[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-404":3,"related-404":55},{"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,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":50,"slug":53,"view_count":21,"doi":8,"paper":8,"created_at":54},404,"四川省农科院作物所杨武云团队在制曲专用小麦品种研究方面取得重要突破——相关研究发表于J Agric Food Res IF=7.2","https:\u002F\u002Fwww.chinawestagr.com\u002Fhomepage\u002Fshowcontent.asp?id=66786","四川省农业科学院作物所杨武云研究员团队在制曲专用小麦品种研究方面取得重要进展。研究发现团队选育的大穗高产弱筋小麦品种川麦93具有制曲工艺低敏感性,即在较低能耗条件下可实现高品质大曲稳定生产,其高淀粉含量\u002F高粉质率特性有利于优质大曲的形成,弱筋结构有助于形成更优的微生物渗透性、酶扩散能力,为高碳水化合物活性酶及有益微生物生长营造适宜微环境。相关研究发表在Journal of Agriculture and Food Research (2026年,IF=7.2),团队成员万洪深研究员为第一作者。该研究从'单因素描述'上升到'多因子定量归因',首次实现原料品种、工艺参数与大曲多维品质因果权重的定量分配。",null,"λԺ                ʱ䣺2026\u002F8\u002F6540\n\n׾Ƶڴṩø΢Ｐζǰ塣ŨʹҪСи£50–60°C̬ͶɣŨͰ׾ƶصͷζҪãԭСƷԼӹӰĹƷʣĿǰ֪ԽΪɢ谭˴׼ƷijգʡũԺоԱŶרСƷоȡҪչ\n\nо֣ŶѡĴ߲СƷִ93յԣڽϵܺʵָƷʴȶߵۺ\u002F߷ʴγɣṹγɸŵ΢͸ԡøɢΪ̼ˮø΢Ӫ˸Ϊ˵΢רСƷ֡оũҵʳƷѧĹ֪ڿJournal of Agriculture and Food Research ( 2026꣬IF=7.2)ŶӳԱоԱΪһߡ\n\nоӴͳ\"\"\"Ӷ\"״ʵԭƷ֡ղάƷ֮ȨصĶ䣬ƷֵλӸϸı\"Ҫյ\"Ĵͳ֪״νС״ն˹ͨ΢ＰøнŽӣһСƷֵԽ鵼Ȩϵߵۺ\u002F߷ʵСƷʴǻ̺΢Ⱥγɣߵ\u002FӲȵСƷٽˮⲢ´Ʒʵ½йСѡߵۺ\u002F߷ʵ֤Խ۷ڡй졷־ϣ2026꣬Ӱ2.8ͨѶ\n\nӣhttps:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jafr.2026.103177","四川省农业科学院 2026-08-07","2026-08-07T02:00:00Z","报道",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},20,15,12,4,1,"省级农科院在制曲专用小麦品种研究上的突破，对农业科技有实质贡献，但时效性稍差。",[24],{"name":10,"url":6},[26,27,28,29,30],"四川省农科院","川麦93","杨武云","小麦制曲","白酒",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},11,true,[34],[34],[],[51,52],"四川省农科院 小麦制曲 杨武云 川麦93","四川省农科院 小麦制曲","四川省农科院小麦制曲杨武云川麦93-404","2026-08-11T23:57:01.737101Z",{"total":56,"page":21,"page_size":56,"items":57},6,[58,94,197,258,308,349],{"id":59,"title":60,"url":61,"summary":62,"summary_zh":8,"content":8,"source_name":63,"source_url":8,"published_at":64,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":65,"score_detail":66,"sources":72,"tags":74,"primary_topic":79,"secondary_topics":80,"classification":81,"search_phrases":88,"slug":91,"view_count":92,"doi":8,"paper":8,"created_at":93},310,"四川省农科院作物所杨武云团队揭示弱筋小麦「川麦93」为优异制曲专用品种——高淀粉\u002F高粉质率特性赋能浓香型大曲品质","https:\u002F\u002Fweibo.com\u002F3811347182\u002F5328963539047109","四川省农科院作物所杨武云研究员团队在制曲专用小麦品种研究方面取得重要进展。研究发现团队选育的大穗高产弱筋小麦品种川麦93具有制曲工艺低敏感性,可在较低能耗条件下实现高品质大曲的稳定生产,其高淀粉含量\u002F高粉质率特性有利于优质大曲的形成。相关研究发表在Journal of Agriculture and Food Research(2026年, IF=7.2)。研究首次实现原料品种、工艺参数与大曲多维品质之间因果权重的定量分配,从根本上改变了\"制曲质量主要靠工艺调控\"的传统认知。","四川省农业科学院作物所 2026-08-05","2026-08-05T00:00:00Z",81,{"impact":17,"substance":67,"depth":68,"authority":69,"freshness":70,"relevant":21,"comment":71},22,18,13,8,"省级科研机构在制曲专用小麦品种上的重要进展，具有行业影响，信息增量高，研究结论新颖，信源权威，时效性较好。",[73],{"name":63,"url":61},[75,26,27,76,77,28,78],"作物所","制曲小麦","浓香型大曲","JAFR",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":82},{"candidates":83,"agricultural_evidence":46},[84],{"code":32,"score":45,"supported":46,"title":85,"abstract":86,"tags":87},[34],[34],[],[89,90],"四川省农科院 浓香型大曲 制曲小麦 作物所","四川省农科院 浓香型大曲","四川省农科院浓香型大曲制曲小麦作物所-310",5,"2026-08-08T23:53:55.626153Z",{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":8,"source_name":100,"source_url":97,"published_at":101,"category":102,"cover_url":8,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":109,"tags":111,"primary_topic":117,"secondary_topics":118,"classification":131,"search_phrases":170,"slug":173,"view_count":174,"doi":175,"paper":176,"created_at":196},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":105,"substance":106,"depth":107,"authority":69,"freshness":13,"relevant":21,"comment":108},16,21,17,"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。",[110],{"name":100,"url":97},[112,113,114,115,116],"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},[119,125],{"code":120,"slug":121,"name":122,"path":123,"parent_code":124},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":126,"slug":127,"name":128,"path":129,"parent_code":130},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":132},{"candidates":133,"agricultural_evidence":46},[134,140,145,150,158,165],{"code":32,"score":135,"supported":46,"title":136,"abstract":138,"tags":139},26,[137,33],"winter wheat",[34,137,33],[116],{"code":120,"score":18,"supported":46,"title":141,"abstract":142,"tags":144},[121],[143,121],"土壤",[115],{"code":126,"score":18,"supported":46,"title":146,"abstract":148,"tags":149},[147],"machine learning",[113,147],[113],{"code":151,"score":13,"supported":46,"title":152,"abstract":153,"tags":157},"I3",[],[154,155,156],"遥感","remote sensing","multispectral",[114],{"code":159,"score":56,"supported":46,"title":160,"abstract":161,"tags":164},"H3",[],[162,163],"灌溉","irrigation",[],{"code":166,"score":20,"supported":46,"title":167,"abstract":168,"tags":169},"I1",[],[112],[112],[171,172],"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",0,"10.3390\u002Fagronomy16202071",{"doi":175,"openalex_id":177,"authors":178,"venue":100,"cited_by_count":174,"oa_url":97,"card":188,"direction":194,"ingested_from":195},"W7223156204",[179,182,184,186],{"name":180,"orcid":181},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":183,"orcid":8},"Yi Zhang",{"name":185,"orcid":8},"WEI Lei",{"name":187,"orcid":8},"Chengcai Zhang",{"tldr":189,"method":190,"finding":191,"direction":192,"opportunity":193},"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",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":8,"content":8,"source_name":202,"source_url":8,"published_at":101,"category":102,"cover_url":8,"hotness":13,"is_selected":14,"score":203,"score_detail":204,"sources":207,"tags":209,"primary_topic":214,"secondary_topics":215,"classification":225,"search_phrases":245,"slug":248,"view_count":21,"doi":8,"paper":249,"created_at":257},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":68,"substance":67,"depth":68,"authority":205,"freshness":13,"relevant":21,"comment":206},14,"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[208],{"name":202,"url":200},[112,210,211,212,213],"农业遥感","作物表型","无人机多光谱","小麦渍害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[216,220],{"code":151,"slug":217,"name":218,"path":219,"parent_code":130},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":221,"slug":222,"name":223,"path":224,"parent_code":124},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":39,"locked":14,"version":40,"method":41,"evidence":226},{"candidates":227,"agricultural_evidence":46},[228,232,236,241],{"code":32,"score":45,"supported":46,"title":229,"abstract":230,"tags":231},[34],[34],[],{"code":151,"score":45,"supported":46,"title":233,"abstract":234,"tags":235},[212],[],[211,210,212],{"code":221,"score":70,"supported":46,"title":237,"abstract":239,"tags":240},[238],"渍害",[],[],{"code":166,"score":21,"supported":14,"title":242,"abstract":243,"tags":244},[],[],[112],[246,247],"江苏省农科院 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