[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2702":3,"related-2702":67},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":62,"slug":65,"view_count":21,"doi":8,"paper":8,"created_at":66},2702,"山东省重点研发计划肉制品智能加工与品质控制关键技术与应用中期推进会召开","http:\u002F\u002Fwww.sdncps.com\u002Farticles\u002Fch12362\u002F202609\u002Ff679ec70-999d-4bf7-b599-17a92064b4af.shtml","山东省重点研发计划肉制品智能加工与品质控制关键技术与应用中期推进会在山东省农科院加工所召开,金锣文瑞食品、中国农科院农产品加工所、齐鲁工大、得利斯食品等项目参与单位20余人参会。围绕带骨畜禽副产品智能异质分离技术、典型肉制品工艺重构与品质保持技术等课题汇报。",null,"![Image 1](http:\u002F\u002Fwww.sdncps.com\u002Fresource\u002Fsdnkyncpyyyj\u002Fimage\u002F202609\u002F08420442-866a-42f1-8243-83c644f83766m.jpg)\n\n9月11日，山东省重点研发计划“肉制品智能加工与品质控制关键技术与应用”中期推进会在我所召开。金锣文瑞食品、中国农科院农产品加工所、山东省农科院农产品所、齐鲁工大、得利斯食品等项目主持及参与单位的领导及科研骨干20余人参加了会议。\n\n会上，刘丽娜所长致欢迎辞，邀请项目组专家加强交流合作。各课题负责人围绕带骨畜禽副产品智能异质分离技术、典型肉制品工艺重构与品质保持技术，肉制品多场景多维协同智能保鲜与监测技术及装备的研发情况进行了汇报。项目主持人张春晖研究员对项目实施情况进行了总结，要求严格按照山东省重点研发计划管理要求完成项目任务考核指标。与会专家们就课题执行中的难点问题深入研讨，认真梳理明确了改进措施与推进路径。\n\n此次会议梳理了阶段性任务，明确了工作时间节点，强化了项目组织管理，为保障项目技术场景落地提供有力支撑。（撰写：王维婷 核稿：刘丽娜）","山东省农科院加工所 2026年9月11日","2026-09-11T00:00:00Z","报道",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},16,14,12,6,1,"省级重点研发计划中期推进会，涉及智能分离、工艺重构与智能保鲜监测等实质技术进展，但属项目例行节点，公共增量有限，可作行业动态收录。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","肉制品加工","智能加工装备","品质控制","农业科技项目",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","E",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,52,57],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},"C",3,true,[],[50],"畜禽",[],{"code":32,"score":46,"supported":47,"title":53,"abstract":54,"tags":56},[],[55],"农产品加工",[],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[26],[63,64],"农业科技项目 智能加工装备 肉制品加工 品质控制","农业科技项目 智能加工装备","农业科技项目智能加工装备肉制品加工品质控制-2702","2026-09-17T00:04:37.317803Z",{"total":20,"page":21,"page_size":20,"items":68},[69,159,206,302,368,401],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":74,"content":8,"source_name":75,"source_url":72,"published_at":76,"category":77,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":78,"sources":82,"tags":84,"primary_topic":89,"secondary_topics":95,"classification":103,"search_phrases":138,"slug":141,"view_count":46,"doi":142,"paper":143,"created_at":158},3613,"AI-Assisted Demand Forecasting and Production Scheduling for High-Volume Food Manufacturing in Saudi Arabia: A Food Security Framework for Vision 2030","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1723457","High-volume food manufacturing in Saudi Arabia is increasingly exposed to volatile demand, short product life cycles, promotion-driven peaks, imported input dependencies, capacity bottlenecks, and the operational consequences of seasonal and religious events. These conditions make demand forecasting and production scheduling inseparable rather than sequential planning tasks. This review synthesizes research published between 2020 and 2025 on artificial intelligence-assisted demand forecasting, perishable-product planning, finite-capacity scheduling, digital-twin-enabled decision support, and food supply-chain resilience. The study develops an integrated framework in which demand sensing produces probabilistic forecasts that are translated into production decisions through capacity, shelf-life, sanitation, changeover, labour, inventory, and service constraints. Evidence indicates that machine-learning and deep-learning methods can improve forecast accuracy when promotions, weather, calendar events, regional variation, and censored demand are represented appropriately, while optimization models can convert these signals into feasible schedules that reduce changeovers, waste, shortages, and unstable resource use [1-8]. The review also identifies a persistent implementation gap: forecasting studies often stop at predictive accuracy, whereas scheduling studies frequently assume demand inputs are fixed and reliable. The proposed framework closes this gap by linking forecast uncertainty, rolling-horizon scheduling, execution feedback, and food-security outcomes. For Saudi manufacturers, the value of this integration lies not only in lower cost, but also in improving product availability, freshness, localized processing capacity, shock recovery, and evidence-based management under Vision 2030. The paper concludes with a staged implementation roadmap and research priorities for explainability, cross-site scheduling, event-aware forecasting, data governance, and resilient planning.","沙特阿拉伯的大规模食品制造日益面临需求波动、产品生命周期短、促销驱动的峰值、进口原料依赖、产能瓶颈以及季节性和宗教事件的运营影响。这些条件使得需求预测与生产调度成为不可分割的任务，而非顺序规划任务。本文综述了2020年至2025年间发表的相关研究，涵盖人工智能辅助需求预测、易腐产品规划、有限产能调度、数字孪生赋能的决策支持以及食品供应链韧性。研究构建了一个集成框架，其中需求感知产生概率预测，并通过产能、保质期、卫生、换产、劳动力、库存和服务约束转化为生产决策。证据表明，当促销、天气、日历事件、区域差异和截尾需求得到适当表征时，机器学习和深度学习方法能够提高预测精度，而优化模型可将这些信号转化为可行的调度方案，从而减少换产、浪费、缺货和不稳定的资源使用[1-8]。本综述还指出了一个持续存在的实施缺口：预测研究往往止步于预测精度，而调度研究则常常假设需求输入是固定且可靠的。所提出的框架通过将预测不确定性、滚动时域调度、执行反馈和食品安全结果联系起来，弥合了这一缺口。对于沙特制造商而言，这种集成的价值不仅在于降低成本，还在于改善产品可得性、新鲜度、本地化加工能力、冲击恢复能力以及“2030愿景”下的循证管理。本文最后提出了分阶段实施路线图以及可解释性、跨厂区调度、事件感知预测、数据治理和韧性规划方面的研究优先事项。","Iconic Research and Engineering Journals","2026-09-25T00:00:00Z","论文",{"impact":19,"substance":79,"depth":17,"authority":20,"freshness":80,"relevant":21,"comment":81},18,8,"综述性论文，提出AI需求预测与生产排程整合框架，对智慧农业与粮食安全有参考价值，但属境外研究且期刊权威性一般。",[83],{"name":75,"url":72},[26,85,86,87,88],"农业人工智能","粮食安全","供应链韧性","生产调度",{"code":90,"slug":91,"name":92,"path":93,"parent_code":94},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[96,97],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":98,"slug":99,"name":100,"path":101,"parent_code":102},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":104},{"candidates":105,"agricultural_evidence":47},[106,112,120,126,132],{"code":32,"score":107,"supported":47,"title":108,"abstract":110,"tags":111},11,[109],"food 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20","文献综述2020-2025年AI预测、易腐品规划、有限产能调度与数字孪生研究。","预测研究止于精度、调度研究假设需求固定，整合框架可弥合实施鸿沟并提升粮食安全。","农业人工智能与决策模型","可研究事件感知预测与滚动调度耦合、跨工厂协同及可解释AI在食品供应链韧性中的应用。","智慧农业 \u002F 农业物联网","openalex","2026-09-27T23:30:14.516185Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":8,"content":8,"source_name":164,"source_url":8,"published_at":165,"category":77,"cover_url":8,"hotness":13,"is_selected":14,"score":166,"score_detail":167,"sources":170,"tags":172,"primary_topic":175,"secondary_topics":176,"classification":177,"search_phrases":193,"slug":196,"view_count":46,"doi":8,"paper":197,"created_at":205},1583,"数字经济对农产品加工业全要素生产率的影响（智慧农业 8(3): 215-225）","https:\u002F\u002Fk.sina.com.cn\u002Farticle_5953466437_162dab0450670bau9c.html","沈阳农业大学韩晓燕、王星蔚、黄泽澔、陈菁、陈迪副教授基于 2012—2024 年中国 A 股上市公司中农产品加工业相关企业数据，测算农产品加工业全要素生产率，利用指标体系和《中国统计年鉴》数据测算省域数字经济发展水平。研究发现：数字经济能够有效提升农产品加工业全要素生产率，企业研发投入在其中发挥部分中介作用；数字经济的作用存在区域和企业上的异质性，数字化政府水平较低时数字经济显著抑制全要素生产率的提高，营商环境水平较高时数字经济显著正向促进全要素生产率的提高。","智慧农业(中英文)期刊","2026-08-31T12:23:00Z",75,{"impact":79,"substance":168,"depth":79,"authority":18,"freshness":46,"relevant":21,"comment":169},22,"基于上市公司数据实证数字经济对农产品加工业TFP的影响，结论有政策参考价值，但时效性较低。",[171],{"name":164,"url":162},[26,173,55,174],"数字经济","全要素生产率",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":178},{"candidates":179,"agricultural_evidence":47},[180,184,188],{"code":32,"score":19,"supported":47,"title":181,"abstract":182,"tags":183},[55],[55],[55],{"code":58,"score":121,"supported":47,"title":185,"abstract":186,"tags":187},[26],[],[26],{"code":189,"score":21,"supported":14,"title":190,"abstract":191,"tags":192},"I6",[],[],[173],[194,195],"全要素生产率 农产品加工 数字经济 智慧农业","全要素生产率 农产品加工","全要素生产率农产品加工数字经济智慧农业-1583",{"doi":8,"openalex_id":8,"authors":198,"venue":8,"cited_by_count":148,"oa_url":8,"card":199,"direction":154,"ingested_from":204},[],{"tldr":200,"method":201,"finding":202,"direction":154,"opportunity":203},"研究数字经济对农产品加工业全要素生产率的影响，发现其有提升作用且存在异质性。","基于A股上市公司数据，测算TFP和数字经济发展水平，采用中介效应和异质性分析。","数字经济提升农产品加工业TFP，研发投入起部分中介作用，效果受数字化政府和营商环境影响。","可探究数字经济对农业细分行业TFP的影响机制，或结合微观企业数据与区域政策环境进行更深入分析。","agent","2026-09-04T00:05:34.226304Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":8,"content":211,"source_name":212,"source_url":8,"published_at":213,"category":214,"cover_url":8,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":220,"tags":222,"primary_topic":228,"secondary_topics":234,"classification":241,"search_phrases":297,"slug":300,"view_count":121,"doi":8,"paper":8,"created_at":301},276,"湖北省人民政府办公厅印发《加快农业科技创新专项行动工作方案(2026—2030年)》——明确种业振兴、生物制造、智能农机、智慧农业、绿色低碳、农产品加工六大攻关方向","https:\u002F\u002Fwww.hubei.gov.cn\u002Fzfwj\u002Fezbf\u002F202607\u002Ft20260731_5987083.shtml","湖北省人民政府办公厅7月31日印发《湖北省加快农业科技创新专项行动工作方案(2026—2030年)》,为湖北加快发展农业新质生产力、培育壮大农业领域新兴产业绘制系统性战略地图。湖北是农业大省、科教大省,农业科技进步贡献率超过68%,位居中部第一。《方案》围绕「创新什么」「谁来创新」「如何保障创新」三方向给出明确指引:重点部署种业振兴、生物制造、智能农机、智慧农业、绿色低碳、农产品加工六大领域攻关任务,提出加快选育高产多抗优质新品种,开发替代蛋白、生物农兽药、生物肥料等标志性产品,创制农业机器人、低空无人机等智能装备,打造一批智慧农场;实施农业科技企业培育工程,力争新培育5家龙头型、50家高速成长型、500家潜力型科技企业;建设10个中试验证平台和30个中试熟化基地,布局一批农业科技大市场和企业孵化器;深入实施院士专家科技服务「515」行动,组建「揭榜挂帅」团队开展人才组团式科技服务。深入实施「人工智能+农业」工程,突破农业专用芯片、智能传感器、空天地跨尺度监测、多模态数据融合及算法、动植物生长与产量预测模型、智能决策调控等关键技术,构建种质资源、耕地、气候、多组学等核心数据库,研发农业大模型与专用智能体。","各市、州、县人民政府，省政府各部门：\n\n《湖北省加快农业科技创新专项行动工作方案（2026—2030年）》已经省政府同意，现印发给你们，请认真贯彻落实。\n\n2026年7月24日\n\n（此件公开发布）\n\n**湖北省加快农业科技创新**\n\n**专项行动工作方案（2026—2030年）**\n\n为深入贯彻习近平总书记关于科技创新的重要论述和考察湖北重要讲话精神，落实国家农业科技创新“十五五”规划要求，主动顺应现代农业发展趋势，坚持需求牵引和供给导向，大力推动科技创新与产业创新深度融合，为全省现代农业发展提供关键支撑，制定本工作方案。到2030年，全省农业科技进步贡献率突破72%、农作物耕种收综合机械化率达到84%、主要农作物良种覆盖率稳定在98%以上，农业领域新兴产业综合产值突破6000亿元。\n\n**一、实施有组织的协同攻关**\n\n**1.加强农业重大新品种培育。**统筹种质资源保护与创新利用，深入实施种业振兴工程和“尖刀工程”，加强农业生物杂种优势形成与固定机制、动物高效节粮减损机理等基础研究，集成应用基因编辑、干细胞育种、全基因组选择、智能设计育种等技术，高通量挖掘鉴定高产、优质、抗逆、抗病等重大基因，创制突破性新种质，推动育种技术体系向智能化、精准化升级。加快选育高产多抗优质粮油作物和经济作物新品种；挖掘特色畜禽资源，构建高效扩繁技术体系，选育高效节粮核心种源及优质高产抗逆新品种（配套系）；迭代无肌间刺鱼等种质创新技术，培育优质淡水水产新品种。\n\n**2.加大生物制造新产品创制。**统筹前沿技术攻关与产业化落地，深入实施合成生物重点研发和农业微生物产业发展重大专项，突破AI驱动的生物元件设计、特色底盘细胞构建、新型生物反应器创制、跨界生物合成、细胞功能编辑等关键技术，创制低成本微生物菌种生产装备。核心菌种自给率达到40%以上。开发未来功能食品、替代蛋白（肽）、动植物疫苗、生物农兽药、生物肥料、非粮生物基产品等标志性产品。\n\n**3.加快智能农机新装备研制。**统筹全程全面机械化与农机装备产业转型升级，深入实施装备制造业高质量发展专项、农机研发制造推广应用一体化项目，加强多场景人机协同智能操控原理研究，突破高精度卫星导航、新能源动力、精准变量作业等关键技术。研制智能播种、低损收获等大田农机和轻量化、小型丘陵山区适用农机，创制农业机器人、低空无人机、农产品产地高效保鲜干燥和精深加工智能装备等，构建涵盖粮油、果菜茶、畜禽水产等领域全程智能化装备技术体系。\n\n**4.加速智慧农业新技术开发。**统筹数字赋能与业态升级，深入实施“人工智能+农业”工程，以人工智能引领农业科研范式变革。突破农业专用芯片与智能传感器、空天地跨尺度监测、多模态数据融合及算法、动植物生长与产量预测模型、智能决策调控等关键技术，构建种质资源、耕地、气候、多组学等核心数据库。研发农情采集、大数据处理、动植物生长（产）智能诊断调控、畜禽健康与兽药智能应用等系统，开发农业大模型与专用智能体。推进农田水利智慧化升级，加快建成适宜耕作、旱涝保收、高产稳产的现代化良田。加快产地仓、烘干、冷链等设施数字化改造，支持企业建设数字工厂和数智生产线，打造一批智慧农场。\n\n**5.加力绿色低碳新模式集成。**统筹高水平保护和高质量发展，突破绿色防控、土壤改良、种养循环、资源综合利用等关键技术，研发绿色农药、生物有机肥、生物基可降解材料等绿色投入品，创制治理农田抗生素、微塑料、重金属等污染物的技术产品，集成推广“黑水虻+微生物”转化有机废弃物等先进技术，构建绿色低碳生产体系。开展碳汇监测、碳足迹核算与碳交易研究，探索生态产品价值实现路径。加强农产品质量安全检测技术研发，围绕农产品农兽药残留、生物毒素污染、新污染物等，攻克风险隐患快速识别与源头阻控关键技术，完善新型农食产品安全性评估技术体系，研发智能快速检测技术装备，支撑农产品质量安全监测合格率稳定在98%以上。\n\n**6.助力农产品加工提质增效。**统筹初加工、精深加工与副产物综合利用，开发营养健康的休闲食品、食药同源的保健食品、风味独特的调味品和定制化的预制食品。围绕粮油、果蔬、畜禽、水产、乳制品等，研发产后品质保持与贮运保鲜技术，开发智能分级、绿色加工与营养强化技术，创制高品质食材、高端食品基料与特需功能食品。推进加工副产物高值利用，开发功能脂质、蛋白（肽）、多糖等产品，加快构建覆盖产前、产中、产后的现代农业产业技术体系，引领产业升级。\n\n**二、推动科技成果高效转化**\n\n**7.畅通科技成果转化渠道。**支持建设一批概念验证中心，推进科技成果中试熟化、产业适配性与市场转化潜力验证。聚焦生物制造、智能农机、智慧农业等方向，建设10个中试验证平台和30个中试熟化基地。发挥国家技术转移中部中心引领作用，强化农业技术经理人队伍建设，布局建设一批农业科技型企业孵化器、农业科技成果转移服务中心、农业科技大市场，建设一批以企业为主体的产学研协同创新平台，促进成果、需求、资本精准对接。\n\n**8.推动重大成果落地应用。**深入实施院士专家科技服务农业产业链“515”行动、农业科技服务支点建设“五大提质行动”“乡村振兴荆楚行”和实用技术大赛等活动，遴选发布一批主推技术、主导品种、先进实用技术，组建一批“揭榜挂帅”团队，开展人才组团式科技服务。积极参与国家标准制定，加快推进转基因、基因编辑、合成生物学、微生物组学等领域科研成果产业化进程。布局建设一批乡村振兴科技创新基地、现代农业科技试验示范基地，集成推广“良田、良种、良机、良法”配套技术模式，推动更多科技成果转化为现实生产力。\n\n**9.提升农技推广服务效能。**积极培育农技社会化服务组织，引导种业、农资等企业和各类农机作业服务主体，通过技术托管、利润分成等方式，与农户结成利益共同体，将技术推广深度嵌入产业链，形成收益共享、持续服务的良性循环。推动供需精准对接和按效付费，探索市场化农技服务新模式。深化基层农技推广体系改革，健全以岗位绩效为核心的薪酬分配制度。支持院地、校地共建科技小院、专家大院等，引导科研院所深入一线开展农技推广。\n\n**三、大力培育农业新质生产力**\n\n**10.建强科技创新平台。**加快建设国家作物表型组学、农业微生物重大设施，推动洪山实验室、武汉国家现代农业产业科技创新中心联动发展，加强与全国重点实验室合作。支持高校、科研院所与企业共建技术创新中心、产业研究院和新型研发机构，推动产学研用协同创新。高标准建设“一带一路”联合实验室，推动区域合作与跨国协同等开放创新。支持襄阳创建国家农业高新技术产业示范区、宜昌建设农业微生物实验室、荆州打造洪湖水生态（水产品）实验室，加快构建以武汉为中心，以襄阳、宜昌、荆州为骨干，全域协同联动的“一芯三核多点支撑”农业科技创新格局。\n\n**11.培育壮大农业科技领军企业。**实施农业科技企业培育工程，力争新培育5家龙头型、50家高速成长型、500家潜力型科技企业。聚焦十大重点农业产业链，招引一批涉农世界500强和中国100强企业，推动在鄂建立区域总部、研发中心和高端生产制造基地。支持企业推进产品技术创新，将企业关键技术需求列为攻关重点。鼓励企业建立研发准备金制度，牵头组建创新联合体和新型研发机构，深度参与创新全过程。支持企业开展商业模式创新，通过并购重组、参股控股、改制上市等方式壮大实力，面向全国、全球重构供应链。鼓励企业推进经营管理模式创新，建立现代企业制度，提升可持续发展能力。\n\n**12.培育优势特色产业集群。**立足各地资源禀赋优化农业布局，支持将武汉都市圈建设成国家生物育种产业创新高地；支持武汉、宜昌、十堰等地打造国内领先的合成生物产业园；依托“汉孝随襄十”汽车走廊，加快建设智能农机装备产业园，形成农机装备产业带；支持江汉平原创建百万亩智慧农（牧、渔）场、鄂西山区及大别山区打造十万亩数字果（菜、茶）园。\n\n**四、持续优化农业科技创新生态**\n\n**13.加强政策服务供给。**健全面向产业征集需求的任务来源机制，完善“揭榜挂帅”“赛马”“定向委托”等项目组织实施机制。开展职务科技成果赋权改革试点，赋予研发人员所有权或10年以上长期使用权。鼓励以技术入股、分红等形式参与成果转化，转让净收入或股权按比例分配给科研团队。推行以创新能力、实效、贡献为导向的人才评价改革，实行高层次人才引进推荐认定制，支持符合条件的企业自主开展职称评审，在科技创新一线发现和培养农业战略科学家。探索“双重聘用、岗位特聘、双向兼职”用人机制，鼓励高校、科研院所选派人员依规兼任“科技副总”。提高企业人才参评“楚天英才计划”的比例，每年培训100名农业科技领军人才。\n\n**14.强化要素资源保障。**统筹财政支农资金和科技创新专项，加大对农业科技创新的支持力度。用好“投、补、贷、保”政策工具，发挥省农业产业发展母基金和农业创投基金作用，构建“创投+产投”基金体系。引导金融机构推出农业科技信贷产品，用好科技型中小微企业知识价值和民营中小企业商业价值信用贷款政策。举办农业科技金融对接活动和创新创业大赛，对关键核心技术领域的科技型企业，加强上市培育和融资服务。探索“先租后让、租让结合、弹性年期出让”等灵活土地供应方式，依法保障重点项目用地需求。注重发挥农村综合改革牵引作用，稳妥推进农村要素资源市场化配置、土地适度规模经营等重点领域改革。\n\n**15.营造良好创新氛围。**制定科技创新容错清单，探索“沙盒监管”方式，加强知识产权保护和科技伦理、科研诚信建设，维护各类创新主体公平竞争环境。加快生物育种、生物制造、人工智能等领域科研成果登记、审定、评价，提升准入审批效率。深化农业科技管理体制机制改革，明晰涉农高校、科研院所功能定位，支持各类创新主体紧密协作、联动发展。弘扬新时代科学家精神，加大科普宣传力度，营造敢为人先、勇于创新的良好生态。\n\n健全完善工作机制，省农业农村厅、省科技厅加强统筹协调，省直相关部门按照工作职责协同推进；相关涉农高校、科研院所和领军企业，聚合资源深度参与；各市县结合实际，同步推进落实。加强跟踪监测、总结分析，激发各地开展农业科技创新的主动性与积极性。\n\n附件：重点任务清单\n\n![Image 1: 1.png](https:\u002F\u002Fwww.hubei.gov.cn\u002Fzfwj\u002Fezbf\u002F202607\u002FW020260731583511140852.png)\n\n![Image 2: 2.png](https:\u002F\u002Fwww.hubei.gov.cn\u002Fzfwj\u002Fezbf\u002F202607\u002FW020260731583512765245.png)\n\n![Image 3: 3.png](https:\u002F\u002Fwww.hubei.gov.cn\u002Fzfwj\u002Fezbf\u002F202607\u002FW020260731583514427701.png)\n\n![Image 4: 4.png](https:\u002F\u002Fwww.hubei.gov.cn\u002Fzfwj\u002Fezbf\u002F202607\u002FW020260731583515956206.png)\n\n![Image 5: 5.png](https:\u002F\u002Fwww.hubei.gov.cn\u002Fzfwj\u002Fezbf\u002F202607\u002FW020260731583517619020.png)\n\n![Image 6: 6.png](https:\u002F\u002Fwww.hubei.gov.cn\u002Fzfwj\u002Fezbf\u002F202607\u002FW020260731583519546555.png)\n\n![Image 7: 7.png](https:\u002F\u002Fwww.hubei.gov.cn\u002Fzfwj\u002Fezbf\u002F202607\u002FW020260731583521119684.png)\n\n编辑：汪 丽\n\n责编：梁唯雅\n\n审核：涂亚卓\n\n姚 盼","湖北省人民政府网 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