[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-613":3,"related-613":55},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":49,"slug":52,"view_count":53,"doi":8,"paper":8,"created_at":54},613,"中国作物学会智慧农业专委会第五届学术年会在郑州举办,朱艳任主委致辞","https:\u002F\u002Fwww.chinacrops.org\u002F44\u002F202608\u002F6952.html","2026 年 8 月 9-10 日,中国作物学会智慧农业专业委员会第五届学术年会在郑州举办,会议由中国作物学会智慧农业专委会主办,河南农业大学、南京农业大学共同承办。年会主题'智慧农业赋能作物学高质量发展',吸引全国 130 余所高校、科研院所及科技企业的 450 余名代表参会,设 104 个报告。开幕式由河南农大副校长赵鹏主持,智慧农业专委会主任委员、江苏农科院院长朱艳致辞。",null,"##### 中国作物学会智慧农业专业委员会第五届学术年会在河南郑州成功举办\n\n发布时间：2026-08-12 来源：阅读量：88 分享：\n\n2026年8月9日-10日，中国作物学会智慧农业专业委员会第五届学术年会在河南郑州成功举办。本届学术年会由中国作物学会智慧农业专业委员会主办，河南农业大学、南京农业大学共同承办。年会主题为“智慧农业赋能作物学高质量发展”，聚焦发展新质生产力、助力作物高效生产、保障国家粮食安全，吸引了来自全国130余所高校、科研院所及科技企业的450余名代表参会。在为期两天的会议里，通过104个报告（其中10个特邀报告、57个专题报告、37个博士后和研究生报告），重点围绕智慧农作、智慧育种、农业大模型、智能装备、智慧农业学科建设与人才培养等议题开展深入研讨，就智慧农业关键技术在保障粮食安全、提升作物高效生产与可持续性方面的突破与应用开展深度对话，促进学术交流与思想碰撞。\n\n![Image 1: 图片1.png](https:\u002F\u002Fcmsfiles.zhongkefu.com.cn\u002Fzuowuxuehui\u002Fupload\u002Fzuowu\u002Fariticle\u002Fueditor\u002F20260812\u002F1786497022543211.png)\n开幕式由河南农业大学副校长赵鹏主持。开幕式上，中国作物学会智慧农业专业委员会主任委员、江苏省农业科学院院长朱艳，河南省农业农村厅厅长孙巍峰，河南农业大学校长周卫分别代表智慧农业专委会、河南省和承办单位祝贺大会胜利召开。\n\n![Image 2: 94631ab2bdab3cc38d7ff398baba53a3.png](https:\u002F\u002Fcmsfiles.zhongkefu.com.cn\u002Fzuowuxuehui\u002Fupload\u002Fzuowu\u002Fariticle\u002Fueditor\u002F20260812\u002F1786497052157349.png)\n\n开幕式上还举行了河南农业大学与中国移动通信集团河南有限公司、阿里云计算有限公司的战略合作签约仪式。三方将聚焦智慧农业科研攻关、数字化人才培育、乡村振兴科技赋能等领域，打造校企协同、科教赋能、服务地方的产教融合标杆，为河南省现代农业高质量发展、数字乡村建设和教育数字化转型贡献力量。\n\n![Image 3: 图片3.png](https:\u002F\u002Fcmsfiles.zhongkefu.com.cn\u002Fzuowuxuehui\u002Fupload\u002Fzuowu\u002Fariticle\u002Fueditor\u002F20260812\u002F1786497117156759.png)\n\n在特邀报告环节，扬州大学张洪程教授作题为“水稻绿色丰产无人化栽培”报告,国家农业信息化工程技术研究中心主任赵春江研究员作题为“数智科技加快传统农耕到智慧农作的转型”的报告,南京农业大学智慧农业研究院名誉院长曹卫星教授作题为“智慧农业学科与团队建设”的报告,河南农业大学校长周卫研究员作题为“农田智慧施肥模型研发与应用”的报告,中国农业科学院都市农业研究所杨其长研究员作题为“智慧植物工厂助力快速育种”的报告,中国农业科学院作物科学研究所邱丽娟研究员作题为“智慧育种赋能耐除草剂大豆创新与绿色高效生产——以中黄6106研发与产业化应用为例”的报告，中国农业大学李民赞教授作题为“智能农业装备创新与思考”的报告,西北农林科技大学张宏鸣教授作题为“全球水力侵蚀胁迫下土壤健康风险评估与智能防控方法研究”的报告,中国农业大学冯利平教授作题为“国际作物模型研究前沿动态及智慧农业视角下的实践”的报告,河南农业大学王道文研究员作分享了“关于利用AI与合成生物学技术进一步改良小麦品质的思考”。\n\n![Image 4: 55916fde4f24874ba9609d19ee4a244d.png](https:\u002F\u002Fcmsfiles.zhongkefu.com.cn\u002Fzuowuxuehui\u002Fupload\u002Fzuowu\u002Fariticle\u002Fueditor\u002F20260812\u002F1786497141228507.png)\n\n在智慧农作、智慧育种、农业大模型、智能装备、智慧农业学科建设与人才培养等5个分会场专题报告中，57名专家围绕作物育种、无人机遥感监测、农业大模型、智能化生产、人才培养等主题分享了科研成果与经验。在博新论坛上，来自浙江大学、中国农业大学、西北农林科技大学、南京农业大学、河南农业大学、山西农业大学等22所高校和科研院所的37名博士后、研究生代表介绍了他们的研究成果。\n\n![Image 5: e0e9449ae9036bbe41e13457d078f07a.png](https:\u002F\u002Fcmsfiles.zhongkefu.com.cn\u002Fzuowuxuehui\u002Fupload\u002Fzuowu\u002Fariticle\u002Fueditor\u002F20260812\u002F1786497161695424.png)\n\n从遥感监测、高通量表型到智能育种，从农业大模型、智能体到机器人、智慧农场和具身智能，一批新技术正在快速进入农业生产和科学研究。特别是“具身智能与自主农业机械”“AI驱动的玉米基因组解析与智能预测”等报告，充分展现了智慧农业正由“看得见、测得准”向“会分析、能决策、自主作业”加速迈进的态势。青年人才的深度参与也成为本次年会的一大亮点，彰显了智慧农业领域后继有人的蓬勃力量。\n\n闭幕式上河南农业大学副校长汤继华教授作大会总结。未来，需要进一步推动人工智能与作物科学深度融合，推动数据、模型、装备和生产场景贯通，真正形成从智能感知、智能认知、智能决策到智能作业的完整技术链条。同时，要持续加强智慧农业学科建设和复合型人才培养，为农业科技创新提供持续的人才支撑。\n\n闭幕式上还举行了会旗交接仪式。山西农业大学副校长李步高教授代表下一届年会承担单位接旗。他对本次会议的成功举办表示热烈祝贺，并向全体与会专家发出诚挚邀请：2027年，让我们相聚山西农业大学，共话智慧农业新未来，共绘乡村振兴新篇章。\n\n![Image 6: 图片6.png](https:\u002F\u002Fcmsfiles.zhongkefu.com.cn\u002Fzuowuxuehui\u002Fupload\u002Fzuowu\u002Fariticle\u002Fueditor\u002F20260812\u002F1786497183906274.png)\n\n本次年会的成功举办，为智慧农业领域专家学者搭建了前沿交流、思想碰撞、协同创新的高端平台，有力推动了作物科学、信息技术、智能装备等多学科的交叉融合。会议既有深入的学术思想交流，也有高校、科研院所和企业协同合作的务实成果，充分体现了智慧农业发展中多主体协同、产学研用贯通的良好态势。与会代表一致表示，将以此次郑州相聚为新起点，进一步凝聚智慧农业领域创新力量，携手推动我国智慧农业和作物学科高质量发展，为保障国家粮食安全、加快建设农业强国作出新的更大贡献。\n\n![Image 7: 图片7.png](https:\u002F\u002Fcmsfiles.zhongkefu.com.cn\u002Fzuowuxuehui\u002Fupload\u002Fzuowu\u002Fariticle\u002Fueditor\u002F20260812\u002F1786497206823164.png)\n\n会议期间还召开了智慧农业专业委员会第一届第五次全体会议，全体委员就专委会近5年工作做了梳理总结，并讨论制定了下一步工作计划。\n\n供稿：中国作物学会智慧农业专业委员会","中国作物学会 2026-08-10 智慧农业专委会","2026-08-10T00:00:00Z","报道",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},25,20,15,12,4,1,"全国性智慧农业学术年会，汇聚多方专家，内容详实，但时效性稍差。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","河南农大","年会","朱艳",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44],{"code":32,"score":20,"supported":45,"title":46,"abstract":47,"tags":48},true,[27],[27],[27],[50,51],"智慧农业 河南农大 年会 朱艳","智慧农业 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The findings can be useful for precision agriculture, crop management, and future research on digital image processing for agricultural applications.","香蕉叶片养分浓度与可测量光学特征之间的关系：一项基于定量图像的研究探讨了利用定量图像分析研究香蕉叶片养分浓度与光学特征之间的关系。该研究聚焦于可测量的叶片特征，包括颜色强度、RGB值及其他图像衍生参数，以识别与植物养分状况相关的模式。香蕉叶片的数字图像可提供关于叶片外观变化的有价值信息，这些变化可能反映养分有效性的差异。通过将这些特征与养分浓度测量结果一并分析，该研究旨在建立可支持植物养分评估的定量关系。该方法可能有助于开发低成本、非破坏性且基于图像的香蕉植株健康监测技术。研究结果可用于精准农业、作物管理以及未来农业应用数字图像处理的研究。","Zenodo (CERN European Organization for Nuclear 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Africa: 2000-2026 Systematic Review.","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22601.71520","Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.。OpenAlex","OpenAlex",37,{"impact":185,"substance":56,"depth":185,"authority":56,"freshness":129,"relevant":22,"comment":186},8,"中非数字农业系统综述，主题相关但摘要信息极少，仅题录级元数据，增量与权威性有限，不建议进入每日精选。",[188,189],{"name":182,"url":180},{"name":182,"url":190},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[192,27,193,194],"数字农业","农业信息化","中非",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":198},{"candidates":199,"agricultural_evidence":45},[200],{"code":32,"score":71,"supported":45,"title":201,"abstract":203,"tags":204},[202],"digital agriculture",[202],[193,192,27],[206,207],"中非 数字农业","农业信息化 数字农业 智慧农业 中非","中非数字农业-5396","10.13140\u002Frg.2.2.22601.71520",{"doi":209,"openalex_id":211,"authors":212,"venue":8,"cited_by_count":100,"oa_url":180,"card":8,"direction":175,"ingested_from":117},"W7222930115",[213,215,217,220],{"name":214,"orcid":8},"Anani Ogou",{"name":216,"orcid":8},"Henry Chotangui",{"name":218,"orcid":219},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":221,"orcid":8},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":8,"content":8,"source_name":182,"source_url":226,"published_at":126,"category":65,"cover_url":8,"hotness":13,"is_selected":14,"score":228,"score_detail":229,"sources":231,"tags":233,"primary_topic":236,"secondary_topics":237,"classification":238,"search_phrases":251,"slug":254,"view_count":100,"doi":255,"paper":256,"created_at":262},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex",56,{"impact":70,"substance":185,"depth":13,"authority":20,"freshness":185,"relevant":22,"comment":230},"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[232],{"name":182,"url":226},[27,234,77,235],"农业机器人","智能采收",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":239},{"candidates":240,"agricultural_evidence":45},[241,246],{"code":32,"score":242,"supported":45,"title":243,"abstract":244,"tags":245},13,[88],[88],[27,77],{"code":247,"score":22,"supported":14,"title":248,"abstract":249,"tags":250},"F6",[],[],[234],[252,253],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":255,"openalex_id":257,"authors":258,"venue":8,"cited_by_count":100,"oa_url":226,"card":8,"direction":261,"ingested_from":117},"W7222971426",[259],{"name":260,"orcid":8},"Joydeep Dey","智慧农业 \u002F 农业物联网","2026-10-20T23:30:08.600540Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":8,"source_name":269,"source_url":266,"published_at":126,"category":65,"cover_url":8,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":275,"tags":277,"primary_topic":282,"secondary_topics":283,"classification":284,"search_phrases":292,"slug":295,"view_count":100,"doi":296,"paper":297,"created_at":322},5375,"Triboelectric Nanogenerator Based on All‐Cellulose Triboelectric Layer for Droplet Energy Harvesting and Self‐Powered Agriculture Monitoring","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsmll.76354","Cellulose-based triboelectric nanogenerators (TENGs) attract increasing attention; however, developing high-performance all-cellulose-based triboelectric layer TENGs remains challenging. In this study, a regenerated cellulose nanofiber film (R-CNF) is used as a substrate, and an aminosilane intermediate layer is introduced. By utilizing the silanol groups on the silane chains as compatible reactive sites for fluorination grafting, and simultaneously enhancing the bonding strength between modified layers through N-H···F-C hydrogen-bond interactions between amino and fluorine groups, a fluorinated regenerated cellulose nanofiber film (F-R-CNF) is successfully prepared. The degradation rate of F-R-CNF within 13 days is approximately 0.96%. When assembled into a TENG with an ethyl cellulose nanofiber film(ECNF), the output voltage of the resulting TENG is ten times higher than that of the R-CNF-based TENGs, while maintaining 85% of its output performance at 80% relative humidity. Furthermore, a droplet energy harvesting TENG (DE-TENG) is constructed to harvest energy from falling water droplets, demonstrating the potential of the DE-TENG to power agricultural micro-sensors. This study provides a feasible strategy for designing high-performance all-cellulose-based triboelectric layer triboelectric nanogenerators and reveals the great potential of F-R-CNF for self-powered devices and smart agriculture applications.","纤维素基摩擦纳米发电机（TENGs）日益受到关注，然而开发高性能全纤维素基摩擦电层TENG仍面临挑战。本研究以再生纤维素纳米纤维膜（R-CNF）为基底，引入氨基硅烷中间层，利用硅烷链上的硅醇基团作为氟化接枝的相容反应位点，同时通过氨基与含氟基团之间的N-H···F-C氢键相互作用增强改性层间的结合强度，成功制备了氟化再生纤维素纳米纤维膜（F-R-CNF）。F-R-CNF在13天内的降解率约为0.96%。将其与乙基纤维素纳米纤维膜（ECNF）组装成TENG后，所得TENG的输出电压比基于R-CNF的TENG高十倍，同时在80%相对湿度下仍保持85%的输出性能。此外，构建了液滴能量收集TENG（DE-TENG）以收集下落水滴的能量，展示了DE-TENG为农业微传感器供电的潜力。本研究为设计高性能全纤维素基摩擦电层摩擦纳米发电机提供了可行策略，并揭示了F-R-CNF在自供电器件和智慧农业应用中的巨大潜力。","Small",81,{"impact":70,"substance":272,"depth":273,"authority":71,"freshness":185,"relevant":22,"comment":274},22,19,"全纤维素摩擦纳米发电机实现液滴能量收集并驱动农业微传感器，方法新颖、数据扎实，属智慧农业自供电传感的实质进展，值得入选。",[276],{"name":269,"url":266},[27,278,279,280,281],"摩擦纳米发电","液滴能量收集","自供电传感","纤维素材料",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":285},{"candidates":286,"agricultural_evidence":45},[287],{"code":32,"score":146,"supported":45,"title":288,"abstract":289,"tags":291},[],[27,290],"smart agriculture",[27],[293,294],"全纤维素 摩擦纳米发电机 液滴能量收集","F-R-CNF 自供电 农业监测","全纤维素摩擦纳米发电机液滴能量收集-5375","10.1002\u002Fsmll.76354",{"doi":296,"openalex_id":298,"authors":299,"venue":269,"cited_by_count":100,"oa_url":8,"card":317,"direction":261,"ingested_from":117},"W7222917387",[300,302,305,307,309,312,314],{"name":301,"orcid":8},"Hengwei Xiong",{"name":303,"orcid":304},"Qiaonan Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9365-0867",{"name":306,"orcid":8},"Weijie Li",{"name":308,"orcid":8},"Kangwen Shi",{"name":310,"orcid":311},"Xinyan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-6901-8977",{"name":313,"orcid":8},"Wenpeng Fan",{"name":315,"orcid":316},"Youqiang Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-9129-8050",{"tldr":318,"method":319,"finding":320,"direction":261,"opportunity":321},"制备全纤维素摩擦电层TENG，收集水滴能量为农业微传感器供电。","再生纤维素纳米纤维膜经氨基硅烷中间层氟化接枝，与乙基纤维素膜组装TENG。","输出达R-CNF基TENG的10倍，80%湿度下保持85%性能，13天降解约0.96%。","可探索全纤维素TENG在田间高湿环境下的长期稳定性及与无线传感节点的集成供电。","2026-10-20T23:30:08.252085Z",{"id":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":8,"source_name":125,"source_url":326,"published_at":126,"category":65,"cover_url":8,"hotness":13,"is_selected":14,"score":66,"score_detail":329,"sources":331,"tags":333,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":350,"slug":353,"view_count":100,"doi":354,"paper":355,"created_at":365},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。",{"impact":70,"substance":272,"depth":70,"authority":20,"freshness":129,"relevant":22,"comment":330},"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[332],{"name":125,"url":326},[27,76,334,77,335],"数字孪生","植物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":339},{"candidates":340,"agricultural_evidence":45},[341,346],{"code":32,"score":18,"supported":45,"title":342,"abstract":344,"tags":345},[343],"digital twin",[77,334,88],[334,27,77],{"code":91,"score":22,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[76],[351,352],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":354,"openalex_id":356,"authors":357,"venue":125,"cited_by_count":100,"oa_url":326,"card":360,"direction":173,"ingested_from":117},"W7222885652",[358],{"name":359,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":361,"method":362,"finding":363,"direction":173,"opportunity":364},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z"]