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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",2,"2026-09-29T00:08:17.794968Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":8,"content":142,"source_name":143,"source_url":8,"published_at":144,"category":11,"cover_url":8,"hotness":12,"is_selected":46,"score":145,"score_detail":146,"sources":151,"tags":153,"primary_topic":158,"secondary_topics":159,"classification":160,"search_phrases":183,"slug":186,"view_count":120,"doi":8,"paper":8,"created_at":187},1550,"中国农业大学王耀君团队\"神农育种 Studio\"9 月 4 日正式发布：1 套小麦育种方案效率提升 20 倍","https:\u002F\u002Fwww.beijing.gov.cn\u002Fywdt\u002Fgzdt\u002F202608\u002Ft20260831_4844377.html","9 月 4 日，\"神农育种 Studio\"正式发布，把原本分散的研究环节自动串联起来。育种人员用自然语言提出目标，Studio 先识别任务所需的数据、模型和软件，再按需调用相应工具，完成资料检索、亲本筛选、品系评价、多环境试验分析等任务。支撑平台运行的是团队多年积累的农业知识、生产数据和育种模型。神农大模型的数据底座已汇集超过 1000 万条农业知识图谱数据、超过 5000 万条农业生产数据。过去设计一套小麦育种方案需投入 10 人月，借助 Studio 可降至 0.5 人月，效率提升约 20 倍。","田野里，无人车沿玉米行前行，机器一边“看”，算法一边“算”；鸡舍里，种鸡的产蛋、体重等数据不断汇入系统，基因芯片与算法共同寻找更有潜力的个体；实验室里，育种人员用自然语言提出目标，人工智能开始检索资料、调用模型、生成方案。\n\n这些正在北京发生的新场景，勾勒出同一个方向：育种正在从主要依靠经验，走向数据与经验结合、人脑与机器协同。\n\n9月4日至5日，第三十三届中国北京种业大会将在北京举行。大会以“AI赋能智慧育种·数智引领种业未来”为主题，首次以智慧育种为主线。人工智能怎样进入田间、鸡舍和实验室？它究竟改变了育种的哪一步？答案藏在正在采集的数据、实际的选种过程和不断落地的技术中。\n\n**机器“看”玉米**\n\n在北京通州区于家务乡的通州国际种业科技园区基地，20亩试验田里的约7万株玉米，正在接受一场“全周期体检”。环绕式无人车拍下植株的颜色、纹理和株型，激光雷达勾勒三维结构，多光谱和热成像设备捕捉长势与温度变化。一台不足半米宽的机器人则钻入冠层之下，从另一个视角补上从上方看不到的信息，近距离记录茎秆粗细、植株疏密和吐丝进度。\n\n从6叶期到9叶期，再到大喇叭口期和吐丝期，设备目前已完成4个阶段的数据采集，后续还将跟踪至成熟期。一次作业由两人操作，每小时可完成约4000株的数据采集。1000株以内的小批量数据，4至6小时可以形成结果，全区域数据可在24小时内完成解析和质控。在海南崖州南繁基地，同类装备还与无人机协同作业，在240亩试验田完成了超过36万株玉米的全生育期数据采集。\n\n交到育种人员手中的，不只是田间照片，还有性状数据表、三维点云模型、品系对比图表和单株表型档案。阶段性数据已能初步区分不同品系的株高增长速度、叶夹角和冠层透光性差异。团队随机抽取100株玉米，以人工测量的株高作对照，机器测量结果的一致性超过95%，误差低于5厘米。\n\n“过去依靠人工作业和经验判断，现在通过机器标准化采集、算法自动化处理，获取的指标更多、维度更多，精度也更高。”北京市农林科学院信息技术研究中心研究员郭新宇介绍，表型是基因型与环境互作的结果，只有把表型数据与基因组数据结合起来，才能更好地认识不同基因型在真实田间究竟会有怎样的表现。\n\n表型大数据工厂所做的，就是把作物真实的“长相”和“长势”变成AI可以学习的语言，帮助育种人员判断哪些材料在耐密、抗倒伏和高产方面更具潜力。\n\n**算法“评”种鸡**\n\n一只尚未产蛋的雏鸡，能不能提前判断它将来产多少蛋、蛋重多大、蛋壳结不结实？在北京市华都峪口禽业有限责任公司，一管血可以帮助育种人员更早读出候选种鸡的遗传潜力。\n\n研发人员闫奕源把选种比作选学生：传统方法要等母鸡产蛋，取得产蛋数、蛋重、蛋品质等“考试成绩”后才能判断。有了基因数据和预测模型，则可以在候选种鸡尚无自身生产记录时，提前评价其遗传潜能。\n\n雏鸡出生后采集血液、提取DNA，再用蛋鸡育种芯片检测基因变异位点。亲本和群体的历史数据中，既有基因型，也有产蛋数、蛋重、体重、蛋壳颜色和强度等表型数据。模型从两类数据的对应关系中学习，再对新一批候选种鸡逐只预测，形成涵盖不同性状的“成绩单”和综合排名。\n\n在这套体系中，芯片负责检测基因变异，算法模型则接过大规模比较、遗传潜能预测和排序等工作，帮助育种人员更早看出哪些个体更有育种潜力。“选得准，才能更快实现育种目标。”闫奕源说。\n\n目前，“京粉6号”已在百万只规模养殖条件下稳定实现“700天产500枚蛋”，相关成果获得2025年度国家科学技术进步奖二等奖。接续这一成熟品种，本届大会将发布“京红2号”“京粉8号”“沃德178”“京翠绿壳”等自主研发蛋（肉）鸡新品种，呈现“京字号”种鸡持续迭代的新成果。\n\n**平台“串”流程**\n\n如果说田间表型工厂解决的是“数据从哪里来”，种鸡精准育种展示的是“算法怎样辅助选择”，那么即将在本届大会发布的“神农育种Studio”，要解决的是分散的数据、模型和软件怎样被育种人员更方便地用起来。\n\n过去，育种人员常常需要在数据库、表格和多种软件间反复切换，有些环节还要请信息技术人员编写程序。现在，育种人员用自然语言提出目标，Studio先识别任务所需的数据、模型和软件，再按需调用相应工具，完成资料检索、亲本筛选、品系评价、多环境试验分析等任务。各环节自动衔接，前一步的结果成为下一步的输入，最后汇成附有资料来源的报告。\n\n支撑平台运行的，是团队多年积累的农业知识、生产数据和育种模型。神农大模型的数据底座已汇集超过1000万条农业知识图谱数据、超过5000万条农业生产数据。在北京市怀柔区，团队借助平台从数百个生菜品种中海选出“雁栖1号”“雁栖2号”。中国农业大学副教授王耀君介绍，过去一个蔬菜新品种往往要五六年才能培育出来，借助海量数据分析，可将时间缩短至两三年。\n\n9月4日，“神农育种Studio”将正式发布。Studio把原本分散的研究环节自动串联起来，效率变化随之显现。以设计一套小麦育种方案为例，王耀君介绍，过去需要投入10人月——即1个人工作10个月，或10个人工作1个月；在育种人员把目标和逻辑梳理清楚后，借助Studio可降至0.5人月。资料检索、数据处理、模型计算和初步方案生成等原本分散的工作，可由平台连续完成。\n\n从机器进田采数据，到算法辅助选材料，再到大模型串起研究环节，3种场景勾勒出智能育种的一条数据链路，也对算力、数据、装备和公共服务提出新要求。\n\n北京市农业农村局有关负责人介绍，北京将依托农业中关村核心区，打造北京农业中关村智能育种创新基地，计划建设智能育种共性技术创新中心和智能育种共性装备创新中心，提升育种专用算力、数据治理、装备研发中试、基因检测、标准规范和成果转化等共性条件；面向作物、畜禽、水产、微生物打造四类智能育种测试平台与产业应用场景。目前，作物逆境表型鉴定平台、作物智能分子育种创新平台、畜禽智能育种技术共性平台等重大平台正在建设中。\n\n育种仍是一场以年为单位的长跑，人工智能不能让种子跳过生长季，却能接过大量重复、耗时的前期工作。它可以把田间观察变成更完整的数据，把海量候选材料排出次序，把分散的研究环节自动串联起来，让每一轮选择少花一些时间、少走一些弯路。当机器真正读懂田间，数据、算法与育种经验形成合力，一粒好种子走向生产的脚步，便能迈得更快、更稳。","北京市人民政府","2026-09-04T00:00:00Z",92,{"impact":75,"substance":147,"depth":148,"authority":149,"freshness":12,"relevant":21,"comment":150},24,18,12,"北京种业大会发布神农育种Studio，展示AI赋能育种全链条，效率提升20倍，具有产业级影响。",[152],{"name":143,"url":140},[154,27,155,156,157],"农业人工智能","种业振兴","大模型","智慧育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":161},{"candidates":162,"agricultural_evidence":46},[163,167,173,178],{"code":32,"score":45,"supported":46,"title":164,"abstract":165,"tags":166},[34],[34],[],{"code":168,"score":169,"supported":46,"title":170,"abstract":171,"tags":172},"I2",5,[],[156],[154,156],{"code":174,"score":21,"supported":13,"title":175,"abstract":176,"tags":177},"F1",[],[],[155],{"code":179,"score":21,"supported":13,"title":180,"abstract":181,"tags":182},"F2",[],[],[157],[184,185],"农业人工智能 智慧育种 种业振兴 大模型","农业人工智能 智慧育种","农业人工智能智慧育种种业振兴大模型-1550","2026-09-04T00:05:30.233316Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":8,"source_name":194,"source_url":191,"published_at":195,"category":196,"cover_url":8,"hotness":12,"is_selected":13,"score":197,"score_detail":198,"sources":203,"tags":205,"primary_topic":210,"secondary_topics":211,"classification":223,"search_phrases":262,"slug":265,"view_count":266,"doi":267,"paper":268,"created_at":288},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":18,"substance":199,"depth":200,"authority":201,"freshness":12,"relevant":21,"comment":202},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。",[204],{"name":194,"url":191},[206,207,208,209,84],"智慧农业","机器学习","遥感监测","土壤墒情",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[212,218],{"code":213,"slug":214,"name":215,"path":216,"parent_code":217},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":168,"slug":219,"name":220,"path":221,"parent_code":222},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":224},{"candidates":225,"agricultural_evidence":46},[226,232,237,242,250,257],{"code":32,"score":227,"supported":46,"title":228,"abstract":230,"tags":231},26,[229,33],"winter 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