[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1802":3,"related-1802":65},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":60,"slug":63,"view_count":43,"doi":8,"paper":8,"created_at":64},1802,"上海育成南方稻区唯一节水抗旱稻\"标准品种\"——\"中旱3号\"累计推广534.96万亩","https:\u002F\u002Fwww.fxbwd.com\u002F128234.html","上海市农业科学院选育的\"中旱3号\"是中国南方稻区第一个通过国家审定的旱稻品种，被指定为国家旱稻品种区域试验南方稻区唯一的对照品种；\"沪旱3号\"是中国南方稻区第一个通过国家审定的粳型旱稻品种。目前累计推广面积已达534.96余万亩，增产稻谷3亿公斤，节约水资源16亿吨，实现经济效益5.89亿元。在一些种植区域，节水可达50%。",null,"![Image 1](https:\u002F\u002Fwww.fxbwd.com\u002Fstatic\u002Ffx\u002Fimages\u002Ffanhui.png)\n\n![Image 2](https:\u002F\u002Fwww.fxbwd.com\u002Fstatic\u002Ffx\u002Fimages\u002Flogosmall.png)\n\n![Image 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上海育成南方稻区惟一节水抗旱稻“标准品种”\n\n来源：农讯网 作者：农讯网编辑  2026-09-07[我要评论](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html#cm)\n\n## 2026年09月07日更新报导，上海市农业科学院16日宣布，上海农业科学家已育成南方稻区惟一的旱稻区域试验对照品种和南方稻区第一个通过国家审定的粳型旱稻品种，并建成国内最完善的水稻抗旱性鉴定和评价体系及种质资源平台。上海已成为全国水稻节水抗旱研究的主要基地。据上海市农科院副院长顾晓君介绍，此间科学家选育出的“中旱3号”是中国南方稻区第一个通过国家审定的旱稻品种，被\n\n FX农业网 09月07日报导。\n\n上海市农业科学院16日宣布，上海农业科学家已育成南方稻区惟一的旱稻区域试验对照品种和南方稻区第一个通过国家审定的粳型旱稻品种，并建成国内最完善的水稻抗旱性鉴定和评价体系及种质资源平台。上海已成为全国水稻节水抗旱研究的主要基地。\n\n据上海市农科院副院长顾晓君介绍，此间科学家选育出的“中旱3号”是中国南方稻区第一个通过国家审定的旱稻品种，被指定为国家旱稻品种区域试验南方稻区惟一的对照品种，也就是说“今后任何新选育的旱稻品种的抗旱性评价都要以‘中旱3号’为标准”。上海选育的“沪旱3号”是中国南方稻区的第一个通过国家审定的粳型旱稻品种；“沪旱7号”也已通过上海的审定。\n\n干旱已成为中国粮食减产的主要原因。水稻生产用水占中国全部农业用水量的七成，消耗了中国总用水量的一半左右。发掘节水抗旱种质资源，培育和推广节水抗旱稻，是保障中国粮食安全、缓解水资源危机的主要出路。\n\n上海旱稻研究项目已育成的3个通过审定的节水抗旱稻品种，已在全国大面积推广种植，主要分布在广西、湖北、浙江、江西、江苏、山东、河北等省的干旱地区。目前累计推广面积已达534．96余万亩，增产稻谷3亿公斤，节约水资源16亿吨，实现经济效益5．89亿元。与抗旱稻相配套的节水栽培技术也在趋于成熟，在一些种植区域，节水可达50％，而且还可在坡耕地实现水土保持，减少近七成的泥沙流失。\n\n据了解，上海还在国内首次建立具有国际先进水平的水稻抗旱性鉴定与评价体系，形成“土壤水分梯度鉴定法”，实现在田间条件下进行全生育期的抗旱性准确鉴定。目前这一鉴定平台已成农业部指定的平台，向国内外开放使用。同时这一技术体系还筛选出86份节水抗旱种质资源，获得一批综合性状较为优良的种质，向国内其它研究单位提供各类节水抗旱资源千余份次，广泛应用于各地旱稻育种和研究之中。\n\n在国家“863”计划、上海科技攻关计划及美国洛克菲勒基金会的长期资助下，上海旱稻研究项目已取得突破性成果。上海成为中国旱稻研究的主要中心。旱稻的推广已形成节水、节能、减污和水土保持四大经济社会效应。\n\n本站推荐: 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\"蝴蝶兰花瓣有小煤点怎么处理呢？蝴蝶兰花朵上有黑点是什么原因？\")\n\n[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html)\n\n[关于我们](https:\u002F\u002Fwww.fxbwd.com\u002Fabout\u002Faboutus.html)[商务合作](https:\u002F\u002Fwww.fxbwd.com\u002Fabout\u002Fpartner.html)[网站地图](https:\u002F\u002Fwww.fxbwd.com\u002Fsitemap.xml)[投稿登录](https:\u002F\u002Fwww.fxbwd.com\u002Fmember)\n\nCopyright ©2014-2022 www.fxbwd.com FX农业网 \n\n分享到： [](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html# \"分享到Facebook\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html# \"分享到Twitter\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html# \"分享到linkedin\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html# \"分享到QQ空间\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html# \"分享到新浪微博\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html# \"分享到豆瓣网\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html# \"分享到微信\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html# \"分享到印象笔记\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html#)","FX农业网2026年9月5日","2026-09-05T00:00:00Z","报道",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},25,22,15,7,1,"报道了上海育成节水抗旱稻标准品种并累计推广超500万亩，具有全国性产业影响，数据详实，来源为行业媒体，时效性尚可。",[24],{"name":10,"url":6},[26,27,28],"种业振兴","品种推广","节水抗旱稻",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,55],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":48},3,true,[],[47],"水资源",[],{"code":50,"score":51,"supported":14,"title":52,"abstract":53,"tags":54},"F1",2,[],[],[27,26],{"code":56,"score":21,"supported":14,"title":57,"abstract":58,"tags":59},"A1",[],[],[28],[61,62],"节水抗旱稻 品种推广 种业振兴","节水抗旱稻 品种推广","节水抗旱稻品种推广种业振兴-1802","2026-09-07T00:04:39.570853Z",{"total":66,"page":21,"page_size":66,"items":67},6,[68,132,206,275,386,477],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":8,"content":73,"source_name":74,"source_url":8,"published_at":75,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":76,"sources":81,"tags":83,"primary_topic":88,"secondary_topics":93,"classification":95,"search_phrases":126,"slug":129,"view_count":130,"doi":8,"paper":8,"created_at":131},5040,"\"深东盐稻1号\"耐盐碱水稻新品系在山东东营全生育期3‰~4‰咸水灌溉下亩产476.09公斤","https:\u002F\u002Fwww.caas.cn\u002F?id=3804","10月8-9日,耐盐碱水稻新品系展示示范与测产观摩会在山东东营举行。经专家组现场实收测产,在全生育期3‰~4‰盐度咸水灌溉条件下,\"深东盐稻1号\"实收产量476.09公斤\u002F亩,较耐盐对照品种增产23%。该新品系突破传统盐碱地水稻产量瓶颈,为滨海盐碱地综合利用与粮食安全拓展新增耕地空间。","[人民日报头版头条]力度更大 “四个面向”为基础研究划重点（总书记的关切·落地的回响）\n\n7月10日，海南商业航天发射场，长征十号乙运载火箭首飞成功，我国运载火箭首次实现可控回收。中国航天一次次创造历史，离不开材料制备、流体动力、空间物理等方面的基础研究突破。","中国农业科学院 2026年10月14日","2026-10-14T00:00:00Z",{"impact":18,"substance":77,"depth":78,"authority":19,"freshness":79,"relevant":21,"comment":80},20,14,8,"国家级科研机构发布的耐盐碱水稻新品系在3‰~4‰咸水灌溉下实现亩产476.09公斤，属盐碱地综合利用与种业创新的实质性突破，数据具体、信源权威，值得进入每日精选。",[82],{"name":74,"url":71},[26,84,85,86,87],"盐碱地治理","耐盐碱水稻","山东东营","咸水灌溉",{"code":56,"slug":89,"name":90,"path":91,"parent_code":92},"rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[94],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":96},{"candidates":97,"agricultural_evidence":44},[98,103,109,116,122],{"code":56,"score":99,"supported":44,"title":100,"abstract":101,"tags":102},12,[90],[90],[85],{"code":30,"score":104,"supported":44,"title":105,"abstract":107,"tags":108},11,[106],"灌溉",[106],[],{"code":110,"score":111,"supported":44,"title":112,"abstract":113,"tags":115},"H2",4,[],[114],"盐碱地",[84],{"code":117,"score":43,"supported":44,"title":118,"abstract":119,"tags":121},"A8",[],[120],"粮食安全",[],{"code":50,"score":21,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[26],[127,128],"深东盐稻1号 东营 耐盐碱水稻","咸水灌溉 水稻 亩产476.09公斤","深东盐稻1号东营耐盐碱水稻-5040",0,"2026-10-16T00:13:11.145577Z",{"id":133,"title":134,"url":135,"summary":136,"summary_zh":137,"content":8,"source_name":138,"source_url":135,"published_at":139,"category":140,"cover_url":8,"hotness":13,"is_selected":14,"score":141,"score_detail":142,"sources":146,"tags":148,"primary_topic":154,"secondary_topics":155,"classification":162,"search_phrases":186,"slug":189,"view_count":130,"doi":190,"paper":191,"created_at":205},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",69,{"impact":79,"substance":18,"depth":143,"authority":99,"freshness":144,"relevant":21,"comment":145},18,9,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[147],{"name":138,"url":135},[149,150,151,152,153],"智慧农业","农业人工智能","灌溉决策","安全保证","试验设计",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[156],{"code":157,"slug":158,"name":159,"path":160,"parent_code":161},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":37,"locked":14,"version":38,"method":39,"evidence":163},{"candidates":164,"agricultural_evidence":44},[165,169,175,181],{"code":30,"score":19,"supported":44,"title":166,"abstract":167,"tags":168},[31],[106,31],[151],{"code":157,"score":99,"supported":44,"title":170,"abstract":172,"tags":174},[171],"artificial intelligence",[173],"decision support",[150],{"code":110,"score":66,"supported":44,"title":176,"abstract":177,"tags":180},[],[178,179],"土壤","soil",[],{"code":182,"score":21,"supported":14,"title":183,"abstract":184,"tags":185},"I1",[],[],[149],[187,188],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":190,"openalex_id":192,"authors":193,"venue":138,"cited_by_count":130,"oa_url":135,"card":198,"direction":202,"ingested_from":204},"W7223131205",[194,196],{"name":195,"orcid":8},"Nassima Benkhelifa",{"name":197,"orcid":8},"Yacine Aït Ahmed",{"tldr":199,"method":200,"finding":201,"direction":202,"opportunity":203},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","农业人工智能与决策模型","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","openalex","2026-10-22T11:21:10.283561Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":8,"source_name":212,"source_url":209,"published_at":213,"category":140,"cover_url":8,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":218,"tags":220,"primary_topic":226,"secondary_topics":227,"classification":228,"search_phrases":253,"slug":256,"view_count":130,"doi":257,"paper":258,"created_at":274},5575,"Wetland vegetation dynamics and its linkage with climatic, hydrological, and land-use\u002F land cover changes in the Foket floodplain of the Fetam River Watershed, Northwestern Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01136-x","Timely monitoring of wetland changes is vital for effective conservation and management of the landscape. This study, therefore, examines vegetation dynamics and their climatic, hydrological, and land-use drivers in the Foket floodplain wetland, Upper Blue Nile basin, Ethiopia. The LULC analysis was employed based on 1985, 2002, and 2020 cloud-free Landsat images. The maximum likelihood classification algorithm was applied to classify the images into eight LULC classes. The spatial and temporal dynamics of the floodplain’s surface water were further analyzed using the Global Surface Water Explorer (1984–2020), focusing on water occurrence, seasonality, transitions, and changes in surface-water dynamics. The Normalized Difference Vegetation Index (NDVI) was used to assess the temporal dynamics of wetland vegetation, while annual rainfall, temperature, and evapotranspiration data were used to examine the climatic context of vegetation change. The results reveal that the area coverage of water body and wetland decreased by 63.2% and 29%, respectively, over the study period. In contrast, cultivated land, settlement, and eucalyptus plantation had expanded by 39.8%, 280.6%, and 380%, respectively, at the expense of wetland, woodland, and shrubland. Surface-water dynamics showed a substantial decline in seasonally inundated areas and changes in the spatial distribution and persistence of surface water. This indicates the reduction of the floodplain’s hydrological connectivity. The NDVI analysis also revealed a significant increasing of wetland vegetation over 1985–2020\u002F21. Vegetation growth was very fast during 1985–2000, slowed down between 2001 and 2012, and dropped slightly from 2013 to 2020\u002F21. Analysis of climatic variables showed no major change in annual rainfall or evapotranspiration, whereas temperature exhibited a significant upward trend. The overall findings revealed the association of the floodplain vegetation dynamics with land-use land cover, hydrological, and climatic changes. This indicates that land-use land cover, hydrological, and climatic changes have contributed to the degradation of the floodplain vegetation, highlighting the urgent need for integrated management interventions that regulate agricultural expansion, grazing, and water diversion while maintaining hydrological connectivity and wetland vegetation. Clinical trial number Not applicable (NA).","及时监测湿地变化对于景观的有效保护和管理至关重要。因此，本研究考察了埃塞俄比亚上青尼罗河流域福凯特洪泛平原湿地的植被动态及其气候、水文和土地利用驱动因素。基于1985年、2002年和2020年无云Landsat影像进行土地利用\u002F土地覆盖（LULC）分析，采用最大似然分类算法将影像分为八个LULC类别。进一步利用全球地表水探索器（1984—2020年）分析洪泛平原地表水的时空动态，重点关注水的出现频率、季节性、转换及地表水动态变化。采用归一化差异植被指数（NDVI）评估湿地植被的时间动态，同时利用年降水量、气温和蒸散发数据考察植被变化的气候背景。结果表明，研究期间水体和湿地的面积覆盖率分别下降了63.2%和29%。相比之下，耕地、居民点和桉树种植园分别扩张了39.8%、280.6%和380%，侵占了湿地、林地和灌丛。地表水动态显示季节性淹没区域大幅减少，地表水的空间分布和持续性发生变化，表明洪泛平原水文连通性降低。NDVI分析还揭示1985—2020\u002F21年间湿地植被显著增加。植被生长在1985—2000年间非常迅速，2001—2012年间放缓，2013—2020\u002F21年间略有下降。气候变量分析显示年降水量或蒸散发无重大变化，而气温呈显著上升趋势。总体研究结果揭示了洪泛平原植被动态与土地利用\u002F土地覆盖、水文和气候变化的关联。这表明土地利用\u002F土地覆盖、水文和气候变化共同导致了洪泛平原植被退化，凸显了迫切需要综合管理干预措施，以调控农业扩张、放牧和引水，同时维持水文连通性和湿地植被。临床试验编号：不适用（NA）。","Discover Environment","2026-10-20T00:00:00Z",65,{"impact":79,"substance":77,"depth":216,"authority":99,"freshness":144,"relevant":21,"comment":217},16,"基于Landsat与NDVI的湿地植被动态实证研究，数据扎实但属区域案例，国际参考价值有限，不宜进入每日精选。",[219],{"name":212,"url":209},[221,222,223,224,225],"NDVI","埃塞俄比亚","土地利用变化","湿地遥感","水文连通性",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":229},{"candidates":230,"agricultural_evidence":44},[231,237,243,248],{"code":30,"score":66,"supported":44,"title":232,"abstract":233,"tags":236},[],[234,235],"蒸散发","evapotranspiration",[],{"code":238,"score":43,"supported":44,"title":239,"abstract":240,"tags":242},"H7",[],[241],"气候变化",[],{"code":244,"score":21,"supported":14,"title":245,"abstract":246,"tags":247},"H6",[],[],[223],{"code":249,"score":21,"supported":14,"title":250,"abstract":251,"tags":252},"I3",[],[],[221],[254,255],"土地利用变化 埃塞俄比亚 水文连通性 湿地遥感","土地利用变化 埃塞俄比亚","土地利用变化埃塞俄比亚水文连通性湿地遥感-5575","10.1007\u002Fs44274-026-01136-x",{"doi":257,"openalex_id":259,"authors":260,"venue":212,"cited_by_count":130,"oa_url":267,"card":268,"direction":272,"ingested_from":204},"W7223064056",[261,264],{"name":262,"orcid":263},"Workiyie Worie Assefa","https:\u002F\u002Forcid.org\u002F0000-0002-4029-9022",{"name":265,"orcid":266},"Belachew Getnet Eneyew","https:\u002F\u002Forcid.org\u002F0000-0003-1196-9004","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01136-x.pdf",{"tldr":269,"method":270,"finding":271,"direction":272,"opportunity":273},"分析埃塞俄比亚Foket洪泛湿地植被动态及其气候、水文与土地利用驱动因素。","用Landsat影像做最大似然分类，结合全球地表水数据与NDVI及气候数据分析。","水体与湿地面积大幅减少，耕地、居民点和桉树种植扩张，温度显著上升。","农业遥感与作物表型","可结合多源遥感与水文连通性指标，量化湿地退化对农业灌溉与乡村生态服务的影响。","2026-10-22T11:20:42.476484Z",{"id":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":8,"source_name":281,"source_url":278,"published_at":139,"category":140,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":282,"sources":284,"tags":286,"primary_topic":290,"secondary_topics":291,"classification":298,"search_phrases":356,"slug":359,"view_count":130,"doi":360,"paper":361,"created_at":385},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",{"impact":143,"substance":18,"depth":143,"authority":99,"freshness":144,"relevant":21,"comment":283},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[285],{"name":281,"url":278},[149,150,287,288,289],"遥感","作物水分胁迫","精准灌溉",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[292,293],{"code":157,"slug":158,"name":159,"path":160,"parent_code":161},{"code":294,"slug":295,"name":296,"path":297,"parent_code":161},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":37,"locked":14,"version":38,"method":39,"evidence":299},{"candidates":300,"agricultural_evidence":44},[301,305,311,318,324,329,336,343,350],{"code":30,"score":19,"supported":44,"title":302,"abstract":303,"tags":304},[31],[106,31],[289],{"code":157,"score":13,"supported":44,"title":306,"abstract":307,"tags":310},[],[308,309,171],"机器学习","machine learning",[150],{"code":294,"score":144,"supported":44,"title":312,"abstract":313,"tags":317},[],[314,315,316],"物联网","internet of things","IoT",[],{"code":182,"score":20,"supported":44,"title":319,"abstract":320,"tags":323},[],[321,322],"数字孪生","digital twin",[149],{"code":249,"score":20,"supported":44,"title":325,"abstract":326,"tags":328},[],[287,327],"remote sensing",[287],{"code":330,"score":66,"supported":44,"title":331,"abstract":332,"tags":335},"A2",[],[333,334],"小麦","wheat",[],{"code":337,"score":66,"supported":44,"title":338,"abstract":339,"tags":342},"A3",[],[340,341],"玉米","maize",[],{"code":344,"score":66,"supported":44,"title":345,"abstract":346,"tags":349},"A5",[],[347,348],"马铃薯","potato",[],{"code":351,"score":43,"supported":44,"title":352,"abstract":353,"tags":355},"B2",[],[354],"葡萄",[],[357,358],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":360,"openalex_id":362,"authors":363,"venue":281,"cited_by_count":130,"oa_url":278,"card":379,"direction":384,"ingested_from":204},"W7223088921",[364,367,370,373,376],{"name":365,"orcid":366},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":368,"orcid":369},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":371,"orcid":372},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":374,"orcid":375},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":377,"orcid":378},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":380,"method":381,"finding":382,"direction":272,"opportunity":383},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":8,"source_name":392,"source_url":389,"published_at":139,"category":140,"cover_url":8,"hotness":13,"is_selected":14,"score":393,"score_detail":394,"sources":397,"tags":399,"primary_topic":402,"secondary_topics":403,"classification":406,"search_phrases":453,"slug":456,"view_count":130,"doi":457,"paper":458,"created_at":476},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":143,"substance":18,"depth":143,"authority":395,"freshness":144,"relevant":21,"comment":396},13,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[398],{"name":392,"url":389},[149,150,314,400,401],"精准农业","气候韧性",{"code":157,"slug":158,"name":159,"path":160,"parent_code":161},[404,405],{"code":294,"slug":295,"name":296,"path":297,"parent_code":161},{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":407},{"candidates":408,"agricultural_evidence":44},[409,417,422,428,432,437,441,447],{"code":157,"score":143,"supported":44,"title":410,"abstract":411,"tags":416},[171],[412,308,413,309,414,415],"深度学习","计算机视觉","deep learning","computer vision",[150],{"code":182,"score":216,"supported":44,"title":418,"abstract":420,"tags":421},[419],"precision agriculture",[400,419],[149,400],{"code":294,"score":13,"supported":44,"title":423,"abstract":424,"tags":427},[],[314,425,316,426],"边缘计算","edge computing",[314],{"code":30,"score":144,"supported":44,"title":429,"abstract":430,"tags":431},[],[106,47,31],[],{"code":238,"score":20,"supported":44,"title":433,"abstract":434,"tags":436},[],[241,435],"climate change",[401],{"code":110,"score":66,"supported":44,"title":438,"abstract":439,"tags":440},[],[178,179],[],{"code":442,"score":43,"supported":44,"title":443,"abstract":444,"tags":446},"A7",[],[445],"间作",[],{"code":448,"score":43,"supported":44,"title":449,"abstract":450,"tags":452},"F3",[],[451],"养分管理",[],[454,455],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":457,"openalex_id":459,"authors":460,"venue":392,"cited_by_count":130,"oa_url":389,"card":471,"direction":384,"ingested_from":204},"W7223121456",[461,464,466,468],{"name":462,"orcid":463},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":465,"orcid":8},"Oluwadare OYEBODE",{"name":467,"orcid":8},"Iretiayo Adelaiye",{"name":469,"orcid":470},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":472,"method":473,"finding":474,"direction":202,"opportunity":475},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":478,"title":479,"url":480,"summary":481,"summary_zh":482,"content":8,"source_name":138,"source_url":480,"published_at":139,"category":140,"cover_url":8,"hotness":13,"is_selected":14,"score":483,"score_detail":484,"sources":486,"tags":488,"primary_topic":492,"secondary_topics":493,"classification":499,"search_phrases":526,"slug":529,"view_count":130,"doi":530,"paper":531,"created_at":541},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":143,"substance":77,"depth":216,"authority":99,"freshness":144,"relevant":21,"comment":485},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[487],{"name":138,"url":480},[150,489,287,490,491],"农业物联网","智慧灌溉","节水农业",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[494,495],{"code":294,"slug":295,"name":296,"path":297,"parent_code":161},{"code":249,"slug":496,"name":497,"path":498,"parent_code":161},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":37,"locked":14,"version":38,"method":39,"evidence":500},{"candidates":501,"agricultural_evidence":44},[502,507,511,515,519],{"code":30,"score":503,"supported":44,"title":504,"abstract":505,"tags":506},19,[31],[106,47,31],[490,491],{"code":294,"score":143,"supported":44,"title":508,"abstract":509,"tags":510},[316],[314,315,316],[489],{"code":249,"score":19,"supported":44,"title":512,"abstract":513,"tags":514},[327],[287,327],[287],{"code":157,"score":99,"supported":44,"title":516,"abstract":517,"tags":518},[171],[171],[150],{"code":520,"score":66,"supported":44,"title":521,"abstract":522,"tags":525},"H8",[],[523,524],"可持续农业","sustainable agriculture",[],[527,528],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":530,"openalex_id":532,"authors":533,"venue":138,"cited_by_count":130,"oa_url":480,"card":536,"direction":384,"ingested_from":204},"W7223090389",[534],{"name":535,"orcid":8},"Patil",{"tldr":537,"method":538,"finding":539,"direction":384,"opportunity":540},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z"]