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17:09·[闽南网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAsnxVLly1u1Oucv8lcaX2LYqyCQMZU4farymb_u4v4ko\u002F?source=tuwen_detail)\n\n近日，福建省农业科学院与闽台金针花栽培科研示范基地签约科技特派团服务实验区台湾金针花产业链专项项目。项目立足平潭海岛环境特点，聚焦台湾金针花全产业链技术攻关，推动平潭金针花产业向规模化、标准化、绿色化方向稳步发展。\n\n当日上午，在闽台金针花栽培科研示范基地，省农科院农业专家、科技特派员走进田间地头，在基地负责人介绍下，详细了解金针花在平潭的种植培育情况。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Ff2bde998bf464a1aa6e9a150e3e6a913~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1793146279&x-signature=J2sXO%2FXrIk%2BgQ%2Ba59LjUo58TU3k%3D)\n农业专家和科技特派员了解金针花在平潭的种植情况。\n\n“今年是基地第一年采花，很荣幸获得省级揭榜挂帅平台支持。目前基地仍存在不少技术难点，希望科研团队和专家持续给予指导，把金针花产业打造成平潭特色产业。”闽台金针花栽培科研示范基地负责人林素芳说。\n\n据了解，实验区成功引种的台湾金针花总种植面积达168亩，共引进9个台湾新品种，主栽品种为“台东6号”金针花。该品种抗风性强，兼具食用、药用与观赏价值，采收期从4月下旬持续至10月，亩产鲜花约3000斤。\n\n据悉，专项项目由福建省农业科学院资环所牵头实施。项目立足平潭海岛环境特点，聚焦台湾金针花全产业链攻关：筛选抗逆性强、高产优质的适栽品种，建立适配海岛的高效种植与绿色防控技术体系；攻克鲜花采后冷链保鲜难题，升级干燥加工装备、优化加工工艺，开发高附加值金针花产品；创新产业发展模式，搭建“产业+直播”营销体系，打通从田间到市场的销售通道。项目覆盖品种选育、种植管护、采后加工、产品开发、品牌营销全链条，推动平潭金针花产业朝着规模化、标准化、绿色化方向稳步发展。\n\n“我们专家团队通过揭榜挂帅承接该项目，重点破解平潭盐碱地金针花产业提质增效难题，同时与台企精致农业相结合，打造我院推动闽台农业科技融合的典型案例。”福建省农业科学院科技对外合作办主任、研究员叶新福说。\n\n“本次合作是省级揭榜挂帅重要项目，也是省农科院帮扶平潭的首个揭榜挂帅项目。合作将促进科技创新与产业创新深度融合，助力平潭农业高质量发展。”实验区经济发展局党组成员董静怡说。（融媒体记者 林舒玲\u002F文 林君斌\u002F摄）\n\n来源：平潭网","闽南网 2026-10-19","2026-10-19T00:00:00Z",63,{"impact":190,"substance":191,"depth":17,"authority":123,"freshness":124,"relevant":21,"comment":192},16,18,"省级科研机构与平潭签约金针花全产业链专项，含品种、亩产、技术攻关等实质信息，属细分行业进展，具闽台农业科技融合参考价值。",[194],{"name":186,"url":183},[196,26,197,198,199],"特色产业","闽台合作","科技特派团","金针花",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":203},{"candidates":204,"agricultural_evidence":51},[205,210,216,222],{"code":39,"score":78,"supported":51,"title":206,"abstract":207,"tags":209},[],[208],"科技特派",[198],{"code":211,"score":170,"supported":51,"title":212,"abstract":213,"tags":215},"E3",[],[214],"冷链",[],{"code":217,"score":170,"supported":51,"title":218,"abstract":219,"tags":221},"F4",[],[220],"绿色防控",[],{"code":223,"score":21,"supported":14,"title":224,"abstract":225,"tags":226},"G4",[],[],[196],[228,229],"福建农科院 金针花 平潭","台东6号 金针花 亩产","福建农科院金针花平潭-5452","2026-10-21T00:10:02.637971Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":8,"content":237,"source_name":238,"source_url":8,"published_at":239,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":244,"tags":246,"primary_topic":251,"secondary_topics":252,"classification":253,"search_phrases":271,"slug":274,"view_count":111,"doi":8,"paper":8,"created_at":275},3404,"广东省农科院与五华县续签战略合作协议并共建特派员工作站——五华菩米全链条产值突破6亿元","https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1339569.html","9月19—20日广东省农科院科技合作部六级职员曾祥山、经信所党总支书记李伟锋带领水稻所、经信所专家赴梅州市五华县开展农业科技服务与产业调研。受院领导委托李伟锋代表院与五华县续签《五华县人民政府 广东省农业科学院关于实施'百县千镇万村高质量发展工程'和绿美广东生态建设科技支撑框架协议》。'五华菩米'入选全国名特优新农产品，2026年上半年全链条产值已突破6亿元；'广东省农村科技特派员工作站（五华县）'揭牌。","### 我院与五华县续签战略合作协议并共建特派员工作站\n\n时间：2026-09-24 16:42 来源：经信所【字体：大 中 小】\n\n为深入贯彻落实省委关于“百县千镇万村高质量发展工程”（下称“百千万工程”）工作部署，扎实推进院地科技合作落地见效，9月19日至20日，我院科技合作部六级职员曾祥山、经信所党总支书记李伟锋带领水稻所、经信所专家一行赴梅州市五华县开展农业科技服务与产业调研，并参加梅州市庆祝2026年中国农民丰收节暨五华菩米文化嘉年华。\n\n9月19日上午，专家组先后到河东镇黄湖村水稻种植基地、五华县大昌粮贸发展有限公司开展实地调研。在黄湖村基地，专家们察看制作五华菩米专用水稻长势，与基地负责人围绕田间管理、基础设施等进行深入交流；在大昌粮贸公司，专家组实地参观五华菩米生产加工车间，详细了解菩米加工、市场销售等情况。\n\n![Image 1: 图片3.png](http:\u002F\u002Fwww.gdaas.cn\u002Fimg\u002F0\u002F890\u002F890010\u002F1339569.png)\n\n随后，在五华县农业农村局召开的五华菩米产业发展研讨会上，县农业农村局系统介绍了五华菩米产业发展情况，与会专家围绕产业布局优化、品牌价值提升、产业链效益挖潜、市场渠道拓展等方面开展深入研讨，为五华菩米产业提质升级建言献策。\n\n![Image 2: 图片4.png](http:\u002F\u002Fwww.gdaas.cn\u002Fimg\u002F0\u002F890\u002F890009\u002F1339569.png)\n\n9月19日下午，专家组参加梅州市庆祝2026年中国农民丰收节暨五华菩米文化嘉年华。活动期间，受院领导委托，李伟锋代表院与五华县续签了《五华县人民政府 广东省农业科学院关于实施“百县千镇万村高质量发展工程”和绿美广东生态建设科技支撑框架协议》。三年来，经信所在院地科技协作框架内组织院内各专业所技术资源，深度对接五华本土企业，立足当地气候、土壤条件，系统开展产业发展战略研究、适种水稻品种选配、菩米试制、营养检测、功效验证等系列科技服务，为菩米产业健康快速发展提供强有力的科技支撑。五华菩米从三年前默默无闻的小众非遗农产品，2026年上半年全链条产值已突破6亿元，“五华菩米”入选全国名特优新农产品。\n\n9月20日，由我院经信所和五华县开晟水务集团共建的“广东省农村科技特派员工作站（五华县）”揭牌仪式暨建设座谈会在五华县举行，李伟锋等参加揭牌仪式，并围绕工作站建设运行、产业需求精准对接等事宜，与市县相关部门开展座谈交流。专家组还与五华县农业科学技术研究所就菩米产业发展、博士工作站建设等合作事项进行深入对接。\n\n![Image 3: 图片5.png](http:\u002F\u002Fwww.gdaas.cn\u002Fimg\u002F0\u002F890\u002F890007\u002F1339569.png)\n\n下一步，我院经信所将持续深化与五华县的合作，聚焦菩米等特色产业，在品种选育、标准体系建设、品牌打造、人才培养等方面提供全链条科技支撑，为五华县深入实施“百千万工程”、全面推进乡村振兴贡献科技力量。","广东省农业科学院 2026-09-20","2026-09-20T00:00:00Z",70,{"impact":191,"substance":242,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":243},20,"院地续签协议并共建特派员工作站，菩米全链条产值破6亿元，数据具体、产业支撑路径清晰，具备精选价值。",[245],{"name":238,"url":235},[26,247,248,249,250],"院地合作","百千万工程","科技特派员","菩米",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":254},{"candidates":255,"agricultural_evidence":51},[256,261,267],{"code":39,"score":257,"supported":51,"title":258,"abstract":259,"tags":260},5,[],[208],[249,247],{"code":262,"score":170,"supported":51,"title":263,"abstract":264,"tags":266},"A1",[],[265],"水稻",[],{"code":223,"score":21,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[248],[272,273],"广东省农科院 五华 菩米","五华菩米 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江苏省农科院农产品加工研究所牵头实施的亚夫科技服务项目\"东台荠菜全产业链关键技术示范推广\"启动暨专题培训会在东台市三仓镇召开，旨在完善荠菜产业技术体系、加快农业标准化技术落地与科技成果转化。项目负责人吴海虹汇报项目实施方案，院成果转化处处长顾军、东台市农业农村局蔬菜栽培站\u002F植保站专家、东台市国家现代农业示范产业园负责人参会。院农经所郑微微研究员、沿江所程玉静副研究员分别围绕江苏蔬菜加工产业发展、荠菜全产业链优质高效生产技术开展专题授课。项目组赴江苏水柠檬农业科技有限公司实地调研。","9月18日，亚夫科技服务项目“东台荠菜全产业链关键技术示范推广”启动暨专题培训会在东台市三仓镇成功召开。本项目由我院农产品加工研究所牵头实施，旨在完善荠菜产业技术体系，加快农业标准化技术落地与科技成果转化，推动地方特色蔬菜产业提质升级，赋能乡村振兴。院成果转化处处长顾军，东台市农业农村局蔬菜栽培站、植保站专家，东台市国家现代农业示范产业园负责人及基层农技人员等参会。\n\n会上，项目负责人吴海虹汇报了项目实施方案，介绍了项目立项背景、核心目标、实施内容、组织保障措施以及工作进展。与会领导、专家充分肯定项目实施方案，并结合东台荠菜种植、加工、销售全链条发展现状，对项目实施、成果示范落地、产业效益提升等方面提出建议。\n\n会后，项目组赴项目示范单位——江苏水柠檬农业科技有限公司实地调研，了解企业生产运营、荠菜加工工艺及新产品研发情况。\n\n本次活动同步开展专题技术培训，院农经所郑微微研究员、沿江所程玉静副研究员分别围绕我省蔬菜加工产业发展、荠菜全产业链优质高效生产技术开展专题授课，现场回应种植、加工环节的技术问题，提升了培训的针对性和实用性。\n\n下一步，项目组将传承弘扬“亚夫精神”，立足东台荠菜产业发展需求，以农业科技创新破解痛点难点，推动荠菜全产业链标准化、优质化、规模化发展，助力农业增效、农民增收和乡村全面振兴。\n\n[![Image 1: 参观照片.jpg](https:\u002F\u002Fjaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1654415635\u002Fpicture\u002F20268\u002FSd7491f6b3d3d4151bdc55736f2a11c22-800.jpg)](https:\u002F\u002Fjaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1654415635\u002Fpicture\u002F20268\u002Fd7491f6b3d3d4151bdc55736f2a11c22.jpg)[](https:\u002F\u002Fjaas.ac.cn\u002Fxww\u002Fkjfw\u002Fart\u002F2026\u002Fart_6ff14a408ffd4147bafa4be052c09718.html)","江苏省农业科学院","2026-09-18T00:00:00Z",56,{"impact":19,"substance":17,"depth":13,"authority":19,"freshness":123,"relevant":21,"comment":286},"省级农科院牵头的特色蔬菜全产业链示范项目启动，有实质内容但属区域性产业动态，适合主题聚合而非每日精选头条。",[288],{"name":282,"url":279},[290,26,291,292,293],"乡村振兴","科技成果转化","特色蔬菜","荠菜产业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":297},{"candidates":298,"agricultural_evidence":51},[299,304,310,316,321],{"code":39,"score":78,"supported":51,"title":300,"abstract":301,"tags":303},[],[302],"成果转化",[291],{"code":305,"score":170,"supported":51,"title":306,"abstract":307,"tags":309},"B1",[],[308],"蔬菜",[],{"code":311,"score":170,"supported":51,"title":312,"abstract":313,"tags":315},"E1",[],[314],"农产品加工",[],{"code":217,"score":170,"supported":51,"title":317,"abstract":318,"tags":320},[],[319],"植保",[],{"code":43,"score":21,"supported":14,"title":322,"abstract":323,"tags":324},[],[],[290],[326,327],"东台荠菜 全产业链","江苏省农科院 亚夫科技服务","东台荠菜全产业链-3110","2026-09-22T00:05:35.998049Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":8,"source_name":336,"source_url":333,"published_at":337,"category":338,"cover_url":8,"hotness":13,"is_selected":14,"score":339,"score_detail":340,"sources":344,"tags":346,"primary_topic":352,"secondary_topics":353,"classification":366,"search_phrases":404,"slug":407,"view_count":64,"doi":408,"paper":409,"created_at":429},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z","论文",77,{"impact":190,"substance":341,"depth":342,"authority":77,"freshness":13,"relevant":21,"comment":343},21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[345],{"name":336,"url":333},[347,348,349,350,351],"智慧农业","机器学习","遥感监测","土壤墒情","冬小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[354,360],{"code":355,"slug":356,"name":357,"path":358,"parent_code":359},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":361,"slug":362,"name":363,"path":364,"parent_code":365},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":367},{"candidates":368,"agricultural_evidence":51},[369,375,381,386,394,399],{"code":32,"score":370,"supported":51,"title":371,"abstract":373,"tags":374},26,[372,33],"winter wheat",[34,372,33],[351],{"code":355,"score":376,"supported":51,"title":377,"abstract":378,"tags":380},15,[356],[379,356],"土壤",[350],{"code":361,"score":376,"supported":51,"title":382,"abstract":384,"tags":385},[383],"machine learning",[348,383],[348],{"code":387,"score":13,"supported":51,"title":388,"abstract":389,"tags":393},"I3",[],[390,391,392],"遥感","remote sensing","multispectral",[349],{"code":163,"score":20,"supported":51,"title":395,"abstract":396,"tags":398},[],[166,397],"irrigation",[],{"code":400,"score":78,"supported":51,"title":401,"abstract":402,"tags":403},"I1",[],[347],[347],[405,406],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":408,"openalex_id":410,"authors":411,"venue":336,"cited_by_count":64,"oa_url":333,"card":421,"direction":427,"ingested_from":428},"W7223156204",[412,415,417,419],{"name":413,"orcid":414},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":416,"orcid":8},"Yi Zhang",{"name":418,"orcid":8},"WEI Lei",{"name":420,"orcid":8},"Chengcai Zhang",{"tldr":422,"method":423,"finding":424,"direction":425,"opportunity":426},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","农业遥感与作物表型","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:18.887301Z"]