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日",80,{"impact":154,"substance":16,"depth":155,"authority":19,"freshness":106,"relevant":20,"comment":156},24,17,"省级科研机构联合发布的杂交水稻智能育种平台，数据规模与功能模块具体，属种业数字化实质进展，值得进入每日精选。",[158],{"name":151,"url":149},[25,26,28,111,112],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":163},{"candidates":164,"agricultural_evidence":45},[165,170,175,179,183],{"code":31,"score":166,"supported":45,"title":167,"abstract":168,"tags":169},20,[33,48],[33],[112],{"code":70,"score":52,"supported":45,"title":171,"abstract":172,"tags":174},[],[173],"品种审定",[28],{"code":128,"score":20,"supported":13,"title":176,"abstract":177,"tags":178},[],[],[111],{"code":51,"score":20,"supported":13,"title":180,"abstract":181,"tags":182},[],[],[25],{"code":80,"score":20,"supported":13,"title":184,"abstract":185,"tags":186},[],[],[26],[188,189],"袁稻思源 智能育种平台","湖南省农科院 杂交水稻 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2026-10-11","2026-10-11T00:00:00Z",25,86,{"impact":154,"substance":16,"depth":104,"authority":18,"freshness":106,"relevant":20,"comment":203},"AI赋能杂交水稻育种的阶段性成果，多方院士背书、功能模块具体，具备行业示范价值，值得进入每日精选。",[205,206],{"name":198,"url":195},{"name":207,"url":208},"湖南省农业科学院(2026-10-11)","http:\u002F\u002Fwww.hunaas.cn\u002Fshownews.asp?nid=17062",[25,26,28,111,112],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":213},{"candidates":214,"agricultural_evidence":45},[215,219,223,227,231],{"code":31,"score":119,"supported":45,"title":216,"abstract":217,"tags":218},[33],[33],[112],{"code":70,"score":20,"supported":13,"title":220,"abstract":221,"tags":222},[],[],[28],{"code":128,"score":20,"supported":13,"title":224,"abstract":225,"tags":226},[],[],[111],{"code":51,"score":20,"supported":13,"title":228,"abstract":229,"tags":230},[],[],[25],{"code":80,"score":20,"supported":13,"title":232,"abstract":233,"tags":234},[],[],[26],[188,236],"岳麓山实验室 杂交水稻 智能育种","袁稻思源智能育种平台-4771","2026-10-13T00:08:35.920031Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":8,"content":244,"source_name":245,"source_url":8,"published_at":199,"category":11,"cover_url":8,"hotness":246,"is_selected":13,"score":201,"score_detail":247,"sources":250,"tags":258,"primary_topic":259,"secondary_topics":260,"classification":261,"search_phrases":284,"slug":285,"view_count":88,"doi":8,"paper":8,"created_at":286},4718,"袁稻思源杂交水稻智能育种平台在岳麓山实验室发布","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_2696acb7f0f89652","10月11日,袁稻思源杂交水稻智能育种平台在岳麓山实验室发布,中国科学院院士李家洋等近200名专家出席。平台集成问答助手、育种资源库、知识图谱、文献库四大功能模块,标志湖南杂交水稻育种迈入数据驱动新阶段","10月11日下午，“袁稻思源”杂交水稻智能育种平台发布会在岳麓山实验室举行。中国科学院院士李家洋、钱前、谢道昕，中国工程院院士柏连阳，以及来自各大科研院所、湖南省相关部门、种业企业的近200名领导专家齐聚现场，共同见证平台正式上线，聚焦智能育种技术创新与产业落地应用展开深度研讨，标志着湖南杂交水稻育种正式告别传统经验模式，迈入数据驱动、智能决策的全新发展阶段。\n\n本次发布会由湖南省农业科学院院长余应弘主持。他表示，“袁稻思源”平台的成功建成与发布，是湖南省落实国家种业振兴行动、推进育种产业数字化、智能化转型升级的关建举措。据悉，7月2日，中国科学院自动化研究所、北京中科闻歌科技股份有限公司与湖南省农业科学院正式签署合作协议，三方将围绕“人工智能+农业科研”展开联合攻关。今天发布了阶段性成果。平台以“饮水思源”之意铭记袁隆平院士的历史贡献，旨在传承和弘扬袁隆平院士的科学精神，把数据资源和智能技术转化为育种创新的现实生产力。\n\n柏连阳院士在致辞中指出，人工智能正在引领新一轮科技革命和产业变革，拥抱人工智能是科研人员的必答题。湖南省农业科学院水稻智能育种团队联合中国科学院自动化研究所，研发出“袁稻思源”智能育种平台。平台目前虽为初级版本，但迈出了AI赋能杂交水稻育种关键而可喜的一步。他希望团队以此为起点，持续迭代完善平台功能，推动人工智能与育种实践深度融合，为种业创新提供坚实支撑。\n\n发布会上，与会人员通过官方宣传视频，系统了解“袁稻思源”平台的建设背景、总体架构与核心能力。湖南省农业科学院副院长李莉现场演示平台核心功能。平台集成“问答助手”、“育种资源库”、“知识图谱”、“文献库”四大功能模块，覆盖杂交水稻育种信息检索、数据查询与智能决策需求。演示中，李莉通过“问答助手”现场提问，展示品种审定智能体的应答能力；在“育种资源库”中演示种质资源查询功能，直观呈现品种系谱关系；通过“知识图谱”展示品种—性状—基因之间的关联网络；结合“文献库”实现育种文献知识快速检索与溯源。目前，平台已在第三代杂交水稻育种中开展应用示范，取得良好成效。\n\n李家洋院士对平台给予高度评价。他指出，当前全球种业竞争加剧，生物技术、信息技术与人工智能加速融合，育种正从“经验驱动”转向“数据驱动、设计育种”，谁率先突破智能育种，谁就赢得下一轮农业产业革命的主动。平台建设是落实种业振兴行动、保障国家粮食安全、抢占农业科技制高点、传承袁隆平精神的重要实践。\n\n李家洋院士还对平台未来发展提出四点建议：一是强化智能决策，整合基因组、表型、环境等多维数据，提高组合推荐与亲本选择精度；二是深化协同创新，连接高校、院所、企业的数据、算法、算力与人才，打破信息孤岛；三是坚持服务产业，形成科研、企业、推广、政府决策闭环，让成果从实验室走向大田；四是建设公共平台，不仅服务湖南，更要面向全国、全球，打造水稻智能育种领域的重要公共产品。\n\n此次“袁稻思源”杂交水稻智能育种平台的发布，标志着湖南杂交水稻育种正式迈入数据驱动、智能决策的新阶段。随着平台功能不断完善和应用持续深化，智能育种技术将加快优良品种选育步伐、降低育种成本与风险，助力实现袁隆平院士“禾下乘凉”的梦想，为保障国家粮食安全贡献湖南力量。","湖南日报 2026-10-11",40,{"impact":102,"substance":16,"depth":155,"authority":248,"freshness":12,"relevant":20,"comment":249},11,"院士云集的省级重大平台发布，AI赋能杂交水稻育种迈出实质一步，信息增量与产业意义兼具，值得进入每日精选。",[251,252,255],{"name":245,"url":242},{"name":253,"url":254},"中国科学院自动化研究所(2026-10-11)","https:\u002F\u002Fia.cas.cn\u002Fkxyj\u002Fkydt_1\u002F202610\u002Ft20261014_8292339.html",{"name":256,"url":257},"中国科学院自动化研究所 2026-10-14","https:\u002F\u002Fwww.ia.ac.cn\u002Fkxyj\u002Fkydt_1\u002F202610\u002Ft20261014_8292339.html",[25,26,28,111,112],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":262},{"candidates":263,"agricultural_evidence":45},[264,268,272,276,280],{"code":31,"score":119,"supported":45,"title":265,"abstract":266,"tags":267},[33],[33],[112],{"code":70,"score":20,"supported":13,"title":269,"abstract":270,"tags":271},[],[],[28],{"code":128,"score":20,"supported":13,"title":273,"abstract":274,"tags":275},[],[],[111],{"code":51,"score":20,"supported":13,"title":277,"abstract":278,"tags":279},[],[],[25],{"code":80,"score":20,"supported":13,"title":281,"abstract":282,"tags":283},[],[],[26],[188,236],"袁稻思源智能育种平台-4718","2026-10-12T00:11:05.010358Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":8,"source_name":293,"source_url":290,"published_at":294,"category":295,"cover_url":8,"hotness":12,"is_selected":13,"score":296,"score_detail":297,"sources":300,"tags":302,"primary_topic":305,"secondary_topics":306,"classification":307,"search_phrases":345,"slug":348,"view_count":144,"doi":349,"paper":350,"created_at":379},2616,"Multi-agent cooperative control for unmanned distributed-drive electric agricultural vehicle in paddy fields: tracking, stability, and energy-aware torque allocation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112422","With the rapid advancement of intelligent agriculture and autonomous field operations, the distributed drive electric plant protection vehicle (DDEPPV) is increasingly adopted for paddy-field plant protection. However, in soft-soil, low-adhesion, and highly disturbed environments, the tight coupling among path tracking, drive\u002Fyaw stability, and energy consumption-together with uncertain ground parameters-poses major challenges to conventional control. In addition, from-scratch reinforcement learning is difficult to deploy, as early exploration can induce yaw instability and wheel entrapment. To overcome these limitations, we propose a vehicle-level distributed electric-drive control framework that integrates physics-informed priors with multi-agent cooperative learning. A mud-water multiphase wheel-soil interaction model is built via CFD-DEM coupling to identify, under the parameter settings and operating conditions considered in this study, an energy- and sinkage-risk-aware slip-ratio window, thereby providing an interpretable ground-mechanics boundary for subsequent controller design. Under the centralized training and decentralized execution paradigm, the task is decomposed into three agents for path tracking, stability\u002Ftraction regulation, and energy-optimal four-wheel allocation, and trained using model predictive control (MPC) expert-supervised pretraining followed by multi-agent twin delayed deep deterministic policy gradient (MATD3) cooperative fine-tuning. Real-time Hardware-in-the-Loop (HIL) experiments verify improved turning performance and enhanced yaw\u002Ftraction stability, while reducing traction-system electrical energy consumption by 29.4% versus MPC and by an additional 5.4% over unpretrained MATD3, demonstrating unified optimization of accuracy-stability-energy efficiency in paddy fields.","随着智能农业与自主田间作业的快速发展，分布式驱动电动植保车辆（DDEPPV）在水稻田植保作业中得到日益广泛的应用。然而，在软土、低附着力和高扰动环境中，路径跟踪、驱动\u002F偏航稳定性与能耗之间的紧密耦合，加之地面参数的不确定性，给传统控制带来了重大挑战。此外，从零开始的强化学习难以部署，因为早期探索可能引发偏航失稳和车轮陷坑。为克服这些局限，我们提出了一种车辆级分布式电驱动控制框架，将物理信息先验与多智能体协同学习相融合。通过CFD-DEM耦合建立了泥水多相轮-土相互作用模型，在本研究所考虑的参数设置和作业条件下识别出兼顾能耗与下陷风险的滑转率窗口，从而为后续控制器设计提供可解释的地面力学边界。在集中训练-分散执行范式下，将任务分解为路径跟踪、稳定性\u002F牵引力调节和能耗最优四轮分配三个智能体，并采用模型预测控制（MPC）专家监督预训练，随后通过多智能体双延迟深度确定性策略梯度（MATD3）进行协同微调。实时硬件在环（HIL）实验验证了转向性能的改善以及偏航\u002F牵引稳定性的增强，同时牵引系统电能消耗较MPC降低29.4%，较未经预训练的MATD3进一步降低5.4%，展示了水稻田中精度-稳定性-能效的统一优化。","Computers and Electronics in Agriculture","2026-09-15T00:00:00Z","论文",82,{"impact":104,"substance":16,"depth":298,"authority":18,"freshness":19,"relevant":20,"comment":299},19,"提出物理先验与多智能体协同学习融合的分布式驱动电动农机控制框架，HIL实验验证能耗降低29.4%，方法新颖、数据扎实，对水田智能装备研发有实质参考价值。",[301],{"name":293,"url":290},[25,26,27,303,304],"水稻生产","多智能体控制",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":308},{"candidates":309,"agricultural_evidence":45},[310,315,321,327,333,337,341],{"code":31,"score":104,"supported":45,"title":311,"abstract":313,"tags":314},[312],"paddy",[33,48,312],[303],{"code":316,"score":58,"supported":45,"title":317,"abstract":318,"tags":320},"A7",[],[319],"间作",[],{"code":322,"score":58,"supported":45,"title":323,"abstract":324,"tags":326},"F4",[],[325],"植保",[],{"code":328,"score":58,"supported":45,"title":329,"abstract":330,"tags":332},"H2",[],[331],"soil",[],{"code":75,"score":20,"supported":13,"title":334,"abstract":335,"tags":336},[],[],[27],{"code":51,"score":20,"supported":13,"title":338,"abstract":339,"tags":340},[],[],[25],{"code":80,"score":20,"supported":13,"title":342,"abstract":343,"tags":344},[],[],[26],[346,347],"农业人工智能 多智能体控制 智慧农业 智能农机","农业人工智能 多智能体控制","农业人工智能多智能体控制智慧农业智能农机-2616","10.1016\u002Fj.compag.2026.112422",{"doi":349,"openalex_id":351,"authors":352,"venue":293,"cited_by_count":144,"oa_url":290,"card":372,"direction":376,"ingested_from":378},"W7213225972",[353,356,358,360,363,365,367,369],{"name":354,"orcid":355},"Wenxiang Xu","https:\u002F\u002Forcid.org\u002F0000-0001-6476-4710",{"name":357,"orcid":8},"Xiaoyu Song",{"name":359,"orcid":8},"Liling Ye",{"name":361,"orcid":362},"Mengnan Liu","https:\u002F\u002Forcid.org\u002F0000-0001-5418-6347",{"name":364,"orcid":8},"He Zheng",{"name":366,"orcid":8},"Mingfeng Wang",{"name":368,"orcid":8},"Ze Liu",{"name":370,"orcid":371},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"tldr":373,"method":374,"finding":375,"direction":376,"opportunity":377},"提出多智能体协同控制框架，实现水田分布式驱动电动农机路径跟踪、稳定性与能耗统一优化。","CFD-DEM泥水轮土模型、MPC专家预训练、MATD3多智能体协同微调、HIL","相比MPC降低牵引电耗29.4%，比未预训练MATD3再降5.4%，提升转向与横摆稳定性。","农业人工智能与决策模型","可探索将物理先验与多智能体强化学习迁移至其他软土农田作业场景，并降低对高保真仿真模型的依赖。","openalex","2026-09-16T23:30:01.996916Z",{"id":381,"title":382,"url":383,"summary":384,"summary_zh":385,"content":8,"source_name":386,"source_url":383,"published_at":387,"category":295,"cover_url":8,"hotness":12,"is_selected":13,"score":388,"score_detail":389,"sources":391,"tags":393,"primary_topic":396,"secondary_topics":397,"classification":404,"search_phrases":438,"slug":441,"view_count":144,"doi":442,"paper":443,"created_at":457},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":104,"substance":16,"depth":104,"authority":119,"freshness":106,"relevant":20,"comment":390},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[392],{"name":386,"url":383},[25,26,33,394,395],"产量预测","遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[398],{"code":399,"slug":400,"name":401,"path":402,"parent_code":403},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":38,"locked":13,"version":39,"method":40,"evidence":405},{"candidates":406,"agricultural_evidence":45},[407,412,417,424,430,434],{"code":31,"score":408,"supported":45,"title":409,"abstract":410,"tags":411},15,[32],[33,32],[33],{"code":399,"score":106,"supported":45,"title":413,"abstract":414,"tags":416},[],[395,415],"remote sensing",[394,395],{"code":418,"score":44,"supported":45,"title":419,"abstract":420,"tags":423},"A8",[],[421,422],"粮食安全","food security",[],{"code":425,"score":58,"supported":45,"title":426,"abstract":427,"tags":429},"J1",[],[428],"农业规划",[],{"code":51,"score":20,"supported":13,"title":431,"abstract":432,"tags":433},[],[],[25],{"code":80,"score":20,"supported":13,"title":435,"abstract":436,"tags":437},[],[],[26],[439,440],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":442,"openalex_id":444,"authors":445,"venue":386,"cited_by_count":144,"oa_url":383,"card":451,"direction":455,"ingested_from":378},"W7222942064",[446,448],{"name":447,"orcid":8},"Nayana Venkatesh",{"name":449,"orcid":450},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":452,"method":453,"finding":454,"direction":455,"opportunity":456},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","农业遥感与作物表型","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z"]