[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3586":3,"related-3586":39},{"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,"search_phrases":33,"slug":36,"view_count":37,"doi":8,"paper":8,"created_at":38},3586,"黑龙江省农科院科技包联现场观摩会举办——'松粳22'优质食味水稻新品种示范田实测亩产557.5公斤","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html","9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，试验田新品种展示区块18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。黑龙江省农科院生物技术研究所（五常水稻研究所）副所长、研究员闫平介绍，新品种'松粳22'150亩示范田按照14.5%标准含水率折算，平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平，抗倒伏能力显著优于传统优质稻。秸秆基质育苗示范点位展示秸秆基质育苗技术，今年在哈尔滨18个点位示范。",null,"![Image 1](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 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[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 3](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 乡村行 看振兴丨科技赋能丰产 定制深耕良田——黑龙江省农科院科技包联现场观摩会举办\n\n 2026-09-25 12:39:56 来源: 科技日报 点击数：\n\n![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n**科技日报记者 朱虹**\n\n金色稻海漫过黑龙江五常的黑土地，稻浪深处，开镰号令响起。随着乔府大院董事长乔文志一声“开镰”，嘉宾们手持镰刀踏入金黄稻田，割下了今秋第一束稻穗。\n\n9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，传统农耕与现代科技撞个满怀，一场丰收开镰，也是一次看得见摸得着的农业科技大考。\n\n在试验田新品种展示区块，18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。该院生物技术研究所（五常水稻研究所）副所长、研究员闫平站在田垄之间，伸手托起沉甸甸的“松粳22”稻穗，招呼围观嘉宾观察茎秆与籽粒状态。现场发布前，实收测产数据显示，150亩示范田按照14.5%标准含水率折算，松粳22平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平。\n\n“传统稻花香大米口感出众，可茎秆偏软，遇上风雨天气极易大面积倒伏。”闫平指着成片挺立的稻株讲解道，“今天展示的新品种，抗倒伏能力显著优于传统优质稻。”\n\n一句“抗倒伏”，点破了好米难产的旧账。传统优质稻往往“娇贵”——品质好却秆软易倒、产量不稳，种高端米像押宝。新品种把“好吃”和“抗倒”拧在一起：抗倒伏，意味着风雨天不趴窝、机械收割进得去、规模种植稳得住，高端米供给才算有了“压舱石”。从“靠天吃饭”到“凭种稳产”，良种攻关为高端稻米产业蹚出一条新路。\n\n米食品鉴区，新米出锅，香气四溢，米粒莹润油亮。定制客户捧着一碗米饭感慨：“一碗好米的根基就在种子，科研选育的新品种，让老百姓既吃得饱，更吃得好。”\n\n观摩队伍在试验田里走走停停。秸秆基质育苗示范点位旁，黑龙江省农科院耕作栽培研究所作物栽培与装备研究室主任董文军被团团围住，他弯腰捧起一把棕褐色基质，讲起秸秆基质育苗的门道。\n\n“原料就是经多年沤熟的秸秆有机肥，配上三成秸秆打包回收时附带的表层土，育苗完全不用再挖黑土。”董文军介绍，杀菌剂、控旺剂都已预配好，农户拿到手直接铺苗床，取土、晒土、混拌这些累活全省了。“今年在哈尔滨18个点位示范，秧苗长势不比传统育苗土差，产量持平就可以得到推广，前期试验发现不同的配方基质比当地基质和黑土能增产5%到15%。”董文军说。秸秆从田间收走、发酵成基质，秧苗育成再回归水田。这条闭环，把曾经的“田间负担”变成“黑土养料”，围观的种植大户连连点头，当场追问推广条件。\n\n智慧农业贯穿作物生长每一环。黑龙江省农科院农业遥感与信息研究所副所长张有智介绍：“无人机图像结合人工智能模型算法，实现作物的长势情况和杂草分布反演识别，自动生成施肥处方图和施药处方图，直接推送到无人机飞手账号进行作业。作业精度能到厘米级。”今年试验，这套技术实现减肥6%、减药17%左右。\n\n黑龙江省农科院耕作栽培研究所所长李柱刚告诉科技日报记者，2023年黑龙江省农科院建立科技包联服务机制以来，哈尔滨已建成粮食单产提升示范田、科技服务田28个，示范推广面积25800亩。\n\n“为龙头企业提供高端定制服务，是科技包联的重头戏。”黑龙江省农科院副院长焦少杰站在田埂上说，“从品种定向选育、栽培技术集成，到植保方案、加工品鉴，全程‘一对一’定制，让高端米从‘卖原粮’升级为‘卖标准、卖品质’。”他介绍，黑龙江省农科院已深化科技包联13个市（地）服务机制，推动“百项技术、千个基地、万名人才”增粮示范落地。\n\n责任编辑：王倩\n\n相关稿件：\n\n![Image 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观摩会","黑龙江省农科院松粳22水稻-3586",0,"2026-09-27T00:05:16.125922Z",{"total":40,"page":22,"page_size":40,"items":41},6,[42,63,87,107,152,187],{"id":43,"title":44,"url":45,"summary":46,"summary_zh":8,"content":8,"source_name":47,"source_url":8,"published_at":48,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":49,"score_detail":50,"sources":53,"tags":55,"search_phrases":58,"slug":61,"view_count":37,"doi":8,"paper":8,"created_at":62},3301,"莲都区107个水稻新品种集中亮相——2026年第四届浙西南水稻新品种数字化展示现场观摩会","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7688050911050007074","9月21日，2026年第四届浙西南水稻新品种数字化展示现场观摩会在莲都区碧湖镇白口村举行。参会嘉宾实地查看107个水稻新品种的长势特点、田间管理和产量等情况。今年观摩会所在的国家级分子育种创新服务平台(长三角)分中心片区，由华智种谷智创科技(浙江)有限公司提供技术支撑，构建了BT生物技术+DT大数据技术的技术体系：运用水稻液相芯片对参试品种开展功能基因图谱鉴定，依托DT大数据技术搭建数字化品种展示评价系统。农业专家团队综合考量茎秆粗壮度、穗粒结构、综合抗性等多项指标，推介出春丰优7号、春优83、春诚优887等15个品种。","今日头条（莲都发布） 2026年09月22日","2026-09-21T18:04:00Z",60,{"impact":51,"substance":17,"depth":51,"authority":40,"freshness":21,"relevant":22,"comment":52},14,"地市级观摩会，107个品种与BT+DT数字化评价体系有实质信息量，但影响层级与信源权威度有限，可作主题页聚合素材。",[54],{"name":47,"url":45},[56,27,28,57,30],"数字农业","分子育种",[59,60],"浙西南 水稻新品种 观摩会","莲都 水稻液相芯片 数字化展示","浙西南水稻新品种观摩会-3301","2026-09-24T00:03:59.270091Z",{"id":64,"title":65,"url":66,"summary":67,"summary_zh":8,"content":68,"source_name":69,"source_url":8,"published_at":70,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":76,"tags":78,"search_phrases":82,"slug":85,"view_count":22,"doi":8,"paper":8,"created_at":86},1363,"北大荒信息有限公司：寒地作物大模型与离朱·智能遥感平台接入 49 颗卫星，垦区每 5 天更新作物长势监测","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_1256a96d87c79652","北大荒信息有限公司已为 4800 余万亩耕地建立数字化档案，研发各类数字化系统 60 余个。寒地作物大模型综合土壤、气象、作物长势信息辅助生成种植方案和变量施肥处方。自主研发的离朱·智能遥感平台接入 49 颗卫星，每 5 天即可完成一轮作物长势监测；垦区田间布设近 5000 台数据采集设备。","新华社哈尔滨9月1日电**题：“黑土粮仓”焕新记**\n\n新华社记者沈易瑾、王优玲、黄腾\n\n从“看天吃饭”到“看屏种地”，从“经验育种”到“精准选种”，从“机械作业”到“智能操控”……新华社记者随“活力中国调研行”采访团在黑龙江走访发现，数字技术、种业创新、智能装备加速走进黑土地，农业生产方式正不断焕新。\n\n走进北大荒信息有限公司，覆盖千万亩耕地的“数字地图”铺满大屏。地块信息、土壤墒情、作物长势、农机轨迹等数据不断更新，卫星遥感、物联网、人工智能等技术逐步融入耕、种、管、收各环节。\n\n北大荒信息有限公司市场运营中心副总经理王浩介绍，目前，公司已为4800余万亩耕地建立数字化档案，研发各类数字化系统60余个。依托长期积累的农业数据，企业研发的寒地作物大模型能够综合土壤、气象、作物长势等信息，辅助生成种植方案和变量施肥处方。\n\n“过去人工巡田，一个人一天大约只能查看200亩地。现在借助无人机、遥感等技术，可以开展大范围巡田，基本每5天就能更新一次垦区作物长势监测结果。”王浩说，数据正成为巡田、施肥、植保等农事决策的重要依据。\n\n从“靠经验”到“看数据”，田间管理越来越精准；从种源端发力，粮食增产潜力也在进一步释放。黑龙江省绥化市北林区的盛昌种子繁育有限责任公司水稻种植基地里，稻穗飘香，稻浪滚滚。技术员卢国臣俯身查看秧苗，边记录边说：“今年新引进的品种长势不错，现在已经进入蜡熟期。”\n\n基地对面，崖州湾国家实验室（绥化）粮油作物创新平台实验室正加紧建设；示范田内，一处新建的抗寒实验设备已经投入使用。\n\n“新引进的种子试种前都要进行抗寒测试。”公司总经理王会说，种子需在恒温15摄氏度的水中持续浸泡，耐寒性达到当地种植要求后，才能进入下一步研发和繁育。\n\n王会介绍，今年5月，绥化市与崖州湾国家实验室签约共建绥化粮油作物创新平台，盛昌种业流转500亩耕地作为试验田，承接南繁北育科研任务，分子育种、基因编辑等前沿技术将应用于寒地作物培育，为新品种选育提供技术支撑。\n\n“好种子”还要“种得好”，新品种不断迭代，农机装备也在向“智”升级。\n\n在哈尔滨市双城区，黑龙江德沃科技开发有限公司，一台台电驱气力式精密播种机整齐排列。风压是否稳定、有没有漏播重播、株距设置多少……这些过去更多依靠机械结构和人工调节的环节，如今可以通过智能终端实时监测和设置。\n\n“电驱系统让作业参数调整更加便捷，排种方式也由机械夹种改为气力吸种，减少种子损伤。”黑龙江德沃科技开发有限公司总工程师杜木军介绍，这款播种机作业速度可达每小时8至12公里，相比传统机械式播种机提高约50%，还可同步完成侧深施肥、覆土镇压等工序。\n\n播得快，还要播得准。企业电驱排种试验室里，每天要开展上百次排种试验。研发人员逐次分析测试数据，再根据试验数据反复调整排种器、变速箱等关键部件。\n\n![Image 1](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-3647446424-C803E5C542109517A24A455AD56CA02F\u002F0?fmt=jpg&size=176&h=768&w=1024&ppv=1)\n\n德沃科技研发的电驱气力式精密播种机（9月1日摄）。新华社记者沈易瑾 摄\n\n“目前，我们的设备在国内高端电驱播种机销量中占比超过60%。”杜木军说，近年来，企业围绕马铃薯全程机械化装备、秸秆处理装备等领域持续研发，更好适应规模化、精准化农业生产需求。\n\n作为我国产粮第一大省，黑龙江粮食总产量已连续16年位居全国首位，全省农作物耕种收综合机械化率达到99.28%。科技创新加快融入粮食生产各环节，持续挖掘粮食稳产增产潜力，为“黑土粮仓”注入新的发展动能。","新华社 · 2026-09-01","2026-09-01T01:00:00Z",82,{"impact":73,"substance":18,"depth":74,"authority":74,"freshness":21,"relevant":22,"comment":75},24,15,"央媒报道北大荒寒地作物大模型与遥感平台应用，覆盖4800万亩耕地，每5天更新长势监测，兼具产业影响与信息增量。",[77],{"name":69,"url":66},[27,79,80,28,81,29],"农业人工智能","智能农机","北大荒",[83,84],"农业人工智能 智慧农业 智能农机 种业振兴","农业人工智能 智慧农业","农业人工智能智慧农业智能农机种业振兴-1363","2026-09-02T00:05:05.669960Z",{"id":88,"title":89,"url":90,"summary":91,"summary_zh":8,"content":8,"source_name":92,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":93,"score_detail":94,"sources":97,"tags":99,"search_phrases":102,"slug":105,"view_count":37,"doi":8,"paper":8,"created_at":106},3589,"武汉市水稻'十大田间表现优秀品种'金奖出炉——80个水稻新品种同田竞'穗'，珞红优211斩获金奖榜首","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7689400795816608292","9月23日武汉市汉南区湘口种业小镇惠民农业基地内稻浪飘香、赛场火热，80个水稻新品种同台开展田间'擂台比拼'。经过省、市专家现场严谨盲评，2026年武汉市水稻'十大田间表现优秀品种'金奖名单揭晓：珞红优211、陵锋优5194、源两优9526、两优3982、源优374、Y两优130、楚两优737、荆两优8622、振优香18、魅两优菊丰占。来自武汉大学、华中农业大学、湖北大学、湖北省农业科学院等高校及科研院所的权威水稻专家组成评审组，从田间长势、株型长相、丰产潜力、抗病能力、抗逆表现、成熟品相六大维度逐项打分。","湖北日报 2026-09-25",59,{"impact":95,"substance":19,"depth":20,"authority":13,"freshness":21,"relevant":22,"comment":96},12,"地市级水稻新品种田间盲评活动，信息具体、信源为省级官媒，但影响范围限于武汉区域，属细分行业进展。",[98],{"name":92,"url":90},[28,100,101,30],"品种审定","武汉",[103,104],"武汉 水稻 十大品种 金奖","珞红优211 水稻新品种","武汉水稻十大品种金奖-3589","2026-09-27T00:05:16.375082Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":8,"source_name":113,"source_url":110,"published_at":11,"category":114,"cover_url":8,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":120,"tags":122,"search_phrases":125,"slug":128,"view_count":37,"doi":129,"paper":130,"created_at":151},3550,"Asymmetric Vegetation Responses to Flood Exposure in the Chi River Basin: A Multi-Temporal Remote Sensing and Machine Learning Investigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18101606","Flooding and drought alternate in many tropical floodplains, but how vegetation responds to both hazards over multiple years remains poorly quantified. We examined vegetation–flood associations in the Chi River Basin, Northeast Thailand, drawing on Sentinel-2 imagery from 2020, 2023, and 2024, flood records from 2017, 2018, 2021, and 2022, and machine learning methods across 230,911 point-based spatial units. Nine forms of asymmetry emerged from the analysis. The most striking was recovery asymmetry: areas that flooded at least twice between 2017 and 2022 gained vegetation during 2020–2023 (mean NDVI change = +0.0270), whereas areas with little or no flood exposure lost vegetation (mean change = −0.0430). This gap was statistically significant (Cohen’s d = 0.5586, p \u003C 0.001) and suggests that repeated flooding may buffer vegetation against subsequent drought. Agricultural land declined less (−0.0316) than forest (−0.0937; ANOVA F = 1717.7, p \u003C 0.001). Baseline vegetation condition, measured as NDVI in 2020, contributed 32.9% to model importance, more than twice the contribution of elevation (14.5%). Spectral indices together accounted for 76.6% of importance, compared with 23.5% for topographic variables. The 2023 El Niño year produced the largest difference between high-flood and low-flood areas (+0.0364); because only one year per ENSO phase was available, we treat this as a case-based comparison rather than a general ENSO response. Threshold analysis identified two distinct values: an operational cut-off at NDVI = 0.05 (overall accuracy 82.67%) and an ecological transition around 0.25–0.30. Spatial clustering was weak but significant (Moran’s I = 0.2179, p \u003C 0.001). Spatial block cross-validation gave lower accuracy (0.597) than random cross-validation (0.627), pointing to spatial autocorrelation in the data. High-flood areas had 1.67 times the vulnerability index of low-flood areas (0.4306 vs. 0.2573). These patterns support differentiated management: elevation-based zoning, warning systems calibrated to local flood regimes, and focused interventions at hotspots.","在许多热带洪泛平原，洪水与干旱交替发生，但植被如何在多年间同时响应这两种灾害，仍缺乏充分的定量研究。我们以泰国东北部栖河（Chi River）流域为研究区，考察植被与洪水的关联，数据来源包括2020年、2023年和2024年的Sentinel-2影像，2017年、2018年、2021年和2022年的洪水记录，并采用机器学习方法，覆盖230 911个基于点的空间单元。分析中出现了九种不对称形式。其中最显著的是恢复不对称：2017—2022年间至少发生两次洪水的区域，在2020—2023年间植被增加（NDVI平均变化=+0.0270），而洪水暴露很少或没有洪水暴露的区域则植被减少（平均变化=−0.0430）。这一差异具有统计显著性（Cohen’s d=0.5586，p\u003C0.001），表明反复洪水可能缓冲植被对随后干旱的响应。农田的下降幅度（−0.0316）小于森林（−0.0937；ANOVA F=1717.7，p\u003C0.001）。以2020年NDVI衡量的基线植被状况对模型重要性的贡献为32.9%，是海拔贡献（14.5%）的两倍多。光谱指数合计占重要性的76.6%，而地形变量占23.5%。2023年厄尔尼诺年（El Niño year）高洪水区与低洪水区之间的差异最大（+0.0364）；由于每个ENSO相位仅有1年数据，我们将其视为基于个案的比较，而非一般性的ENSO响应。阈值分析识别出两个不同的值：操作性截断值为NDVI=0.05（总体精度82.67%），生态过渡值约为0.25~0.30。空间聚类较弱但显著（Moran’s I=0.2179，p\u003C0.001）。空间分块交叉验证的精度（0.597）低于随机交叉验证（0.627），表明数据中存在空间自相关。高洪水区的脆弱性指数是低洪水区的1.67倍（0.4306对0.2573）。这些格局支持差异化治理：基于海拔的分区、根据当地洪水情势校准的预警系统，以及在热点区域的重点干预。","Symmetry","论文",70,{"impact":21,"substance":117,"depth":17,"authority":20,"freshness":118,"relevant":22,"comment":119},22,9,"基于Sentinel-2与机器学习的多时相洪涝-植被响应研究，数据规模大、结论具体，对农业遥感与灾害风险管理有参考价值，但属区域案例研究，公共影响有限。",[121],{"name":113,"url":110},[27,79,29,123,124],"洪涝灾害","植被恢复",[126,127],"湄公河支流 流域 遥感 洪水","Sentinel-2 NDVI 洪涝 植被","湄公河支流流域遥感洪水-3550","10.3390\u002Fsym18101606",{"doi":129,"openalex_id":131,"authors":132,"venue":113,"cited_by_count":37,"oa_url":110,"card":144,"direction":148,"ingested_from":150},"W7214283372",[133,135,138,141],{"name":134,"orcid":8},"Jiradech Majandang",{"name":136,"orcid":137},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":139,"orcid":140},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":142,"orcid":143},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":145,"method":146,"finding":147,"direction":148,"opportunity":149},"利用多时相遥感和机器学习揭示泰国湄公河支流流域植被对洪水的非对称响应。","Sentinel-2影像、洪水记录与机器学习，分析23万个空间单元。","重复洪水区植被增加，低洪水区减少，表明洪水可缓冲干旱影响。","农业遥感与作物表型","可探索洪水-干旱交替下植被恢复机制，并开发基于阈值的差异化预警系统。","openalex","2026-09-26T23:30:29.514361Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":157,"content":8,"source_name":158,"source_url":155,"published_at":159,"category":114,"cover_url":8,"hotness":13,"is_selected":14,"score":160,"score_detail":161,"sources":164,"tags":166,"search_phrases":169,"slug":172,"view_count":37,"doi":173,"paper":174,"created_at":186},3549,"Artificial Intelligence-Based Innovations for Environmental Management in Uganda: A Scoping Review of Current Innovations","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fjatir.141025","Uganda's extraordinary biodiversity, encompassing the Albertine Rift's endangered primate populations, East Africa's most significant freshwater wetland systems, and montane forest corridors of global conservation significance, is under compound and steadily accelerating environmental stress from widespread deforestation, wetland encroachment, agricultural expansion, climate variability, and persistently inadequate environmental governance capacity.Simultaneously, artificial intelligence technologies are undergoing rapid global deployment across environmental monitoring, predictive modelling, natural resource management, and climate adaptation domains, raising the question of whether these innovations are reaching contexts such as Uganda.This scoping review systematically maps the emerging landscape of artificial intelligence applications in environmental management in Uganda, examining which specific technologies have been deployed or piloted, across which environmental domains, by which actor types, and with what documented outcomes or limitations.A systematic search of peer-reviewed literature, grey literature, policy documents, and institutional reports published between 2020 and 2026 was conducted across five databases, ultimately yielding just 18 studies and reports meeting inclusion criteria after careful screening of 247 initial records identified.Findings reveal that artificial intelligence applications in Ugandan environmental management remain genuinely promising yet geographically and thematically concentrated, with remote sensing and machine learning applications for land cover change detection and forest monitoring constituting the single dominant application cluster identified.Applications in wetland management, water quality monitoring, wildlife protection, and climate adaptation do exist but remain isolated, under-resourced, and poorly integrated into government environmental governance frameworks.Critical cross-cutting barriers, including persistent data infrastructure deficits, digital skills shortages, electricity access constraints, and an underdeveloped AI policy ecosystem, systematically limit the effective translation of global AI innovations into practical, Uganda-specific environmental management solutions.The review concludes by proposing an AI-Environmental Governance Integration Framework","乌干达拥有非凡的生物多样性，涵盖艾伯丁裂谷（Albertine Rift）的濒危灵长类动物种群、东非最重要的淡水湿地系统以及具有全球保护意义的山地森林廊道，然而这些生态系统正面临来自大面积森林砍伐、湿地侵占、农业扩张、气候变率以及长期不足的环境治理能力的复合且持续加速的环境压力。与此同时，人工智能技术正在全球范围内迅速部署于环境监测、预测建模、自然资源管理和气候适应等领域，这引发了一个问题：这些创新是否正在惠及乌干达等地区。本范围综述（scoping review）系统梳理了乌干达环境管理中人工智能应用的新兴图景，考察了哪些具体技术已被部署或试点、涉及哪些环境领域、由哪些类型的行动者实施，以及有哪些已记录的结果或局限。研究对2020年至2026年间发表的同行评审文献、灰色文献、政策文件和机构报告在五个数据库中进行了系统检索，在仔细筛选初步识别的247条记录后，最终仅有18项研究和报告符合纳入标准。研究结果表明，人工智能在乌干达环境管理中的应用确实具有前景，但在地理和主题上高度集中，其中用于土地覆盖变化检测和森林监测的遥感与机器学习应用构成了唯一占主导地位的应用集群。湿地管理、水质监测、野生动物保护和气候适应方面的应用确实存在，但仍然孤立、资源不足，且未能有效纳入政府环境治理框架。关键的跨领域障碍，包括持续存在的数据基础设施赤字、数字技能短缺、电力获取限制以及欠发达的人工智能政策生态系统，系统性地限制了全球人工智能创新向乌干达本土化环境管理解决方案的有效转化。综述最后提出了一个人工智能-环境治理整合框架（AI-Environmental Governance Integration Framework）。","OpenAlex","2026-09-24T00:00:00Z",66,{"impact":21,"substance":18,"depth":162,"authority":95,"freshness":118,"relevant":22,"comment":163},17,"系统梳理乌干达AI环境管理应用的综述论文，方法规范、结论可靠，但属区域性研究且与三农信息化关联偏间接，公共影响有限。",[165],{"name":158,"url":155},[27,79,167,29,168],"环境治理","乌干达",[170,171],"乌干达 人工智能 环境管理","AI 遥感 森林监测","乌干达人工智能环境管理-3549","10.64643\u002Fjatir.141025",{"doi":173,"openalex_id":175,"authors":176,"venue":8,"cited_by_count":37,"oa_url":179,"card":180,"direction":185,"ingested_from":150},"W7214195868",[177],{"name":178,"orcid":8},"Henry Omara","https:\u002F\u002Fjatir.org\u002Fpublishedpapers\u002F141025_PAPER.pdf",{"tldr":181,"method":182,"finding":183,"direction":148,"opportunity":184},"综述乌干达环境管理中AI应用现状，发现应用集中于遥感与森林监测，整体零散且受基础设施制约。","系统检索2020-2026年五个数据库的文献与政策报告，筛选出18项研究做范围综","AI应用有前景但地理与主题集中，湿地、水质、野生动物等领域应用孤立，数据与政策短板明显。","可研究低成本遥感与机器学习在乌干达湿地、水质及小农农业环境监测中的落地路径与治理整合。","智慧农业 \u002F 农业物联网","2026-09-26T23:30:15.512560Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":8,"source_name":193,"source_url":190,"published_at":11,"category":114,"cover_url":8,"hotness":13,"is_selected":194,"score":71,"score_detail":195,"sources":198,"tags":200,"search_phrases":204,"slug":207,"view_count":37,"doi":208,"paper":209,"created_at":240},3525,"Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10442-6","Crop growth models (CGMs) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to provide complementary information on crop water status and to contribute to more robust ensemble trajectories. Real-case results confirmed these findings. DA assimilation of SM proved valuable particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation strategies, the joint assimilation yielded consistent results, especially in 2023, where RMSE, nRMSE and bias were 1258.83 kg\u002Fha, 16.62%, and - 220.52 kg\u002Fha, respectively. The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.","作物生长模型（CGM）是农业监测的重要工具。然而，其对大量输入参数的需求、模型参数化与结构相关的不确定性以及空间信息的缺乏，推动了数据同化（DA）等技术的应用。本文提出了一种数据同化框架，以改进玉米生物量估算。采用粒子滤波（PF）将遥感反射率和土壤水分（SM）数据分别独立及同时同化进入农业生产系统模拟器（APSIM）模型。Sentinel-2反射率观测数据通过将APSIM与辐射传输模型（RTM）PROSAIL耦合进行同化，而SMAP L波段土壤水分产品则直接同化进入APSIM。为评估所提方法可靠性而设计的合成实验，凸显了同化反射率在约束作物性状方面的优势，以及同化土壤水分在提供作物水分状况补充信息和促进更稳健集合轨迹方面的作用。实际案例结果证实了这些发现。土壤水分数据同化在数据缺失和干旱条件下尤为有价值。尽管联合同化未能持续超越单源同化策略，但其结果具有一致性，尤其在2023年，RMSE、nRMSE和偏差分别为1258.83 kg\u002Fha、16.62%和-220.52 kg\u002Fha。所提出的框架展示了多源数据同化在增强生物量估算和支持稳健、空间显式作物监测方面的潜力。","Precision Agriculture",true,{"impact":17,"substance":117,"depth":196,"authority":51,"freshness":118,"relevant":22,"comment":197},19,"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型估算玉米生物量，方法新颖、结论可靠，对作物遥感监测有参考价值。",[199],{"name":193,"url":190},[27,201,202,29,203],"玉米","作物模型","数据同化",[205,206],"Sentinel-2 SMAP APSIM 玉米生物量","多源数据同化 玉米 遥感","Sentinel-2SMAPAPSIM玉米生物量-3525","10.1007\u002Fs11119-026-10442-6",{"doi":208,"openalex_id":210,"authors":211,"venue":193,"cited_by_count":37,"oa_url":190,"card":235,"direction":148,"ingested_from":150},"W7214405444",[212,214,217,220,223,225,227,230,232],{"name":213,"orcid":8},"Manuela Montella",{"name":215,"orcid":216},"Christian Bossung","https:\u002F\u002Forcid.org\u002F0000-0003-4651-2645",{"name":218,"orcid":219},"Thanh Huy Nguyen","https:\u002F\u002Forcid.org\u002F0000-0003-2471-350X",{"name":221,"orcid":222},"Marco Chini","https:\u002F\u002Forcid.org\u002F0000-0002-9094-0367",{"name":224,"orcid":8},"Jean François Iffly",{"name":226,"orcid":8},"Thomas Udelhoven",{"name":228,"orcid":229},"Julia Kubanek","https:\u002F\u002Forcid.org\u002F0000-0001-9597-2029",{"name":231,"orcid":8},"Zoltan Szantoi",{"name":233,"orcid":234},"Miriam Machwitz","https:\u002F\u002Forcid.org\u002F0000-0002-4999-673X",{"tldr":236,"method":237,"finding":238,"direction":148,"opportunity":239},"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型，提升玉米生物量估算精度。","粒子滤波同化Sentinel-2反射率（经PROSAIL耦合）与SMAP土壤水分","联合同化结果稳健，2023年RMSE为1258.83 kg\u002Fha，nRMSE 16.62%，偏差-2","可探索多源数据同化在数据缺失与干旱条件下的自适应权重策略，并推广至其他作物与区域。","2026-09-26T23:30:03.247019Z"]