[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-607":3,"related-607":70},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":64,"slug":67,"view_count":68,"doi":8,"paper":8,"created_at":69},607,"中国农业科学院烟草所揭示 RNA ac4C 修饰通过相分离调控植物耐热性的分子机制","https:\u002F\u002Ftric.caas.cn\u002Fbsdt\u002Fzhdt\u002F683c5270c3d6454ebbeb2432adba88d8.htm","中国农业科学院烟草研究所烟草病虫害绿色防控创新团队揭示表观转录组修饰在植物胁迫响应中的重要作用,首次揭示 RNA N4-乙酰胞苷(ac⁴C)修饰通过调控生物大分子相分离参与植物耐热应答的全新机制。NbNAT10 敲除突变体幼苗热胁迫后存活率仅 6%,低于野生型的 44%。研究为作物抗高温分子育种提供新靶点,论文发表于《分子植物》(Molecular Plant)。",null,"近日，中国农业科学院烟草研究所烟草病虫害绿色防控创新团队揭示表观转录组修饰在植物胁迫响应中的重要作用。相关研究成果发表在植物科学顶级期刊《分子植物（Molecular Plant）》（JCR Q1 \u002F IF= 32.7）上。该研究首次揭示了RNA N4-乙酰胞苷（ac⁴C）修饰通过调控生物大分子相分离参与植物耐热应答的全新机制，为作物抗高温分子育种提供了全新的靶点和理论支撑。\n\n![Image 1: 图片 - 副本.jpg](https:\u002F\u002Ftric.caas.cn\u002Fimages\u002F2026-08\u002Fbeae2c769a1c44bab800a2cd24c409b2.jpg)\n\n全球变暖导致极端高温事件频发，严重威胁作物产量与全球粮食安全。研究揭示RNA ac⁴C修饰通过调控液-液相分离参与植物耐热应答的分子机制。N-乙酰转移酶NAT10与多聚腺苷酸结合蛋白PABP互作，选择性将携带ac⁴C修饰的解毒功能相关mRNA招募至热胁迫诱导形成的胁迫颗粒中，避免其在高温下降解。实验表明，NbNAT10敲除突变体幼苗热胁迫后存活率仅6%，低于野生型的44%，表明NbNAT10为耐热正调控因子。多组学分析鉴定272个同时携带ac⁴C修饰、结合PABP且富集于胁迫颗粒的转录本，主要富集于解毒与胁迫应答通路。该研究将RNA ac⁴C修饰与相分离调控胁迫响应建立直接联系，为作物耐热性遗传改良提供全新分子靶点，对提升高温环境下作物产量稳定性具有重要理论价值。\n\n烟草所博士生张万红、博士生卜沿宵、助理研究员焦裕冰为本论文第一作者，杨金广研究员为通讯作者。本研究获得国家自然科学基金及中国农业科学院创新工程等项目的资助。\n\n论文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.molp.2026.08.004","中国农业科学院烟草研究所 2026-08-12","2026-08-12T00:00:00Z","报道",10,true,88,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},25,24,19,15,5,1,"重大科研突破，揭示新机制，为作物耐热育种提供新靶点，信源权威，但时效性稍差。",[25],{"name":10,"url":6},[27,28,29,30],"Molecular Plant","RNA修饰","植物耐热","中国农科院烟草所",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":39,"locked":40,"version":41,"method":42,"evidence":43},"classified",false,"2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":14},[45,51,58],{"code":32,"score":46,"supported":14,"title":47,"abstract":49,"tags":50},11,[48],"烟草",[48],[],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":57},"F2",3,[],[56],"分子育种",[],{"code":59,"score":53,"supported":14,"title":60,"abstract":61,"tags":63},"F4",[],[62],"绿色防控",[],[65,66],"中国农科院烟草所 植物耐热 RNA修饰 Molecular Plant","中国农科院烟草所 植物耐热","中国农科院烟草所植物耐热RNA修饰MolecularPlant-607",14,"2026-08-16T23:55:40.525759Z",{"total":71,"page":22,"page_size":71,"items":72},6,[73,111,182,231,277,335],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":8,"content":78,"source_name":79,"source_url":8,"published_at":80,"category":12,"cover_url":8,"hotness":13,"is_selected":40,"score":81,"score_detail":82,"sources":87,"tags":89,"primary_topic":93,"secondary_topics":94,"classification":95,"search_phrases":106,"slug":109,"view_count":53,"doi":8,"paper":8,"created_at":110},2010,"植物靠RNA“身份标记”增强高温耐受能力","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F704c0e7d3959454c80b7f86ad9668321.htm","中国农业科学院烟草研究所发现，植物可通过RNA修饰标记并将相关分子聚集到保护颗粒中，减少高温条件下的降解。研究锁定272个同时具备标记和聚集特征的RNA分子，为培育耐热作物提供新思路。","[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fcaas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 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[政务新媒体矩阵](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fzwxmtjz\u002Findex.htm) \n\n[返回首页](https:\u002F\u002Fcaas.cn\u002Findex.htm)[English](https:\u002F\u002Fwww.caas.cn\u002Fen\u002F)\n\n[首页](https:\u002F\u002Fcaas.cn\u002Findex.htm)-[新闻中心](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Findex.htm)-[科研活动](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Findex.htm)\n\n分享到\n\n### 植物靠RNA“身份标记”解锁高温生存技能\n\n发布时间：2026-09-08 _|_ 来源： 中国农业科学院烟草研究所 _|_ 作者：焦裕冰\n\n字体[小](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F704c0e7d3959454c80b7f86ad9668321.htm)[中](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F704c0e7d3959454c80b7f86ad9668321.htm)[大](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F704c0e7d3959454c80b7f86ad9668321.htm)\n\n近日，中国农业科学院[烟草研究所](http:\u002F\u002Ftric.caas.cn\u002F)烟草病虫害绿色防控创新团队揭示在高温条件下，植物可通过在RNA上添加修饰标记、并将其召集进细胞内新形成的“保护颗粒”来避免关键分子被破坏，从而增强对高温的耐受能力。相关研究成果发表在《分子植物（Molecular Plant）》上。\n\n该研究发现，植物细胞里负责解毒的RNA分子在高温下很容易被破坏。一种名为乙酰胞苷的修饰标记能保护它们：细胞先由一种酶给这些RNA贴上标签，再由另一种蛋白把它们召集进高温下新形成的保护颗粒里，像住进避难所一样躲过降解。科研人员删除负责添加这种标记的关键蛋白后，幼苗在高温下的存活率骤降。经过进一步分析，团队还找到了272个同时带标签、被召集、进入保护颗粒的RNA分子，它们大多参与解毒和抗逆过程。该研究首次把这种修饰标记、细胞内物质聚集成颗粒的现象与植物耐热能力直接联系起来，为培育耐热作物提供了新思路。\n\n该研究得到国家自然科学基金和中国农业科学院科技创新工程等项目支持。（通讯员 李晓娟）\n\n论文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.molp.2026.08.004\n\n![Image 8: sdfsd.jpg](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-09\u002F88a5112e3760436480ab005dc2e64f07.jpg)\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [杨振海为在京博士新生讲授思政第一课](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fnkyw\u002F1d5c3301dca3431582000904672a6f67.htm)2026-09-08  \n*   [研究发现紫杉烷生物合成基因“群岛”](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F2fabe97339a7485e812daeac8dc0850d.htm)2026-09-08  \n*   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京公网安备11010802048565号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010802048565)","中国农业科学院","2026-09-09T00:00:00Z",76,{"impact":83,"substance":84,"depth":20,"authority":20,"freshness":85,"relevant":22,"comment":86},20,18,8,"中国农科院发布植物高温耐受机制新发现，具科研价值，但正文缺失，影响评估。",[88],{"name":79,"url":76},[28,90,91,92],"基因调控","高温胁迫","作物抗逆",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":96},{"candidates":97,"agricultural_evidence":14},[98,102],{"code":32,"score":53,"supported":14,"title":99,"abstract":100,"tags":101},[],[48],[],{"code":52,"score":22,"supported":40,"title":103,"abstract":104,"tags":105},[],[],[90],[107,108],"作物抗逆 基因调控 高温胁迫 RNA修饰","作物抗逆 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审议全国主推技术推荐工作","https:\u002F\u002Fcss.aaas.org.cn\u002F4311487\u002F71576609.html","10月14日蚕桑所召开2026年第六次学术委员会会议，审议2027年度全国农业主导品种主推技术和重大引领性技术暨全省农业主推技术推荐工作，并专题研讨《安徽省农业科学院蚕桑研究所「十五五」发展规划》。委员们围绕家蚕种质资源保护与创制、突破性新品种选育、绿色高效种养技术与蚕桑资源综合利用等方向建言。","为充分发挥学术委员会学术评议和咨询职能，把好高层次人才引进学术关，谋划推进重大科技任务和“十五五”发展等工作，10月14日上午，安徽省农业科学院蚕桑研究所召开2026年第六次学术委员会会议。会议由所学术委员会主任李冰主持，所学术委员会全体委员参加会议。\n\n会议开展了2026年新进博士面试预答辩。新进博士依次围绕个人基本情况、学习和研究经历、代表性成果以及入职后的工作设想作PPT汇报。与会委员结合学科发展需求，从专业基础、科研能力、学科契合度、发展潜力等方面进行提问和评议，并对提出意见建议，为高层次人才引进把好学术关。\n\n会议审议2027年度全国农业主导品种主推技术和重大引领性技术暨全省农业主推技术推荐工作事宜。牵头科室就推荐工作的政策依据、推荐条件、指标分配和前期组织申报情况作说明。委员们立足蚕桑、食药用菌等学科优势，围绕拟推荐品种和技术的先进性、适用性、示范推广基础和产业带动能力进行审议，提出推荐意见，着力遴选优质成果、促进科技成果转化应用。\n\n会议专题研讨《安徽省农业科学院蚕桑研究所“十五五”发展规划》。编制组就规划编制过程、总体思路、主要目标和重点任务作说明。委员们围绕发展定位、指标设置、重点任务和保障措施提出修改建议，重点就家蚕种质资源保护与创制、突破性新品种选育、绿色高效种养技术、蚕桑资源综合利用等方向建言献策。\n\n本次会议议题聚焦、讨论充分，委员们的意见建议务实中肯，体现了学术委员会的专业水准和责任担当。会议强调，学术委员会要持续发挥专家智库作用，做强蚕桑优势学科，拓展特色经济作物研究，强化科技创新与成果服务，为安徽蚕桑和特色农业产业高质量发展提供科技支撑。","安徽省农业科学院蚕桑研究所 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推荐","安徽农科院蚕桑所十五五规划-5522","2026-10-22T00:07:04.530022Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":8,"content":8,"source_name":236,"source_url":8,"published_at":237,"category":12,"cover_url":8,"hotness":13,"is_selected":40,"score":190,"score_detail":238,"sources":240,"tags":242,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":272,"slug":275,"view_count":171,"doi":8,"paper":8,"created_at":276},5350,"中原电气实验室一行赴河南农科院烟草所考察 达成烟草烘烤节能减排合作意向","https:\u002F\u002Fhnagri.org.cn\u002Fycs\u002Farticle-114211.html","10 月 14 日,中原电气实验室能源研究所所长马志豪、电气研究所所长贾霖、顾问梁东杰等一行 5 人莅临河南农科院烟草所开展调研交流,实地参观了密集烤房设施、天敌昆虫繁育车间以及各学科实验室。双方就烟叶烘烤过程中新能源利用、储能技术应用、热能高效利用、纳米技术在农药、肥料研发等关键环节进行了深入探讨,并达成初步合作意向。","河南省农业科学院烟草研究所 2026-10-14","2026-10-14T00:00:00Z",{"impact":121,"substance":68,"depth":192,"authority":192,"freshness":71,"relevant":22,"comment":239},"省级科研机构与实验室的跨领域合作，涉及烟叶烘烤新能源与储能应用，有实质技术方向但尚处意向阶段，时效一般。",[241],{"name":236,"url":234},[243,244,245,246,247],"院企合作","节能减排","烟草烘烤","新能源利用","储能技术",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":251},{"candidates":252,"agricultural_evidence":14},[253,257,263,268],{"code":32,"score":46,"supported":14,"title":254,"abstract":255,"tags":256},[48],[48],[],{"code":258,"score":53,"supported":14,"title":259,"abstract":260,"tags":262},"F3",[],[261],"肥料",[],{"code":59,"score":53,"supported":14,"title":264,"abstract":265,"tags":267},[],[266],"农药",[],{"code":217,"score":22,"supported":40,"title":269,"abstract":270,"tags":271},[],[],[243],[273,274],"中原电气实验室 河南农科院烟草所","烟草烘烤 节能减排","中原电气实验室河南农科院烟草所-5350","2026-10-20T02:47:44.469410Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":8,"source_name":283,"source_url":280,"published_at":284,"category":118,"cover_url":8,"hotness":13,"is_selected":40,"score":119,"score_detail":285,"sources":287,"tags":289,"primary_topic":292,"secondary_topics":293,"classification":294,"search_phrases":312,"slug":315,"view_count":22,"doi":316,"paper":317,"created_at":334},5234,"A smart agriculture-oriented cross-attention multiscale CNN for sugarcane disease diagnosis","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23080477.2026.2749739","Sugarcane leaf diseases can adversely affect crop health and yield, making timely disease identification important for smart agriculture. Manual inspection of leaf diseases is time-consuming and requires expert knowledge, particularly when large cultivation areas need to be monitored. To tackle this problem, this study proposes a Cross-Attention Multiscale Parallel Convolutional Neural Network for automatic classification of sugarcane leaf diseases. This framework comprises a Parallel Feature Extraction Stem, cross-attention mechanism, Dense Connection Blocks, and Inception Blocks to learn disease features at various spatial scales. The PFES extracts fine and broad disease patterns using parallel convolutional branches, while the cross-attention mechanism refines the extracted features by emphasizing disease-related information. The DCB supports feature propagation and reuse, and the IB further captures multi-scale feature representations. The proposed model was tested on two benchmark datasets of leaf diseases for sugarcane plants. The proposed framework obtained an accuracy (98.79%), precision (98.31%), recall (97.81%), specificity (98.60%), and F1-Score (98.05%). For assessing the classification accuracy and consistency of the framework, the comparative study, ablation studies, and five-fold cross-validation are carried out. The results show that CA-MPCNN provides accurate sugarcane leaf disease classification and can support automated disease diagnosis in smart agriculture applications.","甘蔗叶片病害会对作物健康与产量造成不利影响，因此及时识别病害对智慧农业具有重要意义。人工检查叶片病害耗时且需要专业知识，尤其是在需要监测大面积种植区域时。为解决该问题，本研究提出一种用于甘蔗叶片病害自动分类的交叉注意力多尺度并行卷积神经网络（Cross-Attention Multiscale Parallel Convolutional Neural Network）。该框架由并行特征提取主干（Parallel Feature Extraction Stem）、交叉注意力机制、密集连接块（Dense Connection Blocks）和Inception块组成，用于学习不同空间尺度上的病害特征。PFES通过并行卷积分支提取细粒度与广域病害模式，而交叉注意力机制通过强调病害相关信息对提取的特征进行细化。DCB支持特征传播与复用，IB进一步捕获多尺度特征表示。所提出的模型在两个甘蔗植株叶片病害基准数据集上进行了测试。该框架取得了准确率（98.79%）、精确率（98.31%）、召回率（97.81%）、特异性（98.60%）和F1分数（98.05%）。为评估该框架的分类准确性和一致性，开展了对比研究、消融研究和五折交叉验证。结果表明，CA-MPCNN能够实现准确的甘蔗叶片病害分类，并可支持智慧农业应用中的自动化病害诊断。","Smart Science","2026-10-17T00:00:00Z",{"impact":192,"substance":83,"depth":121,"authority":192,"freshness":124,"relevant":22,"comment":286},"该研究提出一种面向甘蔗叶部病害自动分类的交叉注意力多尺度CNN，在两个基准数据集上准确率达98.79%，方法新颖、实验充分，但属于细分作物应用研究，产业影响有限，时效性强，值得进入每日精选。",[288],{"name":283,"url":280},[129,130,290,147,291],"甘蔗","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":295},{"candidates":296,"agricultural_evidence":14},[297,302,307],{"code":32,"score":20,"supported":14,"title":298,"abstract":300,"tags":301},[299],"sugarcane",[290,299],[290],{"code":158,"score":20,"supported":14,"title":303,"abstract":305,"tags":306},[304],"smart agriculture",[129,304],[129],{"code":137,"score":308,"supported":40,"title":309,"abstract":310,"tags":311},2,[],[],[130,147],[313,314],"甘蔗 叶部病害 智能诊断","CA-MPCNN 甘蔗 病害","甘蔗叶部病害智能诊断-5234","10.1080\u002F23080477.2026.2749739",{"doi":316,"openalex_id":318,"authors":319,"venue":283,"cited_by_count":171,"oa_url":8,"card":326,"direction":332,"ingested_from":333},"W7222832245",[320,322,324],{"name":321,"orcid":8},"Bala Subramanian Chokkalingam",{"name":323,"orcid":8},"Uvarani Meenrajan",{"name":325,"orcid":8},"Shanmugapriya Sankaran",{"tldr":327,"method":328,"finding":329,"direction":330,"opportunity":331},"提出交叉注意力多尺度并行CNN，实现甘蔗叶病自动分类。","并行卷积分支、交叉注意力、密集连接与Inception块，两个基准数据集验证。","模型准确率达98.79%，消融与五折交叉验证证明其有效稳定。","农业人工智能与决策模型","可探索田间复杂背景与轻量化部署，并扩展到多作物病害实时诊断。","智慧农业 \u002F 农业物联网","openalex","2026-10-18T23:30:07.777036Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":8,"source_name":341,"source_url":338,"published_at":284,"category":118,"cover_url":8,"hotness":13,"is_selected":40,"score":342,"score_detail":343,"sources":345,"tags":347,"primary_topic":351,"secondary_topics":352,"classification":353,"search_phrases":378,"slug":381,"view_count":22,"doi":382,"paper":383,"created_at":391},5233,"Cotton pest and disease multi-class classification with weather and temporal predictors features across Indian agroclimatic zones","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.73s.45","The Pink Bollworm (Pectinophora gossypiella) remains one of the most destructive pests affecting cotton productivity, with infestation levels strongly driven by climatic fluctuations and crop phenology.This study presents a machine learning-based framework for early multi-classification and prediction of Pink Bollworm incidence using agro-meteorological variables.The dataset includes maximum and minimum air temperature, relative humidity, rainfall, sunshine hours, evaporation, and wind speed, all of which influence Pink Bollworm development, migration, and reproductive cycles.After preprocessing, applying cyclic encoding to temporal variables, and generating lagged climatic features, multiple machine learning algorithms-including SVM, Neural Networks, KNN, Regression models, Ensemble methods, and Naïve Bayes-were trained and systematically compared.SVM model demonstrated the best test accuracy, effectively capturing dynamic climate-pest interactions.The resulting MATLAB-based classification tool enables rapid assessment and supports climate-informed pest surveillance.A stratified 80:20 train-test split was employed, with 5-fold cross-validation on the training set for hyperparameter tuning, ensuring no data leakage between partitions.The validation and training accuracies of these models are 89.4% and 96 % respectively.Overall, the model offers a practical and interpretable solution for early detection of Pink Bollworm risk, thereby contributing to informed decision-making in integrated pest management (IPM) and precision agriculture.","棉红铃虫（Pectinophora gossypiella）仍是影响棉花产量最具破坏性的害虫之一，其发生程度受气候波动和作物物候的强烈驱动。本研究提出了一种基于机器学习的框架，利用农业气象变量对棉红铃虫的发生进行早期多分类与预测。数据集包括最高和最低气温、相对湿度、降雨量、日照时数、蒸发量和风速，这些因素均影响棉红铃虫的发育、迁移和繁殖周期。经过预处理、对时间变量应用循环编码并生成滞后气候特征后，训练并系统比较了多种机器学习算法，包括支持向量机（SVM）、神经网络、K近邻（KNN）、回归模型、集成方法和朴素贝叶斯。SVM模型表现出最佳的测试准确率，有效捕捉了气候与害虫之间的动态相互作用。所构建的基于MATLAB的分类工具可实现快速评估，并支持气候信息驱动的害虫监测。采用分层80：20的训练-测试划分，并在训练集上进行5折交叉验证以调优超参数，确保各分区之间无数据泄漏。这些模型的验证准确率和训练准确率分别为89.4%和96%。总体而言，该模型为棉红铃虫风险的早期检测提供了一种实用且可解释的解决方案，从而有助于综合害虫管理（IPM）和精准农业中的科学决策。","International Journal of Drug Delivery Technology",55,{"impact":85,"substance":84,"depth":68,"authority":71,"freshness":124,"relevant":22,"comment":344},"研究用多类机器学习模型结合气象与时序特征预测棉铃虫风险，方法扎实但属细分领域应用研究，信源期刊权威性一般，公共影响有限。",[346],{"name":341,"url":338},[129,348,153,349,350],"机器学习","农业气象","病虫害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":354},{"candidates":355,"agricultural_evidence":14},[356,360,367,372],{"code":32,"score":20,"supported":14,"title":357,"abstract":358,"tags":359},[155],[153,155],[153],{"code":158,"score":361,"supported":14,"title":362,"abstract":363,"tags":366},7,[],[364,365],"精准农业","precision agriculture",[129],{"code":137,"score":361,"supported":14,"title":368,"abstract":369,"tags":371},[],[348,370],"machine learning",[348],{"code":373,"score":374,"supported":14,"title":375,"abstract":376,"tags":377},"H7",4,[],[349],[349],[379,380],"棉铃虫 机器学习 预测","棉花 病虫害 气象因子","棉铃虫机器学习预测-5233","10.25258\u002Fijddt.16.73s.45",{"doi":382,"openalex_id":384,"authors":385,"venue":341,"cited_by_count":171,"oa_url":338,"card":386,"direction":332,"ingested_from":333},"W7222826491",[],{"tldr":387,"method":388,"finding":389,"direction":330,"opportunity":390},"用农业气象变量与时间特征构建机器学习模型，实现印度棉区棉铃虫多级发生预测。","气象数据加周期编码与滞后特征，比较SVM等模型，5折交叉验证。","SVM测试精度最高达89.4%，能有效刻画气候与虫害动态关系。","可引入遥感植被指数与实时气象物联网数据，构建跨区域可迁移的病虫害预警系统。","2026-10-18T23:30:07.725427Z"]