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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",false,77,{"impact":584,"substance":585,"depth":586,"authority":587,"freshness":385,"relevant":455,"comment":588},16,21,17,13,"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。",[590],{"name":578,"url":575},[592,593,594,595,596],"智慧农业","机器学习","遥感监测","土壤墒情","冬小麦",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[599,600],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":604},"classified","rules",{"candidates":605,"agricultural_evidence":608},[606,613,619,624,631,637],{"code":216,"score":607,"supported":608,"title":609,"abstract":611,"tags":612},26,true,[610,217],"winter 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农业物联网","openalex","2026-10-22T11:20:18.887301Z",{"id":668,"title":669,"url":670,"summary":671,"summary_zh":11,"content":11,"source_name":672,"source_url":11,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":673,"score_detail":674,"sources":676,"tags":678,"primary_topic":683,"secondary_topics":684,"classification":687,"search_phrases":707,"slug":710,"view_count":455,"doi":11,"paper":711,"created_at":719},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":361,"substance":321,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":675},"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。",[677],{"name":672,"url":670},[592,679,680,681,682],"农业遥感","作物表型","无人机多光谱","小麦渍害",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[685,686],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":602,"locked":581,"version":4,"method":603,"evidence":688},{"candidates":689,"agricultural_evidence":608},[690,694,698,703],{"code":216,"score":524,"supported":608,"title":691,"abstract":692,"tags":693},[218],[218],[],{"code":32,"score":524,"supported":608,"title":695,"abstract":696,"tags":697},[681],[],[680,679,681],{"code":184,"score":173,"supported":608,"title":699,"abstract":701,"tags":702},[700],"渍害",[],[],{"code":16,"score":455,"supported":581,"title":704,"abstract":705,"tags":706},[],[],[592],[708,709],"江苏省农科院 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秋粮收购","全国秋粮收获中晚稻收购-5502","2026-10-22T00:06:58.371818Z",{"id":769,"title":770,"url":771,"summary":772,"summary_zh":11,"content":11,"source_name":773,"source_url":11,"published_at":727,"category":728,"cover_url":11,"hotness":385,"is_selected":581,"score":329,"score_detail":774,"sources":776,"tags":778,"primary_topic":783,"secondary_topics":784,"classification":785,"search_phrases":801,"slug":804,"view_count":424,"doi":11,"paper":11,"created_at":805},5443,"四川省农科院作物所优质弱筋小麦新品种「川麦632」品种权转让公示","https:\u002F\u002Fwww.chinawestagr.com\u002Fhomepage\u002FshowAffiche.asp?id=3026","四川省农业科学院作物研究所（四川省种质资源中心）公示小麦品种「川麦632」品种权转让结果，受让方为四川均隆生物科技有限公司。「川麦632」是该所选育的优质弱筋小麦新品种，已于 2026 年 10 月通过品种报审，由蒲宗君等 9 位育种人完成。转让费采用入门费加提成费方式，入门费 30 万元，签订合同后支付 5 万元、审定公告出号后支付尾款 25 万元；提成费自 2029 年起按每销售一公斤种子提成 0.1 元，公示期至 2026 年 11 月 2 日。","四川省农业科学院 2026-10-19",{"impact":584,"substance":508,"depth":416,"authority":587,"freshness":632,"relevant":455,"comment":775},"省级科研机构小麦新品种品种权转让公示，含转让费与提成条款等实质信息，对种业成果转化有参考价值，但属细分行业进展。",[777],{"name":773,"url":771},[779,780,218,781,782],"种业振兴","成果转化","品种权转让","弱筋小麦",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":786},{"candidates":787,"agricultural_evidence":608},[788,792,797],{"code":216,"score":416,"supported":608,"title":789,"abstract":790,"tags":791},[218],[218],[218],{"code":80,"score":400,"supported":608,"title":793,"abstract":794,"tags":796},[],[795],"种质资源",[779],{"code":488,"score":455,"supported":581,"title":798,"abstract":799,"tags":800},[],[],[780],[802,803],"川麦632 品种权转让","四川省农科院 弱筋小麦","川麦632品种权转让-5443","2026-10-21T00:10:01.857857Z",{"id":807,"title":808,"url":809,"summary":810,"summary_zh":11,"content":811,"source_name":812,"source_url":11,"published_at":813,"category":728,"cover_url":11,"hotness":385,"is_selected":581,"score":814,"score_detail":815,"sources":818,"tags":820,"primary_topic":824,"secondary_topics":825,"classification":826,"search_phrases":841,"slug":844,"view_count":424,"doi":11,"paper":11,"created_at":845},5431,"山东农科院作物所阳信小麦试验示范基地 100 余亩展示田开始播种","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20261020A0B4TW00","10 月 20 日，山东省农科院作物研究所阳信小麦试验示范基地 2026 年小麦品种播种现场启动，100 多亩展示田开始秋播。该基地由作物所与阳信县农村专业技术协会共建，9 月 15 日签约揭牌，作物所聘请科技成果转化推广专员刘洪彬担任技术指导员。基地以展示济麦系列优质高产及特色营养小麦新品种为核心，同步开展新品种新技术推广与人才培训，并从良种繁育前端与特色营养小麦加工后端两端拉长产业链。","10月20日，在位于阳信县农村技术协会的小麦播种田里，大型播种机在100多亩小麦品种展示基地回穿梭，不时传出技术人员下达的技术指令声，“播种机进地播种时一定检查好播种定量指针”“底肥和种子同期播种必须保持好安全距离”等技术措施不时从现场技术指导老师刘洪彬的嘴里发出。\n\n原来这是山东省农科院作物所山东省农业科学院作物研究所阳信小麦试验示范基地2026年的小麦品种播种现场，阳信县农技协会会长王保友在忙着记录数据、搬运种子，确保严格程序，完成高质量播种工作。\n\n今年9月15日，山东省农业科学院作物研究所阳信县小麦试验示范基地在阳信举行签约揭牌仪式。在随后召开的座谈会上，省农科院作物研究所小麦专家围绕济麦系列小麦新品种的特征特性、栽培要点等进行详细介绍。\n\n近年来，阳信县持续深化产学研合作，积极引进推广小麦新品种、新技术，粮食综合生产能力稳步提升。此次小麦试验示范基地的共建，将进一步推动济麦系列优良品种在该县规模化应用，打造农业科技创新与示范推广的县域样板，为全县小麦等粮食作物单产提升注入科技动能。\n\n基地由山东省农业科学院作物研究所与阳信农村专业技术协会共同打造建设，聘请山东省农科院作物所科技成果转化推广专员刘洪彬为小麦高质量栽培技术指导员。坚持“技术成果带动、试验示范结合、责任权利清晰、合作互利共赢”的原则，围绕山东省农业科学院作物研究所育成的小麦新品种（系）进行试验示范，开展新品种、新技术推广及人才培训，打造科技引领型小麦产业提质增效的阳信样板。旨在建设小麦新品种（系）展示平台，展示济麦系列优质高产及特色营养小麦新品种（系）。建立优质原粮生产基地，促进优质特色营养小麦产业化开发。以基地为核心，带动周边发展高产优质特色营养小麦生产，为当地面粉加工企业提供优质原料.延长小麦产业链，打造小麦产业提质增效的样板。\n\n在新品种展示示范的基础上，从两端拉长小麦产业链，前端引入种业企业，进一步发展小麦良种繁育，打造良种繁育基地；后端开发特色营养小麦加工，形成良种繁育—示范推广—产品加工完整产业链，不断提升小麦产业附加值。","山东省农业科学院 2026-10-20","2026-10-20T00:00:00Z",56,{"impact":416,"substance":587,"depth":385,"authority":416,"freshness":816,"relevant":455,"comment":817},9,"省级科研机构与县域共建小麦新品种示范基地的常规进展报道，有具体合作机制与产业链思路，但属区域性动态，公共影响有限。",[819],{"name":812,"url":809},[779,218,821,822,823],"济麦系列","院地合作","试验示范",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":827},{"candidates":828,"agricultural_evidence":608},[829,833,837],{"code":216,"score":587,"supported":608,"title":830,"abstract":831,"tags":832},[218],[218],[218,821],{"code":488,"score":400,"supported":608,"title":834,"abstract":835,"tags":836},[],[780],[822],{"code":80,"score":455,"supported":581,"title":838,"abstract":839,"tags":840},[],[],[779],[842,843],"山东农科院 阳信 小麦试验示范基地","济麦系列 小麦 示范推广","山东农科院阳信小麦试验示范基地-5431","2026-10-21T00:10:00.947422Z",{"id":847,"title":848,"url":849,"summary":850,"summary_zh":11,"content":851,"source_name":852,"source_url":11,"published_at":727,"category":728,"cover_url":11,"hotness":385,"is_selected":581,"score":329,"score_detail":853,"sources":855,"tags":857,"primary_topic":860,"secondary_topics":861,"classification":862,"search_phrases":878,"slug":881,"view_count":424,"doi":11,"paper":11,"created_at":882},5429,"山东农科院作物所矮秆耐逆小麦新品种「济麦1806」生产经营权签约转让","http:\u002F\u002Fsdcrops.cn\u002Farticles\u002Fch11158\u002F202610\u002Fe4b9c977-8933-49d2-9adb-481fb88754ae.shtml","10 月 19 日，济麦 1806 生产经营权独占实施许可签约仪式在济南举办，山东省农科院作物所与山东东润种业有限公司签约。该品种是作物所育成的矮秆、耐逆、高产、广适小麦新品种，2026 年通过国家审定，两年区试全生育期 234.2 天，株高 68.3 厘米，倒伏达标点率 100%，亩穗数 35.8 万穗、穗粒数 35.7 粒、千粒重 40.7 克；生产试验平均亩产 346.6 公斤、增产 8.96%。品质稳定时间 5.3 至 6.4 分钟，适合制作馒头。","10月19日，济麦1806生产经营权独占实施许可签约仪式在济南举办。作物所理事长兼党委书记赵海军、山东东润种业有限公司总经理刘延福，小麦遗传育种团队、成果转化科以及东润种业相关人员参加活动。\n\n小麦遗传育种团队执行专家李豪圣介绍了济麦1806品种特征特性以及栽培技术要点。双方针对济麦1806的推广进行座谈交流，下一步将以该品种的转让为合作启点，加快新品种推广步伐，同时在项目申报、人才培养、成果培育上开展深入合作。\n\n济麦1806是作物所育成的矮秆、耐逆、高产、广适小麦新品种，2026年通过国家审定。两年区试全生育期234.2天，株高68.3厘米，倒伏达标点率100%；亩穗数35.8万穗，穗粒数35.7粒，千粒重40.7克，亩产435公斤，较对照增产4.6%；生产试验平均亩产346.6公斤，增产8.96%。品质较稳定，适合制作馒头。多年品质分析结果表明，容重792 g\u002FL-814 g\u002FL，蛋白质（干基）12.3%-13.4%，湿面筋（14%湿基）26.5% -29.6%，吸水率60.0%-62.0%，稳定时间5.3-6.4 min。（撰写：郭军 核稿：李峰）","山东省农业科学院 2026-10-19",{"impact":584,"substance":361,"depth":416,"authority":587,"freshness":173,"relevant":455,"comment":854},"省级科研机构小麦新品种独占许可转让，含审定数据与品种特性，具行业参考价值但属常规成果转化动态。",[856],{"name":852,"url":849},[779,780,858,859],"小麦育种","耐逆品种",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":863},{"candidates":864,"agricultural_evidence":608},[865,869,874],{"code":216,"score":416,"supported":608,"title":866,"abstract":867,"tags":868},[218],[218],[858],{"code":80,"score":400,"supported":608,"title":870,"abstract":871,"tags":873},[],[872],"种业",[779],{"code":488,"score":455,"supported":581,"title":875,"abstract":876,"tags":877},[],[],[780],[879,880],"济麦1806 生产经营权 转让","山东农科院 小麦新品种 济麦1806","济麦1806生产经营权转让-5429","2026-10-21T00:10:00.769274Z",{"id":884,"title":885,"url":886,"summary":887,"summary_zh":888,"content":11,"source_name":889,"source_url":886,"published_at":890,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":582,"score_detail":891,"sources":893,"tags":895,"primary_topic":898,"secondary_topics":899,"classification":901,"search_phrases":920,"slug":923,"view_count":424,"doi":924,"paper":925,"created_at":948},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",{"impact":584,"substance":321,"depth":361,"authority":587,"freshness":173,"relevant":455,"comment":892},"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[894],{"name":889,"url":886},[592,896,594,596,897],"无人机","生物量估算",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[900],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":902},{"candidates":903,"agricultural_evidence":608},[904,908,912,916],{"code":216,"score":607,"supported":608,"title":905,"abstract":906,"tags":907},[610,217],[218,610,217],[596],{"code":32,"score":584,"supported":608,"title":909,"abstract":910,"tags":911},[628],[627,628],[897,594],{"code":24,"score":632,"supported":608,"title":913,"abstract":914,"tags":915},[],[593,621],[],{"code":16,"score":455,"supported":581,"title":917,"abstract":918,"tags":919},[],[],[592],[921,922],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":924,"openalex_id":926,"authors":927,"venue":889,"cited_by_count":424,"oa_url":886,"card":943,"direction":662,"ingested_from":665},"W7222806066",[928,930,932,934,937,939,941],{"name":929,"orcid":11},"Guangtao Wang",{"name":931,"orcid":11},"Sumei Yao",{"name":933,"orcid":11},"Yanzheng Xie",{"name":935,"orcid":936},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":938,"orcid":11},"Xiangdong Chen",{"name":940,"orcid":11},"Yuhao Zhang",{"name":942,"orcid":11},"Lina Jiang",{"tldr":944,"method":945,"finding":946,"direction":662,"opportunity":947},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":950,"title":951,"url":952,"summary":953,"summary_zh":11,"content":11,"source_name":954,"source_url":11,"published_at":890,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":955,"score_detail":956,"sources":958,"tags":960,"primary_topic":965,"secondary_topics":966,"classification":967,"search_phrases":987,"slug":990,"view_count":424,"doi":11,"paper":991,"created_at":998},5365,"基于经济型多光谱成像与知识蒸馏模型压缩的小麦线条花叶病毒早期检测","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-10\u002F20261017000601916.htm","研究人员利用配备六个光谱滤光片的消费级数码单反(DSLR)相机在自然环境条件下获取了一个新的小麦线条花叶病毒(WhSMV)多光谱数据集。评估十二个深度学习模型识别 WhSMV 感染不同阶段的性能,并首次将模型压缩技术应用于多光谱图像用于 WhSMV 感染的早期检测。比较了深度学习算法与压缩模型的性能,揭示压缩模型达到与大型深度学习模型相当的性能。WhSMV 是小麦土传媒介传播病毒,巴西与南非均有报道。","Smart Agricultural Technology 2026-10-17",78,{"impact":361,"substance":321,"depth":586,"authority":587,"freshness":173,"relevant":455,"comment":957},"该研究构建了新的多光谱数据集，并首次将模型压缩用于小麦病毒早期检测，方法新颖、结论可靠，对智慧农业植保具有参考价值。",[959],{"name":954,"url":952},[592,961,962,963,964],"农业人工智能","多光谱成像","小麦病害","模型压缩",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":968},{"candidates":969,"agricultural_evidence":608},[970,974,979,983],{"code":216,"score":524,"supported":608,"title":971,"abstract":972,"tags":973},[218],[218],[],{"code":24,"score":400,"supported":608,"title":975,"abstract":976,"tags":978},[],[977],"深度学习",[961],{"code":16,"score":455,"supported":581,"title":980,"abstract":981,"tags":982},[],[],[592],{"code":32,"score":455,"supported":581,"title":984,"abstract":985,"tags":986},[],[],[962],[988,989],"小麦线条花叶病毒 多光谱 检测","知识蒸馏 模型压缩 小麦","小麦线条花叶病毒多光谱检测-5365",{"doi":11,"openalex_id":11,"authors":992,"venue":11,"cited_by_count":424,"oa_url":11,"card":993,"direction":662,"ingested_from":718},[],{"tldr":994,"method":995,"finding":996,"direction":662,"opportunity":997},"用经济型多光谱成像结合知识蒸馏压缩模型，实现小麦线条花叶病毒早期检测。","消费级单反加六波段滤光片采集多光谱数据，评估12个深度学习模型并做模型压缩。","压缩模型性能与大型深度学习模型相当，可用于WhSMV感染早期识别。","可探索低成本多光谱设备与轻量模型在田间病害早期预警中的部署与跨区域泛化。","2026-10-20T02:47:54.487590Z",{"id":1000,"title":1001,"url":1002,"summary":1003,"summary_zh":11,"content":11,"source_name":1004,"source_url":11,"published_at":1005,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1006,"score_detail":1007,"sources":1009,"tags":1011,"primary_topic":1015,"secondary_topics":1016,"classification":1017,"search_phrases":1036,"slug":1039,"view_count":424,"doi":11,"paper":1040,"created_at":1047},5362,"深度语义分割模型的时序泛化评估:无人机小麦倒伏监测 5 年跨年测试","https:\u002F\u002Fbioengineer.org\u002Fai-models-trained-on-one-year-of-wheat-fail-the-next-five-year-drone-study-shows\u002F","Zhang G., Wang X., Wang D., Sun D., Zhang Y., Zhang D. (2026) 利用 5 年田间试验评估深度语义分割模型在无人机(UAV)小麦倒伏监测中的时序泛化能力。研究发现:同年内高精度不能保证跨年可靠性,模型从训练年份迁移到其他年份时性能显著下降,基于注意力的模型通常比全卷积网络更稳健,可能因为层级窗口自注意力能整合更长范围上下文,而卷积强调局部模式;即便最优架构在大时序漂移下也出现严重退化。强调时序泛化测试应成为农业 AI 模型部署前的必备环节。DOI: 10.1016\u002Fj.atech.2026.102631。","Smart Agricultural Technology Vol.15 Article 102631 2026-10-16","2026-10-16T00:00:00Z",79,{"impact":361,"substance":321,"depth":361,"authority":587,"freshness":173,"relevant":455,"comment":1008},"基于5年跨年田间试验揭示农业AI模型时序泛化瓶颈，结论扎实且具部署指导价值，值得入选。",[1010],{"name":1004,"url":1002},[592,961,1012,1013,1014],"无人机遥感","小麦倒伏","模型泛化",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1018},{"candidates":1019,"agricultural_evidence":608},[1020,1024,1028,1032],{"code":216,"score":524,"supported":608,"title":1021,"abstract":1022,"tags":1023},[218],[218],[],{"code":16,"score":455,"supported":581,"title":1025,"abstract":1026,"tags":1027},[],[],[592],{"code":24,"score":455,"supported":581,"title":1029,"abstract":1030,"tags":1031},[],[],[961],{"code":32,"score":455,"supported":581,"title":1033,"abstract":1034,"tags":1035},[],[],[1012],[1037,1038],"无人机 小麦倒伏 监测","深度语义分割 时序泛化","无人机小麦倒伏监测-5362",{"doi":11,"openalex_id":11,"authors":1041,"venue":11,"cited_by_count":424,"oa_url":11,"card":1042,"direction":662,"ingested_from":718},[],{"tldr":1043,"method":1044,"finding":1045,"direction":662,"opportunity":1046},"用5年田间试验评估深度语义分割模型在无人机小麦倒伏监测中的跨年泛化能力。","5年无人机小麦倒伏影像，对比注意力模型与全卷积网络的跨年迁移表现。","同年内高精度不能保证跨年可靠，注意力模型比全卷积网络更稳健，但大时序漂移下仍严重退化。","可研究时序域自适应与跨年增量学习，建立农业AI模型部署前的时序泛化评测基准。","2026-10-20T02:47:54.289085Z",{"id":1049,"title":1050,"url":1051,"summary":1052,"summary_zh":11,"content":1053,"source_name":1054,"source_url":11,"published_at":727,"category":728,"cover_url":11,"hotness":385,"is_selected":608,"score":1055,"score_detail":1056,"sources":1058,"tags":1060,"primary_topic":1065,"secondary_topics":1066,"classification":1067,"search_phrases":1091,"slug":1094,"view_count":424,"doi":11,"paper":11,"created_at":1095},5328,"全国秋粮收获过七成 农业农村部部署应对有系统性监测以来最强厄尔尼诺","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202610\u002Ft20261019_8876973.htm","据农业农村部最新农情调度,全国秋粮收获已过七成:东北过六成、黄淮海和西南过八成、西北近八成、长江中下游过六成、华南过三成;冬小麦播种过一成、冬油菜过三成。农业农村部制定印发防范应对超强厄尔尼诺夺取粮食和农业丰收相关预案,明确今年秋季防秋汛防旺长、冬季防冻害和雨雪冰冻;明年春季抗倒春寒冷抗旱抗低温春涝,夏季防干热风防烂场雨防洪涝防台风防高温。国家气候中心监测显示此次厄尔尼诺将成为有系统性监测以来最强,可能从今年秋种持续传导至明年春耕和夏季生产。各地秋粮旺季收购已收购中晚稻 1800 万吨。","[![Image 1](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fny_logo.png)](https:\u002F\u002Fwww.agri.cn\u002F)\n\n[无障碍](javascript:void(0);)[农芯慧答](https:\u002F\u002Fllm.trscd.com.cn\u002Fllm-app\u002F#\u002Fintelligent-qa?code=345059188574435d82b124eb1013e8d1&appId=910)[中华人民共和国农业农村部](https:\u002F\u002Fwww.moa.gov.cn\u002F)\n\n*   [首页](https:\u002F\u002Fwww.agri.cn\u002F)\n*   [机构](https:\u002F\u002Fwww.agri.cn\u002Fjg)\n*   [资讯](https:\u002F\u002Fwww.agri.cn\u002Fzx)\n*   [数据](https:\u002F\u002Fwww.agri.cn\u002Fsj)\n*   [生产](https:\u002F\u002Fwww.agri.cn\u002Fsc)\n*   [信息化](https:\u002F\u002Fwww.agri.cn\u002Fxxh)\n*   [专题](https:\u002F\u002Fwww.agri.cn\u002Fzt)\n*   [视频](https:\u002F\u002Fwww.agri.cn\u002Fvideo)\n\n[首页](https:\u002F\u002Fwww.agri.cn\u002F \"首页\")>[资讯](https:\u002F\u002Fwww.agri.cn\u002Fzx\u002F \"资讯\")>[农业要闻](https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F \"农业要闻\")\n\n 全国秋粮收获超七成 秋粮旺季收购有序开展，已收购中晚稻1800万吨 \n\n时间：2026-10-19 作者：郁静娴 王东辉 来源：人民日报\n\n字号：大 中 小![Image 2](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fnxw_printIcon.png) 打印 \n\n分享:[](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202610\u002Ft20261019_8876973.htm&title=%E5%85%A8%E5%9B%BD%E7%A7%8B%E7%B2%AE%E6%94%B6%E8%8E%B7%E8%B6%85%E4%B8%83%E6%88%90%20%E7%A7%8B%E7%B2%AE%E6%97%BA%E5%AD%A3%E6%94%B6%E8%B4%AD%E6%9C%89%E5%BA%8F%E5%BC%80%E5%B1%95%EF%BC%8C%E5%B7%B2%E6%94%B6%E8%B4%AD%E4%B8%AD%E6%99%9A%E7%A8%BB1800%E4%B8%87%E5%90%A8 \"qq空间\")[](javascript:void(0); \"微信\")[](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202610\u002Ft20261019_8876973.htm&title=%E5%85%A8%E5%9B%BD%E7%A7%8B%E7%B2%AE%E6%94%B6%E8%8E%B7%E8%B6%85%E4%B8%83%E6%88%90%20%E7%A7%8B%E7%B2%AE%E6%97%BA%E5%AD%A3%E6%94%B6%E8%B4%AD%E6%9C%89%E5%BA%8F%E5%BC%80%E5%B1%95%EF%BC%8C%E5%B7%B2%E6%94%B6%E8%B4%AD%E4%B8%AD%E6%99%9A%E7%A8%BB1800%E4%B8%87%E5%90%A8 \"新浪微博\")\n\n 人民日报北京10月18日电 据农业农村部最新农情调度，全国秋粮收获过七成。其中，东北过六成，黄淮海、西南过八成，西北近八成，长江中下游过六成，华南过三成。同时，各地秋冬种有序推进，冬小麦播种过一成，冬油菜播种过三成。\n\n 记者从国家粮食和物资储备局获悉：各地秋粮旺季收购正有序展开。初步统计，各类经营主体已收购中晚稻1800万吨。\n\n 当前秋粮已开始收获上市，秋粮产量占全年粮食产量的3\u002F4左右，涉及品种多、数量规模大、覆盖区域广。相关部门将加强秋粮收购工作统筹组织，抓好市场化收购和政策性收储，落实落细各项政策举措，全力保障农民售粮渠道顺畅、市场平稳运行。\n\n![Image 3](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fnxw_back.png)\n返回顶部\n\n*   [外交部](https:\u002F\u002Fwww.fmprc.gov.cn\u002Fweb\u002F)\n*   [国防部](http:\u002F\u002Fwww.mod.gov.cn\u002F)\n*   [国家发展和改革委员会](https:\u002F\u002Fwww.ndrc.gov.cn\u002F)\n*   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2026-10-19",84,{"impact":607,"substance":508,"depth":345,"authority":614,"freshness":816,"relevant":455,"comment":1057},"全国秋粮收获进度与收购数据权威及时，叠加最强厄尔尼诺应对部署，具备全国产业级参考价值，值得进入每日精选。",[1059],{"name":1054,"url":1051},[1061,596,1062,1063,1064],"厄尔尼诺","秋冬种","秋粮收获","粮食收购",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1068},{"candidates":1069,"agricultural_evidence":608},[1070,1074,1078,1083,1087],{"code":216,"score":400,"supported":608,"title":1071,"abstract":1072,"tags":1073},[],[218],[596],{"code":232,"score":750,"supported":608,"title":1075,"abstract":1076,"tags":1077},[],[753],[],{"code":184,"score":750,"supported":608,"title":1079,"abstract":1080,"tags":1082},[],[1081],"洪涝",[],{"code":563,"score":69,"supported":581,"title":1084,"abstract":1085,"tags":1086},[],[],[1062,1063],{"code":263,"score":455,"supported":581,"title":1088,"abstract":1089,"tags":1090},[],[],[1064],[1092,1093],"农业农村部 厄尔尼诺 秋粮","中晚稻 收购 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紫麦全产业链促增收","http:\u002F\u002Fwww.hblys.com\u002Fhtml\u002F1\u002F1\u002F118\u002F5651.html","10月14日河北省农科院粮油所小麦中心资源室赴石家庄市栾城区开展黑小麦播种专项技术培训,武丽芬博士介绍冀资麦19号、冀资麦16号等优质黑小麦品种的营养优势和保健价值;栾城区天亮种植专业合作社开发四大类17个紫麦深加工产品,形成富硒紫麦\"粮头食尾\"全产业链发展模式,产品销售范围覆盖全国。","为有效应对近年秋播期间气候多变等不利天气对小麦播种生产带来的影响，进一步提升农户黑小麦规范种植技术水平，助力农业提质增效、农民增产增收，2026年10月14日，我所小麦中心资源室赴石家庄市栾城区开展了黑小麦播种专项技术培训。当地农民、种植大户等50余人参加了培训。\n\n培训现场，我所小麦中心资源室武丽芬博士详细介绍了冀资麦19号、冀资麦16号等优质黑小麦品种的营养优势与保健价值。相较于普通小麦相比，黑小麦属于优质特色功能性主粮，富含花青素、优质膳食纤维以及硒、镁、钙等多种矿质元素，蛋白质、氨基酸含量显著高于常规白小麦，具有营养高、品质优、功能性强的突出特性。同时，武丽芬博士围绕黑小麦标准化种植开展技术授课，从播种前整地、种子筛选、药剂拌种，到适期适量播量、苗期田间管护等关键环节，逐一明确技术标准，精准解答农民在生产中遇到的播种不均、种子架空、晚播弱苗等常见问题，切实补齐当地黑小麦种植技术的短板。\n\n随后，栾城区天亮种植专业合作社技术人员介绍了合作社的发展情况。该合作社是国家级示范社、全国农民合作社国家300强，坐落于栾城区东牛村的天然富硒带上，依托农业社会化服务体系，聚焦功能性富硒紫麦特色种植，现已形成富硒紫麦“粮头食尾”全产业链发展模式。目前，合作社已开发四大类17个紫麦深加工产品，市场反响良好，销售范围覆盖全国，品牌影响力持续提升。\n\n培训结束后，参会人员实地参观了天亮合作社的产品展厅，展厅集中展示了冀资麦19号、冀资麦16号和冀紫439等优质黑小麦籽粒，以及富硒紫麦全麦粉、全麦挂面、全麦馍丁、紫麦酒、紫麦醋、无糖月饼、沙琪玛等系列深加工产品。参会人员现场品鉴后普遍认为，紫麦产品口感好，既适配糖尿病等特殊群体食用，也是大众日常膳食的优质健康食材。","河北省农林科学院粮油作物研究所(2026-10-14)","2026-10-14T00:00:00Z",53,{"impact":385,"substance":345,"depth":524,"authority":416,"freshness":632,"relevant":455,"comment":1197},"省级科研机构开展的黑小麦播种技术培训，内容具体，但属地方性技术推广动态，公共影响有限。",[1199],{"name":1193,"url":1190},[1201,1202,1203,1204,1205],"全产业链","黑小麦","功能性小麦","富硒紫麦","种植技术培训",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1208],{"code":488,"slug":489,"name":490,"path":491,"parent_code":480},{"status":602,"locked":581,"version":4,"method":603,"evidence":1210},{"candidates":1211,"agricultural_evidence":608},[1212,1216],{"code":216,"score":524,"supported":608,"title":1213,"abstract":1214,"tags":1215},[218],[218],[],{"code":488,"score":524,"supported":608,"title":1217,"abstract":1219,"tags":1220},[1218],"技术培训",[1218],[],[1222,1223],"河北省农科院 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富硒紫麦","河北省农科院黑小麦栾城-5275","2026-10-19T00:07:41.048097Z",{"id":1227,"title":1228,"url":1229,"summary":1230,"summary_zh":11,"content":1231,"source_name":1232,"source_url":11,"published_at":1194,"category":728,"cover_url":11,"hotness":385,"is_selected":581,"score":1233,"score_detail":1234,"sources":1236,"tags":1238,"primary_topic":1243,"secondary_topics":1244,"classification":1246,"search_phrases":1270,"slug":1273,"view_count":424,"doi":11,"paper":11,"created_at":1274},5274,"河北全省小麦高质量播种观摩培训在曲阳举行:智慧农机赋能秋种","https:\u002F\u002Fwww.hebei.gov.cn\u002Fcolumns\u002F62725381-642a-4327-9196-e1e0923e22bd\u002F202610\u002F14\u002Ff8275eb2-feff-474d-80f5-f0e0e065cae6.html","10月13日2026年河北省\"大面积单产提升\"整建制推进县和\"作物丰产工程\"项目县小麦高质量播种关键技术观摩培训活动在曲阳县举行;现场集中展示北斗自动驾驶拖拉机、秸秆粉碎还田机、小麦精量复式播种机、小麦耙播一体机、小麦立体等深匀播机等智能农机装备;曲阳县冬小麦预计播种面积13万亩,着力打造高标准秋种样板。","10月13日，2026年河北省“大面积单产提升”整建制推进县和“作物丰产工程”项目县小麦高质量播种关键技术观摩培训活动在曲阳县举行。活动采取现场观摩与专题培训相结合的方式，展示先进播种装备，普及小麦丰产技术，为全省冬小麦规范化、标准化、高质量播种提供示范。\n\n本次观摩地点设在曲阳县西流德绿源家庭农场。现场集中展示北斗自动驾驶拖拉机、秸秆粉碎还田机、小麦精量复式播种机、小麦耙播一体机、小麦立体等深匀播机等一批智能化、现代化农机装备，全程演示深耕整地、精准施肥、精量播种、绿色植保一体化作业流程，充分展现智慧农机赋能粮食稳产增产的实践成效，为高标准秋种提供了可复制、可推广的实操样板。\n\n专题培训环节，石家庄市农林科学研究院、河北省农林科学院谷子研究所专家紧扣今年气候特点，重点解读小麦“四适一压”一播全苗、深翻耕作、病虫草害生态防控等核心关键技术，针对性讲解当前播种难点问题。\n\n曲阳县地处太行山东麓，耕地条件优良，是省市重要粮食生产基地。2026年，全县粮食播种面积47.79万亩，预计总产量16.1万吨，粮产形势稳中有升。其中，夏粮小麦播种12.96万亩、总产量5.64万吨，实现稳产增收；秋粮播种34.19万亩，种植结构持续优化，玉米、谷子、甘薯、高粱多元发展，粮食品质效益稳步提升。\n\n当前，曲阳县冬小麦播种工作高效有序推进，预计播种面积13万亩。曲阳县农业农村局局长谷天龙表示，将以此次活动为契机，持续抓实抓细秋种管理，深化农技下沉指导，高标准高质量完成秋种任务，为来年夏粮丰产丰收筑牢坚实根基。","河北省人民政府(2026-10-14)",65,{"impact":361,"substance":584,"depth":587,"authority":416,"freshness":632,"relevant":455,"comment":1235},"省级小麦高质量播种观摩培训，展示北斗自动驾驶与精量播种装备，有实操示范价值，但属常规农技推广动态，未达重大突破层级。",[1237],{"name":1232,"url":1229},[592,1239,1240,1241,1242],"北斗导航","智能农机","农技培训","小麦播种",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1245],{"code":112,"slug":113,"name":114,"path":115,"parent_code":72},{"status":602,"locked":581,"version":4,"method":603,"evidence":1247},{"candidates":1248,"agricultural_evidence":608},[1249,1254,1258,1262,1266],{"code":112,"score":587,"supported":608,"title":1250,"abstract":1252,"tags":1253},[1251],"农机",[1251],[1239,1240],{"code":216,"score":524,"supported":608,"title":1255,"abstract":1256,"tags":1257},[218],[218],[],{"code":263,"score":750,"supported":608,"title":1259,"abstract":1260,"tags":1261},[],[1157],[],{"code":488,"score":455,"supported":581,"title":1263,"abstract":1264,"tags":1265},[],[],[1241],{"code":16,"score":455,"supported":581,"title":1267,"abstract":1268,"tags":1269},[],[],[592],[1271,1272],"曲阳 小麦 智慧农机","河北 小麦 高质量播种","曲阳小麦智慧农机-5274","2026-10-19T00:07:40.989680Z",{"id":1276,"title":1277,"url":1278,"summary":1279,"summary_zh":11,"content":1280,"source_name":1281,"source_url":11,"published_at":1005,"category":728,"cover_url":11,"hotness":385,"is_selected":581,"score":1282,"score_detail":1283,"sources":1285,"tags":1287,"primary_topic":1290,"secondary_topics":1291,"classification":1292,"search_phrases":1303,"slug":1306,"view_count":424,"doi":11,"paper":11,"created_at":1307},5215,"山东省农科院发布小麦新品种\"济麦51\"独占许可公告","http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch14047\u002F202610\u002Ff2d31316-caee-4f09-b97a-ceba2d6abcba.shtml","2026年10月16日,山东省农业科学院发布小麦新品种\"济麦51\"独占许可公告,登记编号国审麦20261037,半冬性、全生育期234.2天,株高79.4厘米,平均亩产583.7-628.7千克,拟转化方式为独占许可,截止日期为2026年10月20日。","品种简介：半冬性，全生育期234.2天，与对照品种济麦22熟期相当。株高79.4厘米，抗倒性较好，平均亩穗数42.3万穗，穗粒数34.6粒，千粒重46.1克。高感赤霉病，高感条锈病，高感叶锈病，中感纹枯病，中感白粉病。抗寒性中等。籽粒容重815克\u002F升、838克\u002F升，蛋白质含量13.2%、13.6%，湿面筋含量29.4%、31.5%，稳定时间2.6分钟、3.2分钟，吸水率61.0%、63.3%。2022—2023年度参加黄淮冬麦区北片水地组区域试验，平均亩产583.7千克，比对照济麦22增产5.38%；2023—2024年度续试，平均亩产603.0千克，比对照济麦22增产4.31%；2024—2025年度生产试验，平均亩产628.7千克，比对照济麦22增产3.78%。","山东省农业科学院成果转化处(2026-10-16)",75,{"impact":361,"substance":321,"depth":345,"authority":587,"freshness":173,"relevant":455,"comment":1284},"省级科研机构发布小麦新品种独占许可，含完整区试产量与品质数据，对种业转化有实质参考价值。",[1286],{"name":1281,"url":1278},[779,218,1288,1289],"品种许可","济麦51",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1293},{"candidates":1294,"agricultural_evidence":608},[1295,1299],{"code":216,"score":416,"supported":608,"title":1296,"abstract":1297,"tags":1298},[218],[218],[218],{"code":80,"score":455,"supported":581,"title":1300,"abstract":1301,"tags":1302},[],[],[779],[1304,1305],"山东省农科院 济麦51 独占许可","济麦51 小麦品种","山东省农科院济麦51独占许可-5215","2026-10-18T00:05:23.255694Z",{"id":1309,"title":1310,"url":1311,"summary":1312,"summary_zh":1313,"content":11,"source_name":1314,"source_url":1311,"published_at":1005,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1102,"score_detail":1315,"sources":1317,"tags":1319,"primary_topic":1324,"secondary_topics":1325,"classification":1326,"search_phrases":1337,"slug":1340,"view_count":424,"doi":1341,"paper":1342,"created_at":1356},5144,"Agricultural production services and farmland reallocation among wheat-growing households in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1968687","Introduction Agricultural production services may influence farmland allocation, but their associations with farm expansion and land retention may differ. This study examines how service use relates to operated farmland area and land rented in and out among wheat-growing households in four Chinese provinces. Methods Using cross-sectional survey data, we measure service use by expenditure and, alternatively, by the number of production stages served, which is less mechanically related to cultivated area. We also use two-part models to distinguish rental participation from rented-in area conditional on participation. Fixed-effects estimates from a separate three-wave rural panel provide a complementary longitudinal check. Results Service use is positively associated with operated area and rented-in area and negatively associated with rented-out area, although the strength of the rented-out association varies by measure and model specification. Service-stage breadth is associated with participation in renting in land, but not significantly with conditional rented-in area. In the longitudinal panel, wheat service expenditure per mu is positively associated with operated area and wheat-growing area. Discussion These findings are consistent with agricultural production services being linked to farmland reallocation through both expansion and land-retention margins. Proposed labor-substitution and expected-return mechanisms remain correlational. Because the main survey is cross-sectional and limited to active wheat growers, the findings should not be generalized beyond comparable wheat-producing settings.","引言 农业生产服务可能影响农地配置，但其与农场扩张和土地保留的关联可能有所不同。本研究考察在中国四省小麦种植户中，服务使用与经营农地面积及土地租入租出的关系。方法 利用横截面调查数据，我们以支出衡量服务使用，并以所服务生产环节数量作为替代衡量方式，后者与耕地面积的相关性较少出于机械性关联。我们还使用两部分模型，将租入参与与参与条件下的租入面积区分开来。来自一个独立的三期农村面板数据的固定效应估计提供了补充性的纵向验证。结果 服务使用与经营面积和租入面积呈正相关，与租出面积呈负相关，尽管租出关联的强度因衡量方式和模型设定而异。服务环节广度与租入土地的参与相关，但与条件租入面积无显著相关。在纵向面板中，每亩小麦服务支出与经营面积和小麦种植面积呈正相关。讨论 这些发现与农业生产服务通过扩张和土地保留边际与农地再配置相关联的观点一致。所提出的劳动替代和预期回报机制仍属相关性关系。由于主要调查为横截面数据且仅限于活跃的小麦种植者，研究结果不应推广至可比小麦生产环境之外。","Frontiers in Sustainable Food Systems",{"impact":584,"substance":585,"depth":586,"authority":587,"freshness":816,"relevant":455,"comment":1316},"基于四省截面调查与三期面板数据，发现农业服务使用与麦农经营面积扩大、租入增加及租出减少相关，方法较严谨但属相关性证据，对农地流转与托管服务研究有参考价值。",[1318],{"name":1314,"url":1311},[1320,1321,1322,1323],"农业社会化服务","小麦种植","农地流转","土地规模经营",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1327},{"candidates":1328,"agricultural_evidence":608},[1329,1333],{"code":216,"score":345,"supported":608,"title":1330,"abstract":1331,"tags":1332},[217],[218,217],[],{"code":496,"score":69,"supported":581,"title":1334,"abstract":1335,"tags":1336},[],[],[1320,1322],[1338,1339],"农业生产服务 农地流转 小麦种植户","Frontiers Sustainable Food Systems 农地再配置","农业生产服务农地流转小麦种植户-5144","10.3389\u002Ffsufs.2026.1968687",{"doi":1341,"openalex_id":1343,"authors":1344,"venue":1314,"cited_by_count":424,"oa_url":1349,"card":1350,"direction":1354,"ingested_from":665},"W7222725123",[1345,1347],{"name":1346,"orcid":11},"Yihang Jiang",{"name":1348,"orcid":11},"Yue Sun","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1968687\u002Fpdf",{"tldr":1351,"method":1352,"finding":1353,"direction":1354,"opportunity":1355},"研究农业生产服务使用与小麦种植户耕地经营规模及土地租入租出的关联。","四省截面调查数据，以服务支出和服务环节数度量，用两部模型与面板固定效应检验。","服务使用与经营面积和租入面积正相关、与租出面积负相关，环节广度影响租入参与。","数字乡村与农业信息化","可结合服务组织数字化数据，用面板或准实验方法识别服务使用对土地流转的因果机制。","2026-10-17T23:30:07.100958Z",{"id":1358,"title":1359,"url":1360,"summary":1361,"summary_zh":11,"content":11,"source_name":1362,"source_url":11,"published_at":1363,"category":728,"cover_url":11,"hotness":385,"is_selected":581,"score":1364,"score_detail":1365,"sources":1367,"tags":1369,"primary_topic":1371,"secondary_topics":1372,"classification":1373,"search_phrases":1392,"slug":1395,"view_count":424,"doi":11,"paper":11,"created_at":1396},4859,"机器人变身小麦育种专家——山东农大小麦育种具身机器人原型机公开亮相","https:\u002F\u002Fnews.sciencenet.cn\u002Fsbhtmlnews\u002F2026\u002F10\u002F391582.shtm","近日在山东泰安举行的小麦智能育种研讨会上,小麦育种具身机器人原型机首次公开亮相。该机器人由山东农业大学教授王宏伟、张亮团队联合研发,采用实时动态差分定位与视觉融合导航技术,可在试验田小区间自主移动作业;搭载滑台高清相机,可对田间小麦进行连续影像采集,并利用视觉算法自动量化株高、穗部性状等表型参数。系统结合智能决策模型对田间单株进行高通量初筛,自主识别具有进一步考察价值的候选植株。研究团队与山东极智生物科技有限公司达成合作意向。","中国科学报 科学网 2026 年 10 月 13 日 第 1 版","2026-10-13T00:00:00Z",81,{"impact":321,"substance":508,"depth":584,"authority":587,"freshness":385,"relevant":455,"comment":1366},"国内小麦育种具身机器人原型机首次公开亮相，技术路线与产学研合作信息具体，具备行业示范价值，值得进入每日精选。",[1368],{"name":1362,"url":1360},[592,961,779,858,1370],"具身机器人",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1374},{"candidates":1375,"agricultural_evidence":608},[1376,1380,1384,1388],{"code":216,"score":416,"supported":608,"title":1377,"abstract":1378,"tags":1379},[218],[218],[858],{"code":80,"score":455,"supported":581,"title":1381,"abstract":1382,"tags":1383},[],[],[779],{"code":16,"score":455,"supported":581,"title":1385,"abstract":1386,"tags":1387},[],[],[592],{"code":24,"score":455,"supported":581,"title":1389,"abstract":1390,"tags":1391},[],[],[961],[1393,1394],"山东农业大学 小麦育种 具身机器人","王宏伟 小麦 表型采集","山东农业大学小麦育种具身机器人-4859","2026-10-14T00:04:08.166418Z",{"id":1398,"title":1399,"url":1400,"summary":1401,"summary_zh":1402,"content":11,"source_name":1403,"source_url":1400,"published_at":1404,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1364,"score_detail":1405,"sources":1407,"tags":1409,"primary_topic":1413,"secondary_topics":1414,"classification":1415,"search_phrases":1442,"slug":1445,"view_count":424,"doi":1446,"paper":1447,"created_at":1468},4820,"Integrated strategies for improving lodging resistance in wheat: physiological mechanisms, plant growth regulators and agronomic management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1925460","Wheat lodging remains a major constraint to sustainable wheat production because it reduces grain yield and quality, increases harvesting losses and lowers production efficiency. The widespread cultivation of high-yielding cultivars, intensive nitrogen fertilization, higher planting densities, expansion of irrigated agriculture, and increasingly frequent extreme weather events have collectively increased lodging susceptibility, making effective management an important priority. Lodging is a complex phenomenon governed by interactions among plant architecture, stem mechanical properties, root anchorage, crop management practices and environmental conditions. Consequently, improving lodging resistance requires an integrated approach that combines physiological understanding with practical agronomic interventions. This review synthesizes recent advances in the structural, physiological, biochemical and agronomic mechanisms underlying lodging resistance in wheat. Particular emphasis is placed on stem biomechanics, basal internode characteristics, cell wall composition, lignin and cellulose deposition, root system architecture, and genetic factors that collectively determine plant stability. The review further examines the influence of major agronomic practices, including nitrogen management, plant population, sowing time, irrigation scheduling and cultivar selection, on lodging risk under diverse production environments. In addition, the physiological responses, field performance, practical applications and limitations of commonly used plant growth regulators, including chlormequat chloride, trinexapac-ethyl, paclobutrazol, uniconazole and ethephon, are critically evaluated in relation to their roles in improving stem strength and reducing lodging. The available evidence indicates that no single strategy provides consistent lodging control across all production systems. Instead, durable improvement in lodging resistance depends on integrating genetic improvement, balanced crop nutrition, optimized agronomic management, and judicious use of plant growth regulators according to local environmental conditions. The review also highlights emerging opportunities offered by precision agriculture, high-throughput phenotyping, advanced spray technologies, and improved understanding of stem biomechanics and root anchorage. Together, these advances provide a framework for developing sustainable, climate-resilient lodging management strategies that enhance wheat productivity, crop stability and resource-use efficiency, while also highlighting key research priorities for future investigation.","小麦倒伏仍是可持续小麦生产的主要制约因素，因为它降低籽粒产量和品质，增加收获损失并降低生产效率。高产栽培品种的广泛种植、集约化氮肥施用、更高的种植密度、灌溉农业的扩张以及日益频繁的极端天气事件，共同增加了倒伏易感性，使有效管理成为重要优先事项。倒伏是一种复杂现象，受植株结构、茎秆力学特性、根系锚定、作物管理措施和环境条件之间相互作用的支配。因此，提高抗倒伏性需要将生理学认识与实际农艺干预相结合的综合方法。本综述综合了小麦抗倒伏性所依据的结构、生理、生化和农艺机制方面的最新进展。特别强调了茎秆生物力学、基部节间特征、细胞壁组成、木质素和纤维素沉积、根系构型以及共同决定植株稳定性的遗传因素。本综述进一步考察了主要农艺措施，包括氮素管理、植株群体、播期、灌溉制度和品种选择，在不同生产环境下对倒伏风险的影响。此外，对常用植物生长调节剂，包括矮壮素(chlormequat chloride)、抗倒酯(trinexapac-ethyl)、多效唑(paclobutrazol)、烯效唑(uniconazole)和乙烯利(ethephon)，在其提高茎秆强度和减少倒伏的作用方面，对其生理响应、田间表现、实际应用和局限性进行了批判性评价。现有证据表明，没有任何单一策略能够在所有生产系统中提供一致的倒伏控制。相反，抗倒伏性的持久改善取决于将遗传改良、平衡作物营养、优化农艺管理以及根据当地环境条件合理使用植物生长调节剂相结合。本综述还强调了精准农业、高通量表型分析、先进喷雾技术以及对茎秆生物力学和根系锚定认识的提高所提供的新兴机遇。总之，这些进展为开发可持续、气候适应型的倒伏管理策略提供了框架，从而提高小麦生产","Frontiers in Plant Science","2026-10-12T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":816,"relevant":455,"comment":1406},"系统综述小麦抗倒伏的生理机制、生长调节剂与农艺管理，信息增量与专业深度较高，对小麦稳产与智慧化管理有参考价值。",[1408],{"name":1403,"url":1400},[592,218,1410,1411,1412],"抗倒伏","植物生长调节剂","农艺管理",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1416},{"candidates":1417,"agricultural_evidence":608},[1418,1422,1428,1432,1437],{"code":216,"score":614,"supported":608,"title":1419,"abstract":1420,"tags":1421},[217],[218,217],[218],{"code":16,"score":731,"supported":608,"title":1423,"abstract":1424,"tags":1427},[],[1425,1426],"精准农业","precision agriculture",[592],{"code":152,"score":632,"supported":608,"title":1429,"abstract":1430,"tags":1431},[],[635,153],[],{"code":96,"score":750,"supported":608,"title":1433,"abstract":1434,"tags":1436},[],[1435],"fertilization",[],{"code":184,"score":750,"supported":608,"title":1438,"abstract":1439,"tags":1441},[],[1440],"气候适应",[],[1443,1444],"小麦 抗倒伏 栽培管理","小麦 植物生长调节剂 矮壮素","小麦抗倒伏栽培管理-4820","10.3389\u002Ffpls.2026.1925460",{"doi":1446,"openalex_id":1448,"authors":1449,"venue":1403,"cited_by_count":424,"oa_url":1461,"card":1462,"direction":662,"ingested_from":665},"W7221517218",[1450,1452,1454,1456,1459],{"name":1451,"orcid":11},"Kavita Devi",{"name":1453,"orcid":11},"Jasdev Singh Deol",{"name":1455,"orcid":11},"Amit Kaul",{"name":1457,"orcid":1458},"Arun Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-0106-2735",{"name":1460,"orcid":11},"Rahul Sharma","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1925460\u002Fpdf",{"tldr":1463,"method":1464,"finding":1465,"direction":1466,"opportunity":1467},"综述小麦抗倒伏的生理机制、植物生长调节剂与农艺管理，提出综合防控策略。","文献综述，整合茎秆生物力学、细胞壁、根系及农艺与调节剂证据。","单一措施难以稳定控倒，需结合遗传、营养、农艺与调节剂并因地制宜。","农业人工智能与决策模型","可结合高通量表型与精准农业构建抗倒伏智能决策模型，实现因地制宜动态调控。","2026-10-13T23:30:19.035054Z",{"id":1470,"title":1471,"url":1472,"summary":1473,"summary_zh":1474,"content":11,"source_name":1403,"source_url":1472,"published_at":1404,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1364,"score_detail":1475,"sources":1477,"tags":1479,"primary_topic":1483,"secondary_topics":1484,"classification":1486,"search_phrases":1503,"slug":1506,"view_count":455,"doi":1507,"paper":1508,"created_at":1535},4806,"A study on the identification of wheat powdery mildew using chlorophyll fluorescence imaging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1837843","Introduction Wheat powdery mildew poses a serious threat to global wheat production, and its accurate identification is key to smart agriculture. Methods This study combines chlorophyll fluorescence imaging with deep learning algorithms to investigate the temporal dynamics of key fluorescence parameters (such as maximum photochemical efficiency, QYmax) during the progression of powderymildew. A QYmax-based chlorophyll fluorescence image dataset for wheat powdery mildew was constructed. Building on this, the LCRNet model was proposed, which effectively processes fluorescence image data through a collaborative mechanism integrating the Large Selective Adaptive Fusion (LSAF) and Coordinated Attention (CA) modules. Results On an independent test set, the model achieved an accuracy of 94% and an F1 score of 94.1%, outperforming comparison models. Discussion This study proposes a method for classifying the severity of powdery mildew in wheat that reduces the computational cost of fluorescence data processing, providing important methodological support and technical insights for advancing the intelligent, precise, and green transformation of crop disease prevention and control systems.","引言 小麦白粉病对全球小麦生产构成严重威胁，其准确识别是实现智慧农业的关键。方法 本研究将叶绿素荧光成像与深度学习算法相结合，探究白粉病发展过程中关键荧光参数（如最大光化学效率QYmax）的时间动态变化，构建了基于QYmax的小麦白粉病叶绿素荧光图像数据集。在此基础上，提出了LCRNet模型，该模型通过融合大选择性自适应融合（LSAF）模块与协调注意力（CA）模块的协同机制，有效处理荧光图像数据。结果 在独立测试集上，该模型达到了94%的准确率和94.1%的F1分数，优于对比模型。讨论 本研究提出了一种小麦白粉病严重程度分类方法，降低了荧光数据处理的计算成本，为推动作物病害防控体系向智能化、精准化和绿色化转型提供了重要的方法学支撑和技术启示。",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":816,"relevant":455,"comment":1476},"将叶绿素荧光成像与深度学习结合用于小麦白粉病严重度识别，方法新颖、数据可靠，对智慧植保有实质参考价值。",[1478],{"name":1403,"url":1472},[592,961,1480,1481,1482],"病害识别","叶绿素荧光","小麦白粉病",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1485],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":1487},{"candidates":1488,"agricultural_evidence":608},[1489,1493,1498],{"code":216,"score":345,"supported":608,"title":1490,"abstract":1491,"tags":1492},[217],[218,217],[],{"code":24,"score":173,"supported":608,"title":1494,"abstract":1495,"tags":1497},[],[977,1496],"deep learning",[961,1480],{"code":16,"score":731,"supported":608,"title":1499,"abstract":1500,"tags":1502},[],[592,1501],"smart agriculture",[592],[1504,1505],"小麦白粉病 叶绿素荧光 深度学习","LCRNet 小麦病害识别","小麦白粉病叶绿素荧光深度学习-4806","10.3389\u002Ffpls.2026.1837843",{"doi":1507,"openalex_id":1509,"authors":1510,"venue":1403,"cited_by_count":424,"oa_url":1529,"card":1530,"direction":664,"ingested_from":665},"W7221549180",[1511,1513,1516,1519,1521,1523,1525,1527],{"name":1512,"orcid":11},"Yijia Chen",{"name":1514,"orcid":1515},"Chengyuan Hao","https:\u002F\u002Forcid.org\u002F0000-0002-6002-3963",{"name":1517,"orcid":1518},"Zhiyu Li","https:\u002F\u002Forcid.org\u002F0000-0003-3771-0199",{"name":1520,"orcid":11},"Xupeng An",{"name":1522,"orcid":11},"Jingxiang Huang",{"name":1524,"orcid":11},"Xinchang Chen",{"name":1526,"orcid":11},"Shidong Wang",{"name":1528,"orcid":11},"ZhiChao Chen","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1837843\u002Fpdf",{"tldr":1531,"method":1532,"finding":1533,"direction":1466,"opportunity":1534},"该研究利用叶绿素荧光成像与深度学习识别小麦白粉病并分级。","构建QYmax荧光图像数据集，提出融合LSAF与CA模块的LCRNet模型。","模型在独立测试集上准确率达94%、F1为94.1%，优于对比模型。","可探索多病害、多作物荧光图像通用模型，并融合田间时序数据实现早期预警。","2026-10-13T23:30:09.060156Z",{"id":1537,"title":1538,"url":1539,"summary":1540,"summary_zh":1541,"content":11,"source_name":1542,"source_url":1539,"published_at":1543,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1544,"score_detail":1545,"sources":1549,"tags":1551,"primary_topic":1554,"secondary_topics":1555,"classification":1557,"search_phrases":1588,"slug":1591,"view_count":750,"doi":1592,"paper":1593,"created_at":1608},4693,"Scalable, cloud-native decision support system for Variable Rate Application (VRA) based on multi-year NDRE time series in arable precision agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23282433","This paper presents an automated, cloud-native geospatial Decision Support System (DSS) that generates variable rate prescription maps directly from Sentinel-2 satellite observations. Designed as an end-to-end arable field VRA architecture, the system operates completely autonomously with minimal input, requiring only the field boundary polygon, the crop phenology class (winter or spring crop), and the multi-year observation window. The computation engine streams Cloud-Optimized GeoTIFF (COG) rasters via asynchronous cloud connectivity through the Microsoft Planetary Computer (MPC) SpatioTemporal Asset Catalog (STAC) Application Programming Interface (API), enforcing data quality through multi-stage Scene Classification Layer (SCL) filtering. Instead of the rapidly saturating Normalized Difference Vegetation Index (NDVI), the model relies on the Normalized Difference Red Edge (NDRE) index, which accurately captures canopy chlorophyll and green biomass dynamics. For each native 10×10 m grid cell, the system extracts the 95th percentile (P95) peak value within a crop-specific reproductive phenological window, eliminating single-day snapshot noise while validating winter or spring growth trajectories via automated phenological ratio calculations. The decision and prescription engine is built upon two core agronomic safety pillars: An exponential empirical Bayesian stability shrinkage that dampens unstable, high-variance zones toward the safe field average, preventing the over-application of inputs on drought-prone areas. A trigonometric Horn slope attenuation derived from digital elevation models that systematically reduces application rates on erosion-prone steep slopes. Cell-level rates are distributed within the target tolerance range using a rank-allocation algorithm coupled with a Hamilton quota (largest remainder method) correction, guaranteeing strict integer-based machine compatibility and zero-sum conservation of the total field input budget. The technology was validated on a 139.03 ha commercial production field across four reference seasons (2022, 2023, 2024, and 2026) for Variable Rate Seeding (VRS) in winter wheat (Triticum aestivum L., 230 kg\u002Fha baseline seed rate) in a rolling hilly landscape in Hungary, benchmarked against conventional uniform application and conservative multi-year VRS planning. The primary quantitative findings are summarized below: · Failure of conventional uniform application: Across the 14,250 cells, the flat 230 kg\u002Fha rate was optimal on only 1.1% of the field area. · Input reallocation: The model reallocated 1,633.7 kg of seed (and 1,167.1 kg compared to conservative VRS) from unproductive areas into high-capacity zones (Figure 1.). Across 98.9% of the field, conventional uniform management generated systematic under- or over-seeding, inducing yield losses or wasted operational expenditure. · Drought mitigation: In temporally unstable zones subject to extreme weather risk (covering 33.6% of the field, 46.69 ha), Bayesian stability shrinkage reduced seed allocation by 495 kg compared to uniform seeding. Establishing lower canopy densities in these vulnerable zones mitigates intra-crop water competition and prevents early-season forced ripening (Lollato et al., 2017). · Erosion control: On steep slopes exceeding 12% inclination (accounting for 9.75% of the field, 13.56 ha), the Horn topographic attenuation engine reduced seed input by 553 kg relative to the fixed 230 kg\u002Fha baseline. This intervention minimizes surface runoff damage and agrochemical leaching. · Economic return: Spatially optimizing plant density based on pedological and topographic constraints delivers substantial yield gains. Reallocating seed across the field—without any expenditure on supplemental fertilizer—produces a projected yield increase of +0.20 to +0.40 t\u002Fha (+28 to +56 tons of grain across the evaluated field) (Kindred et al., 2016). At a baseline wheat price of €192\u002Ft (70,000 HUF\u002Ft), this generates an estimated net value of €5,376 to €10,752 (1.96 to 3.92M HUF) on this single 139 ha field.","本文提出了一种自动化、云原生的地理空间决策支持系统（DSS），可直接从Sentinel-2卫星观测数据生成变量施肥处方图。该系统设计为端到端的耕地变量施肥（VRA）架构，完全自主运行且输入需求极低，仅需田块边界多边形、作物物候类别（冬作或春作）以及多年观测窗口。计算引擎通过Microsoft Planetary Computer（MPC）时空资产目录（STAC）应用程序接口（API）以异步云连接方式流式传输云优化GeoTIFF（COG）栅格数据，并通过多阶段场景分类层（SCL）滤波确保数据质量。该模型不采用易快速饱和的归一化差异植被指数（NDVI），而是依赖归一化差异红边指数（NDRE），后者能够准确捕捉冠层叶绿素和绿色生物量动态。对于每个原生10×10 m网格单元，系统提取作物特定生殖物候窗口内的第95百分位数（P95）峰值，消除单日快照噪声，同时通过自动化物候比率计算验证冬作或春作生长轨迹。决策与处方引擎建立在两个核心农艺安全支柱之上：指数经验贝叶斯稳定性收缩，将不稳定的高方差区域向安全的田块平均值收缩，防止在易旱区域过量施用投入品；基于数字高程模型推导的三角函数Horn坡度衰减，系统性地降低易侵蚀陡坡上的施用量。单元级施用量通过秩分配算法结合Hamilton配额（最大余数法）校正，在目标容差范围内分配，确保严格的基于整数的机器兼容性和田块总投入预算的零和守恒。该技术已在匈牙利起伏丘陵地貌的139.03 ha商业化生产田块上，针对冬小麦（Triticum aestivum L.，基准播种量230 kg\u002Fha）的变量播种（VRS），在四个参考季节（2022、2023、2024和2026年）进行了验证，并以常规均一施用和保守的多年VRS规划为基准进行比较。主要定量结果总结如下：· 常规均一施用的失败：在14,250个单元中，统一的230 kg\u002Fha施","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",83,{"impact":361,"substance":1546,"depth":1547,"authority":587,"freshness":385,"relevant":455,"comment":1548},23,19,"基于多年度NDRE时序的云原生变量施肥决策系统，方法新颖、验证数据扎实，对精准农业信息化有较高参考价值。",[1550],{"name":1542,"url":1539},[1552,592,1553,1425,627],"数字农业","变量施肥",{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[1556],{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},{"status":602,"locked":581,"version":4,"method":603,"evidence":1558},{"candidates":1559,"agricultural_evidence":608},[1560,1564,1569,1573,1579,1584],{"code":16,"score":524,"supported":608,"title":1561,"abstract":1562,"tags":1563},[1426],[],[1552,592,1425],{"code":24,"score":524,"supported":608,"title":1565,"abstract":1567,"tags":1568},[1566],"decision support",[1566],[],{"code":216,"score":816,"supported":608,"title":1570,"abstract":1571,"tags":1572},[],[218,610,217],[],{"code":96,"score":731,"supported":608,"title":1574,"abstract":1575,"tags":1578},[],[1576,1577],"施肥","fertilizer",[1553],{"code":184,"score":750,"supported":608,"title":1580,"abstract":1581,"tags":1583},[],[1582],"drought",[],{"code":32,"score":455,"supported":581,"title":1585,"abstract":1586,"tags":1587},[],[],[627],[1589,1590],"Sentinel-2 NDRE 变量施肥","云原生 决策支持系统 精准农业","Sentinel-2NDRE变量施肥-4693","10.5281\u002Fzenodo.23282433",{"doi":1592,"openalex_id":1594,"authors":1595,"venue":1542,"cited_by_count":424,"oa_url":1539,"card":1603,"direction":1354,"ingested_from":665},"W7221365319",[1596,1598,1600],{"name":1597,"orcid":11},"Zoltán Szenek",{"name":1599,"orcid":11},"Donát Szám",{"name":1601,"orcid":1602},"Gábor Milics","https:\u002F\u002Forcid.org\u002F0000-0002-4499-8322",{"tldr":1604,"method":1605,"finding":1606,"direction":662,"opportunity":1607},"构建云原生决策支持系统，用多年NDRE时序自动生成精准农业变量施肥\u002F播种处方图。","Sentinel-2 NDRE、COG\u002FSTAC云流式处理、贝叶斯收缩与坡度衰减","在匈牙利139公顷冬小麦田验证，系统优于传统均一施用与保守多年VRS规划。","可探索将NDRE时序DSS扩展至多作物、多区域及实时物联网数据融合的通用处方框架。","2026-10-11T23:30:36.790037Z",{"technologies":1610,"keywords":1631},[1611,1612,1613,1614,1615,1616,1617,1619,1620,1622,1623,1624,1625,1627,1629],{"name":961,"count":516},{"name":627,"count":1547},{"name":594,"count":416},{"name":679,"count":632},{"name":680,"count":400},{"name":593,"count":400},{"name":1618,"count":400},"生物育种",{"name":1425,"count":400},{"name":1621,"count":750},"分子育种",{"name":1012,"count":750},{"name":962,"count":69},{"name":681,"count":69},{"name":1626,"count":455},"基因编辑",{"name":1628,"count":455},"大模型",{"name":1630,"count":455},"高光谱",[1632,1633,1634,1635,1637,1638,1639,1640,1642,1644,1646,1648],{"name":592,"count":93},{"name":218,"count":299},{"name":779,"count":125},{"name":858,"count":1636},24,{"name":596,"count":584},{"name":780,"count":524},{"name":736,"count":524},{"name":1641,"count":408},"作物模型",{"name":1643,"count":400},"精准施肥",{"name":1645,"count":750},"产量预测",{"name":1647,"count":750},"保护性耕作",{"name":1241,"count":750}]