37,"parent_code":38},[338],{"code":339,"slug":340,"name":341,"path":342,"parent_code":343},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":41,"locked":14,"version":42,"method":43,"evidence":345},{"candidates":346,"agricultural_evidence":47},[347,351,356,362,368,373],{"code":35,"score":18,"supported":47,"title":348,"abstract":349,"tags":350},[36],[29,36],[29],{"code":339,"score":123,"supported":47,"title":352,"abstract":353,"tags":355},[],[335,354],"remote sensing",[334,335],{"code":150,"score":72,"supported":47,"title":357,"abstract":358,"tags":361},[],[359,360],"粮食安全","food security",[],{"code":363,"score":54,"supported":47,"title":364,"abstract":365,"tags":367},"J1",[],[366],"农业规划",[],{"code":369,"score":21,"supported":14,"title":370,"abstract":371,"tags":372},"I1",[],[],[332],{"code":374,"score":21,"supported":14,"title":375,"abstract":376,"tags":377},"I2",[],[],[333],[379,380],"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":382,"openalex_id":384,"authors":385,"venue":323,"cited_by_count":162,"oa_url":320,"card":391,"direction":395,"ingested_from":397},"W7222942064",[386,388],{"name":387,"orcid":8},"Nayana Venkatesh",{"name":389,"orcid":390},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":392,"method":393,"finding":394,"direction":395,"opportunity":396},"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"]