[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-445":3,"related-445":75},{"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":70,"slug":73,"view_count":45,"doi":8,"paper":8,"created_at":74},445,"黑龙江:'数智龙江智启新程'全面启动——多场景落地激活黑土地新质生产力","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-08\u002F10\u002Fcontent_561999.html","科技日报报道:8月7日至8日,'数智龙江 智启新程'主题媒体调研采访团走访农业、算力、科创、政务、医疗等一线场景。北大荒信息公司覆盖4800多万亩垦区的全域耕地数字大屏实时滚动,26万个地块、59万种植户、78万台农机全部拥有专属'数字身份证'。北大荒集团基于DeepSeek底层架构的寒地作物大模型融合超30万条寒地农业专业知识,是国内首个寒地作物垂直大模型,AI变量施肥技术已在八五五农场、鹤山农场等试点落地,覆盖1.5万亩耕地,实现亩均增产30余斤,肥料用量减少20%、人工成本降低50%,病害AI识别准确率超83%。",null,"走进北大荒信息公司，覆盖4800多万亩垦区的全域耕地数字大屏实时滚动。26万个地块、59万种植户、78万台农机全部拥有专属“数字身份证”。\n\n“传统农业最大的问题是靠经验、靠人力、靠天气。”北大荒信息有限公司市场运营副总经理王浩告诉记者，“我们就是用AI替代传统经验、用数据指导种植、用无人设备替代重复性人工劳作。”\n\n北大荒集团依托“天空地人机”一体化监测体系，推出基于DeepSeek底层架构的寒地作物大模型，融合超30万条寒地农业专业知识，是国内首个寒地作物垂直大模型，适配黑龙江无霜期短、冻害多发的独特气候。目前，AI变量施肥技术已在八五五农场、鹤山农场等试点落地，覆盖1.5万亩耕地，实现亩均增产30余斤，肥料用量减少20%、人工成本降低50%，病害AI识别准确率超83%。眼下，仅智能灌排系统一项，120万亩示范田已累计节水1.2亿立方米。\n\n在黑龙江省数字政府运营中心，“龙政智数”政务大模型打破传统政务数字化重复建设弊端，走出集约化建设新路径。\n\n该省政务大数据中心高级工程师曹占立算了一笔账：“传统分散建设需600P算力、1600余张算力卡；而龙政智数仅用100P算力、328张卡，算力利用率达80%，可节省超九成成本。”平台打造80余个政务智能体，其中，低保核验智能体已完成近3万轮次数据核验，排查存疑数据14.6万余项。\n\n哈尔滨精神专科白渔泡医院是黑龙江首个AI精神医疗落地机构，整套数字化平台由中国电信TeleAgent智能体平台提供底层支撑。中国电信哈尔滨分公司高级解决方案经理王恕伟解释：“TeleAgent不是单一的AI工具，而是一套完整的AI应用开发运行平台。”针对精神科定制运营、随访专属AI功能；院内本地部署保障病历隐私；模块化架构可向基层医院复制。\n\n医院信息科主任孙忠平结合一线工作，道出最直观改变：“以往科室运营分析需要1至2名工作人员耗时两到三天完成，如今AI系统十分钟即可生成完整报告，自动标注数据异常、分析运营趋势。”同时，AI随访系统为出院患者设置5个标准化随访节点，将患者随访覆盖率从60%—70%提升至95%以上，搭建三级风险预警机制，用数字化手段守护特殊群体健康。\n\n在中华巴洛克历史文化街区，5G-A大上行网络让百年老街有了新体验。街区年游客量超千万人次，高峰期瞬时人流超1.3万人。中国联通哈尔滨市分公司网络优化工程师万隆告诉记者：“过去游客拍视频上传慢、直播卡顿。我们部署的3.5G+2.1G双频聚合5G-A大上行网络，实现4K视频秒传秒发。”大上行还支撑着多路高清监控实时回传、AI人流密度识别、AR数字人导览等应用。“大上行不是锦上添花，是数字时代的刚需。”万隆说。","科技日报 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寒地大模型 智慧农业 北大荒","黑龙江省农科院 寒地大模型","黑龙江省农科院寒地大模型智慧农业北大荒-445","2026-08-12T23:57:22.691491Z",{"total":45,"page":22,"page_size":45,"items":76},[77,121,197,280,376,419],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":8,"content":82,"source_name":83,"source_url":8,"published_at":84,"category":12,"cover_url":8,"hotness":13,"is_selected":46,"score":85,"score_detail":86,"sources":92,"tags":94,"primary_topic":99,"secondary_topics":100,"classification":101,"search_phrases":116,"slug":119,"view_count":21,"doi":8,"paper":8,"created_at":120},520,"国家人工智能应用中试基地(农业)落地黑龙江,规划185P算力","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7673080782793015842","国家人工智能应用中试基地(农业领域农作物种植方向)正式落地黑龙江,由黑龙江移动承建,依托中国移动(哈尔滨)算力中心规划建设不低于185P Flops的全国产化智能算力集群。北大荒集团AI变量施肥亩均增产30斤、节水1.2亿立方米,标志AI赋能中华大粮仓进入新阶段,人工智能+农业产业联盟同步成立。","**《科创板日报》8月12日讯（记者 黄心怡）**近日，国家人工智能应用中试基地 (农业领域农作物种植方向) 正式落地黑龙江，作为国家发改委在该方向唯一批复的国家级人工智能应用创新载体，基地将构建集AI技术试验、系统调试与成果转化于一体的专业化平台，推动智慧农业跨越式发展。\n\n据悉，该基地由黑龙江移动承建，依托中国移动（哈尔滨）算力中心，规划建设不低于185P Flops的全国产化智能算力集群。\n\n记者在一线采访中了解到，今年以来，黑龙江省“人工智能+”行动在农业、制造、文旅等十余个行业纵深推进。其中，北大荒集团已构建起“天空地人机”一体化智慧农业体系：AI变量施肥带动亩均增产30余斤，肥料减量20%，人工成本下降50%；智能灌排覆盖120万亩，累计节水达1.2亿立方米。\n\n**▍国家人工智能应用农业中试基地落地**\n\n黑龙江省发展和改革委员会党组书记、主任袁锋表示，省“十五五”规划纲要中将人工智能作为首要发展引擎的重要内容作出详细明确部署。黑龙江省拥有智能农业、先进制造、冰雪经济、生态保护、对俄合作等丰富场景，承担国家人工智能应用中试基地（农业领域农作物种植方向）建设任务；成功纳入国家3个算电协同布局区范围，“十五五”期间算力规模有望实现大幅跃升。他提出，期待未来深化多方合作，培育新质生产力，助力东北振兴。\n\n中国移动通信集团黑龙江有限公司党委书记、董事长、总经理金巍表示，国家人工智能应用中试基地（农业领域农作物种植方向）是贯彻落实国家粮食安全战略和“人工智能+”行动的重要抓手，是国家发改委在农作物种植方向唯一批复的国家级人工智能应用创新载体。项目申报共历经816个日日夜夜终于获得正式批复。基地将聚焦行业共性技术难题，打造可复制推广的典型场景，着力筑成国内一流智慧农业创新高地。\n\n依托中国移动（哈尔滨）算力中心，**该基地规划建设不低于185P的全国产化智能算力资源，实现算力、网络、存储统一调度运维，满足农业大模型训练、推理迭代和场景研发需求。**基地以万卡级稳定集群、高速低延时国产组网、PB级分级存储和全生命周期智能运维体系，解决农业场景数据量大、模型迭代频繁、训练稳定性要求高等痛点，为农业AI技术攻关、场景适配、成果验证到产业落地提供支撑。\n\n作为东北地区顶尖的智算核心基座、全球运营商最大单集群智算中心，中国移动（哈尔滨）算力中心坐落哈南工业新城，总占地1294亩，规划建筑面积60万平方米，累计投资超120亿元，建成5座高标准机楼，已建成面积超16万平方米，可提供1.8万架装机容量，硬件底座达到行业顶尖水平。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F2cb236caffbe4a50b355ea80900818a6~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1787356981&x-signature=TAyGXW1sG0lwdo3HW8%2F6cqKw3ho%3D)\n中国移动（哈尔滨）算力中心 基础维护专业主管刘伟介绍，**在建B04、B05 机楼及配套项目总投资12亿元，建筑面积4.45万平方米，提供4278架装机能力，IT负荷达61.25MW，2026年将正式投产**。**拟建项目B06 机房项目已申报国家发改委窗口指导，建成后将进一步拓展智算集群承载空间，提升智算服务能力**。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F8b6c390907eeb2b6556978351f8d8386~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1787356981&x-signature=EKrBOplCYgw2oivPzVljfNK2fV0%3D)\n在技术层面，该数据中心实现AI芯片100%国产化，依托自研天池SDN、GSE自主组网技术，搭建高速低延时、无阻塞两级国产组网架构。配套极效分级存储与全生命周期智能运维体系，可实现分钟级故障定位、小时级断点续训，保障超大算力集群长期稳定运行，为各类AI训练、行业算力应用提供底层支撑。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fa65d7676c40d5d5839cf3db85a1ed976~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1787356981&x-signature=aqXOd3O0HER5r%2FHUOW71lcldP7Y%3D)\n**▍共建“人工智能+农业”产业生态**\n\n在农业中试基地落地的同期，人工智能+农业产业联盟成立，将整合上下游产业资源，，构建智慧农业产业生态。记者获悉，多家政务单位、地方政府、本土上市企业、省内知名高校，与华为已建立战略合作，将在政务数字化、城区智慧建设、航天卫星智能技术、农业人工智能科研攻关等板块共建。\n\n华为高级副总裁杨瑞凯表示，AI正深度驱动产业数智化转型，黑龙江坐拥农业、智能制造、向北开放等丰富场景，绿电资源优势突出，为发展绿色算力奠定良好基础。华为将从四方面助力当地新质生产力建设：依托昇腾、鲲鹏构筑自主算力底座，探索算电协同新模式；下沉数字技术赋能多行业转型；秉持硬件开放、软件开源理念聚合产业生态；深化产教融合，联合本地高校培育复合型AI人才。\n\n**▍AI赋能“中华大粮仓”**\n\n记者走进北大荒集团，实地探访AI赋能大田生产的真实成效。依托4800余万亩耕地规模化优势，北大荒集团依托4800余万亩耕地规模化优势，构建“天空地人机”一体化智慧农业体系，建成农垦用地“一张图”数字底座，五维一体感知体系归集120余个专题、600余个图层数据，实现4800多万亩耕地精细化管理；接入49颗卫星，2小时完成全垦区作物长势监测，传感器24小时采集田间数据。推出国内首个寒地作物大模型，融合30万条寒地农业专业知识与80年种植历史数据，产前生成“一地一策”方案，产中病害识别准确率超83%，产后智能预判烘干仓储。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F856f7cbd9a788df1ea83221ba6d990ff~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1787356981&x-signature=3U%2Ft%2BbJLkGy8wNAreSMEQ%2F%2Bc6z8%3D)\n北大荒信息公司市场运营副总经理王浩介绍，AI带来的最大变革，是让农业生产告别经验化、碎片化、靠天吃饭，走向标准化、数据化、智能化——无论谁来种、无论什么年份，都能实现稳定产出。\n\n目前，北大荒寒地作物大模型已贯穿种植全链条，输入地块、土壤、品种信息，即可输出完整种植方案；拍摄作物照片上传，即可识别长势、诊断病害并给出防治建议；结合气象与市场数据，还能辅助产量预估和风险预警。模型每天注入新的田间数据，持续进化，越用越“懂”北大荒的土地。\n\n王浩将智慧农业建设成效归纳为 “三节一提” ——节肥、节水、节劳、提产。节肥方面，AI变量施肥技术推广后，肥料用量减少20%，亩均增产逾30斤。在4800万亩耕地上，这意味着年增产量达数十亿斤，年节约化肥数十万吨；节水方面，智能水闸与精准灌溉系统覆盖120万亩农田，累计节水1.2亿立方米；节劳方面，智能农机与无人农场的应用，使单人作业效率提升至原来的十倍。\n\n![Image 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fe567563ff913f2505fe04a2ad28a8b19~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1787356981&x-signature=ua0JYuJsyEYtPIJZDa2fXWAoNUc%3D)\n除了生产端，可复制的“北大荒未来农场”模式已经成型，目前试点推广面积超过1.5万亩、\n\n面向未来，北大荒集团明确三大方向：一是持续深化寒地作物大模型应用，覆盖更多作物与场景，使其成为每位种植户的“AI农技专家”；二是加快智能农机升级，推动AI拖拉机、农业机器人等装备规模化应用；三是拓展全产业链数字化，从“耕种管收”向“储加销”延伸，用AI打通全链条，让北大荒不仅是“中华大粮仓”，更是“智慧农业的样板间”。\n\n王浩称，“我们的目标是用人工智能把北大荒打造成世界一流的现代农业示范区。”","科创板日报","2026-08-12T09:52:00Z",89,{"impact":87,"substance":88,"depth":89,"authority":20,"freshness":90,"relevant":22,"comment":91},28,23,18,8,"国家级农业AI中试基地落地，算力规划与产业数据详实，对智慧农业具有重大推动意义。",[93],{"name":83,"url":80},[27,95,96,29,97,98],"黑龙江","人工智能","中试基地","算力",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":102},{"candidates":103,"agricultural_evidence":46},[104,108,112],{"code":32,"score":54,"supported":46,"title":105,"abstract":106,"tags":107},[],[49],[],{"code":66,"score":22,"supported":14,"title":109,"abstract":110,"tags":111},[],[],[27],{"code":60,"score":22,"supported":14,"title":113,"abstract":114,"tags":115},[],[],[96],[117,118],"中试基地 人工智能 智慧农业 北大荒","中试基地 人工智能","中试基地人工智能智慧农业北大荒-520","2026-08-15T00:02:52.854291Z",{"id":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":8,"source_name":127,"source_url":124,"published_at":128,"category":129,"cover_url":8,"hotness":17,"is_selected":14,"score":130,"score_detail":131,"sources":137,"tags":141,"primary_topic":146,"secondary_topics":147,"classification":148,"search_phrases":177,"slug":180,"view_count":181,"doi":182,"paper":183,"created_at":196},5398,"Correlation Analysis of Banana Leaf Colour and Texture Features with Chemically Determined Nutrient Concentrations","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470857","Correlation Analysis of Banana Leaf Colour and Texture Features with Chemically Determined Nutrient Concentrations examines the relationship between measurable colour and texture characteristics of banana leaves and their chemically determined nutrient concentrations. The study uses digital image processing to extract leaf features, including RGB colour values, colour intensity, texture patterns, and related statistical parameters. These image-based measurements are compared with laboratory-based nutrient analysis to identify significant correlations between optical features and nutrient levels. The research aims to determine whether leaf colour and texture can serve as reliable indicators of plant nutritional status. This approach may support the development of non-destructive, cost-effective, and efficient techniques for monitoring banana plant health. The findings can contribute to precision agriculture, improved nutrient management, and the advancement of image-based methods for crop assessment and agricultural research.","香蕉叶片颜色和纹理特征与化学测定养分浓度的相关性分析探讨了香蕉叶片可测量的颜色和纹理特征与其化学测定养分浓度之间的关系。该研究利用数字图像处理提取叶片特征，包括RGB颜色值、颜色强度、纹理模式及相关统计参数。将这些基于图像的测量结果与实验室养分分析进行比较，以识别光学特征与养分水平之间的显著相关性。该研究旨在确定叶片颜色和纹理能否作为植物营养状况的可靠指标。该方法可能有助于开发无损、经济高效且高效的香蕉植株健康监测技术。研究结果可为精准农业、改进养分管理以及推进基于图像的作物评估和农业研究方法作出贡献。","International Journal of Emerging Technologies and Innovative Research","2026-10-19T00:00:00Z","论文",50,{"impact":90,"substance":132,"depth":133,"authority":134,"freshness":135,"relevant":22,"comment":136},16,13,4,9,"该研究探索用叶片颜色与纹理图像无损判断香蕉营养状况，思路有价值，但期刊权威性低、样本规模未明，暂不宜进入每日精选。",[138,139],{"name":127,"url":124},{"name":127,"url":140},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23471101",[27,142,143,144,145],"无损检测","图像识别","香蕉","营养诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":149},{"candidates":150,"agricultural_evidence":46},[151,157,162,168,173],{"code":66,"score":21,"supported":46,"title":152,"abstract":153,"tags":156},[],[154,155],"精准农业","precision agriculture",[27],{"code":32,"score":54,"supported":46,"title":158,"abstract":159,"tags":161},[],[160],"养分管理",[],{"code":163,"score":54,"supported":46,"title":164,"abstract":165,"tags":167},"I7",[],[166],"研究方法",[],{"code":169,"score":22,"supported":14,"title":170,"abstract":171,"tags":172},"B2",[],[],[144],{"code":60,"score":22,"supported":14,"title":174,"abstract":175,"tags":176},[],[],[143],[178,179],"香蕉叶片 营养浓度 图像特征","图像识别 无损检测 智慧农业 营养诊断","香蕉叶片营养浓度图像特征-5398",0,"10.5281\u002Fzenodo.23470857",{"doi":182,"openalex_id":184,"authors":185,"venue":127,"cited_by_count":181,"oa_url":124,"card":188,"direction":194,"ingested_from":195},"W7222963764",[186],{"name":187,"orcid":8},"Anantjit Publication",{"tldr":189,"method":190,"finding":191,"direction":192,"opportunity":193},"分析香蕉叶片颜色与纹理特征同化学测定养分浓度的相关性。","数字图像处理提取RGB、颜色强度与纹理参数，与实验室养分分析对比。","叶片光学特征与养分水平存在显著相关，可作营养状态指标。","农业遥感与作物表型","可扩展到多作物、多生育期及高光谱纹理融合的养分无损诊断模型构建。","数字乡村与农业信息化","openalex","2026-10-20T23:30:23.713550Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":8,"source_name":203,"source_url":200,"published_at":204,"category":129,"cover_url":8,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":209,"tags":211,"primary_topic":215,"secondary_topics":216,"classification":217,"search_phrases":235,"slug":238,"view_count":181,"doi":239,"paper":240,"created_at":279},5327,"Poloxamer‐Enabled Thermoresponsive Biobased Controlled‐Release Fertilizer for On‐Demand Nutrient Delivery","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fadvs.78371","ABSTRACT Synchronizing nutrient release of fertilizers with crop uptake remains a central challenge in precision agriculture. Here, we present a novel temperature‐responsive biobased controlled‐release fertilizer (TRBFs) incorporating Poloxamer 407 (F127) as a thermal switch within a castor oil‐derived polyurethane coating. The optimized TRBF10 (3 wt.% coating rate, 10 wt.% F127 in coating) exhibits a sharp, reversible phase transition at ∼20°C, matching the phenological requirements of overwintering crops. As quantified by digital refractometry, the 30‐day cumulative nitrogen release is suppressed to 9.22% at 10°C (crop dormancy) and increases 8.7‐fold to 80.32% at 30°C (active growth). This scalable technology overcomes intrinsic limitations of conventional diffusion‐controlled coated fertilizers and offers potential for on‐demand nutrient delivery with reduced environmental loss. Our work establishes sustainable strategies for climate‐adaptive precision agriculture and offers new insights and design paradigms for intelligent controlled‐release materials.","摘要 实现肥料养分释放与作物吸收的同步仍是精准农业的核心挑战。本研究提出一种新型温度响应型生物基控释肥料（TRBFs），在蓖麻油衍生聚氨酯包膜中引入泊洛沙姆407（F127）作为热开关。优化后的TRBF10（包膜率3 wt.%，包膜中F127含量10 wt.%）在约20°C时表现出急剧且可逆的相变，与越冬作物的物候需求相匹配。经数字折光法定量测定，30天累积氮释放量在10°C（作物休眠期）被抑制至9.22%，而在30°C（活跃生长期）增加8.7倍至80.32%。该可规模化技术克服了传统扩散控制型包膜肥料的内在局限，为按需养分供给并减少环境损失提供了潜力。本研究为气候适应性精准农业建立了可持续策略，并为智能控释材料提供了新见解与设计范式。","Advanced Science","2026-10-18T00:00:00Z",81,{"impact":89,"substance":18,"depth":89,"authority":207,"freshness":135,"relevant":22,"comment":208},14,"该研究提出温敏生物基控释肥，实现按需养分释放，方法新颖、数据扎实，对气候适应性精准农业有参考价值。",[210],{"name":203,"url":200},[27,154,212,213,214],"控释肥","温度响应","蓖麻油基材料",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":218},{"candidates":219,"agricultural_evidence":46},[220,225,229],{"code":32,"score":207,"supported":46,"title":221,"abstract":223,"tags":224},[222],"fertilizer",[50,222],[],{"code":66,"score":90,"supported":46,"title":226,"abstract":227,"tags":228},[],[154,155],[27,154],{"code":230,"score":54,"supported":46,"title":231,"abstract":232,"tags":234},"H7",[],[233],"气候适应",[],[236,237],"Poloxamer 温敏控释肥","蓖麻油基材料 智慧农业 温度响应 精准农业","Poloxamer温敏控释肥-5327","10.1002\u002Fadvs.78371",{"doi":239,"openalex_id":241,"authors":242,"venue":203,"cited_by_count":181,"oa_url":200,"card":273,"direction":194,"ingested_from":195},"W7222872607",[243,245,248,250,252,255,257,259,261,264,267,270],{"name":244,"orcid":8},"Jiahui Liu",{"name":246,"orcid":247},"Li J","https:\u002F\u002Forcid.org\u002F0009-0008-8760-1365",{"name":249,"orcid":8},"Ziyao Li",{"name":251,"orcid":8},"Xiangjie Zhao",{"name":253,"orcid":254},"Fengyan Fang","https:\u002F\u002Forcid.org\u002F0009-0009-0081-4796",{"name":256,"orcid":8},"Yan Liu",{"name":258,"orcid":8},"Songyuan Wang",{"name":260,"orcid":8},"Boning Su",{"name":262,"orcid":263},"Yuanyuan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-7439-2970",{"name":265,"orcid":266},"Bin Gao","https:\u002F\u002Forcid.org\u002F0000-0003-3769-0191",{"name":268,"orcid":269},"Shugang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1428-9082",{"name":271,"orcid":272},"Yuechao Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4045-0252",{"tldr":274,"method":275,"finding":276,"direction":277,"opportunity":278},"研发含F127温敏开关的生物基包膜控释肥，实现按温度按需释放养分。","蓖麻油基聚氨酯包膜中嵌入Poloxamer 407作热开关，数字折光法测氮释放。","10℃时30天氮累积释放仅9.22%，30℃时增至80.32%，提升8.7倍。","智慧农业 \u002F 农业物联网","可探索多响应（温湿\u002FpH）智能包膜与田间作物需肥曲线耦合的精准释放调控。","2026-10-19T23:30:28.317764Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":8,"source_name":286,"source_url":283,"published_at":287,"category":129,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":288,"sources":290,"tags":292,"primary_topic":298,"secondary_topics":299,"classification":300,"search_phrases":357,"slug":360,"view_count":361,"doi":362,"paper":363,"created_at":375},5235,"Role of Smart Fertilization in Modern Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijpss\u002F2026\u002Fv38i106378","Global food demand is increasing rapidly owing to population growth, placing considerable pressure on agricultural systems to enhance productivity under constraints on natural resources. Although conventional fertiliser use has contributed significantly to improved yields, its excessive and imbalanced application has resulted in soil degradation, nutrient leaching, groundwater contamination, and increased emissions of nitrous oxide (N₂O), thereby reducing nutrient use efficiency (NUE). In this context, smart fertilisation has emerged as a science-driven nutrient management strategy that emphasises the application of the right nutrient source at the right rate, time and place (4R principle), supported by advanced tools such as soil and plant sensors, remote sensing, geographic information systems, and decision-support platforms. Key components include controlled-release fertilisers, nano-fertilisers, bio-formulations, fertigation systems, variable rate technology (VRT), and unmanned aerial vehicle (UAV)-based crop monitoring. Evidence from multi-location field studies across diverse agro-climatic regions of India and other countries indicates that smart fertilisation can enhance nutrient use efficiency by 50–80%, reduce fertiliser input by 19–30%, and improve crop yields by 22–30% in major crops such as rice, wheat, maize and pulses. With the global fertiliser market exceeding USD 200 billion, the adoption of precision nutrient management technologies is gaining momentum. Overall, smart fertilisation offers a sustainable intensification pathway by improving productivity while simultaneously minimising environmental externalities. However, challenges related to technology accessibility, cost, and farmer adoption remain critical constraints that require policy and institutional support.","由于人口增长，全球粮食需求迅速上升，给农业系统带来了巨大压力，要求在自然资源约束下提高生产力。尽管常规施肥对提高产量作出了显著贡献，但过量和不平衡施肥已导致土壤退化、养分淋失、地下水污染以及氧化亚氮（N₂O）排放增加，从而降低了养分利用效率（NUE）。在此背景下，智能施肥作为一种以科学为驱动的养分管理策略应运而生，强调在正确的时间、正确的地点以正确的用量施用正确的养分来源（4R原则），并借助土壤与植物传感器、遥感、地理信息系统和决策支持平台等先进工具加以支撑。其关键组成部分包括控释肥料、纳米肥料、生物制剂、水肥一体化系统、变量施肥技术（VRT）以及基于无人机（UAV）的作物监测。来自印度及其他国家不同农业气候区多点田间研究的数据表明，智能施肥可使养分利用效率提高50%–80%，减少肥料投入19%–30%，并使水稻、小麦、玉米和豆类等主要作物增产22%–30%。随着全球肥料市场规模超过2000亿美元，精准养分管理技术的采用正在加速推进。总体而言，智能施肥通过提高生产力同时最大限度减少环境负外部性，提供了一条可持续集约化路径。然而，技术可及性、成本和农民采用等方面的挑战仍是关键制约因素，需要政策和制度支持。","International Journal of Plant & Soil Science","2026-10-17T00:00:00Z",{"impact":18,"substance":18,"depth":89,"authority":133,"freshness":90,"relevant":22,"comment":289},"系统综述智能施肥技术体系与多国田间数据，量化增产节肥效果，对智慧农业与绿色投入品方向有参考价值，但属综述性论文且非国内案例，时效与权威度中等。",[291],{"name":286,"url":283},[27,293,294,295,296,297],"无人机","精准施肥","变量施肥","遥感监测","养分利用效率",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":301},{"candidates":302,"agricultural_evidence":46},[303,311,318,325,332,339,346,353],{"code":32,"score":304,"supported":46,"title":305,"abstract":307,"tags":310},20,[306],"fertilization",[49,50,308,160,309],"水肥一体化","fertigation",[297,295,294],{"code":312,"score":21,"supported":46,"title":313,"abstract":314,"tags":317},"I3",[],[315,316],"遥感","remote sensing",[296],{"code":319,"score":45,"supported":46,"title":320,"abstract":321,"tags":324},"A1",[],[322,323],"水稻","rice",[],{"code":326,"score":45,"supported":46,"title":327,"abstract":328,"tags":331},"A2",[],[329,330],"小麦","wheat",[],{"code":333,"score":45,"supported":46,"title":334,"abstract":335,"tags":338},"A3",[],[336,337],"玉米","maize",[],{"code":340,"score":45,"supported":46,"title":341,"abstract":342,"tags":345},"H2",[],[343,344],"土壤","soil",[],{"code":347,"score":45,"supported":46,"title":348,"abstract":349,"tags":352},"I4",[],[350,351],"地理信息","geographic information",[],{"code":66,"score":22,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[27],[358,359],"智能施肥 4R原则","变量施肥技术 无人机","智能施肥4R原则-5235",2,"10.9734\u002Fijpss\u002F2026\u002Fv38i106378",{"doi":362,"openalex_id":364,"authors":365,"venue":286,"cited_by_count":181,"oa_url":283,"card":370,"direction":277,"ingested_from":195},"W7222838957",[366,368],{"name":367,"orcid":8},"S. T. Panusha",{"name":369,"orcid":8},"T. M. Soumya",{"tldr":371,"method":372,"finding":373,"direction":277,"opportunity":374},"综述智能施肥（4R原则+传感器\u002F遥感\u002FVRT）提升养分利用效率与产量的机制与证据。","整合多地点田间试验证据，梳理传感器、遥感、GIS、控释\u002F纳米肥与无人机监测等技术","智能施肥可提高养分利用效率50–80%、减施化肥19–30%、增产22–30%，但受成本与农户采纳制","可研究低成本传感器+无人机变量施肥在小农户场景的适配与采纳机制，及减氮减排的量化评估。","2026-10-18T23:30:07.850055Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":8,"content":8,"source_name":381,"source_url":8,"published_at":382,"category":12,"cover_url":8,"hotness":17,"is_selected":14,"score":383,"score_detail":384,"sources":386,"tags":391,"primary_topic":395,"secondary_topics":396,"classification":397,"search_phrases":414,"slug":417,"view_count":181,"doi":8,"paper":8,"created_at":418},5212,"人保财险联合浙江省农科院发布《无人机施肥施药作业质量划分》团体标准","https:\u002F\u002Fwww.cnr.cn\u002Fzjfw\u002Fmlnb\u002Fnbgd\u002F20261016\u002Ft20261016_527843650.shtml","在宁波举办的首届四明保险论坛科技保险主题活动上,人保财险联合浙江省农业科学院发布《无人机施肥施药作业质量划分》团体标准,为农业无人机作业划定质量标尺,促进低空农业高质量发展和风险防控。","央广网(2026-10-16)","2026-10-16T00:00:00Z",68,{"impact":89,"substance":132,"depth":20,"authority":207,"freshness":90,"relevant":22,"comment":385},"省级科研机构与保险企业联合发布团体标准，为农业无人机作业质量提供标尺，具行业参考价值，但属细分领域进展，未达重大突破层级。",[387,388],{"name":381,"url":379},{"name":389,"url":390},"新浪财经(2026-10-15)","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-15\u002Fdoc-inivhyai8038690.shtml",[27,392,293,393,394],"低空经济","农业保险","农业标准",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":398},{"candidates":399,"agricultural_evidence":46},[400,405,410],{"code":32,"score":401,"supported":46,"title":402,"abstract":403,"tags":404},11,[49],[49],[],{"code":406,"score":22,"supported":14,"title":407,"abstract":408,"tags":409},"G3",[],[],[393],{"code":66,"score":22,"supported":14,"title":411,"abstract":412,"tags":413},[],[],[27],[415,416],"人保财险 浙江省农科院 无人机施肥","无人机施肥施药 作业质量划分","人保财险浙江省农科院无人机施肥-5212","2026-10-18T00:05:23.058110Z",{"id":420,"title":421,"url":422,"summary":423,"summary_zh":424,"content":8,"source_name":425,"source_url":422,"published_at":426,"category":129,"cover_url":8,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":436,"secondary_topics":437,"classification":438,"search_phrases":459,"slug":462,"view_count":181,"doi":463,"paper":464,"created_at":506},4892,"RGB-Derived Haralick Edge-Texture Features Discriminate Mung Bean Responses to Mineral and Biofertilizer Regimes","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202026","Low-cost RGB imaging can accelerate crop phenotyping, but its ability to capture plant responses to fertilizer regimes through edge-based texture descriptors remains insufficiently tested. Mung bean [Vigna radiata (L.) R. Wilczek] was grown in a randomized complete block design with six fertilizer regimes and four replicates. Side-view RGB images acquired under controlled conditions were segmented, converted to grayscale, transformed into Canny edge maps, and summarized using six gray-level co-occurrence matrix (GLCM) features. Agronomic variables were evaluated: plant height, stem diameter, nodule weight, fresh weight, pod weight, shoot dry matter, grain yield per plot, the chlorophyll index, root length, and root volume. Contrast, energy, homogeneity, entropy, and variance differed across regimes, whereas the GLCM correlation did not. Mineral fertilization resulted in the highest values of stem diameter, nodule mass, fresh biomass, shoot dry matter, and the chlorophyll index. Baker’s Gamma coefficient (0.9369) and the cophenetic correlation (0.9498) showed high similarity between the hierarchical structures, indicating that the texture descriptors preserved a treatment organization highly similar to that observed for the agronomic variables. The Haralick features calculated from controlled RGB-derived edge maps revealed fertilizer-associated differences in the mung bean canopy structure. This method is promising for nondestructive screening, but replicate-level predictive validation, full reporting of image-processing parameters, and external testing are needed before agronomic traits can be estimated from images.","低成本RGB成像可加速作物表型分析，但其通过基于边缘的纹理描述符捕捉植物对施肥制度响应的能力仍缺乏充分验证。绿豆[Vigna radiata (L.) R. Wilczek]采用随机完全区组设计种植，设6个施肥处理，4次重复。在受控条件下获取的侧视RGB图像经分割、转换为灰度图、转化为Canny边缘图，并利用6个灰度共生矩阵(GLCM)特征进行汇总。评估的农艺指标包括：株高、茎粗、根瘤重、鲜重、荚重、地上部干物质、每小区籽粒产量、叶绿素指数、根长和根体积。对比度、能量、同质性、熵和方差在不同施肥处理间存在差异，而GLCM相关性则无显著差异。矿质施肥处理下茎粗、根瘤质量、鲜生物量、地上部干物质和叶绿素指数均最高。Baker's Gamma系数(0.9369)和共表型相关系数(0.9498)表明层次结构之间高度相似，说明纹理描述符所保留的处理组织结构与农艺变量所观察到的结构高度一致。由受控RGB衍生边缘图计算的Haralick特征揭示了绿豆冠层结构中与施肥相关的差异。该方法在无损筛选中具有前景，但在能够从图像估算农艺性状之前，仍需进行重复水平的预测验证、图像处理参数的完整报告以及外部测试。","Agronomy","2026-10-13T00:00:00Z",66,{"impact":90,"substance":304,"depth":132,"authority":133,"freshness":135,"relevant":22,"comment":429},"低成本RGB边缘纹理特征用于绿豆施肥响应的无损表型筛选，方法新颖但尚需重复级预测验证，属细分领域方法学进展。",[431],{"name":425,"url":422},[27,433,142,434,435],"绿豆","作物表型","RGB成像",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":439},{"candidates":440,"agricultural_evidence":46},[441,445,450,455],{"code":32,"score":135,"supported":46,"title":442,"abstract":443,"tags":444},[],[49,222,306],[],{"code":312,"score":21,"supported":46,"title":446,"abstract":447,"tags":449},[],[434,448],"crop phenotyping",[434],{"code":451,"score":22,"supported":14,"title":452,"abstract":453,"tags":454},"A5",[],[],[433],{"code":66,"score":22,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[27],[460,461],"绿豆 施肥 表型","RGB图像 纹理特征 作物","绿豆施肥表型-4892","10.3390\u002Fagronomy16202026",{"doi":463,"openalex_id":465,"authors":466,"venue":425,"cited_by_count":181,"oa_url":422,"card":501,"direction":192,"ingested_from":195},"W7221615335",[467,470,473,476,479,482,485,488,491,494,496,498],{"name":468,"orcid":469},"Eder Pereira Neves","https:\u002F\u002Forcid.org\u002F0000-0001-9645-7061",{"name":471,"orcid":472},"Jorge González Aguilera","https:\u002F\u002Forcid.org\u002F0000-0002-7308-0967",{"name":474,"orcid":475},"Werverth Costa Martins","https:\u002F\u002Forcid.org\u002F0000-0002-2813-4276",{"name":477,"orcid":478},"Roseline da Silva Coêlho","https:\u002F\u002Forcid.org\u002F0000-0002-4412-7471",{"name":480,"orcid":481},"Fábio Steiner","https:\u002F\u002Forcid.org\u002F0000-0001-9091-1737",{"name":483,"orcid":484},"Marco Aparecido Queiroz Duarte","https:\u002F\u002Forcid.org\u002F0000-0003-2494-448X",{"name":486,"orcid":487},"Alan Mário Zuffo","https:\u002F\u002Forcid.org\u002F0000-0001-9704-5325",{"name":489,"orcid":490},"César Augusto Masgo Soto","https:\u002F\u002Forcid.org\u002F0000-0003-0720-0833",{"name":492,"orcid":493},"Carlos Genaro Morales Aranibar","https:\u002F\u002Forcid.org\u002F0000-0002-4184-2365",{"name":495,"orcid":8},"María Paulina Aliaga Martínez",{"name":497,"orcid":8},"Nataniel Linares-Gutiérrez",{"name":499,"orcid":500},"Luis Fortunato Morales Aranibar","https:\u002F\u002Forcid.org\u002F0000-0002-9421-9833",{"tldr":502,"method":503,"finding":504,"direction":192,"opportunity":505},"用RGB图像Canny边缘图的Haralick纹理特征区分绿豆对不同施肥处理的响应。","侧视RGB图像分割、灰度化、Canny边缘检测与GLCM六特征，结合农艺性状聚类","对比度、能量、同质性、熵和方差随施肥制度显著差异，纹理与农艺变量层次结构高度相似。","可探索边缘纹理特征与具体农艺性状的定量预测模型，并开展重复级验证与跨品种外部测试。","2026-10-14T23:30:20.148045Z"]