[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5328":3,"related-5328":81},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":75,"slug":78,"view_count":79,"doi":8,"paper":8,"created_at":80},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。",null,"[![Image 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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*   [教育部](http:\u002F\u002Fwww.moe.gov.cn\u002F)\n*   [科学技术部](https:\u002F\u002Fwww.most.gov.cn\u002F)\n*   [工业和信息化部](https:\u002F\u002Fwww.miit.gov.cn\u002F)\n*   [国家民族事务委员会](https:\u002F\u002Fwww.neac.gov.cn\u002F)\n*   [公安部](https:\u002F\u002Fwww.mps.gov.cn\u002F)\n*   [国家安全部](https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202610\u002Ft20261019_8876973.htm)\n*   [民政部](https:\u002F\u002Fwww.mca.gov.cn\u002F)\n*   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2026-10-19","2026-10-19T00:00:00Z","报道",10,true,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},26,20,14,15,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"厄尔尼诺","冬小麦","秋冬种","秋粮收获","粮食收购",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":40,"locked":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,51,58,64,70],{"code":33,"score":47,"supported":14,"title":48,"abstract":49,"tags":50},4,[],[35],[28],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":57},"A4",3,[],[56],"油菜",[],{"code":59,"score":53,"supported":14,"title":60,"abstract":61,"tags":63},"H7",[],[62],"洪涝",[],{"code":65,"score":66,"supported":41,"title":67,"abstract":68,"tags":69},"J4",2,[],[],[29,30],{"code":71,"score":22,"supported":41,"title":72,"abstract":73,"tags":74},"A8",[],[],[31],[76,77],"农业农村部 厄尔尼诺 秋粮","中晚稻 收购 1800万吨","农业农村部厄尔尼诺秋粮-5328",0,"2026-10-20T02:47:36.345729Z",{"total":82,"page":22,"page_size":82,"items":83},6,[84,151,251,293,364,432],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":8,"content":89,"source_name":90,"source_url":8,"published_at":91,"category":92,"cover_url":8,"hotness":13,"is_selected":14,"score":93,"score_detail":94,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":105,"classification":111,"search_phrases":146,"slug":149,"view_count":66,"doi":8,"paper":8,"created_at":150},3682,"农业农村部部署全国三秋农业生产 抓秋管秋收稳冬小麦","https:\u002F\u002Fzzys.moa.gov.cn\u002Fgzdt\u002F202609\u002Ft20260927_6488144.htm","9月21日，农业农村部召开全国三秋农业生产工作视频会议，部署秋管秋收、秋冬种、防灾减灾和菜篮子稳产保供。强调分区分类抓细后期田管、组织农机供需对接、压紧压实粮食安全党政同责，针对超强厄尔尼诺气候细化应急预案，加快高标准农田建设与烘干储藏能力补短板。","9月21日，农业农村部召开全国“三秋”农业生产工作视频会议。会议强调，要深入学习贯彻习近平总书记重要指示精神，落实党中央、国务院决策部署，全力以赴抓好“三秋”农业生产，奋力夺取全年粮食和农业丰收，不误农时推进秋冬种，打牢明年粮油丰收基础。农业农村部党组书记、部长张柱出席会议并讲话。\n\n会议指出，今年是“十五五”开局之年，夺取全年粮食丰收意义重大。各级农业农村部门要从全局大局高度，深刻认识抓好今年“三秋”农业生产的重要性紧迫性，自觉增强责任感使命感，扎实有力抓好秋管、秋收、秋冬种，及早谋划部署，周密组织实施，确保秋粮颗粒归仓，冬小麦、冬油菜种足种好。\n\n会议强调，要紧盯不放抓好秋管秋收，分区分类抓细后期田管，提早组织农机供需对接，抢抓晴好天气推进秋粮收获。要千方百计稳定冬小麦生产，压紧压实粮食安全党政同责，落实播种面积，提高播种质量，控旺与促壮相结合培育冬前壮苗，夯实明年夏粮生产基础。农机农艺融合推动油菜大面积单产提升，多措并举提升油料产能。要密切关注超强厄尔尼诺气候影响，细化气象灾害和病虫草害应急预案，扎实推进高标准农田建设，持续开展农田沟渠修复整治和平原涝区治理，加快补齐烘干储藏等应急能力短板，提升农业防灾减灾救灾能力。要推动做好化肥等农资稳价保供，大力推广水肥一体化等节水节肥技术，切实保障农业生产用肥需要。\n\n会议要求，要统筹做好秋冬农业生产各项工作，着力保障“菜篮子”产品均衡供应，大力加强农产品质量安全监管，持续推进生猪产能综合调控，加快推进草食畜牧业高质量发展，强化动物疫病防控，抓好农产品产销衔接，切实保障粮食等重要农产品稳定安全供给。要抓好秋冬农业安全生产，聚焦海洋渔船、农机、有限空间作业等重点领域，全面排查整治风险隐患，紧盯不放落细防范救援措施，坚决遏制重特大事故发生。\n\n农业农村部党组成员、副部长潘文博主持会议。国家气象中心正研级高级工程师郭安红介绍秋冬季气象条件，黑龙江、江苏、山东、河南、湖南、四川、陕西农业农村部门负责同志发言。","农业农村部种植业管理司","2026-09-21T12:00:00Z","政策",85,{"impact":95,"substance":18,"depth":96,"authority":20,"freshness":82,"relevant":22,"comment":97},28,16,"农业农村部全国性三秋生产部署会议，政策层级高、条款具体，虽发布已逾一周仍具指导价值，值得进入每日精选。",[99],{"name":90,"url":87},[101,102,103,28,29],"高标准农田","农业防灾减灾","三秋生产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[106],{"code":65,"slug":107,"name":108,"path":109,"parent_code":110},"seasonal-agriculture","农时与节庆活动","\u002Ftopic\u002Fpolicy\u002Fseasonal-agriculture","J",{"status":40,"locked":41,"version":42,"method":43,"evidence":112},{"candidates":113,"agricultural_evidence":14},[114,119,123,130,135,140],{"code":65,"score":96,"supported":14,"title":115,"abstract":117,"tags":118},[116],"秋收",[116,29],[103,29],{"code":33,"score":21,"supported":14,"title":120,"abstract":121,"tags":122},[35],[],[28],{"code":124,"score":125,"supported":14,"title":126,"abstract":127,"tags":129},"H1",7,[],[101,128],"农田建设",[101],{"code":59,"score":47,"supported":14,"title":131,"abstract":132,"tags":134},[],[133],"防灾减灾",[102],{"code":71,"score":53,"supported":14,"title":136,"abstract":137,"tags":139},[],[138],"粮食安全",[],{"code":141,"score":53,"supported":14,"title":142,"abstract":143,"tags":145},"F5",[],[144],"农机",[],[147,148],"农业农村部 三秋农业生产 视频会议","冬小麦 秋冬种 播种面积","农业农村部三秋农业生产视频会议-3682","2026-09-29T00:08:17.794968Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":8,"source_name":157,"source_url":154,"published_at":158,"category":159,"cover_url":8,"hotness":13,"is_selected":41,"score":160,"score_detail":161,"sources":166,"tags":168,"primary_topic":173,"secondary_topics":174,"classification":187,"search_phrases":225,"slug":228,"view_count":79,"doi":229,"paper":230,"created_at":250},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":96,"substance":162,"depth":163,"authority":164,"freshness":13,"relevant":22,"comment":165},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。",[167],{"name":157,"url":154},[169,170,171,172,28],"智慧农业","机器学习","遥感监测","土壤墒情",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[175,181],{"code":176,"slug":177,"name":178,"path":179,"parent_code":180},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":182,"slug":183,"name":184,"path":185,"parent_code":186},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":41,"version":42,"method":43,"evidence":188},{"candidates":189,"agricultural_evidence":14},[190,195,200,205,213,220],{"code":33,"score":17,"supported":14,"title":191,"abstract":193,"tags":194},[192,34],"winter wheat",[35,192,34],[28],{"code":176,"score":20,"supported":14,"title":196,"abstract":197,"tags":199},[177],[198,177],"土壤",[172],{"code":182,"score":20,"supported":14,"title":201,"abstract":203,"tags":204},[202],"machine learning",[170,202],[170],{"code":206,"score":13,"supported":14,"title":207,"abstract":208,"tags":212},"I3",[],[209,210,211],"遥感","remote sensing","multispectral",[171],{"code":214,"score":82,"supported":14,"title":215,"abstract":216,"tags":219},"H3",[],[217,218],"灌溉","irrigation",[],{"code":221,"score":47,"supported":14,"title":222,"abstract":223,"tags":224},"I1",[],[169],[169],[226,227],"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":229,"openalex_id":231,"authors":232,"venue":157,"cited_by_count":79,"oa_url":154,"card":242,"direction":248,"ingested_from":249},"W7223156204",[233,236,238,240],{"name":234,"orcid":235},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":237,"orcid":8},"Yi Zhang",{"name":239,"orcid":8},"WEI Lei",{"name":241,"orcid":8},"Chengcai Zhang",{"tldr":243,"method":244,"finding":245,"direction":246,"opportunity":247},"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":252,"title":253,"url":254,"summary":255,"summary_zh":8,"content":256,"source_name":257,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":41,"score":93,"score_detail":258,"sources":261,"tags":263,"primary_topic":267,"secondary_topics":268,"classification":269,"search_phrases":288,"slug":291,"view_count":79,"doi":8,"paper":8,"created_at":292},5502,"全国秋粮收获超七成 中晚稻已收购1800万吨","https:\u002F\u002Fwww.peopleweekly.cn\u002Fhtml\u002F2026\u002Fsannong_1019\u002F279469.html","农业农村部最新农情调度显示，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。冬小麦播种过一成，冬油菜播种过三成。国家粮食和物资储备局数据显示，各类经营主体已收购中晚稻1800万吨；秋粮产量占全年粮食产量约四分之三。","本报北京10月18日电  （记者郁静娴）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本报北京10月18日电  （记者王东辉）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。","人民日报 2026-10-19",{"impact":17,"substance":259,"depth":20,"authority":20,"freshness":125,"relevant":22,"comment":260},22,"全国性秋粮收获与收购权威数据，信息增量足、信源权威，时效稍滞后但值得进入每日精选。",[262],{"name":257,"url":254},[138,28,264,265,266],"秋粮收购","农情调度","中晚稻",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":270},{"candidates":271,"agricultural_evidence":14},[272,276,280,284],{"code":33,"score":47,"supported":14,"title":273,"abstract":274,"tags":275},[],[35],[28],{"code":52,"score":53,"supported":14,"title":277,"abstract":278,"tags":279},[],[56],[],{"code":71,"score":66,"supported":41,"title":281,"abstract":282,"tags":283},[],[],[264,138],{"code":206,"score":22,"supported":41,"title":285,"abstract":286,"tags":287},[],[],[265],[289,290],"全国秋粮收获 中晚稻收购","国家粮食和物资储备局 秋粮收购","全国秋粮收获中晚稻收购-5502","2026-10-22T00:06:58.371818Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":8,"source_name":299,"source_url":296,"published_at":300,"category":159,"cover_url":8,"hotness":13,"is_selected":41,"score":160,"score_detail":301,"sources":305,"tags":307,"primary_topic":310,"secondary_topics":311,"classification":316,"search_phrases":335,"slug":338,"view_count":79,"doi":339,"paper":340,"created_at":363},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":96,"substance":259,"depth":302,"authority":164,"freshness":303,"relevant":22,"comment":304},18,8,"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。",[306],{"name":299,"url":296},[169,308,171,28,309],"无人机","生物量估算",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[312],{"code":206,"slug":313,"name":314,"path":315,"parent_code":186},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":41,"version":42,"method":43,"evidence":317},{"candidates":318,"agricultural_evidence":14},[319,323,327,331],{"code":33,"score":17,"supported":14,"title":320,"abstract":321,"tags":322},[192,34],[35,192,34],[28],{"code":206,"score":96,"supported":14,"title":324,"abstract":325,"tags":326},[210],[209,210],[309,171],{"code":182,"score":82,"supported":14,"title":328,"abstract":329,"tags":330},[],[170,202],[],{"code":221,"score":22,"supported":41,"title":332,"abstract":333,"tags":334},[],[],[169],[336,337],"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":339,"openalex_id":341,"authors":342,"venue":299,"cited_by_count":79,"oa_url":296,"card":358,"direction":246,"ingested_from":249},"W7222806066",[343,345,347,349,352,354,356],{"name":344,"orcid":8},"Guangtao Wang",{"name":346,"orcid":8},"Sumei Yao",{"name":348,"orcid":8},"Yanzheng Xie",{"name":350,"orcid":351},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":353,"orcid":8},"Xiangdong Chen",{"name":355,"orcid":8},"Yuhao Zhang",{"name":357,"orcid":8},"Lina Jiang",{"tldr":359,"method":360,"finding":361,"direction":246,"opportunity":362},"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":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":8,"source_name":157,"source_url":367,"published_at":370,"category":159,"cover_url":8,"hotness":13,"is_selected":41,"score":371,"score_detail":372,"sources":374,"tags":376,"primary_topic":380,"secondary_topics":381,"classification":384,"search_phrases":408,"slug":411,"view_count":412,"doi":413,"paper":414,"created_at":431},4433,"PROSAIL-DNN-TL: A Hyperspectral Inversion Framework for Winter Wheat Leaf Chlorophyll Content Based on Fine-Tuned Transfer Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191971","Accurate and efficient estimation of leaf chlorophyll content (LCC) is essential for crop physiological monitoring and precision agriculture. Current remote sensing approaches for LCC estimation mainly rely on machine learning and deep learning methods, which exhibit strong nonlinear modelling capabilities but require sufficient and representative samples. Radiative transfer models (RTMs) can simulate canopy spectral response based on physical mechanisms and generate large-scale datasets, thereby overcoming the limitations of purely data-driven approaches. However, their applications under diverse environmental conditions remain challenging due to uncertainties caused by model parameterization and discrepancies between simulated and observed data. This study proposed a hybrid framework, PROSAIL-DNN-TL, integrating the PROSAIL RTM with full-network fine-tuned transfer learning for winter wheat LCC estimation. First, 85,680 simulated samples generated by PROSAIL were used to pre-train a deep neural network (DNN), and field observations (n = 270) were subsequently used for full-network fine-tuning. A conventional DNN model trained only with field data and machine learning models based on different feature selection strategies were established for comparison. The results showed that: (1) UVE-selected bands were distributed across visible, red-edge, and near-infrared regions; CARS retained more bands in the visible region, whereas SPA mainly selected near-infrared bands; (2) RR and SVR achieved higher accuracy than KNN, with the full-spectrum and CARS feature sets showing superior performance and SPA showing the lowest accuracy; (3) PROSAIL-DNN-TL achieved reliable LCC estimation performance, with an average test R2 of 0.79 across 500 repeated experiments, and showed improved performance compared with conventional machine learning models; (4) PROSAIL-DNN-TL achieved reliable predictive performance when evaluated using datasets collected during the 2024 and 2025 growing seasons, with R2 values of 0.75 and 0.92, respectively. Overall, the proposed hybrid framework integrates physical knowledge from RTMs with data-driven modelling through transfer learning, providing a potential approach for crop chlorophyll monitoring. However, the SPAD-to-LCC conversion used in this study was not specifically calibrated for the instrument and crop, and therefore, the absolute LCC values should be interpreted with caution.","准确、高效地估算叶片叶绿素含量（leaf chlorophyll content，LCC）对作物生理监测和精准农业至关重要。当前遥感估算LCC的方法主要依赖机器学习和深度学习方法，这类方法具有较强的非线性建模能力，但需要充足且具有代表性的样本。辐射传输模型（radiative transfer models，RTMs）能够基于物理机制模拟冠层光谱响应并生成大规模数据集，从而克服纯数据驱动方法的局限性。然而，由于模型参数化带来的不确定性以及模拟数据与观测数据之间的差异，其在不同环境条件下的应用仍面临挑战。本研究提出了一种混合框架PROSAIL-DNN-TL，将PROSAIL辐射传输模型与全网络微调迁移学习相结合，用于冬小麦LCC估算。首先，利用PROSAIL生成的85 680个模拟样本预训练深度神经网络（deep neural network，DNN），随后利用田间观测数据（n = 270）进行全网络微调。同时建立了仅使用田间数据训练的传统DNN模型以及基于不同特征选择策略的机器学习模型作为对比。结果表明：（1）UVE选择的波段分布在可见光、红边和近红外区域；CARS在可见光区域保留了更多波段，而SPA主要选择近红外波段；（2）RR和SVR的精度高于KNN，全光谱和CARS特征集表现较优，SPA精度最低；（3）PROSAIL-DNN-TL取得了可靠的LCC估算性能，在500次重复实验中平均测试R²为0.79，且相比传统机器学习模型表现出更优的性能；（4）利用2024年和2025年生长季采集的数据集进行评估时，PROSAIL-DNN-TL均表现出可靠的预测性能，R²分别为0.75和0.92。总体而言，所提出的混合框架通过迁移学习将辐射传输模型的物理知识与数据驱动建模相结合，为作物叶绿素监测提供了一种潜在途径。然而，本研究中使用的SPAD到LCC的转换并未针对仪器和作物进行专门校准，因此绝对LCC值应谨慎解读。","2026-10-07T00:00:00Z",81,{"impact":302,"substance":259,"depth":302,"authority":19,"freshness":21,"relevant":22,"comment":373},"将PROSAIL辐射传输模型与深度神经网络迁移学习结合，用8万余模拟样本预训练、270个田间样本微调，冬小麦叶绿素反演精度可靠，方法新颖且数据规模扎实，对作物生理监测与精准农业有参考价值。",[375],{"name":157,"url":367},[169,377,28,378,379],"农业遥感","迁移学习","叶绿素监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[382,383],{"code":206,"slug":313,"name":314,"path":315,"parent_code":186},{"code":182,"slug":183,"name":184,"path":185,"parent_code":186},{"status":40,"locked":41,"version":42,"method":43,"evidence":385},{"candidates":386,"agricultural_evidence":14},[387,391,396,402],{"code":33,"score":17,"supported":14,"title":388,"abstract":389,"tags":390},[192,34],[35,192,34],[28],{"code":206,"score":20,"supported":14,"title":392,"abstract":394,"tags":395},[393],"hyperspectral",[209,210],[377],{"code":182,"score":13,"supported":14,"title":397,"abstract":398,"tags":401},[],[399,170,202,400],"深度学习","deep learning",[378],{"code":221,"score":125,"supported":14,"title":403,"abstract":404,"tags":407},[],[405,406],"精准农业","precision agriculture",[169],[409,410],"PROSAIL 冬小麦 叶绿素","高光谱 叶绿素 反演","PROSAIL冬小麦叶绿素-4433",5,"10.3390\u002Fagronomy16191971",{"doi":413,"openalex_id":415,"authors":416,"venue":157,"cited_by_count":79,"oa_url":367,"card":426,"direction":246,"ingested_from":249},"W7220844556",[417,419,421,424],{"name":418,"orcid":8},"Xueqing Zhu",{"name":420,"orcid":8},"Ying Nian",{"name":422,"orcid":423},"Jikai Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2365-5221",{"name":425,"orcid":8},"Xinwei Li",{"tldr":427,"method":428,"finding":429,"direction":246,"opportunity":430},"提出PROSAIL-DNN-TL框架，用PROSAIL模拟数据预训练DNN并全网络微调，反演冬小麦叶","PROSAIL辐射传输模型生成85680样本预训练DNN，270个田间样本全网络","500次重复实验平均测试R²为0.79，2024和2025年生长季验证R²分别为0.75和0.92，","SPAD到LCC转换未针对仪器和作物标定，可研究跨传感器、跨作物、跨生长季的迁移学习泛化与绝对叶绿素标定。","2026-10-08T23:30:29.383957Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":8,"source_name":438,"source_url":435,"published_at":439,"category":159,"cover_url":8,"hotness":13,"is_selected":41,"score":440,"score_detail":441,"sources":444,"tags":446,"primary_topic":451,"secondary_topics":452,"classification":453,"search_phrases":479,"slug":482,"view_count":66,"doi":483,"paper":484,"created_at":506},4424,"Productivity of winter wheat depending on predecessors and systems of basic tillage in short crop rotations in conditions of the Southern Steppe","https:\u002F\u002Fdoi.org\u002F10.31073\u002Fagrovisnyk202609-02","Goal. To develop environmentally friendly technologies for competitive production of high-quality winter wheat products in the Southern Steppe. Methods. The following methods were used in the study: field (for monitoring plant development and yield), laboratory (for analyzing grain quality), mathematical-statistical (for processing data and determining the reliability of the results). Results. The results of research on the influence of different systems of the main soil tillage (southern unwashed heavy loam chornozem) on the productivity of winter wheat in short crop rotations of the Southern Steppe of Ukraine are given. The experimental part was performed in the 2023\u002F2024 and 2024\u002F2025 agricultural years on the experimental field of the Odesa State Agricultural Experimental Station of the Institute of Climate-Smart Agriculture of the NAAS (vil. Khlibodarske, Odesa district, Odesa oblast). It was established that various predecessors and systems of soil tillage significantly influenced the yield of winter wheat. The use of sideral fallow with winter vetch increased the yield of winter wheat grain compared to black fallow, sideral fallow with peas, white mustard, and the predecessor — peas for grain. In the case of using sideral fallow with winter vetch, yields increased by 1.4 – 7.5% due to improved nitrogen regime of the soil and more efficient use of moisture. Less productive was the option after peas for grain, in which the yield decreased by 9.7 – 15.0% compared to black fallow. Depending on the systems of basic tillage, the largest yield (4.42 t\u002Fha) was provided by the BMMBM system (no-till cultivation). The excess in comparison with the PMMPM system (moldboard tillage) was 5.2 – 10.2%. Conclusions. The obtained results confirmed the expediency of the use of sideral fallow with winter vetch in short crop rotations as an effective agrotechnical technique for increasing the productivity of winter wheat and rational use of soil moisture. The complex combination of sideral fallow with winter vetch as a precursor and no-till basic tillage made it possible to increase the yield of winter wheat, while combinations of peas for grain + differentiated tillage reduced the efficiency of production.","目标。开发环境友好型技术，以实现南方草原高品质冬小麦产品的有竞争力生产。方法。本研究采用以下方法：田间法（用于监测植株发育和产量）、实验室法（用于分析籽粒品质）、数理统计法（用于处理数据和确定结果的可靠性）。结果。本文给出了关于不同主耕作制度（南方未冲刷重壤质黑钙土）对乌克兰南方草原短轮作中冬小麦生产力影响的研究结果。试验部分于2023\u002F2024和2024\u002F2025农业年度在乌克兰国家农业科学院气候智慧型农业研究所敖德萨州农业试验站试验田（敖德萨州敖德萨区赫利博达尔斯克村）进行。研究确定，不同前茬和土壤耕作制度对冬小麦产量有显著影响。与黑色休闲、豌豆绿肥休闲、白芥绿肥休闲以及前茬——粒用豌豆相比，采用冬箭筈豌豆绿肥休闲提高了冬小麦籽粒产量。在使用冬箭筈豌豆绿肥休闲的情况下，由于土壤氮素状况改善和水分利用效率提高，产量增加了1.4–7.5%。粒用豌豆之后处理的生产力较低，与黑色休闲相比产量下降了9.7–15.0%。取决于基本耕作制度，BMMBM系统（免耕栽培）提供了最高产量（4.42吨\u002F公顷）。与PMMPM系统（有壁犁耕作）相比，增产5.2–10.2%。结论。所得结果证实了在短轮作中使用冬箭筈豌豆绿肥休闲作为提高冬小麦生产力和合理利用土壤水分的有效农艺措施的合理性。将冬箭筈豌豆绿肥休闲作为前茬与免耕基本耕作相结合的综合措施，使冬小麦产量得以提高，而粒用豌豆+差异化耕作组合则降低了生产效率。","Visnyk agrarnoi nauky","2026-10-06T00:00:00Z",69,{"impact":442,"substance":18,"depth":96,"authority":164,"freshness":303,"relevant":22,"comment":443},12,"乌克兰南方草原冬小麦轮作与免耕试验，数据具体但属区域性农艺研究，公共影响有限。",[445],{"name":438,"url":435},[447,448,28,449,450],"保护性耕作","轮作","土壤耕作","免耕",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":454},{"candidates":455,"agricultural_evidence":14},[456,460,464,469,474],{"code":33,"score":17,"supported":14,"title":457,"abstract":458,"tags":459},[192,34],[35,192,34],[28],{"code":176,"score":125,"supported":14,"title":461,"abstract":462,"tags":463},[],[198,177],[449],{"code":465,"score":47,"supported":14,"title":466,"abstract":467,"tags":468},"A7",[],[448],[448],{"code":221,"score":53,"supported":14,"title":470,"abstract":471,"tags":473},[],[472],"smart agriculture",[],{"code":475,"score":22,"supported":41,"title":476,"abstract":477,"tags":478},"H8",[],[],[447],[480,481],"敖德萨试验站 冬小麦 免耕","乌克兰南方草原 冬小麦 轮作","敖德萨试验站冬小麦免耕-4424","10.31073\u002Fagrovisnyk202609-02",{"doi":483,"openalex_id":485,"authors":486,"venue":438,"cited_by_count":79,"oa_url":499,"card":500,"direction":248,"ingested_from":249},"W7220668456",[487,490,493,496],{"name":488,"orcid":489},"С.В. Почколіна","https:\u002F\u002Forcid.org\u002F0000-0003-2288-9595",{"name":491,"orcid":492},"I.M. Kohut","https:\u002F\u002Forcid.org\u002F0000-0002-4418-5954",{"name":494,"orcid":495},"Oleksandr Melnyk","https:\u002F\u002Forcid.org\u002F0000-0002-0717-5116",{"name":497,"orcid":498},"V. Rudenko","https:\u002F\u002Forcid.org\u002F0000-0002-8651-7689","https:\u002F\u002Fagrovisnyk.com\u002Findex.php\u002Fagrovisnyk\u002Farticle\u002Fview\u002F2026_09_02\u002F5188",{"tldr":501,"method":502,"finding":503,"direction":504,"opportunity":505},"研究乌克兰南部草原短轮作中前作与耕作制度对冬小麦产量的影响。","田间试验、实验室品质分析及数理统计，2023-2025年黑钙土。","冬箭筈豌豆绿肥前作配合免耕使产量最高达4.42吨\u002F公顷，豌豆前作减产。","农业绿色发展与碳","可探究绿肥前作与免耕协同提升土壤碳氮及水分利用效率的长期机制。","2026-10-08T23:30:11.217866Z"]