[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3122":3,"related-3122":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},3122,"Advances in Information Sensing and Intelligent Monitoring of Field Crops Throughout the Full Growth Cycle（大田作物全生育期信息感知与智能监测研究进展）","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1852","江苏大学农业工程学院 Tang Ruifan 等在《Agronomy》16(18): 1852 发表综述（2026-09-20 发表）：大田作物在不同生育阶段持续变化、呈现显著空间异质性、需在短作业窗口内进行管理。研究以生育阶段为主线组织文献，通过\"农业需求—可观测变量—感知平台—数据处理方法—验证设计—状态解释—管理或装备输出\"通用链条分析。从卫星遥感、无人机感知、地面与近端感知、田间物联网、机载传感器、多源融合、作物模型与机器学习方法按空间支撑、时间连续性、尺度匹配、田间稳健性、迁移条件、不确定性与操作适用性比较。综述报告作物表型反演、田间环境表征、生物胁迫识别在特定条件下已建立；跨阶段状态继承、一致参考测量、独立验证、监测结果向可执行任务转化仍不充分。提出生命周期导向的信息处理视角，未来应加强跨作物跨区域验证、机理性与数据驱动模型协同、不确定性报告、互操作性和田间反馈。",null,"MDPI Agronomy","2026-09-20T00:00:00Z","论文",10,false,76,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,20,18,13,9,1,"江苏大学团队在核心期刊发表的综述，系统梳理大田作物全生育期感知与监测技术链条，专业深度与信息增量较高，但属学术综述、产业影响有限，适合进入主题聚合而非头条精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业物联网","作物表型","遥感监测","大田作物",[32,33],"江苏大学 大田作物 智能监测","Agronomy 作物全生育期 信息感知","江苏大学大田作物智能监测-3122",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"综述大田作物全生育期信息感知与智能监测，按生育阶段梳理技术并指出转化不足。","以生育阶段为主线，比较卫星、无人机、地面物联网、模型与机器学习等方法。","表型反演与胁迫识别已有条件建立，但跨阶段继承、独立验证与可执行转化不足。","农业遥感与作物表型","可研究跨生育阶段状态继承建模、一致参考测量与监测结果向田间作业指令的转化。","agent","2026-09-22T00:05:38.276747Z",{"total":47,"page":21,"page_size":47,"items":48},6,[49,81,138,162,198,234],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":8,"content":8,"source_name":54,"source_url":8,"published_at":55,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":56,"score_detail":57,"sources":63,"tags":65,"search_phrases":68,"slug":71,"view_count":35,"doi":8,"paper":72,"created_at":80},2610,"整合人工智能、物联网与遥感技术的大田作物智能灌溉管理 综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260913082658847.htm","发表于Biosystems Engineering。对人工智能(AI)、物联网(IoT)和遥感(RS)技术在灌溉管理中的应用进行全面且结构化分析，特别是在优化基于天气、土壤和作物的灌溉调度方面。智能灌溉系统实现了水资源节约(用水量减少高达20-60%)、降低能源消耗和提高作物生产力。未来研究应优先考虑成本效益高的传感器开发和用户友好的AI界面。","Biosystems Engineering","2026-09-13T01:00:00Z",83,{"impact":58,"substance":59,"depth":18,"authority":60,"freshness":61,"relevant":21,"comment":62},22,21,14,8,"核心期刊综述，系统梳理AI、物联网与遥感在大田灌溉调度中的融合应用，给出节水20-60%等量化结论，对智慧农业技术路线有参考价值。",[64],{"name":54,"url":52},[26,66,27,67,29],"农业人工智能","智能灌溉",[69,70],"农业人工智能 农业物联网 智慧农业 智能灌溉","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业智能灌溉-2610",{"doi":8,"openalex_id":8,"authors":73,"venue":8,"cited_by_count":35,"oa_url":8,"card":74,"direction":78,"ingested_from":44},[],{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"综述AI、物联网与遥感在大田作物智能灌溉调度中的应用与成效。","结构化综述AI、IoT、RS在基于天气、土壤和作物的灌溉调度中的应用。","智能灌溉可节水20-60%，降低能耗并提高作物生产力。","智慧农业 \u002F 农业物联网","低成本传感器与用户友好AI界面是落地瓶颈，可研究轻量化模型与低成本感知方案。","2026-09-16T00:03:52.381950Z",{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":8,"source_name":87,"source_url":84,"published_at":88,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":89,"score_detail":90,"sources":94,"tags":96,"search_phrases":99,"slug":102,"view_count":35,"doi":103,"paper":104,"created_at":137},2522,"Estimation of Grain Yield and Quality in Awned and Awnless Wheat Genotypes Using UAV and Proximal Multispectral Sensors","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15182819","This study aimed to evaluate the complementary potential of UAV-based and proximal multispectral sensing using the Plant-O-Meter (POM) sensor for the assessment of wheat grain yield and quality across different phenological stages and two growing seasons. The analysis was based on three vegetation indices, the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Red Edge Index (NDRE), and included two morphologically distinct genotype groups, awned and awnless. The study included nine awned and nine awnless genotypes. Vegetation indices showed pronounced seasonal dynamics, with higher values during intensive vegetative development and a decline during later growth stages. The strongest relationship with grain yield was observed for GNDVI-A during the heading to beginning of flowering stage (BBCH 51–61) in Season I (r = 0.85), whereas in Season II, the strongest relationship was observed for NDRE-A during the flag leaf stage (BBCH 37–39) (r = 0.80). Awned genotypes generally showed stronger VI–yield relationships, while associations with grain quality parameters varied among genotype groups and seasons. Linear mixed-effects models showed that both VI-related effects and genotype variability contributed to the variation in grain yield and quality. For yield, marginal R2 was 0.43 in Season I and 0.50 in Season II, while conditional R2 was 0.78 and 0.64, respectively. For protein and wet gluten content, model performance was more variable, with marginal R2 values ranging from 0.30 to 0.39 for protein and from 0.31 to 0.33 for wet gluten. Genotype-level LOGO cross-validation further indicated variation in model performance when genotypes not included in model development were evaluated. Overall, the results indicate that relationships between multispectral vegetation indices and wheat grain yield and quality depend on sensing method, phenological stage, genotype characteristics, and growing season. The findings provide an exploratory basis for the application of multispectral sensing in wheat phenotyping and assessment of grain yield and quality, while further validation across broader genetic and environmental conditions is required.","本研究旨在评估基于无人机与近地多光谱传感（采用Plant-O-Meter（POM）传感器）在不同物候期和两个生长季中对小麦籽粒产量和品质评估的互补潜力。分析基于三个植被指数，即归一化差异植被指数（NDVI）、绿色归一化差异植被指数（GNDVI）和归一化差异红边指数（NDRE），并纳入两个形态学上不同的基因型组，即有芒和无芒。研究包括9个有芒基因型和9个无芒基因型。植被指数表现出明显的季节性动态变化，在旺盛营养发育期数值较高，在生长后期下降。与籽粒产量关系最强的是抽穗至开花初期（BBCH 51–61）第I季的GNDVI-A（r = 0.85），而在第II季，关系最强的是旗叶期（BBCH 37–39）的NDRE-A（r = 0.80）。有芒基因型总体上表现出更强的植被指数-产量关系，而与籽粒品质参数的关联因基因型组和季节而异。线性混合效应模型表明，植被指数相关效应和基因型变异均对籽粒产量和品质的变异有贡献。对于产量，第I季的边际R²为0.43，第II季为0.50，而条件R²分别为0.78和0.64。对于蛋白质和湿面筋含量，模型表现更为多变，蛋白质的边际R²范围为0.30至0.39，湿面筋为0.31至0.33。基因型水平的LOGO交叉验证进一步表明，在评估未纳入模型开发的基因型时，模型表现存在变异。总体而言，结果表明多光谱植被指数与小麦籽粒产量和品质之间的关系取决于传感方法、物候期、基因型特征和生长季。研究结果为多光谱传感在小麦表型分析及籽粒产量和品质评估中的应用提供了探索性依据，但仍需在更广泛的遗传和环境条件下进一步验证。","Plants","2026-09-14T00:00:00Z",75,{"impact":91,"substance":59,"depth":92,"authority":19,"freshness":20,"relevant":21,"comment":93},15,17,"该研究利用无人机与近地多光谱传感器评估有芒\u002F无芒小麦产量与品质，方法新颖、数据跨两季，对智慧农业遥感育种有参考价值，但属探索性论文，产业影响有限。",[95],{"name":87,"url":84},[26,97,98,28,29],"产量预测","小麦育种",[100,101],"产量预测 作物表型 小麦育种 智慧农业","产量预测 作物表型","产量预测作物表型小麦育种智慧农业-2522","10.3390\u002Fplants15182819",{"doi":103,"openalex_id":105,"authors":106,"venue":87,"cited_by_count":35,"oa_url":84,"card":131,"direction":42,"ingested_from":136},"W7212560710",[107,110,113,116,119,122,125,128],{"name":108,"orcid":109},"Irina Marina","https:\u002F\u002Forcid.org\u002F0000-0002-5894-363X",{"name":111,"orcid":112},"Vesna Kandić","https:\u002F\u002Forcid.org\u002F0000-0003-1999-2030",{"name":114,"orcid":115},"Marko Kostić","https:\u002F\u002Forcid.org\u002F0000-0001-9446-994X",{"name":117,"orcid":118},"Nataša Ljubičić","https:\u002F\u002Forcid.org\u002F0000-0001-5982-9401",{"name":120,"orcid":121},"Biljana Bošković","https:\u002F\u002Forcid.org\u002F0000-0003-4977-3170",{"name":123,"orcid":124},"Kosta Gligorević","https:\u002F\u002Forcid.org\u002F0000-0001-5783-4637",{"name":126,"orcid":127},"Miloš Pajić","https:\u002F\u002Forcid.org\u002F0000-0002-8905-3293",{"name":129,"orcid":130},"Milan Dražić","https:\u002F\u002Forcid.org\u002F0000-0002-0416-5174",{"tldr":132,"method":133,"finding":134,"direction":42,"opportunity":135},"用无人机与近地多光谱传感器评估有芒\u002F无芒小麦产量与品质，比较不同生育期和年份的植被指数关系。","无人机与Plant-O-Meter多光谱传感器，NDVI\u002FGNDVI\u002FNDRE，","GNDVI与NDRE在抽穗至开花期和旗叶期与产量相关性最强，有芒基因型关系更强，模型对品质预测较弱。","可探索多源遥感融合与基因型分层建模，提升跨年份、跨环境的小麦产量与品质预测泛化能力。","openalex","2026-09-15T23:30:17.124343Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":8,"content":8,"source_name":143,"source_url":8,"published_at":144,"category":145,"cover_url":8,"hotness":12,"is_selected":13,"score":146,"score_detail":147,"sources":152,"tags":154,"search_phrases":157,"slug":160,"view_count":35,"doi":8,"paper":8,"created_at":161},2261,"北大荒集团智慧农业:4800万亩耕地打造\"地块链\"式数字孪生管理","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260907\u002F20260907_8\u002Fnmrb_20260907_13398_8_2096696093043691617.html","北大荒信息有限公司智慧农业大数据中心将4800万亩耕地切分成27万个地块,每块都有编号和二维码。遥感平台接入48颗卫星每5天做一次体检,5000台田间采集设备、600个气象站、500台虫情测报灯昼夜值守,3.3万台物联网设备统一接入\"地块链\"管理。5年累计为农户线上放贷400亿元。","农民日报","2026-09-06T16:00:00Z","报道",85,{"impact":148,"substance":149,"depth":18,"authority":150,"freshness":47,"relevant":21,"comment":151},26,23,12,"北大荒4800万亩耕地实现地块级数字孪生管理，卫星遥感、物联网与地块链数据规模具体，属产业级智慧农业标杆案例，值得入选每日精选。",[153],{"name":143,"url":141},[155,26,27,156,29],"数字乡村","数字孪生",[158,159],"农业物联网 数字乡村 数字孪生 智慧农业","农业物联网 数字乡村","农业物联网数字乡村数字孪生智慧农业-2261","2026-09-13T00:04:03.310614Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":8,"source_name":168,"source_url":165,"published_at":169,"category":11,"cover_url":8,"hotness":170,"is_selected":13,"score":14,"score_detail":171,"sources":173,"tags":179,"search_phrases":182,"slug":185,"view_count":35,"doi":186,"paper":187,"created_at":197},2139,"Connecting the Canopy: C-Band and Ku-Band Satellite Technologies for Sustainable Oil Palm Plantation Management in Indonesia—A Review","https:\u002F\u002Fdoi.org\u002F10.47191\u002Fetj\u002Fv11i09.06","Indonesia's oil palm plantations are increasingly expected to combine high productivity with environmental protection, traceability, worker welfare, and smallholder inclusion. Achieving these objectives requires reliable digital connectivity across plantation landscapes that are frequently extensive, remote, and inadequately served by terrestrial telecommunications. This qualitative literature review examines the characteristics of C-band and Ku-band satellite technologies and critically explores their roles in supporting sustainable oil palm plantation management in Indonesia. Drawing on interdisciplinary literature published since 2020 covering satellite communications, Internet of Things, smart agriculture, remote sensing, oil palm agronomy, and sustainability governance, the review develops a socio-technical synthesis rather than a systematic or meta-analytic assessment. C-band generally provides stronger resilience to tropical rain attenuation and is attractive for fixed, availability-critical backbone connectivity, whereas Ku-band enables smaller terminals, greater deployment flexibility, and practical broadband access but requires more deliberate rain-fade mitigation. Neither band is intrinsically superior, and actual performance depends on link design, service architecture, traffic requirements, cost, and local conditions. The review identifies applications in environmental surveillance, precision agronomy, disease detection, harvesting, logistics, traceability, worker safety, and sustainability assurance. It proposes an Observe–Connect–Decide–Act–Verify framework and a hybrid connectivity architecture integrating field networks, satellite backhaul, terrestrial networks, edge computing, remote sensing, and analytics. Sustainable benefits ultimately depend not merely on connectivity but on inclusive governance, institutional capacity, appropriate agronomic action, and mechanisms preventing digitalization from reinforcing existing inequalities.","印度尼西亚的油棕种植园日益被期望将高生产力与环境保护、可追溯性、工人福利和小农包容性结合起来。实现这些目标需要在种植园景观中建立可靠的数字连接，而这些景观往往面积广阔、地处偏远，且地面电信服务不足。本定性文献综述考察了C波段和Ku波段卫星技术的特征，并批判性地探讨了它们在支持印度尼西亚可持续油棕种植园管理中的作用。综述借鉴了2020年以来发表的多学科文献，涵盖卫星通信、物联网、智慧农业、遥感、油棕农学和可持续性治理，构建了一种社会技术综合，而非系统性或元分析评估。C波段通常对热带降雨衰减具有更强的抵御能力，适用于固定的、可用性至关重要的骨干连接；而Ku波段则支持更小的终端、更大的部署灵活性和实用的宽带接入，但需要更有针对性的雨衰缓解措施。两者并无本质上的优劣之分，实际性能取决于链路设计、服务架构、流量需求、成本和当地条件。综述识别了其在环境监测、精准农艺、病害检测、收获、物流、可追溯性、工人安全和可持续性保障方面的应用。它提出了一个“观察—连接—决策—行动—验证”框架，以及一种混合连接架构，整合田间网络、卫星回传、地面网络、边缘计算、遥感和分析。可持续效益最终不仅取决于连接性，还取决于包容性治理、机构能力、适当的农艺行动，以及防止数字化加剧现有不平等的机制。","Engineering and Technology Journal","2026-09-10T00:00:00Z",40,{"impact":18,"substance":17,"depth":92,"authority":19,"freshness":61,"relevant":21,"comment":172},"综述系统梳理C波段与Ku波段卫星通信在印尼油棕可持续种植中的应用，提出“观测—连接—决策—行动—验证”框架与混合组网架构，对热带经济作物产区的农业信息化建设具有参考价值，但属文献综述、非原始数据研究，且地域局限于印尼。",[174,175,177],{"name":168,"url":165},{"name":168,"url":176},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22689903",{"name":168,"url":178},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22689902",[155,26,27,29,180,181],"卫星通信","油棕种植",[183,184],"农业物联网 卫星通信 数字乡村 智慧农业","农业物联网 卫星通信","农业物联网卫星通信数字乡村智慧农业-2139","10.47191\u002Fetj\u002Fv11i09.06",{"doi":186,"openalex_id":188,"authors":189,"venue":168,"cited_by_count":35,"oa_url":165,"card":192,"direction":78,"ingested_from":136},"W7212110357",[190],{"name":191,"orcid":8},"Loso Judijanto",{"tldr":193,"method":194,"finding":195,"direction":78,"opportunity":196},"综述C波段与Ku波段卫星技术在印尼油棕可持续种植管理中的应用与选择。","2020年以来跨学科文献定性综述，提出社会技术综合框架。","两波段无绝对优劣，性能取决于链路设计、成本与本地条件，可持续性更依赖治理。","可实证比较C\u002FKu波段在热带雨林衰减下的物联网回传性能，并评估小农户数字包容机制。","2026-09-11T23:30:10.114685Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":8,"content":8,"source_name":54,"source_url":201,"published_at":203,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":204,"score_detail":205,"sources":207,"tags":209,"search_phrases":210,"slug":211,"view_count":35,"doi":212,"paper":213,"created_at":233},2024,"Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104589","Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review。Biosystems Engineering","2026-09-09T00:00:00Z",78,{"impact":18,"substance":17,"depth":92,"authority":60,"freshness":20,"relevant":21,"comment":206},"核心期刊综述，系统梳理AI、物联网与遥感融合的田间作物智能灌溉研究进展，对智慧农业技术路线有参考价值，但属综述类成果、非突破性原创，适合进入每日精选。",[208],{"name":54,"url":201},[26,66,27,67,29],[69,70],"农业人工智能农业物联网智慧农业智能灌溉-2024","10.1016\u002Fj.biosystemseng.2026.104589",{"doi":212,"openalex_id":214,"authors":215,"venue":54,"cited_by_count":35,"oa_url":8,"card":8,"direction":8,"ingested_from":136},"W7212048658",[216,219,221,223,225,227,230],{"name":217,"orcid":218},"Ehab H. Hegazi","https:\u002F\u002Forcid.org\u002F0000-0002-1468-1420",{"name":220,"orcid":8},"Jian Liu",{"name":222,"orcid":8},"Ruixia Ai",{"name":224,"orcid":8},"Lin Liu",{"name":226,"orcid":8},"Xuemei Liu",{"name":228,"orcid":229},"Jin Yuan","https:\u002F\u002Forcid.org\u002F0000-0002-5803-6626",{"name":231,"orcid":232},"G. Papadakis","https:\u002F\u002Forcid.org\u002F0000-0002-1805-5056","2026-09-10T23:30:05.849251Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":8,"content":239,"source_name":240,"source_url":8,"published_at":241,"category":145,"cover_url":8,"hotness":12,"is_selected":13,"score":242,"score_detail":243,"sources":245,"tags":247,"search_phrases":249,"slug":252,"view_count":35,"doi":8,"paper":8,"created_at":253},3103,"山东省农科院举办人工智能专题舜耕论坛暨培训交流会——浙江大学数字农业农村研究中心主任何勇教授作\"作物表型多源多尺度智能感知技术与装备\"专题报告","http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10717\u002F202609\u002F1c1dbcca-09c4-421f-80dd-09910a603152.shtml","9-16 山东省农业科学院举办人工智能专题舜耕论坛暨培训交流会，落实院党委\"人工智能驱动科技创新智慧引领高质量发展\"专题活动部署，促进人工智能与各学科创新团队重点攻关方向深度耦合。论坛特邀浙江大学数字农业农村研究中心主任何勇教授作专题报告，围绕植物表型采集解析、智慧农业技术装备前沿领域，从细胞\u002F组织器官\u002F表型获取装备三个层级系统阐释作物表型智能感知技术创新实践，介绍该技术在水稻\u002F草莓\u002F茶叶等作物的示范应用。院长李向东要求各创新团队推动人工智能与作物栽培、畜禽育种、病虫害防控、种质资源鉴定、农产品质量安全等领域深度融合。会议设主会场和视频分会场，全院科研人员代表 500 余人参会。","![Image 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\n\n![Image 18](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Fny_banner02.jpg)\n\nYour browser does not support the HTML5 canvas tag.Your browser does not support the HTML5 canvas tag.Your browser does not support the HTML5 canvas tag.\n\n新闻中心![Image 19](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Flm_icon01png)\n\n*   [图片新闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10910\u002F)\n*   [农科要闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10717\u002F)\n*   [综合新闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10719\u002F)\n*   [媒体聚焦](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10723\u002F)\n*   [媒体聚焦(图片)](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch17317\u002F)\n*   [公开公示](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10800\u002F)\n*   [通知公告](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10798\u002F)\n\n![Image 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智慧引领高质量发展”专题活动部署，促进人工智能与各学科创新团队重点攻关方向深度耦合，激活科研创新内生动力。论坛特邀浙江大学数字农业农村研究中心主任何勇教授作专题报告，院党委副书记、院长李向东主持会议并讲话。\n\n报告题为《作物表型多源多尺度智能感知技术与装备》，围绕植物表型采集解析、智慧农业技术装备前沿领域，从细胞、组织器官、表型获取装备三个层级，系统阐释作物表型智能感知技术创新实践，介绍该技术在水稻、草莓、茶叶等作物的示范应用，解读无人机、作物智慧管理装备关键技术及应用路径，提出农业科技创新要加速人工智能深度融入，以多技术交叉融合助推智慧农业高质量发展，为我院农业人工智能科研布局提供重要参考。\n\n李向东指出，报告紧扣智慧农业发展前沿，兼具理论深度和实践价值，对我院科研迭代升级具有重要指导意义。他强调，要提高政治站位，把握战略导向。深入学习贯彻习近平总书记关于人工智能创新发展的重要指示精神，把智慧农业摆在全院科技创新突出位置，强化机遇意识，开辟农业科研新赛道。要聚焦主责主业，精准靶向攻坚。各创新团队依托现有科研基础，推动人工智能与作物栽培、畜禽育种、病虫害防控、种质资源鉴定、农产品质量安全等领域深度融合，坚持问题导向，紧扣产业瓶颈凝练攻关方向，推动智能技术赋能科研实践。要压实闭环管理，推动落地见效。细化攻关任务清单，强化项目、平台、人才、经费要素保障，健全调度考核机制，将人工智能攻关及成果产出纳入评价体系，力争产出高水平科研成果、实用技术与智能装备，形成可复制推广的农业人工智能应用模式。全院科研人员要以此次论坛为契机，拓宽科研视野，聚力攻关，推动我院智慧农业科技创新再上新台阶。\n\n会议设主会场和视频分会场，院属各单位主要负责人、科研分管负责人，拟组建创新团队首席、副首席及45岁以下青年科研人员代表500余人参加会议。\n\n（撰写：陈英凯 核稿：张文君）\n\n分享\n\n分享到\n\n[微信](http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10717\u002F202609\u002F1c1dbcca-09c4-421f-80dd-09910a603152.shtml 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