[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-433":3,"related-433":69},{"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":64,"slug":67,"view_count":22,"doi":8,"paper":8,"created_at":68},433,"华北首个AI育种垂类大模型项目落地武清——AI育种虚拟试验田将育种周期压缩30%以上","https:\u002F\u002Ftj.news.163.com\u002F26\u002F0811\u002F13\u002FL4287EAO042098PG_pa11y.html","近日,华北首个农业领域AI育种垂类大模型在京津产业新城高村数智创新园正式落地,标志着AI技术从通用对话场景深度下沉至种业科研一线,打通'基因数据—智能模拟—品种选育'全链条。天津百奥华兴基因科技有限公司深耕生物育种智能化赛道,自研MFMGP、LGBMY、PFMGP三款国产可控AI育种模型以及下一代AI育种大模型,适配全物种、多场景育种,多项核心指标达到国际先进水平。传统育种往往需要6—8年完成海量田间杂交试验,如今通过AI育种大模型打造的'虚拟试验田',大量试验推演在数字空间完成,育种周期可压缩30%以上,土地、人力和农资成本得到有效节约。",null,"近日，华北首个农业领域AI育种垂类大模型在京津产业新城高村数智创新园正式落地，标志着AI技术从通用对话场景深度下沉至种业科研一线，打通“基因数据—智能模拟—品种选育”全链条，开启智能育种产业化应用新阶段。\n\n天津百奥华兴基因科技有限公司成立于2023年，深耕生物育种智能化赛道，主攻AI育种大模型和基因编辑技术研发与产业化，依托高村数智创新园的算力底座、孵化服务与政企协同资源，快速搭建科研团队，迭代核心技术，对接上下游资源，建起高层次科研体系。\n\n公司科研板块由来自张献龙院士团队等多位博士牵头，吸纳清华、中科院、华中农大等院校人才，先后获评国家级科技型中小企业、创新型中小企业和国家高新技术企业等荣誉，多次在市级创新创业大赛斩获荣誉，综合创新根基持续筑牢。\n\n“大模型研发和基因编辑工具模拟十分消耗算力。过去靠企业自身完成大模型训练至少需要一个月，现在借助园区“零延迟”算力基础，三天就能完成，研发周期大幅压缩。”公司董事长兼总经理梁齐齐表示，强大的算力底座，支撑企业100亿参数人工智能模型运行所需的计算资源，推动科研成果加速从实验室走向产业应用。\n\n区别于通用大模型“万金油”式的能力，企业自研MFMGP、LGBMY、PFMGP三款国产可控AI育种模型以及下一代AI育种大模型，适配全物种、多场景育种，多项核心指标达到国际先进水平。\n\n传统育种往往需要6—8年完成海量田间杂交试验，投入大、周期长、不确定性高。如今通过AI育种大模型打造的“虚拟试验田”，大量试验推演在数字空间完成，育种周期可压缩30%以上，土地、人力和农资成本得到有效节约。\n\n依托自研模型，企业打造软硬件一体化智能育种工作站，支持本地化部署，现已为国内近百家育种机构提供技术服务。今年3月，企业结合华中农大基因编辑专利，实现AI大模型与基因编辑技术深度融合，同时与华中农大牵头国家重点研发计划棉花抗逆精准育种专项，推动传统经验育种向数字化精准育种跨越。\n\n“这种跨越体现在育种数据分析领域从‘点’到‘面’的迭代。”梁齐齐介绍，过去育种领域只单独研判土壤、气候、盐碱度等环境因素和分子层面与表型关系的单项指标，在接入大模型后，对单点数据波动和作物生长性状开展模拟画像，直观评估育种成效。\n\n在AI育种领域，欧美等老牌生物软件公司具备先发优势，但无法兼容本土数据，在多模态分析方面存在短板。“我们的大模型适配国内各类特有数据，计算精度较国外模型提升18.4%，综合成本下降约50%，实现了AI育种大模型领域的国产化原始创新，成功应用于中亚、非洲等‘一带一路’沿线国家项目。”梁齐齐骄傲地说。\n\n近年来，高村数智创新园走出一条“算力基建为基、生产服务为核、产业集聚为果”的差异化发展路径，通过顶级算力底座，助力京津冀数字产业高端化、专业化、集聚化。\n\n从海偲生物胚胎生命素的投产，到百奥华兴AI育种大模型的落地，武清正持续补齐农业领域AI+生物制造创新产业链条，促进更多前沿技术落地转化，不断壮大生物育种新质生产力，为保障国家粮食安全筑牢科技根基。","美丽武清 2026-08-11","2026-08-11T00:00:00Z","报道",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,18,15,8,9,1,"AI育种大模型落地，技术突破显著，但信源为地方媒体，影响层级为省级产业进展。",[25],{"name":10,"url":6},[27,28,29,30],"种业振兴","AI育种","大模型","京津冀",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"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":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,57],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[29],[29],[29],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"F1",4,[],[55],"种业",[27],{"code":58,"score":59,"supported":46,"title":60,"abstract":61,"tags":63},"F2",3,[],[62],"生物育种",[],[65,66],"种业振兴 京津冀 大模型 AI育种","种业振兴 京津冀","种业振兴京津冀大模型AI育种-433","2026-08-12T23:57:20.569366Z",{"total":70,"page":22,"page_size":70,"items":71},6,[72,141,200,279,357,439],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":8,"content":77,"source_name":78,"source_url":8,"published_at":79,"category":80,"cover_url":8,"hotness":13,"is_selected":46,"score":81,"score_detail":82,"sources":87,"tags":89,"primary_topic":95,"secondary_topics":96,"classification":97,"search_phrases":136,"slug":139,"view_count":52,"doi":8,"paper":8,"created_at":140},4376,"农业农村部办公厅征集 2027 年度农业主导品种、主推技术和重大引领性技术","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7693543001843204643","农业农村部科学技术司发布通知,2026-09-30 至 11-13 期间在全国范围内征集 2027 年度农业主导品种、主推技术与重大引领性技术;主导品种覆盖农作物、饲草、畜禽、水产及名特优新品种,主推技术覆盖良田、良种、良机、良法融合集成技术,优先推荐农业人工智能、生物育种、农机装备等领域的引领性技术。","近日，农业农村部科学技术司发布关于开展2027年度农业主导品种、主推技术和重大引领性技术申报推荐工作的通知。\n\n**关于开展2027年度农业主导品种、主推技术和重大引领性技术申报推荐工作的通知**\n\n**各省、自治区、直辖市农业农村（农牧）、畜牧兽医、农垦、渔业厅（局、委），新疆生产建设兵团农业农村局，北大荒农垦集团有限公司，有关部直属单位，国家现代农业产业技术体系，有关单位：**\n\n为加快先进适用的农业科技成果大面积推广应用，发挥科技对粮油作物大面积单产提升和农业高质量发展的支撑作用，根据《农业农村部关于修订农业主导品种和主推技术推介发布办法的通知》（农科技发〔2025〕10号）的规定，我部决定在全国范围内征集推介2027年度农业主导品种、主推技术和重大引领性技术。现将有关事项通知如下。\n\n**一、推荐时间**\n\n2026年9月30日至2026年11月13日。\n\n**二、推荐范围**\n\n（一）主导品种\n\n主要农作物、饲草、畜禽、水产及重要名特优新品种等，具有增产潜力大、适应性广、抗性强、品质优、产量高等特点。\n\n（二）主推技术\n\n1. 农作物和饲草综合栽培技术及病虫草鼠害防治技术；\n\n2. 畜禽水产科学繁育、饲养及疫病综合防治技术；\n\n3. 重要名特优新品种的种养技术；\n\n4. 先进适用的农业机械化、设施化、智能化与农产品加工储运等农业产业化关键技术；\n\n5. 有利于农业防灾减灾、耕地质量提升、农产品质量安全、农业生物安全、农业资源高效利用和生态环境保护等可持续发展的先进适用技术。\n\n（三）重大引领性技术\n\n重大引领性技术是主推技术的前导性技术，重点推荐高效种养、生物育种、农业生物制造、农业人工智能、耕地保护与利用、防灾减灾、动植物疫病防控、农业资源环境、精深加工、绿色低碳、农机装备等领域技术。\n\n**三、推荐条件**\n\n（一）品种（配套系、遗传资源）须经全国或省级品种审定（登记）机构审定（登记、鉴定）。优先推荐在同等条件下获得品种权的农作物品种。\n\n（二）技术要点、操作规范和适宜区域清晰，须经国家农业技术推广机构试验示范，涉及的投入品、装备等符合我国法律法规要求。优先推荐良田、良种、良机、良法“四良”融合的集成技术。\n\n（三）成果权属清晰且无纠纷。\n\n（四）应具有较大的应用区域和规模且保持较快的增长速度，在增产、提质、增效、增收等方面潜力大，应用效果好；推荐的重大引领性技术应在一定区域示范应用中取得明显成效，具有重大引领作用和发展潜力。\n\n（五）在应用过程中得到用户、基层国家农业技术推广机构或地方政府的充分认可。\n\n（六）在相应技术领域发挥重要作用，代表轻简化、机械化、可持续的先进农业技术发展方向，适应产业规模化、信息化、智能化发展需要。\n\n（七）已经推介为主导品种、主推技术的，3年内再次申报推荐，应在条件（四）和（五）方面取得重大应用进展。\n\n**四、申报单位**\n\n申报单位为各级国家农业技术推广机构、农业科研单位、有关学校、涉农企业等。农业科研单位、有关学校、涉农企业等原则上应与国家农业技术推广机构联合申报。国家种业阵型企业可单独申报主导品种，鼓励与国家农业技术推广机构联合申报。\n\n**五、推荐单位和推荐数量**\n\n（一）主导品种、主推技术\n\n1、各省、自治区、直辖市农业农村（农牧）、畜牧兽医、农垦、渔业厅（局、委），新疆生产建设兵团农业农村局，北大荒农垦集团有限公司推荐品种和技术各不超过10项。\n\n2、中国农业科学院推荐品种和技术各不超过15项，中国水产科学研究院、中国热带农业科学院、农业农村部规划设计研究院推荐各不超过10项；农业农村部农业生态与资源保护总站、全国农业技术推广服务中心、农业农村部农业机械化总站、全国畜牧总站、中国动物疫病预防控制中心、中国农垦经济发展中心、全国水产技术推广总站等推广机构推荐本单位作为成果完成单位或技术依托单位的各不超过5项。\n\n3、国家现代农业产业技术体系每个体系推荐品种和技术各不超过5项。\n\n（二）重大引领性技术\n\n1、各省、自治区、直辖市农业农村（农牧）、畜牧兽医、农垦、渔业厅（局、委），新疆生产建设兵团农业农村局，北大荒农垦集团有限公司推荐不超过2项。\n\n2、中国农业科学院推荐不超过3项；中国水产科学研究院、中国热带农业科学院、农业农村部规划设计研究院推荐不超过2项；农业农村部农业生态与资源保护总站、全国农业技术推广服务中心、农业农村部农业机械化总站、全国畜牧总站、中国动物疫病预防控制中心、中国农垦经济发展中心、全国水产技术推广总站等推广机构推荐本单位作为技术依托单位的不超过2项。\n\n3、农业生物育种重大专项、农业农村部作为主责单位的国家重点研发计划等农业科技重大项目推荐数量各不超过2项，由有关项目管理专业机构组织推荐。\n\n**六、材料报送**\n\n（一）推荐单位在10月底前完成申报推荐与公示，并于10月26日—11月13日期间组织拟推荐主导品种、主推技术和重大引领性技术的申报单位将相关材料录入“农业主导品种主推技术和重大引领性技术管理系统”（简称“管理系统”，网址另行通知）。\n\n（二）推荐单位出具推荐函，内容应包括申报推荐过程、公示情况和结果等。11月13日前将加盖公章的推荐函及《2027年度农业主导品种（农作物）推荐汇总表》（附件1）、《2027年度农业主导品种（畜禽水产）推荐汇总表》（附件2）、《2027年度农业主推技术推荐汇总表》（附件3）、《2027年度农业重大引领性技术推荐汇总表》（附件4）提交管理系统。\n\n（三）拟推荐的主导品种申报单位，在管理系统中填写《2027年度农业主导品种（农作物）申报表》（附件5）或《2027年度农业主导品种（畜禽水产）申报表》（附件6）、《2027年度农业主导品种介绍材料（模板）》（附件7）。同时提供品种审定、鉴定、登记等证明文件，获奖证书，种子、种苗及品种产出商品图片（图片大小不低于1.0M），品种推广应用规模等情况的证明材料。\n\n（四）拟推荐的主推技术、重大引领性技术申报单位，分别按照《2027年度农业主推技术介绍材料（模板）》（附件8）、《2027年度农业重大引领性技术介绍材料（模板）》（附件9）在管理系统中提交相应技术介绍材料。材料字数不超过5000字，插入3~5幅有助于理解该项技术的高清图片（图片大小不低于1.0M），并附图注。同时提供知识产权、获奖证书（成果鉴定）、技术推广应用规模等情况的证明材料。\n\n**七、有关要求**\n\n（一）各推荐单位按照本通知要求组织申报和推荐工作，对拟推荐的材料要严格审核把关。拟推荐名单须按程序进行公示，接受社会监督，公示期为5个工作日。申报单位和技术（品种）持有者对所提交的技术（品种）信息及相关证明材料的真实性和规范性负责，证明文件的出具单位（机构）应具有权威性和代表性，推荐品种2024年和2025年生产应用面积（规模）数据应与种业信息管理部门的统计数据相符。\n\n（二）主导品种应列出国家农业技术推广机构，主推技术、重大引领性技术应将国家农业技术推广机构作为技术依托单位。推荐的重大引领性技术应有配套的试验示范场所和经费支持，能够组织开展技术集成熟化和示范推广等任务。\n\n（三）农业农村部直属单位的成果经相关行业司局同意后推荐；国家现代农业产业技术体系推荐工作由体系首席科学家牵头开展，成果经执行专家组一致同意并签字后推荐，由体系首席科学家所在单位组织报送。\n\n（四）同一品种、技术严格按照单一渠道推荐；同一技术不得同时申报主推技术和重大引领性技术；往年已经入选主推技术的，不得推荐为重大引领性技术。\n\n（五）主推技术、重大引领性技术的申报单位，须在管理系统填报项目基本情况，上传技术介绍材料word版和pdf版；技术介绍材料pdf版须加盖技术所列全部依托单位公章，系统填报的基础信息须与上传的技术介绍材料内容一致。\n\n（六）强化科研诚信与监督。对存在材料夸大应用效果，提供虚假数据、虚假证明、虚假合同等虚假材料，打招呼、请托等行为，技术内容包含特定商品品牌投入品以及知识产权权属不清、职务发明权属不清、存在纠纷等情形的，取消本次推介资格。存在违纪违规行为的，视情形轻重给予约谈、通报、列入科研失信档案等处理。\n\n（七）未按上述要求申报推荐和提供材料的，不予推介。\n\n**八、联系方式**\n\n**（一）中国农学会对外合作处**\n\n联系人：韩 溦\n\n电 话：010−59194213\n\n邮 箱：nxhgjc@126.com\n\n**（二）农业农村部科学技术司科技推广处**\n\n联系人：高修歌张 良\n\n电 话：010−59193003\n\n**（三）管理系统技术支持**\n\n联系人：陈金超 王菲菲\n\n电 话：010−81128410 010−81128409\n\n**附件：**\n\n_1．2027年度农业主导品种（农作物）推荐汇总表_\n\n_2．2027年度农业主导品种（畜禽水产）推荐汇总表_\n\n_3．2027年度农业主推技术推荐汇总表_\n\n_4．2027年农业重大引领性技术推荐汇总表_\n\n_5．2027年度农业主导品种（农作物）申报表_\n\n_6．2027年度农业主导品种（畜禽水产）申报表_\n\n_7．2027年度农业主导品种介绍材料（模板）_\n\n_8．2027年度农业主推技术介绍材料（模板）_\n\n_9．2027年农业重大引领性技术介绍材料（模板）_\n\n**农业农村部科学技术司**\n\n**2026年9月29日**\n\n![Image 1: 图片](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Ffe995aaa8db04f25a288853d804aef59~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792022771&x-signature=u8hH91qQKJNGwZ8gdDk9tGE%2BZVA%3D)\n来源丨农业农村部\n\n编辑 | 农财君\n\n联系农财君请加微信：18565265490\n\n新时代 新种业\n\n南方农村报丨农财宝典","今日头条 农财网种业宝典 2026-10-06","2026-10-06T00:00:00Z","政策",92,{"impact":83,"substance":84,"depth":18,"authority":19,"freshness":85,"relevant":22,"comment":86},28,24,7,"农业农村部全国性技术推介申报通知，条款、时间节点与推荐限额详实，对种业与农技推广主体有直接指导价值，值得进入每日精选。",[88],{"name":78,"url":75},[90,27,91,92,93,94],"农业人工智能","主导品种","主推技术","重大引领性技术","农业科技推广",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":98},{"candidates":99,"agricultural_evidence":46},[100,104,110,116,122,126,130],{"code":32,"score":52,"supported":46,"title":101,"abstract":102,"tags":103},[],[90],[90],{"code":105,"score":59,"supported":46,"title":106,"abstract":107,"tags":109},"C",[],[108],"畜禽",[],{"code":111,"score":59,"supported":46,"title":112,"abstract":113,"tags":115},"C4",[],[114],"饲草",[],{"code":117,"score":59,"supported":46,"title":118,"abstract":119,"tags":121},"D",[],[120],"水产",[],{"code":51,"score":59,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[91,92,27],{"code":58,"score":59,"supported":46,"title":127,"abstract":128,"tags":129},[],[62],[],{"code":131,"score":59,"supported":46,"title":132,"abstract":133,"tags":135},"F5",[],[134],"农机",[],[137,138],"农业农村部 2027年度 主导品种 主推技术","重大引领性技术 申报推荐","农业农村部2027年度主导品种主推技术-4376","2026-10-08T00:06:06.498353Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":8,"content":146,"source_name":147,"source_url":8,"published_at":148,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":149,"score_detail":150,"sources":153,"tags":155,"primary_topic":159,"secondary_topics":164,"classification":166,"search_phrases":195,"slug":198,"view_count":21,"doi":8,"paper":8,"created_at":199},4263,"山东实施人工智能+农业行动 省农科院舜耕粮安遥感大模型精度95%","http:\u002F\u002Fgzylhyzx.com\u002Fdetail.html?newsId=52418975&cpid=T17","山东省农科院发布舜耕粮安农业遥感大模型，基于20年2000万份人工样本，覆盖山东全域全地貌全物候周期，构建耕地智能提取-肥力墒情预测-作物精准识别-长势动态监测-病虫草害预警-作物智能估产全链条智能监测体系，精度95%、效率提升超100%。山东启动40个实数融合数字农业发展县建设，培育智慧农业应用场景近1000个。","### 山东实施“人工智能+农业”行动：数据“种”进泥土，云端守护丰收\n\n2026-10-06 02:39:49\n\n![Image 1](https:\u002F\u002Fappimg.dzwww.com\u002Fshare\u002F2026\u002F08\u002F02\u002Ff9c9a29336dd947_750.JPEG)\n\n麦田上空，无人机低空掠过，哪片麦苗缺氮、哪处有蚜虫，屏幕上即时标红；大棚内，人工智能（AI）算法值守，缺水、缺肥时，系统立刻报警；分拣车间里，AI视觉系统每秒“端详”上百颗苹果，按色泽、果径、瑕疵精准分级，误判率不到百分之一……\n\n从“靠天吃饭”到“知天而作”，当前人工智能正成为助力农业稳产提质、带动农民增收致富的“好帮手”，为农业农村现代化提供全新范式。\n\n2026年中央一号文件明确提出“因地制宜发展农业新质生产力，促进人工智能与农业发展相结合”，将AI技术从单点应用升级为贯穿农业全产业链的战略引擎。多家行业机构预测，2026年中国智慧农业市场规模将突破1340亿元，年复合增长率保持在15%以上。\n\n刚刚召开的省委十二届十一次全会，着眼把农业建成现代化大产业，推动农业数智化转型升级，明确提出实施“人工智能+农业”行动。何谓“人工智能+农业”？简言之，就是以人工智能技术为核心，驱动传统农业向智能化、高效化转型的新型生产体系。与会同志认为，从更深层次看，“人工智能+农业”不是简单的技术叠加，而是农业生产方式、组织形式和价值链条的系统性重塑。\n\n**AI种地，田野阡陌释放出更多可能**\n\n技术的生命力在于解决实际问题。放眼全国，农业面临着劳动力老龄化、气候变化风险加剧等压力，也存在特色产业附加值低、品牌力不强等问题。人工智能的介入推动农业从“靠经验”转向“靠数据”，为端牢“中国饭碗”、塑强农业产业注入确定性。在不少人看来，人工智能的应用推广，恰逢其时。\n\n“山东是农业大省，也是农业产业化发源地，产业规模大、业态全，生产者队伍素质高、接受新事物能力强。与此同时，山东工业基础雄厚，人工智能技术发展迅速。新技术在农业方面的转化推广具有显著的成本和效率优势。”省委党校（山东行政学院）教授、山东乡村振兴研究院院长刘岳认为，山东有着发展“人工智能+农业”的坚实基础，在完整的产业体系、可观市场规模、丰富应用场景等优势加持下，田野阡陌将释放出更多可能。\n\n夏日的鲁北平原，玉米拔节生长，一片生机盎然。在齐河县金穗粮食种植专业合作社的示范田里，一套黑色管网发挥着大作用。“这是我们铺设的水肥一体化设施。每30厘米有一个出水孔，能精准滴灌到玉米根底部位置。相比大水漫灌，它的灌溉速度快3倍，可省水30%，一人一天能管理200亩地。”合作社理事长袁本刚说。依托大数据平台，合作社还可在“云端”实施全程监管，动动手指就能依据检测数据制订分地块水肥管理方案，为各村玉米田定制个性化基肥配方，同步开展测土配方、作物叶绿素检测等服务。\n\n今年山东夏粮逆势丰收，连续4年实现面积、单产、总产“三增”，这离不开水肥一体化技术在大田的推广。数据显示，2025年，全省应用水肥一体化技术的粮油作物面积已达到630万亩。“水肥一体化与减垄增地、‘大小行’密植等结合，加速了良种、良法、良机、良田深度融合，有效提升了农田规模化、标准化种植水平。”袁本刚体会深切，水肥一体化的落地，让丰收有了更充实的底气。\n\n![Image 2](https:\u002F\u002Fappimg.dzwww.com\u002Fshare\u002F2026\u002F08\u002F02\u002Ff9c9a2a30f6bab0_750.JPEG)\n\n种子是农业的“芯片”，也是保障粮食安全的关键，智能育种已成为育种科研人员聚焦的热点领域。\n\n今年4月，由中国农业科学院作物科学研究所与山东极智生物科技有限公司联合研发的“iWheat小麦智能设计育种平台”发布。对青岛农业大学小麦遗传育种团队来说，iWheat的应用正在帮助其攻克小麦育种“周期长、效率低、成本高”等行业难题。\n\n这一平台整合3万余份小麦种质资源的基因型、442个功能基因、6000余个功能位点及40余万条表型数据，为每一份种质建立起“数字身份证”，实现资源的精准检索与高效利用。“结合快速繁育体系，能将传统8年的育种周期压缩2至3年。”团队负责人张玉梅说，加速新品种的培育进程、降低育种成本，意味着更能提升整个种业行业的创新能力和竞争力，进而赋能小麦产业链上下游实现高质量发展。\n\n**让“技术盆景”真正落地为“产业风景”**\n\n农业大模型能够整合气象、土壤、作物生长等多维度数据，显著提升农业生产的精准性和预见性。我省明确，要加强农业大模型与人工智能体落地应用。\n\n在“中国蔬菜之乡”寿光，蔬菜大棚迎来了一批特殊“员工”。一群脚步灵活的机器狗，沿着田垄灵活穿梭，搭载的高清摄像头与传感器如同“火眼金睛”，精准识别病虫害迹象。巡检结束，数据实时传回后台，人工智能模型迅速给出种植决策。如今，寿光新建大棚已迭代升级至第七代智能物联网“云棚”，物联网应用率超过85%，劳动生产效率较以往翻倍。\n\n![Image 3](https:\u002F\u002Fappimg.dzwww.com\u002Fshare\u002F2026\u002F08\u002F02\u002Ff9c9a2a2155c1ff_750.JPEG)\n\n就在一个多月前，省农科院发布“舜耕·粮安”农业遥感大模型。省农科院党委书记梁金光介绍，该模型基于20年来积累的2000万份人工样本的海量数据，覆盖山东全域全地貌、全物候周期，构建起“耕地智能提取—肥力墒情预测—作物精准识别—长势动态监测—病虫草害预警—作物智能估产”全链条智能监测体系，精度高达95%，效率提升超100%。\n\n在中游加工环节，大模型也能有效提升产品附加值与标准化水平。在烟台莱山区，由浪潮云洲旗下数农云（山东）信息科技有限公司联合中国农业大学计算中心、烟台葡乡农业开发有限公司共同打造的“莱山葡萄种植和酿造大模型”，一方面在种植端自动生成气候适应性调控方案，实现水肥精准配施、病虫害智能预警与防治，葡萄产量与品质同步提升；另一方面在酿造端用AI模拟发酵、陈酿全过程，动态优化关键参数，完成原料分级、发酵控制和酒体风味数字化建模。以此，让每一批葡萄酒的口感更稳定、品质更可控。\n\n当然，让人工智能与广袤乡村实现更深层次的双向赋能，还有很长的路要走——\n\n一套AI系统，传感终端、边缘计算、决策模型全算上，初始投入比较大。普通农户能不能用得起、用得好？\n\n农业生产周期长、环境复杂、区域差异大，怎样才能收集利用一批高质量农业数据，推动AI模型迭代？\n\n下沉到村、懂技术懂农业的青年人才较为短缺，农业人工智能技术人才怎样引育？\n\n让“技术盆景”真正落地为“产业风景”，刘岳认为，关键是鼓励龙头企业、高校院所与新型农业经营主体协同攻关，围绕粮食、果蔬、畜牧、水产等优势产业，打造一批“听得懂农事、算得清账本、管得了全程”的行业大模型。\n\n此外，刘岳还建议，要更加重视构建与农村新质生产力相适应的农村新型生产关系，盘活整合农村各类资产资源，为充分发挥数字化、智能化技术进步作用提供保障。“推动农村数字化从‘局域网’向‘互联网’转化，要破除长期存在的‘信息孤岛’，构建乡村振兴的数据底座。”\n\n（大众新闻记者 于新悦 刘兵）\n\n  \n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。\n本文来源：大众新闻·大众日报\n\n查看更多","每日热点 大众新闻 2026-10-04","2026-10-04T00:00:00Z",82,{"impact":84,"substance":151,"depth":18,"authority":13,"freshness":20,"relevant":22,"comment":152},22,"省级“人工智能+农业”行动部署叠加舜耕·粮安遥感大模型95%精度等实质数据，信息增量与专业深度俱佳，值得进入每日精选。",[154],{"name":147,"url":144},[156,157,90,27,158],"数字乡村","智慧农业","遥感大模型",{"code":160,"slug":161,"name":162,"path":163,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[165],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":167},{"candidates":168,"agricultural_evidence":46},[169,176,180,186,190],{"code":160,"score":170,"supported":46,"title":171,"abstract":173,"tags":175},14,[172],"遥感",[172,174],"估产",[],{"code":32,"score":45,"supported":46,"title":177,"abstract":178,"tags":179},[29],[29],[90],{"code":181,"score":85,"supported":46,"title":182,"abstract":183,"tags":185},"I1",[],[157,184],"数字农业",[157],{"code":51,"score":22,"supported":14,"title":187,"abstract":188,"tags":189},[],[],[27],{"code":191,"score":22,"supported":14,"title":192,"abstract":193,"tags":194},"I6",[],[],[156],[196,197],"舜耕粮安 遥感大模型","山东 人工智能 行动","舜耕粮安遥感大模型-4263","2026-10-06T00:05:02.084808Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":8,"source_name":206,"source_url":203,"published_at":207,"category":208,"cover_url":8,"hotness":13,"is_selected":46,"score":209,"score_detail":210,"sources":214,"tags":216,"primary_topic":219,"secondary_topics":220,"classification":221,"search_phrases":236,"slug":239,"view_count":52,"doi":240,"paper":241,"created_at":278},3864,"A Knowledge‐Augmented Wearable System for Interpretable Plant Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.1002\u002Ffle2.70020","ABSTRACT In precision agriculture, modern sensing technologies can autonomously collect and process vast plant data using statistical or machine‐learning models. However, an “interpretability gap” persists: these outputs often remain black‐box correlations rather than interpretable physiological insights, which means users cannot validate their credibility, trace causal links to plant physiology, or translate them into actionable decisions. To bridge this gap, we propose a knowledge‐augmented wearable system for interpretable plant health monitoring (K‐Wear). By coupling a soft wearable sensing platform with a knowledge‐guided reasoning framework, K‐Wear leverages a large language model to transform raw sensing signals into mechanistic diagnoses and recommendations for proactive management. In simulated evaluations, the system achieved 99% diagnostic accuracy and first correctly detected plant stress on day 4.95 on average. In a real 14‐day potassium‐deficiency monitoring experiment, K‐Wear successfully identified early stress signals on day 4 and generated targeted intervention recommendations, enabling timely treatment and recovery of plant health.","在精准农业中，现代传感技术能够利用统计或机器学习模型自主采集并处理海量植物数据。然而，“可解释性鸿沟”依然存在：这些输出往往仍是黑箱式的相关性结果，而非可解释的生理学洞见，这意味着用户无法验证其可信度、追溯其与植物生理的因果联系，或将其转化为可操作的决策。为弥合这一鸿沟，我们提出了一种用于可解释植物健康监测的知识增强可穿戴系统（K‐Wear）。通过将柔性可穿戴传感平台与知识引导的推理框架相结合，K‐Wear利用大语言模型将原始传感信号转化为机理诊断和主动管理建议。在模拟评估中，该系统达到了99%的诊断准确率，并最早在平均第4.95天正确检测到植物胁迫。在一项为期14天的真实缺钾监测实验中，K‐Wear成功在第4天识别出早期胁迫信号，并生成有针对性的干预建议，从而使植物健康得到及时治疗和恢复。","FlexTech","2026-09-29T00:00:00Z","论文",84,{"impact":151,"substance":211,"depth":18,"authority":212,"freshness":20,"relevant":22,"comment":213},23,13,"将可穿戴传感与大模型知识推理结合，实现可解释的植物健康诊断，方法新颖、实验数据扎实，对智慧农业感知决策有参考价值。",[215],{"name":206,"url":203},[157,90,29,217,218],"可穿戴传感","植物健康监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":222},{"candidates":223,"agricultural_evidence":46},[224,230],{"code":32,"score":20,"supported":46,"title":225,"abstract":226,"tags":229},[],[227,228],"机器学习","large language model",[90,29],{"code":181,"score":85,"supported":46,"title":231,"abstract":232,"tags":235},[],[233,234],"精准农业","precision agriculture",[157],[237,238],"K-Wear 可穿戴 植物健康监测","大语言模型 植物胁迫 诊断","K-Wear可穿戴植物健康监测-3864","10.1002\u002Ffle2.70020",{"doi":240,"openalex_id":242,"authors":243,"venue":206,"cited_by_count":269,"oa_url":203,"card":270,"direction":276,"ingested_from":277},"W7214803187",[244,246,248,250,253,255,258,260,263,266],{"name":245,"orcid":8},"Xingxing Dong",{"name":247,"orcid":8},"Yihan Luo",{"name":249,"orcid":8},"Jiang Qin",{"name":251,"orcid":252},"Jie Ye","https:\u002F\u002Forcid.org\u002F0000-0001-7728-3588",{"name":254,"orcid":8},"Yihui Fan",{"name":256,"orcid":257},"Menghao Pu","https:\u002F\u002Forcid.org\u002F0009-0003-1421-6785",{"name":259,"orcid":8},"Zisheng Zong",{"name":261,"orcid":262},"Guoqiang Xu","https:\u002F\u002Forcid.org\u002F0009-0007-5481-1422",{"name":264,"orcid":265},"Han Ding","https:\u002F\u002Forcid.org\u002F0000-0003-2905-6369",{"name":267,"orcid":268},"Zhigang Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3719-406X",0,{"tldr":271,"method":272,"finding":273,"direction":274,"opportunity":275},"提出知识增强可穿戴系统K-Wear，将植物传感信号转化为可解释诊断与建议。","软可穿戴传感平台+知识引导推理框架+大语言模型。","模拟诊断准确率99%，真实钾缺乏实验第4天检出早期胁迫并给出干预建议。","智慧农业 \u002F 农业物联网","可探索知识增强LLM推理在更多作物胁迫类型与田间长期监测中的泛化能力。","农业人工智能与决策模型","openalex","2026-09-30T23:30:55.022226Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":8,"content":284,"source_name":285,"source_url":8,"published_at":286,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":287,"score_detail":288,"sources":291,"tags":293,"primary_topic":298,"secondary_topics":304,"classification":312,"search_phrases":351,"slug":354,"view_count":355,"doi":8,"paper":8,"created_at":356},3396,"从吃到用——中国农科院第八届农科开放日现场直击：彩色胡萝卜、丙醇二酸黄瓜、甜糯同粒玉米、转运机器人、动物疾病风险大模型","https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml","9月19日中国农科院36个研究所的560多项最新科研成果集中亮相：彩色胡萝卜、橘红心白菜、特色彩椒等全新果蔬品种轮番展示；富含丙醇二酸的晶莹剔透黄瓜能阻止糖类转化为脂肪；低豆腥味、高异黄酮、适合榨汁的大豆及'甜糯同粒'玉米；农业转运机器人能扛100公斤以上作物在丘陵山区跑、具备原地掉头能力；正在研发的全球动物疾病风险大模型将对动物疫病实现实时预报预警。","[首页](https:\u002F\u002Fwww.cctv.cn\u002F)\n\n[时政](javascript:;)\n\n[新闻](https:\u002F\u002Fnews.cctv.cn\u002F)\n\n[评论](https:\u002F\u002Fopinion.cctv.cn\u002F)\n\n[视频](https:\u002F\u002Fv.cctv.cn\u002F)\n\n[财经](https:\u002F\u002Ffinance.cctv.cn\u002Findex.shtml)\n\n[体育](https:\u002F\u002Fsports.cctv.cn\u002F)\n\n[教育](https:\u002F\u002Fedu.cctv.cn\u002F)\n\n[熊猫](https:\u002F\u002Fwww.ipanda.cn\u002F)\n\n[直播中国](https:\u002F\u002Flivechina.cctv.cn\u002F)\n\n[4K](https:\u002F\u002Ftv.cctv.cn\u002F4K\u002Findex.shtml)\n\n[CCTV.直播](https:\u002F\u002Ftv.cctv.cn\u002Findex.shtml)\n\n[中](https:\u002F\u002Fwww.cctv.cn\u002Findex.shtml)\n\n[](https:\u002F\u002Faigc.cctv.cn\u002Findex.shtml 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13:58:17\n\n###### 原标题：\n\n正在加载\n\n[Video 3](https:\u002F\u002Fdh5.cntv.cdn20.com\u002Fasp\u002Fh5e\u002Fhls\u002Fmain\u002F0303000a\u002F3\u002Fdefault\u002F8d26c0f8c36e4e1e8ed02d6406007686\u002Fmain.m3u8?maxbr=2048&contentid=15120519184043)\n\n静音(m)全屏(f)\n\nADCountdown Time | 广告\n\n![Image 51](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)关闭广告\n\n![Image 52](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n尊贵的用户，已为您跳过广告\n\n0:00\n\n正在加载 视频播放器。\n\n播放(p)播放视频\n\n播放(p)\n\n当前时间 0:00\n\n\u002F\n\n时长 2:12\n\n媒体流类型 直播\n\n高清\n\n画质\n\n*   超清\n*   高清, 选择\n*   标清\n*   流畅\n\n静音(m)\n\n全屏(f)\n\n加载完成: 0%\n\n-\n\n88:88\n\n[](javascript:void(0);)\n\n*   core:undefined.undefined\n*   wrap:3.8.2.260723\n\n画中画\n\n**央视网消息：**9月19日，中国农业科学院36个研究所的560多项最新科研成果集中面向社会公众亮相，把农业实验室里的“新鲜事”带到公众眼前。\n\n![Image 53](https:\u002F\u002Fp3.img.cntv.cn\u002Fphotoworkspace\u002Fcontentimg\u002F2026\u002F09\u002F20\u002F2026092013212476326.png)\n\n在果蔬成果展区，最受欢迎的不是常见的香甜水果，而是一杯胡萝卜汁。  \n  \n 科研人员通过调节不同品种胡萝卜的收获时间和栽培模式，培育出口感更甜更细腻的品种以及减糖版胡萝卜。  \n  \n 中国农业科学院蔬菜花卉研究所大宗蔬菜产业技术体系岗位专家庄飞云介绍，胡萝卜口感整体受环境影响特别大，通过调节里面的一些组分，很多让人不愉快的组分把它尽量减少到很低，当然它的营养还能保持原有的营养。\n\n![Image 54](https:\u002F\u002Fp5.img.cntv.cn\u002Fphotoworkspace\u002Fcontentimg\u002F2026\u002F09\u002F20\u002F2026092013213274797.png)\n\n另一边的展区里，这种晶莹剔透的黄瓜不仅口感脆嫩，还富含一种能有效阻止糖类转化为脂肪的功能小分子——“丙醇二酸”。吃根黄瓜还能顺带帮着管理体重。  \n  \n 作物展区里，作物科学研究所带来了新的攻关成果。低豆腥味、高异黄酮、适合榨汁的大豆，还有一款新研发的“甜糯同粒”玉米。\n\n![Image 55](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n中国农业科学院作物科学研究所育种专家肖永贵介绍，原来的玉米要么是甜，要么是糯，要么就是7粒糯1粒甜，要么3粒糯1粒甜，把甜和糯放到一个粒里按照不同比例调配口感会更好。  \n  \n 机械展区，各种新型农用机器人集体亮相。这台农业转运机器人不仅能扛100公斤以上的作物在丘陵山区跑，还会“凌波微步”。\n\n![Image 56](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F20\u002FARTIkUEnHV7gFcVPFEweSMwS260920.shtml)\n\n农业农村部南京农业机械化研究所助理研究员宫庆硕介绍，可以进行左右横移，在狭窄的田间环境中可以实现快速换行，也可以实现原地掉头能力。  \n  \n 正在研发的全球动物疾病风险大模型，未来将对动物疫病实现实时预报预警，相当于给全球畜牧业装上一张“天气图”，让餐桌上的肉蛋奶更安全。从吃到用，实验室与餐桌的距离正在被这些“看得见、尝得到、用得上”的成果不断拉近。\n\n编辑：杨书杰 责任编辑：刘亮\n\n*   [从吃到用](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E4%BB%8E%E5%90%83%E5%88%B0%E7%94%A8)\n*   [实验室](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%AE%9E%E9%AA%8C%E5%AE%A4)\n*   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23:42[大国外交进行时](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%A4%A7%E5%9B%BD%E5%A4%96%E4%BA%A4%E8%BF%9B%E8%A1%8C%E6%97%B6)   \n*   [![Image 60](https:\u002F\u002Fp1.img.cntv.cn\u002Fphotoworkspace\u002F2026\u002F09\u002F25\u002F2026092507401440491.jpg)](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIQKqznf9idYWS6iNTtQnG260925.shtml) [大国外交进行时丨习近平主席夫妇抵达白宫出席欢迎宴会](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIQKqznf9idYWS6iNTtQnG260925.shtml) [当地时间9月24日晚，国家主席习近平和夫人彭丽媛乘车抵达白宫，出席美国总统特朗普和夫人梅拉尼娅举行的欢迎宴会。](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIQKqznf9idYWS6iNTtQnG260925.shtml) 2026-09-24 23:41[大国外交进行时丨习近平主席夫妇抵达白宫出席欢迎宴会](https:\u002F\u002Fsearch.cctv.cn\u002Fsearch.php?qtext=%E5%A4%A7%E5%9B%BD%E5%A4%96%E4%BA%A4%E8%BF%9B%E8%A1%8C%E6%97%B6%E4%B8%A8%E4%B9%A0%E8%BF%91%E5%B9%B3%E4%B8%BB%E5%B8%AD%E5%A4%AB%E5%A6%87%E6%8A%B5%E8%BE%BE%E7%99%BD%E5%AE%AB%E5%87%BA%E5%B8%AD%E6%AC%A2%E8%BF%8E%E5%AE%B4%E4%BC%9A)   \n*   [![Image 61](https:\u002F\u002Fp4.img.cntv.cn\u002Fphotoworkspace\u002F2026\u002F09\u002F25\u002F2026092507245664866.jpg)](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIrkOg3GKxtFuslY0DqNVz260925.shtml) [大国外交进行时丨白宫：礼兵和军乐团就位 迎候习近平主席](https:\u002F\u002Fnews.cctv.cn\u002F2026\u002F09\u002F25\u002FARTIrkOg3GKxtFuslY0DqNVz260925.shtml) [当地时间9月2","央视网 2026-09-20","2026-09-20T00:00:00Z",65,{"impact":18,"substance":19,"depth":45,"authority":19,"freshness":289,"relevant":22,"comment":290},5,"央媒对中国农科院开放日的综合报道，集中展示彩色胡萝卜、丙醇二酸黄瓜、甜糯同粒玉米及转运机器人、动物疾病风险大模型等成果，信息量足但属活动报道，时效已过一周，可作主题聚合素材。",[292],{"name":285,"url":282},[294,27,295,296,297],"农业机器人","动物疫病防控","农业科技","功能型农产品",{"code":299,"slug":300,"name":301,"path":302,"parent_code":303},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[305,311],{"code":306,"slug":307,"name":308,"path":309,"parent_code":310},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":313},{"candidates":314,"agricultural_evidence":46},[315,321,326,330,336,342,346],{"code":299,"score":151,"supported":46,"title":316,"abstract":319,"tags":320},[317,318],"黄瓜","萝卜",[317,318],[],{"code":306,"score":322,"supported":46,"title":323,"abstract":324,"tags":325},11,[308],[308],[],{"code":32,"score":322,"supported":46,"title":327,"abstract":328,"tags":329},[29],[29],[],{"code":331,"score":52,"supported":46,"title":332,"abstract":333,"tags":335},"C5",[],[334],"动物疫病",[295],{"code":337,"score":59,"supported":46,"title":338,"abstract":339,"tags":341},"A4",[],[340],"大豆",[],{"code":51,"score":22,"supported":14,"title":343,"abstract":344,"tags":345},[],[],[27],{"code":347,"score":22,"supported":14,"title":348,"abstract":349,"tags":350},"F6",[],[],[294],[352,353],"中国农科院 农科开放日","彩色胡萝卜 丙醇二酸黄瓜","中国农科院农科开放日-3396",2,"2026-09-25T00:09:29.650043Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":8,"source_name":363,"source_url":360,"published_at":364,"category":208,"cover_url":8,"hotness":13,"is_selected":14,"score":365,"score_detail":366,"sources":368,"tags":370,"primary_topic":374,"secondary_topics":379,"classification":387,"search_phrases":413,"slug":416,"view_count":52,"doi":417,"paper":418,"created_at":438},3263,"Identifying drought-responsive candidate genes in root and shoot tissues of wheat (Triticum aestivum L.) through RNA sequencing, cluster analysis, weighted gene co-expression network analysis, and machine learning models","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1952456","Drought stress severely affects the growth, development, and yield formation of wheat. However, the differentiated regulatory mechanisms of wheat roots and shoots during drought adaptation remain to be further analyzed. Therefore, this study used RNA-seq datasets of roots and shoots from multiple wheat varieties, combined with differential expression analysis, Gene Ontology (GO) enrichment analysis, clustering analysis, weighted gene co-expression network analysis (WGCNA), and machine learning were used to characterize drought-responsive transcriptional changes. Differential expression analysis identified 90 and 3,991 differentially expressed genes (DEGs) in roots under DS1 and DS2 respectively, compared with 34 and 1,940 DEGs in shoots. Functional enrichment analysis indicated that root DEGs were mainly involved in root growth, cell wall remodeling, water transport, and osmotic adjustment processes, whereas shoot DEGs are mainly enriched in processes related to stomatal regulation, photosynthesis, lipid metabolism, and oxidative stress responses. WGCNA identified 18 and 19 co-expression modules in roots and shoots, respectively, among which the yellow module was significantly correlated with the response to drought stress in root and shoot tissues. To further screen drought-responsive candidate genes from these modules, we compared multiple machine learning models. The XGBoost model achieved the highest and AUC in both roots and shoots. Combined with SHAP feature contribution analysis, 30 drought-responsive candidate genes with high discriminatory power were identified separately in roots and shoots. Analysis of cis -acting elements in candidate gene promoters revealed abundant drought- and stress-responsive elements, including MYB, G-box, ABRE, MYC, MBS, and LTR. MYB (183 in roots and 164 in shoots), G-box (168 and 98), and ABRE (148 and 86) were particularly abundant, and several candidate genes contained more than five ABRE copies in their promoters, suggesting potential involvement in ABA-mediated drought responses. This study reveals the different transcriptional response processes of wheat roots and shoots under drought stress, and identifies key co-expression modules and core candidate genes through WGCNA combined with XGBoost and SHAP analysis. These findings provide a basis for understanding organ-specific molecular responses to drought stress and offer potential targets for improving drought tolerance in wheat.","干旱胁迫严重影响小麦的生长发育和产量形成。然而，小麦根系和地上部在干旱适应过程中的差异化调控机制仍有待深入解析。因此，本研究利用多个小麦品种根系和地上部的RNA-seq数据集，结合差异表达分析、基因本体（GO）富集分析、聚类分析、加权基因共表达网络分析（WGCNA）和机器学习方法，表征干旱响应转录变化。差异表达分析在DS1和DS2条件下分别鉴定出根系中90个和3,991个差异表达基因（DEGs），而地上部中分别为34个和1,940个DEGs。功能富集分析表明，根系DEGs主要参与根系生长、细胞壁重塑、水分运输和渗透调节过程，而地上部DEGs主要富集于气孔调控、光合作用、脂质代谢和氧化胁迫响应相关过程。WGCNA在根系和地上部中分别鉴定出18个和19个共表达模块，其中黄色模块与根系和地上部组织对干旱胁迫的响应显著相关。为进一步从这些模块中筛选干旱响应候选基因，我们比较了多种机器学习模型。XGBoost模型在根系和地上部中均取得了最高的AUC。结合SHAP特征贡献分析，分别在根系和地上部中鉴定出30个具有高判别力的干旱响应候选基因。候选基因启动子中的顺式作用元件分析揭示了丰富的干旱和胁迫响应元件，包括MYB、G-box、ABRE、MYC、MBS和LTR。其中MYB（根系183个，地上部164个）、G-box（168个和98个）和ABRE（148个和86个）尤为丰富，且多个候选基因的启动子中含有超过5个ABRE拷贝，表明其可能参与ABA介导的干旱响应。本研究揭示了小麦根系和地上部在干旱胁迫下不同的转录响应过程，并通过WGCNA结合XGBoost和SHAP分析鉴定了关键共表达模块和核心候选基因。这些发现为理解干旱胁迫的器官特异性分子响应提供了基础，并为提高小麦耐旱性提供了潜在靶点。","Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z",81,{"impact":18,"substance":151,"depth":18,"authority":212,"freshness":13,"relevant":22,"comment":367},"该研究结合多组学与机器学习挖掘小麦抗旱候选基因，方法新颖、数据扎实，对分子育种有参考价值，值得进入每日精选。",[369],{"name":363,"url":360},[90,27,371,372,373],"基因挖掘","作物育种","小麦抗旱",{"code":375,"slug":376,"name":377,"path":378,"parent_code":310},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[380,381],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":382,"slug":383,"name":384,"path":385,"parent_code":386},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":388},{"candidates":389,"agricultural_evidence":46},[390,395,399,405,409],{"code":32,"score":19,"supported":46,"title":391,"abstract":393,"tags":394},[392],"machine learning",[227,392],[90],{"code":375,"score":170,"supported":46,"title":396,"abstract":397,"tags":398},[376],[377,376],[],{"code":382,"score":170,"supported":46,"title":400,"abstract":402,"tags":404},[401],"drought",[403,401],"干旱",[],{"code":51,"score":22,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[27],{"code":58,"score":22,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[371],[414,415],"小麦 抗旱基因 RNA测序","小麦 根系 地上部 干旱响应","小麦抗旱基因RNA测序-3263","10.3389\u002Ffsufs.2026.1952456",{"doi":417,"openalex_id":419,"authors":420,"venue":363,"cited_by_count":269,"oa_url":360,"card":433,"direction":276,"ingested_from":277},"W7214062209",[421,423,426,428,430],{"name":422,"orcid":8},"Tianle Ji",{"name":424,"orcid":425},"Baoyue Cui","https:\u002F\u002Forcid.org\u002F0009-0000-5881-9682",{"name":427,"orcid":8},"Jinjie Yin",{"name":429,"orcid":8},"Xianyong Meng",{"name":431,"orcid":432},"Jun Yan","https:\u002F\u002Forcid.org\u002F0000-0001-6883-8529",{"tldr":434,"method":435,"finding":436,"direction":276,"opportunity":437},"通过RNA-seq、WGCNA和XGBoost-SHAP分析，鉴定小麦根和茎中响应干旱的候选基因。","RNA-seq、差异表达、WGCNA、XGBoost与SHAP特征分析。","根和茎干旱响应机制不同，各鉴定出30个候选基因，启动子富含MYB、G-box和ABRE元件。","可结合多组学与深度学习构建器官特异性干旱调控网络，并开展候选基因功能验证。","2026-09-23T23:30:07.062126Z",{"id":440,"title":441,"url":442,"summary":443,"summary_zh":8,"content":8,"source_name":444,"source_url":8,"published_at":445,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":446,"score_detail":447,"sources":452,"tags":454,"primary_topic":458,"secondary_topics":459,"classification":460,"search_phrases":483,"slug":486,"view_count":59,"doi":8,"paper":8,"created_at":487},2815,"WAFI2026人工智能与农业论坛:中国方案助力农民种得好、种得起、种得稳、种得赚","https:\u002F\u002Fbaike.baidu.com\u002Fitem\u002F2026%E4%B8%96%E7%95%8C%E5%86%9C%E4%B8%9A%E7%A7%91%E6%8A%80%E5%88%9B%E6%96%B0%E5%A4%A7%E4%BC%9A\u002F68651190","9月16日WAFI2026举行\"人工智能与农业论坛\",中国农业大学全球食物经济与政策研究院院长樊胜根提出\"人工智能如何造福农民\"议题。论坛介绍神农大模型(2023年1.0版到2025年3.0版,3.0版为\"小麦育种智能助手\",可识别70类、600余种病虫害,已在非洲落地)、农业食物经济与政策AI模型、北大荒\"未来农场\"平台(覆盖111个农场、接入8.4万台智能装备、为60万种植户服务)、北京市\"智京园\"智慧设施管控技术体系等案例。","百度百科 \u002F 中国农业大学","2026-09-16T00:00:00Z",86,{"impact":448,"substance":449,"depth":450,"authority":170,"freshness":20,"relevant":22,"comment":451},26,21,17,"国际论坛上集中展示神农大模型、北大荒未来农场等中国AI农业方案，案例数据具体、信源权威，时效性强，值得进入每日精选。",[453],{"name":444,"url":442},[157,90,455,456,29,457],"未来农场","智能育种","病虫害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":461},{"candidates":462,"agricultural_evidence":46},[463,467,471,475,479],{"code":32,"score":70,"supported":46,"title":464,"abstract":465,"tags":466},[],[29],[90,29,457],{"code":375,"score":59,"supported":46,"title":468,"abstract":469,"tags":470},[],[377],[],{"code":58,"score":22,"supported":14,"title":472,"abstract":473,"tags":474},[],[],[456],{"code":347,"score":22,"supported":14,"title":476,"abstract":477,"tags":478},[],[],[455],{"code":181,"score":22,"supported":14,"title":480,"abstract":481,"tags":482},[],[],[157],[484,485],"农业人工智能 病虫害识别 智慧农业 智能育种","农业人工智能 病虫害识别","农业人工智能病虫害识别智慧农业智能育种-2815","2026-09-18T00:03:24.639818Z"]