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平台等联合攻关，依托巴西家庭农业场景开展技术验证与成果转化，打造智慧农业南南合作标杆。\n\n近日，江苏省农业科学院与巴西农牧业研究公司(Embrapa)、南京农业大学、国机数科、国际民众合作协会共同签署合作谅解备忘录，携手共建“中国—巴西人工智能应用合作中心”，以科技创新赋能全球粮食安全与农业现代化。江苏省农科院党委委员、副院长孙洪武出席签约仪式并作主旨发言。\n\n孙洪武重点介绍了江苏省农科院在科技服务与国际合作方面的工作成效。他建议合作各方应落实好人员互访、专家考察、联合研讨等任务，聚焦中小型智能农机、农业AI平台、作物生长模型等方向开展联合攻关，建设好智慧农机服务中心示范点，搭建好人员交流桥梁，携手推进合作中心建设，为深化中巴农业科技合作、保障全球粮食安全、推动农业现代化贡献力量。\n\n巴西农牧业研究公司总裁席尔维娅·马苏鲁哈介绍，Embrapa是拉美地区最大的国家级公立农业科研机构，巴西家庭农户占比超七成，热带农业种植与生产场景丰富，拥有成熟的农业试点研发体系。她期待深度依托合作中心，与包括江苏省农业科学院在内的中方单位深化协作，开展田间技术验证、农业数据共享与技术成果转化，打造金砖农业合作标杆案例。\n\n中国—巴西人工智能应用合作中心将深度整合产学研优势资源，以巴西家庭农业实际应用场景为需求牵引，以中方智慧农业先进技术为重要支撑，推进中巴双方在联合科研攻关、示范基地建设、人才交流培养等方面实现双向赋能、双向落地，探索形成可复制、可推广的智慧农业南南合作新模式。\n\n引用： 共建中国—巴西人工智能应用合作中心 助力智慧农业南南合作 江苏省农业科学院 [引用日期：2026年10月8日]\n\n本文系转载，转发此文目的在于促进信息交流，不存在盈利性目的，此文观点与本站立场无关，不承担任何责任，不做任何投资和交易根据。如无意侵犯媒体或个人知识产权请告知我们，我们将在第一时间做删除处理。\n\n上一篇：混动争锋、沃野竞技！200马力及以上混合动力拖拉机田间实地试验验证活动即将举行","农业商情网 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智慧农业","江苏省农科院Embrapa人工智能-4768","2026-10-13T00:08:35.638471Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":8,"content":173,"source_name":174,"source_url":8,"published_at":175,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":176,"score_detail":177,"sources":182,"tags":184,"primary_topic":187,"secondary_topics":188,"classification":195,"search_phrases":217,"slug":220,"view_count":112,"doi":8,"paper":8,"created_at":221},4256,"人民论坛实评：当秋收遇上AI 智慧农机迭代重塑农耕","https:\u002F\u002Fwww.rmlt.com.cn\u002F2026\u002F1005\u002F756557.shtml","人民论坛评论文章：今年秋收AI从实验室走进田间地头成为新农具。湖北枣阳农机手收割机北斗智能终端将行距误差控制在厘米级别；湖南衡阳烘干中心2.4万个传感器为20多个稻谷品种量身定制烘干曲线；浙江粮仓平台覆盖232家收储企业6750个仓房。十五五规划纲要提出农作物耕种收综合机械化率提高到80%以上。","湖北枣阳的农机手，靠着收割机的北斗智能终端，把行距误差控制在厘米级别；湖南衡阳的烘干中心里，2.4万个传感器，为20多个稻谷品种“量身定制”烘干曲线……今年秋收，AI从实验室走进田间地头，成为百姓身边的“新农具”。\n\n习近平总书记强调，中国式现代化离不开农业农村现代化。不同于传统农耕模式，伴随AI助手、北斗终端、智能烘干、数字仓储等科技装备全面落地，深度融入粮食收割、烘干、储运、加工全链条，智慧农机迭代升级、数字技术深耕田野，正是我国现代农业高质量发展的缩影。\n\nAI助手的意义，不只是让农民“少流汗”，更在于让每一粒粮食“颗粒归仓”。湖南衡阳角山米业的智慧粮食系统进入3.0版本后，较去年上一代系统出米率提升1%，整精米率提升3%以上。减损就是增产，提质就是增收。智慧农业的价值，首先体现在“减损增效”的硬账本上。\n\n智慧农业的想象力，远不止于效率的提升。以往农民种地是“看天吃饭、凭手感烘干”，而现在是靠数据“系统识别品种、自动派仓、变温烘干”。从“人脑决策”转向“人机协同”，从“粗放管理”转向“精准施策”。“浙江粮仓”平台，打通收、储、运、管全链数据，覆盖全省232家收储企业、6750个仓房，这种系统性的数字化改造，让粮食从田间到仓库的每一步都“有迹可循”。\n\n目前，我国人工智能在农业领域的应用取得显著成效，但仍面临瓶颈。这主要表现在：一是成本门槛高。一台搭载北斗终端的收割机、一套智慧粮食系统，投入动辄数十万甚至上百万，对中小农户而言，存在“看得见、用不起”的困境。同时，后续运维、流量、软件升级等隐性成本不低，投入回报周期偏长，也在一定程度上抑制购置和使用意愿。二是“数据孤岛”现象仍存。各地平台各建各的，标准不一、接口不通，农机数据、粮库数据难以共享，数据权属、隐私安全和收益分配机制有待健全。三是操作技术转型， 一时跟不上机器的迭代。一些农民面对智能设备仍手足无措，设备更新了，技能跟不上，再好的AI也成了“摆设”。为此，既要有基础设施、智慧农机等“硬投入”，也需优化数据治理、培训指导等“软服务”，让人工智能扎根田间地头。\n\n“十五五”规划纲要对“强化农业科技和装备支撑”作出部署，提出“农作物耕种收综合机械化率提高到80%以上”的目标。针对成本门槛，加大农机购置补贴力度，探索智慧农业设备“共享租赁”“按需付费”等模式，降低中小农户的使用门槛；针对“数据孤岛”，加快制定统一的数据标准、接口规范和安全规则，实现数据共享；针对人才短板，把培训送到田间地头，依托农技站、合作社和龙头企业开展场景化教学，带动更多农民跨越“数字鸿沟”。\n\n智慧农业不是“替代农民”，而是“赋能农民”，让农民从繁重体力劳动中解放出来，成为“会种地”更“慧种地”的新型农民。为此，看待智慧农业发展，还需用辩证眼光。既看到其提效减损的巨大潜力，也正视其落地生根的现实障碍；既拥抱技术进步，也守住“为农服务”的初心。\n\n让AI在田埂上生根发芽，需要的不仅是算法的迭代，更是政策、人才、基础设施的协同发力。当农民在秋收时节收获的不仅是沉甸甸的稻谷，还有实实在在的获得感，“智慧农业”才算踏踏实实落了地。","人民论坛网 2026-10-05","2026-10-05T00:00:00Z",88,{"impact":178,"substance":179,"depth":138,"authority":82,"freshness":180,"relevant":20,"comment":181},26,21,9,"央媒评论系统梳理AI秋收落地成效与成本、数据孤岛、人才三大瓶颈，兼具政策高度与产业细节，值得精选。",[183],{"name":174,"url":171},[25,26,29,185,186],"北斗终端","数字粮仓",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[189],{"code":190,"slug":191,"name":192,"path":193,"parent_code":194},"J4","seasonal-agriculture","农时与节庆活动","\u002Ftopic\u002Fpolicy\u002Fseasonal-agriculture","J",{"status":38,"locked":39,"version":40,"method":41,"evidence":196},{"candidates":197,"agricultural_evidence":12},[198,203,209,213],{"code":31,"score":138,"supported":12,"title":199,"abstract":200,"tags":202},[47],[47,50,201],"收割机",[29],{"code":190,"score":204,"supported":12,"title":205,"abstract":207,"tags":208},11,[206],"秋收",[206],[],{"code":53,"score":20,"supported":39,"title":210,"abstract":211,"tags":212},[],[],[25],{"code":58,"score":20,"supported":39,"title":214,"abstract":215,"tags":216},[],[],[26],[218,219],"浙江粮仓 数字收储","北斗智能终端 收割机","浙江粮仓数字收储-4256","2026-10-06T00:04:59.998793Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":8,"source_name":228,"source_url":225,"published_at":229,"category":230,"cover_url":8,"hotness":11,"is_selected":12,"score":231,"score_detail":232,"sources":234,"tags":236,"primary_topic":239,"secondary_topics":240,"classification":241,"search_phrases":273,"slug":276,"view_count":151,"doi":277,"paper":278,"created_at":303},3564,"Research Progress on Intelligent Seeding Technology and Equipment: The Development of Seeders from Multi-Functional Integration to Agricultural Intelligent Agents","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191884","Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems.","播种是决定种子空间分布、作物群体结构和潜在产量形成的关键作物生产环节，也是精准、高效、绿色农业的基础。然而，田间土壤特性、区域气候和作物农艺要求具有强烈的时空异质性。基于人工经验和固定预设参数的传统播种作业无法满足大规模精准农业的需求。在精准农业、智能感知、人工智能和自主机械等领域的进步推动下，现代智能播种系统集成了精密排种、高精度环境感知和闭环动态自适应调节。此类系统可在标准露地条件下提高株距均匀性并实现精量播种深度控制，但在全球导航卫星系统（GNSS）拒止的复杂环境中，包括密植作物冠层和温室，仍面临明显的性能局限。本文综述了播种机械的演进轨迹，总结了精量播种、多功能装备集成、多源信息感知和智能决策方面的研究进展。阐述了涵盖机械优化、电子控制和感知驱动决策系统的集成设计原则。识别出播种装备的四个发展阶段：机械精量作业、电子智能调控、多功能模块集成和智能认知集成。当前智能播种技术受限于对复杂农田条件的适应性不足、多源数据融合不稳定、长期运行可靠性不够以及多场景部署成本高昂，制约了其在田间的广泛规模化应用。未来研究应将农艺知识与人工智能相结合，提升环境感知和自主决策能力，开发低成本、高可靠性的集成播种装备，支撑智能农机体系建设。","Agronomy","2026-09-25T00:00:00Z","论文",82,{"impact":16,"substance":179,"depth":138,"authority":83,"freshness":19,"relevant":20,"comment":233},"系统梳理智能播种装备从机械化到智能体四阶段演进，指出GNSS受限环境与多源数据融合瓶颈，对智慧农业装备研发有参考价值。",[235],{"name":228,"url":225},[25,26,27,237,238],"精准农业","智能播种",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":242},{"candidates":243,"agricultural_evidence":12},[244,249,255,262,267],{"code":53,"score":19,"supported":12,"title":245,"abstract":246,"tags":248},[],[237,247],"precision agriculture",[25,237],{"code":31,"score":250,"supported":12,"title":251,"abstract":252,"tags":254},7,[],[47,253],"agricultural machinery",[27],{"code":256,"score":69,"supported":12,"title":257,"abstract":258,"tags":261},"H2",[],[259,260],"土壤","soil",[],{"code":58,"score":151,"supported":12,"title":263,"abstract":264,"tags":266},[],[265],"artificial intelligence",[26],{"code":268,"score":112,"supported":12,"title":269,"abstract":270,"tags":272},"B7",[],[271],"温室",[],[274,275],"智能播种 装备","Agronomy 智能播种 装备","智能播种装备-3564","10.3390\u002Fagronomy16191884",{"doi":277,"openalex_id":279,"authors":280,"venue":228,"cited_by_count":126,"oa_url":225,"card":295,"direction":301,"ingested_from":302},"W7214297818",[281,283,285,287,289,292],{"name":282,"orcid":8},"Yuting Dong",{"name":284,"orcid":8},"Yapeng Wu",{"name":286,"orcid":8},"Shiguo Wang",{"name":288,"orcid":8},"Xiaohu Guo",{"name":290,"orcid":291},"Xin Lu","https:\u002F\u002Forcid.org\u002F0000-0003-4462-3472",{"name":293,"orcid":294},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"tldr":296,"method":297,"finding":298,"direction":299,"opportunity":300},"综述智能播种技术装备从多功能集成到农业智能体的四阶段演进及瓶颈。","文献综述，梳理精量播种、多源感知与智能决策的集成设计。","智能播种在开阔农田表现良好，但复杂环境下适应性、数据融合与成本仍受限。","智慧农业 \u002F 农业物联网","GNSS拒止的冠层与温室环境下低成本高可靠感知与自主决策播种装备是研究空白。","农业人工智能与决策模型","openalex","2026-09-26T23:30:47.821171Z",{"id":305,"title":306,"url":307,"summary":308,"summary_zh":8,"content":309,"source_name":310,"source_url":8,"published_at":311,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":312,"score_detail":313,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":356,"slug":359,"view_count":151,"doi":8,"paper":8,"created_at":360},3210,"【光明论坛】藏粮于地、藏粮于技 扎实推进乡村全面振兴——农业新质生产力引领\"十五五\"种植业高质量发展","https:\u002F\u002Fepaper.gmw.cn\u002Fgmrb\u002Fhtml\u002Fcontent\u002F202609\u002F23\u002Fcontent_25817.html","9-23 光明日报\"光明论坛\"刊文解读农业农村部近日印发的《全国种植业发展\"十五五\"规划》：规划在深入实施藏粮于地、藏粮于技战略的同时，专门就\"发挥农业新质生产力引领作用\"作出部署。种植业科技战略正由以良田、良种、良机、良法集成为主，向数据、算法、智能装备与生物技术深度融合拓展。2025年全国粮食总产量达14298亿斤、增长1.2%，单位面积产量增长1.1%，单产提升是增产的主要来源；农作物耕种收综合机械化率达76.7%，农业科技进步贡献率超过64%；农用无人机保有量超过30万架，年作业面积突破4.6亿亩。规划首次提出以精准监测、AI决策、自主作业为重点发展智慧种植，推动无人机、具身机器人等先进装备与传统植保深度融合。","**【光明论坛】**\n\n习近平总书记指出，建设农业强国，利器在科技，关键靠改革。近日，农业农村部印发《全国种植业发展“十五五”规划》，在深入实施藏粮于地、藏粮于技战略的同时，专门就“发挥农业新质生产力引领作用”作出部署。这表明，种植业科技战略正由以良田、良种、良机、良法集成为主，向数据、算法、智能装备与生物技术深度融合拓展。以新质生产力引领科技战略升级是未来五年种植业高质量发展的重要路径。\n\n实施藏粮于地、藏粮于技战略并非抽象概念，它实实在在体现在产量、装备和技术水平的变化里。相关数据显示，2025年全国粮食总产量达14298亿斤，比上年增长1.2%，其中单位面积产量增长1.1%，单产提升是增产的主要来源；农作物耕种收综合机械化率达76.7%，农业科技进步贡献率超过64%；农用无人机保有量超过30万架，年作业面积突破4.6亿亩，农机北斗终端设备应用超过220万台（套），智慧农场、智能育秧工厂等智能化场景不断涌现。《“十四五”全国种植业发展规划》把“坚持创新驱动，转型升级”作为基本原则，把“增强科技支撑”列入保障措施，智能化内容散见于相关章节，提出“探索应用智慧农业技术”。新规划则将发挥农业新质生产力引领作用单列为一个部分，与增强供给保障能力、推进绿色转型、增强防灾减灾救灾能力并列，科技由“支撑”走向“引领”，这是这份规划最鲜明的亮点。\n\n科技引领具体体现在五个方面的部署上。在品种上，把抗赤霉病小麦、耐密宜机收玉米、高油高产大豆等列为选育重点，向突破性品种要产量；在技术上，强调集成推广引领性技术，并把提高技术到位率和覆盖率作为推广要求；在装备上，一手抓大型高端智能农机，一手抓丘陵山区轻简高效农机，让不同地形都有合用的机具；在投入品上，在种植业五年规划中首次提出运用基因编辑、核酸干扰等前沿手段创制核糖核酸农药等新型产品；在场景上，首次提出以精准监测、AI决策、自主作业为重点发展智慧种植，推动无人机、具身机器人等先进装备与传统植保深度融合，并提出培育无人农机操作员等专业技术人员。这些部署与2026年中央一号文件促进人工智能与农业发展相结合的要求相衔接，也服务于国务院《加快农业农村现代化“十五五”规划》提出的“到2030年，粮食等重要农产品供给保障能力稳步提升，粮食安全根基持续夯实，农业质量效益和竞争力不断提高”的目标。其实质，是生产力三要素的同步跃升，劳动对象从良田良种扩展到数据资源，劳动资料从机械装备升级为智能装备和模型算法，劳动者从传统农机手拓展到新型专业人才。“藏粮于地”稳的是根基，“藏粮于技”强的是手段，粮食产能不仅沉淀在耕地和良种里，也生成于以数据集成与智能作业为代表的农业新质生产力之中。\n\n目前，把产能真正蓄积到数据和智能作业之中，还要经历一段爬坡过坎的过程，应清晰地看到，这场生产方式变革还处在“起步期”。因此，未来需要给农业新质生产力发展加点“催化剂”，推动其由示范应用走向大面积普及。\n\n应加快关键核心技术攻关，使其成为农业新质生产力的“发动机”。以国家重大科技任务为牵引，设立智慧农业科技创新专项，聚焦高精度低成本传感器、农业专用芯片、作物生长模型与核心算法、智能决策系统、重型智能农机装备等领域，实行“揭榜挂帅”机制，推动产学研深度联动，明确企业创新主体地位，引导创新资源向农业科技领军企业集聚。同步夯实数据要素基础，加快构建“天空地”一体化农情灾情监测网络，健全数据标准和管理体系，面向育种、栽培、植保、农机作业等场景建设高质量数据集，形成“场景牵引数据、数据驱动模型、模型赋能应用、应用创造价值”的良性循环，并探索建立数据权益确认和收益分享机制，促进数据安全有序流通。\n\n应发展农业社会化服务，使其成为智能技术进村入田的“摆渡船”。通过发挥专业化服务组织和农机社会化服务中心的作用，通过托管、代耕代种等方式，将智能农机、植保无人机和智能决策服务导入小农户生产，使农户不必购置装备也能用上智能技术。通过鼓励服务主体提供贯穿全生产周期的农事指导、灾害预警等信息服务，把技术优势转化为农户的经营收益。通过加快智慧农场等应用场景示范推广，以技术到位率覆盖率和增产增效实绩检验成效，推动智能技术由点上示范向面上应用拓展。\n\n应完善配套制度体系，使其成为推动农业新质生产力发展的“稳压器”。通过建立适应智能农机、算法模型、数据产品等新型成果的分类评价机制，让科研力量向大田需求集聚。通过推动农机购置与应用补贴向智能装备和智能作业服务延伸，引导政策资源向智能化方向倾斜。通过加快前沿产品的登记资料要求、命名规则和评价标准制定，同步完善农业数据安全管理，使新技术新产品有标可依、有规可循。通过推进基层农技推广体系改革与建设，培育大田无人农机操作员、现代设施农业技术员等专业技术人才，为智能技术落地提供人才支撑。\n\n从“藏粮于地”到“藏粮于技”，体现了种植业科技战略在新一轮科技革命和产业变革中的深化拓展。“十五五”时期是基本实现农业农村现代化的关键时期，面向未来，当数据跃升为产能提升的核心要素，算法沉淀为生产决策的智能引擎，智能装备演化为田间作业的新型农具，农业新质生产力便会从战略蓝图照进田野现实。我们期待中国人的饭碗端得更稳、成色更足。\n\n**（作者：王洋，系东北农业大学经济管理学院副院长、教授）**","光明日报","2026-09-22T23:00:00Z",93,{"impact":314,"substance":315,"depth":138,"authority":82,"freshness":180,"relevant":20,"comment":316},28,24,"央媒权威解读种植业“十五五”规划，首次将农业新质生产力单列部署，政策条款与数据详实，值得进入每日精选。",[318],{"name":310,"url":307},[25,26,27,320,321,322],"农业新质生产力","藏粮于技","种植业规划",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":326},{"candidates":327,"agricultural_evidence":12},[328,332,338,343,347,351],{"code":31,"score":151,"supported":12,"title":329,"abstract":330,"tags":331},[],[50],[27],{"code":333,"score":112,"supported":12,"title":334,"abstract":335,"tags":337},"A8",[],[336],"单产提升",[],{"code":111,"score":112,"supported":12,"title":339,"abstract":340,"tags":342},[],[341],"植保",[],{"code":53,"score":20,"supported":39,"title":344,"abstract":345,"tags":346},[],[],[25],{"code":58,"score":20,"supported":39,"title":348,"abstract":349,"tags":350},[],[],[26],{"code":352,"score":20,"supported":39,"title":353,"abstract":354,"tags":355},"J2",[],[],[320],[357,358],"农业新质生产力 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16:09\n\n为学习农业领域前沿科技创新成果，加强跨区域科研交流合作，9月17日至19日，2026年华东地区农学会学术年会在江苏扬州中兴天成国际酒店召开。本次年会以“AI赋能农业新质生产力发展”为主题，汇聚华东六省一市农业科研院所、高等院校、农技推广部门及农业科技企业专家代表。江苏沿江地区农科所王顺祥所长、陆兵副所长带队，组织10余名科研人员赴扬州参会交流。\n\n会议由江苏省农学会、扬州大学承办。开幕式后，主旨报告环节大咖云集。中国工程院院士张洪程作《水稻绿色丰产无人化栽培技术》报告；日本工程院院士邓明聪分享人工智能与非线性控制技术融合及其在农业工程领域的应用；南京农业大学陆明洲教授围绕畜禽养殖智能化技术、装备与农业大模型展开交流；南京工业大学徐虹教授介绍基于AI赋能的新型生物刺激素绿色生物制造研究。专家们从智慧种植、智能养殖、生物制造等方面，展现AI技术赋能现代农业的最新进展。\n\n9月18日下午，年会同步开设四大平行专题论坛：智慧种植·丰产提质、智慧养殖·节本增效、智造农机·装备赋能、智驱加工·延链强链。智慧种植专场聚焦AI大田生产管控、农业碳汇、水肥智能决策、数字化育种、大豆抗逆新品种选育等研究；智慧养殖专场研讨畜禽水产智能感知、稻虾生态种养智慧管控、畜禽行为智能识别与养殖环境调控技术；智造农机专场交流名优茶采摘机器人、设施蔬菜装备、智能育种加速器、丘陵山地农机装备研发实践；智驱加工专场围绕食品加工数字孪生、全溶性植物蛋白开发、果蔬及特色农产品精深加工技术开展研讨，覆盖农业生产全链条数字化转型。\n\n参会期间，我所科研人员结合自身研究方向，分赴各专题会场认真聆听报告，积极参与学术讨论，与华东地区同行专家深入交流，重点学习智慧农业装备、绿色高效栽培、农产品加工增值等领域创新成果，探讨科研项目协同合作路径。\n\n此次参会，有效拓宽了科研人员学术视野，进一步搭建起跨区域科研协作桥梁。下一步，江苏沿江地区农科所将充分吸收本次年会的新理念、新技术，立足南通沿江农业产业实际需求，持续推进农业科技创新，加快科技成果示范转化，为培育农业新质生产力、推动区域农业高质量发展贡献科研力量。\n\n[![Image 1: 1.jpeg](https:\u002F\u002Fyj.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002FS254e30553383497089ded7cfd802aec4-800.jpeg)](https:\u002F\u002Fyj.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002F254e30553383497089ded7cfd802aec4.jpeg)\n\n（图文：唐明霞；责任编辑：陈满峰；审核人：陈国清）","江苏省农业科学院沿江地区农业科学研究所","2026-09-20T10:00:00Z",58,{"impact":106,"substance":82,"depth":106,"authority":204,"freshness":180,"relevant":20,"comment":371},"区域性学术年会参会通稿，信息以会议议程与报告概览为主，无新增数据或独家结论，但AI赋能农业主题契合度高、时效新，可作为智慧农业主题页的聚合素材。",[373],{"name":367,"url":364},[25,26,375,27,376],"新质生产力","数字育种",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":380},{"candidates":381,"agricultural_evidence":12},[382,386,392,398,402,406],{"code":31,"score":151,"supported":12,"title":383,"abstract":384,"tags":385},[],[47],[27],{"code":387,"score":112,"supported":12,"title":388,"abstract":389,"tags":391},"A1",[],[390],"水稻",[],{"code":393,"score":112,"supported":12,"title":394,"abstract":395,"tags":397},"G1",[],[396],"农技推广",[],{"code":53,"score":20,"supported":39,"title":399,"abstract":400,"tags":401},[],[],[25],{"code":58,"score":20,"supported":39,"title":403,"abstract":404,"tags":405},[],[],[26],{"code":352,"score":20,"supported":39,"title":407,"abstract":408,"tags":409},[],[],[375],[411,412],"华东地区农学会 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