[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3228":3,"related-3228":84},{"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,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":79,"slug":82,"view_count":59,"doi":8,"paper":8,"created_at":83},3228,"卫星技术赋能黑土良田 佳木斯打造\"卫星+农业\"特色样板——\"中国智慧农业第一镇\"郊区沿江镇先行示范区项目正式启动","https:\u002F\u002Fwww.hubpd.com\u002Fdetail\u002Findex.html?contentId=5188146770736475190&appKey=5fdaacc0c3f0c80001d1c5b3&key=5fdaacc0c3f0c80001d1c5b4","9-19 人民网报道：第四届佳木斯卫星产业发展大会暨商业航天产业技术创新联盟会议将于9-21~23召开。作为全国首批北斗规模应用试点城市，佳木斯充分发挥黑土、水系、农林等地理优势及全国重要商品粮基地的产业优势，大力发展\"卫星+农业\"应用场景。今年5月由佳木斯市工信局、郊区人民政府、黑龙江省农科院佳木斯分院等单位联合发起的中国智慧农业第一镇暨\"卫星+农业\"项目正式启动，并以郊区沿江镇为先行示范区，按照\"单品示范破题—农户规模参与—镇域全链产业化\"三步走路径梯次推进，搭建集遥感苗情监测、AI病虫害诊断、墒情灾情预警、线上农技服务、农产品溯源等于一体的智慧农业管理平台。",null,"人民日报全国党媒信息公共平台","2026-09-19T04:28:00Z","报道",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,15,13,7,1,"全国首批北斗规模应用试点城市的卫星+农业先行示范项目，有明确路径与平台内容，具备行业参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","黑土地保护","卫星遥感","北斗应用",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,51,57,64,69,74],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},"I1",12,true,[27],[27],[27],{"code":32,"score":52,"supported":47,"title":53,"abstract":54,"tags":56},4,[],[55],"遥感",[29],{"code":58,"score":59,"supported":47,"title":60,"abstract":61,"tags":63},"E4",3,[],[62],"农产品溯源",[],{"code":65,"score":21,"supported":13,"title":66,"abstract":67,"tags":68},"F5",[],[],[30],{"code":70,"score":21,"supported":13,"title":71,"abstract":72,"tags":73},"H1",[],[],[28],{"code":75,"score":21,"supported":13,"title":76,"abstract":77,"tags":78},"I6",[],[],[26],[80,81],"佳木斯 卫星 沿江镇","北斗 智慧农业 黑土","佳木斯卫星沿江镇-3228","2026-09-23T00:04:30.375650Z",{"total":85,"page":21,"page_size":85,"items":86},6,[87,142,192,237,294,352],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":8,"content":92,"source_name":93,"source_url":8,"published_at":94,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":95,"score_detail":96,"sources":100,"tags":102,"primary_topic":106,"secondary_topics":107,"classification":113,"search_phrases":136,"slug":139,"view_count":140,"doi":8,"paper":8,"created_at":141},4464,"山东省农科院发布舜耕粮安农业遥感大模型 精度95%效率提升超100%","http:\u002F\u002Fwww.md2we.com\u002Fitems\u002F4263","山东省农科院发布舜耕粮安农业遥感大模型，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%；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。","每日热点 大众新闻 2026-10-04·10月4日 · 周日 报道\n\n阅读 0\n\nAI 评分\n\n精选\n\n影响力 24\n\n信息增量 22\n\n专业深度 18\n\n信源权威 10\n\n时效性 8\n\n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。\n\n摘要\n\n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。\n\n文 全文\n\n### 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: Image 1](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a29336dd947_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: Image 2](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a2a30f6bab0_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: Image 3](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a2a2155c1ff_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\n\n相关检索","大众新闻 2026-10-06","2026-10-06T00:00:00Z",76,{"impact":16,"substance":97,"depth":98,"authority":12,"freshness":85,"relevant":21,"comment":99},21,17,"省级农科院发布农业遥感大模型，含95%精度、2000万份样本等实质数据，信息增量与专业深度俱佳，值得进入每日精选。",[101],{"name":93,"url":90},[26,27,103,104,105],"农业人工智能","农业大模型","遥感大模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[108],{"code":109,"slug":110,"name":111,"path":112,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":13,"version":40,"method":41,"evidence":114},{"candidates":115,"agricultural_evidence":47},[116,122,127,132],{"code":32,"score":117,"supported":47,"title":118,"abstract":119,"tags":121},14,[55],[55,120],"估产",[],{"code":109,"score":19,"supported":47,"title":123,"abstract":125,"tags":126},[124],"大模型",[124],[103,104],{"code":45,"score":20,"supported":47,"title":128,"abstract":129,"tags":131},[],[27,130],"数字农业",[27],{"code":75,"score":21,"supported":13,"title":133,"abstract":134,"tags":135},[],[],[26],[137,138],"山东省农科院 舜耕粮安 遥感大模型","舜耕粮安 农业遥感 精度95%","山东省农科院舜耕粮安遥感大模型-4464",5,"2026-10-09T00:04:56.436776Z",{"id":143,"title":144,"url":145,"summary":146,"summary_zh":8,"content":147,"source_name":148,"source_url":8,"published_at":149,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":150,"score_detail":151,"sources":155,"tags":157,"primary_topic":159,"secondary_topics":160,"classification":162,"search_phrases":186,"slug":189,"view_count":190,"doi":8,"paper":8,"created_at":191},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":152,"substance":16,"depth":17,"authority":12,"freshness":153,"relevant":21,"comment":154},24,8,"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。",[156],{"name":148,"url":145},[26,27,103,158,105],"种业振兴",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[161],{"code":109,"slug":110,"name":111,"path":112,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":163},{"candidates":164,"agricultural_evidence":47},[165,169,173,177,182],{"code":32,"score":117,"supported":47,"title":166,"abstract":167,"tags":168},[55],[55,120],[],{"code":109,"score":46,"supported":47,"title":170,"abstract":171,"tags":172},[124],[124],[103],{"code":45,"score":20,"supported":47,"title":174,"abstract":175,"tags":176},[],[27,130],[27],{"code":178,"score":21,"supported":13,"title":179,"abstract":180,"tags":181},"F1",[],[],[158],{"code":75,"score":21,"supported":13,"title":183,"abstract":184,"tags":185},[],[],[26],[187,188],"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",9,"2026-10-06T00:05:02.084808Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":8,"content":197,"source_name":198,"source_url":8,"published_at":199,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":200,"score_detail":201,"sources":203,"tags":205,"primary_topic":206,"secondary_topics":210,"classification":211,"search_phrases":234,"slug":235,"view_count":59,"doi":8,"paper":8,"created_at":236},3417,"卫星技术赋能黑土良田——佳木斯打造'卫星+农业'特色样板，沿江镇'中国智慧农业第一镇'项目启动","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7687111511378117146\u002F","第四届佳木斯卫星产业发展大会暨商业航天产业技术创新联盟会议将于9月21—23日召开。作为全国首批北斗规模应用试点城市，佳木斯大力发展'卫星+农业'应用场景。今年5月由佳木斯市工信局、郊区人民政府、黑龙江省农科院佳木斯分院等单位联合发起的中国智慧农业第一镇暨'卫星+农业'项目正式启动，以郊区沿江镇为先行示范区，按照'单品示范破题—农户规模参与—镇域全链产业化'三步走路径梯次推进。","## 卫星技术赋能黑土良田佳木斯打造“卫星＋农业”特色样板\n\n2026-09-19 13:19·[金台资讯](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAL8m7IqT5eR3-VgZjzlMhg66-z57FyRmydYIImMLAsQ4\u002F?source=tuwen_detail)\n\n人民网哈尔滨9月19日电 (记者尚城)第四届佳木斯卫星产业发展大会暨商业航天产业技术创新联盟会议将于9月21日—23日正式拉开帷幕。这是“华夏东极”连续举办的第四届卫星产业大会，也是佳木斯再一次向全国公众展示佳木斯商业航天发展成果的舞台。\n\n作为全国首批北斗规模应用试点城市，佳木斯充分发挥黑土、水系、农林等地理优势及全国重要商品粮基地的产业优势，大力发展“卫星+农业”应用场景。今年5月，由佳木斯市工信局、郊区人民政府、黑龙江省农科院佳木斯分院等单位联合发起的中国智慧农业第一镇暨“卫星+农业”项目正式启动，并以郊区沿江镇为先行示范区，依托其独特区位禀赋、优质黑土农业资源、完备基础设施及卫星产业配套优势，着力打造国家级智慧农业技术集成高地和现代农业示范观摩窗口，积极探索可复制、可推广的智慧农业发展佳木斯样板。\n\n据了解，该项目按照“单品示范破题—农户规模参与—镇域全链产业化”三步走路径梯次推进。搭建集遥感苗情监测、AI病虫害诊断、墒情灾情预警、线上农技服务、农产品溯源等于一体的智慧农业管理平台，构建全链条数字化闭环管理体系，有效减少了化肥、农药施用量，提升主要粮油作物平均单产。通过卫星数据与农业生产的深度融合，把卫星数据转化为田间生产力，探索可复制、可推广的“卫星+农业”佳木斯模式，为保障国家粮食安全注入航天科技力量。\n\n依托本届卫星产业发展大会，佳木斯将持续做强卫星测控优势，拓展空天信息应用场景，以卫星技术赋能黑土粮仓，全力打造国内知名的卫星测控与空天应用产业高地。","金台资讯\u002F人民网哈尔滨 2026-09-19","2026-09-19T00:00:00Z",72,{"impact":16,"substance":17,"depth":18,"authority":46,"freshness":140,"relevant":21,"comment":202},"央媒报道的卫星遥感赋能黑土农业示范项目，路径清晰、应用场景具体，具备行业参考价值。",[204],{"name":198,"url":195},[26,27,28,29,30],{"code":45,"slug":207,"name":208,"path":209,"parent_code":36},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":212},{"candidates":213,"agricultural_evidence":47},[214,218,222,226,230],{"code":45,"score":46,"supported":47,"title":215,"abstract":216,"tags":217},[27],[27],[27],{"code":65,"score":21,"supported":13,"title":219,"abstract":220,"tags":221},[],[],[30],{"code":70,"score":21,"supported":13,"title":223,"abstract":224,"tags":225},[],[],[28],{"code":32,"score":21,"supported":13,"title":227,"abstract":228,"tags":229},[],[],[29],{"code":75,"score":21,"supported":13,"title":231,"abstract":232,"tags":233},[],[],[26],[80,81],"佳木斯卫星沿江镇-3417","2026-09-25T00:09:31.179492Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":8,"content":242,"source_name":243,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":244,"score_detail":245,"sources":247,"tags":249,"primary_topic":252,"secondary_topics":253,"classification":254,"search_phrases":289,"slug":292,"view_count":59,"doi":8,"paper":8,"created_at":293},2701,"山东实施人工智能+农业行动——数据种进泥土云端守护丰收","http:\u002F\u002Fgzylhyzx.com\u002Fdetail.html?newsId=52418975","山东省农科院发布舜耕·粮安农业遥感大模型,基于20年积累的2000万份人工样本数据,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,精度高达95%,效率提升超100%。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%。","### 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\n2026-09-17 03:40:55\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","大众新闻 2026年9月",83,{"impact":152,"substance":16,"depth":17,"authority":12,"freshness":190,"relevant":21,"comment":246},"省级\"人工智能+农业\"行动部署，含舜耕遥感大模型、iWheat育种平台、寿光云棚等具体案例与数据，政策与产业信息增量充足，值得入选每日精选。",[248],{"name":243,"url":240},[26,27,103,104,250,251],"智能育种","水肥一体化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":255},{"candidates":256,"agricultural_evidence":47},[257,261,265,271,276,281,285],{"code":32,"score":85,"supported":47,"title":258,"abstract":259,"tags":260},[],[55,120],[],{"code":109,"score":140,"supported":47,"title":262,"abstract":263,"tags":264},[],[124],[103,104],{"code":266,"score":59,"supported":47,"title":267,"abstract":268,"tags":270},"I5",[],[269],"物联网",[],{"code":272,"score":21,"supported":13,"title":273,"abstract":274,"tags":275},"F2",[],[],[250],{"code":277,"score":21,"supported":13,"title":278,"abstract":279,"tags":280},"F3",[],[],[251],{"code":45,"score":21,"supported":13,"title":282,"abstract":283,"tags":284},[],[],[27],{"code":75,"score":21,"supported":13,"title":286,"abstract":287,"tags":288},[],[],[26],[290,291],"农业人工智能 农业大模型 水肥一体化 数字乡村","农业人工智能 农业大模型","农业人工智能农业大模型水肥一体化数字乡村-2701","2026-09-17T00:04:37.256062Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":8,"source_name":300,"source_url":297,"published_at":301,"category":302,"cover_url":8,"hotness":12,"is_selected":13,"score":303,"score_detail":304,"sources":308,"tags":310,"primary_topic":313,"secondary_topics":314,"classification":315,"search_phrases":327,"slug":330,"view_count":306,"doi":331,"paper":332,"created_at":351},1757,"Pengaruh Satellite Monitoring terhadap Efektivitas Pemantauan Lahan dengan Data-Driven Decision Making sebagai Variabel Mediasi pada Agritech PT XYZ","https:\u002F\u002Fdoi.org\u002F10.61132\u002Flokawati.v4i5.2768","Farming on a large scale at Agritech PT XYZ calls for satellite monitoring so that supervision actually works, yet a heavy schedule of field visits has not translated into effective monitoring on its own. Earlier work largely stops at the direct link between monitoring technology and farming performance, leaving the pathway joining the two thinly and inconsistently evidenced, especially across the Indonesian agritech landscape. This study therefore weighs how four dimensions of satellite monitoring implementation, covering information availability, information accuracy, monitoring speed, and operational utilisation, shape land monitoring effectiveness, while asking whether data-driven decision making carries that influence. The design is quantitative and causal, drawing on 120 field agronomists enumerated as a census, with estimation through partial least squares path modelling. Every dimension lifted both land monitoring effectiveness and data-driven decision making at a meaningful level; the latter in turn raised effectiveness and conveyed the influence of all four. Each dimension travels a different route, since information availability moves largely along the direct path whereas information accuracy contributes almost wholly through decision making, with monitoring speed standing out overall. Investment therefore needs sorting by mechanism rather than treating remote sensing as one uniform construct.","在Agritech PT XYZ的大规模农业经营中，卫星监测对于实现有效监督至关重要，然而繁重的实地巡查日程并未单独转化为有效的监控效果。以往的研究大多止步于监测技术与农业绩效之间的直接关联，对于连接两者的路径所提供的证据既薄弱又不一致，在印尼农业科技领域尤为如此。因此，本研究评估了卫星监测实施的四个维度——信息可用性、信息准确性、监测速度及操作利用——如何影响土地监测有效性，同时探讨数据驱动的决策是否在其中传递这种影响。研究设计采用定量与因果分析方法，基于对120名田间农艺师的全员普查数据，并通过偏最小二乘路径建模进行估计。每个维度均显著提升了土地监测有效性和数据驱动的决策能力；后者反过来又增强了有效性，并传递了所有四个维度的影响。各维度所走的路径各不相同，因为信息可用性主要沿直接路径发挥作用，而信息准确性几乎完全通过决策过程产生贡献，其中监测速度的整体效应最为突出。因此，投资需按机制进行分类，而非将遥感视为一个统一的整体概念。","Lokawati Jurnal Penelitian Manajemen dan Inovasi Riset","2026-09-05T00:00:00Z","论文",56,{"impact":46,"substance":17,"depth":305,"authority":153,"freshness":306,"relevant":21,"comment":307},16,2,"研究卫星监测对农业土地管理有效性的影响，细分维度分析，但发表于地方期刊且时效性低。",[309],{"name":300,"url":297},[27,311,29,312],"农业管理","数据驱动",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":316},{"candidates":317,"agricultural_evidence":47},[318,323],{"code":32,"score":20,"supported":47,"title":319,"abstract":320,"tags":322},[],[55,321],"remote sensing",[29],{"code":45,"score":21,"supported":13,"title":324,"abstract":325,"tags":326},[],[],[27],[328,329],"农业管理 卫星遥感 数据驱动 智慧农业","农业管理 卫星遥感","农业管理卫星遥感数据驱动智慧农业-1757","10.61132\u002Flokawati.v4i5.2768",{"doi":331,"openalex_id":333,"authors":334,"venue":300,"cited_by_count":341,"oa_url":342,"card":343,"direction":349,"ingested_from":350},"W7208706175",[335,337,339],{"name":336,"orcid":8},"Rani Mutiarawati",{"name":338,"orcid":8},"Widi Winarso",{"name":340,"orcid":8},"Dewi Sri Woelandari Pantjolo Giningroem",0,"https:\u002F\u002Fjournal.arimbi.or.id\u002Findex.php\u002FLokawati\u002Farticle\u002Fdownload\u002F2768\u002F2708",{"tldr":344,"method":345,"finding":346,"direction":347,"opportunity":348},"研究卫星监测对土地监测有效性的影响，并检验数据驱动决策的中介作用。","对120名农艺师进行普查，采用偏最小二乘路径建模分析。","卫星监测各维度均提升监测有效性，数据驱动决策起中介作用，但各维度路径不同。","农业遥感与作物表型","可探索不同监测维度在特定作物或区域下的差异化机制，优化遥感投资策略。","智慧农业 \u002F 农业物联网","openalex","2026-09-06T23:30:08.482478Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":8,"source_name":358,"source_url":355,"published_at":359,"category":302,"cover_url":8,"hotness":12,"is_selected":13,"score":360,"score_detail":361,"sources":363,"tags":365,"primary_topic":369,"secondary_topics":370,"classification":372,"search_phrases":428,"slug":431,"view_count":341,"doi":432,"paper":433,"created_at":455},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":46,"substance":17,"depth":305,"authority":190,"freshness":12,"relevant":21,"comment":362},"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[364],{"name":358,"url":355},[27,103,366,367,368],"无损检测","多光谱成像","食品质量检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[371],{"code":109,"slug":110,"name":111,"path":112,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":373},{"candidates":374,"agricultural_evidence":47},[375,380,391,397,404,411,418,424],{"code":32,"score":46,"supported":47,"title":376,"abstract":378,"tags":379},[377],"multispectral",[377],[367],{"code":109,"score":12,"supported":47,"title":381,"abstract":382,"tags":390},[],[383,384,385,386,387,388,389],"深度学习","机器学习","计算机视觉","machine learning","deep learning","artificial intelligence","computer vision",[103],{"code":58,"score":20,"supported":47,"title":392,"abstract":393,"tags":396},[],[394,395],"食品安全","food safety",[366],{"code":398,"score":85,"supported":47,"title":399,"abstract":400,"tags":403},"A1",[],[401,402],"稻米","rice",[],{"code":405,"score":85,"supported":47,"title":406,"abstract":407,"tags":410},"B1",[],[408,409],"番茄","tomato",[],{"code":412,"score":85,"supported":47,"title":413,"abstract":414,"tags":417},"B2",[],[415,416],"苹果","apple",[],{"code":419,"score":59,"supported":47,"title":420,"abstract":421,"tags":423},"E1",[],[422],"农产品加工",[],{"code":45,"score":21,"supported":13,"title":425,"abstract":426,"tags":427},[],[],[27],[429,430],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":432,"openalex_id":434,"authors":435,"venue":358,"cited_by_count":341,"oa_url":355,"card":448,"direction":454,"ingested_from":350},"W7223139905",[436,438,440,442,444,446],{"name":437,"orcid":8},"Eshwari Take",{"name":439,"orcid":8},"Avanish Raut",{"name":441,"orcid":8},"Vedant Joshi",{"name":443,"orcid":8},"Shravani Akolkar",{"name":445,"orcid":8},"R. M. Gharat",{"name":447,"orcid":8},"Shruti Dive",{"tldr":449,"method":450,"finding":451,"direction":452,"opportunity":453},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","农业人工智能与决策模型","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","2026-10-22T11:20:48.684398Z"]