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9月20日国家能源局消息，2026年8月，全社会用电量再破万亿千瓦时，达到10332亿千瓦时，同比增长1.7%；用电负荷创历史新高，达到15.6亿千瓦，较上年最大负荷高4969万千瓦，共有7天超过上年最大负荷。\n\n■ 国家市场监督管理总局副局长束为介绍，“十五五”时期，我国将建设国家质检中心不少于30家、检验检测公共服务平台不少于300个。市场监管总局将推动认证在北斗导航、集成电路等重点产业的应用，深入推进养老、健康等领域服务认证。\n\n■ 根据国家卫生健康委9月20日公布的通知，到2026年底前，各省、自治区、直辖市及新疆生产建设兵团均应当至少依托1家省属及以上医院开设罕见病门诊，辖区内300万人口以上的地市均应当至少依托1家医院开设罕见病门诊，安排相关专科医师坐诊，提供规范的罕见病诊疗服务。\n\n**经济金融**\n\n■ 2026年9月15日，国家金融监督管理总局党委书记、局长丁向群会见友邦保险集团董事会主席杜嘉祺。双方就保险业最新发展、友邦保险集团在华经营情况等议题进行交流。\n\n■2026年9月18日，中国人民银行与老挝人民民主共和国银行在广西南宁联合举办中老跨境数字支付互联互通启动会。中国人民银行副行长陆磊、老挝人民民主共和国银行副行长苏里萨克·萨姆努翁出席并致辞。\n\n■中国人民银行授权全国银行间同业拆借中心公布，2026年9月20日贷款市场报价利率（LPR）为：1年期LPR为3.0%，5年期以上LPR为3.5%。以上LPR在下一次发布LPR之前有效。（详情）\n\n■ 中国证券投资者保护基金有限责任公司协助中国证监会办理“吹哨人”奖励申领登记事宜。自2026年9月18日至2026年12月18日，17起案件线索的“吹哨人”可登录中国证券投资者保护基金有限责任公司官方网站“吹哨人”奖励专栏办理奖励申领登记，逾期未按要求登记确认的，视为放弃奖励。（详情）\n\n■ 近日，证监会披露两份行政处罚决定书，杨将、翁宇轩两名当事人均因内幕交易被处罚。当事人均在内幕信息敏感期内控制使用多个证券账户买卖相关公司股票。两人合计获利约246.88万元，合计罚没约943.75万元。其中，杨将被“没一罚三”。（详情）\n\n■ 据国家开发银行网站“领导班子”栏目显示，黄志强已任国开行党委副书记、副董事长、行长。调任国开行前，黄志强担任内蒙古自治区党委常委，自治区人民政府常务副主席、党组副书记。\n\n**社会热点**\n\n■9月20日，话题“秦始皇陵为什么不挖”冲上热搜。据此前央视采访报道，秦始皇帝陵博物院名誉院长吴永琪曾表示“地下的文物确实很脆弱，我们要对天地、对自然、对祖宗有敬畏之心；如果我们有先进技术，能够在地面不打开陵墓的条件下，了解里面的东西，而不去打扰这个睡了两千年的老人，我出自内心的说法，以后也不要动。”\n\n■ 据央视新闻消息，北京时间9月20日，名古屋亚运会男子100米自由泳决赛，中国选手潘展乐以47秒61的成绩夺得金牌成功卫冕，这也是本赛季亚洲最好成绩。\n\n**国际动态**\n\n■ 新华社伦敦9月18日电，英国海上贸易行动办公室18日通报说，一艘油轮在霍尔木兹海峡被不明投射物击中后起火。通报说，火已被扑灭，船员均安全，事件造成的环境影响尚不清楚。\n\n■新华社德黑兰9月19日报道，伊朗最高国家安全委员会秘书穆赫辛·雷扎伊19日表示，卡塔尔作为调解方已向美国方面转达伊朗的谈判条件，正等待美国总统特朗普的回应。\n\n■据央视新闻援引美国方面19日消息，五角大楼调查人员发现，情报缺陷、图像过时以及过度依赖人工智能，是导致2月28日伊朗米纳卜市一所小学所在区域遭到空袭，并造成大量儿童死亡的原因。\n\n编辑：杨晶贻\n\n转载请注明来源","新华社·腾讯新闻","2026-09-20T11:00:00Z","政策",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,16,8,15,9,1,"国务院任命潘文博为农业农村部副部长，属部委高层人事变动，对农业政策走向有参考价值，但资讯本身为任免通稿、信息增量有限。",[25],{"name":10,"url":6},[27,28,29],"农业农村部","农业政策","人事任免",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},3,true,[],[48],"烟草",[],[51,52],"潘文博 农业农村部 副部长","国务院 任免 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2026-10-19","2026-10-19T00:00:00Z","报道",60,{"impact":18,"substance":140,"depth":141,"authority":141,"freshness":56,"relevant":22,"comment":142},14,12,"省级科研院所学术委员会会议通稿，涉及蚕桑学科规划与全国主推技术推荐，有行业参考价值但属常规会议动态。",[144],{"name":135,"url":132},[146,147,148,149,150],"十五五规划","成果转化","种质资源","主推技术","蚕桑",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":154},{"candidates":155,"agricultural_evidence":45},[156,160,166,171],{"code":31,"score":98,"supported":45,"title":157,"abstract":158,"tags":159},[150],[150],[],{"code":161,"score":162,"supported":45,"title":163,"abstract":164,"tags":165},"F1",5,[],[148],[149,148],{"code":167,"score":22,"supported":14,"title":168,"abstract":169,"tags":170},"G1",[],[],[147],{"code":172,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},"J1",[],[],[146],[177,178],"安徽农科院 蚕桑所 十五五规划","蚕桑 主推技术 推荐","安徽农科院蚕桑所十五五规划-5522","2026-10-22T00:07:04.530022Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":8,"content":8,"source_name":186,"source_url":8,"published_at":187,"category":137,"cover_url":8,"hotness":13,"is_selected":14,"score":138,"score_detail":188,"sources":190,"tags":192,"primary_topic":198,"secondary_topics":199,"classification":200,"search_phrases":223,"slug":226,"view_count":118,"doi":8,"paper":8,"created_at":227},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":18,"substance":140,"depth":141,"authority":141,"freshness":56,"relevant":22,"comment":189},"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。",[191],{"name":186,"url":184},[193,194,195,196,197],"院企合作","节能减排","烟草烘烤","新能源利用","储能技术",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":201},{"candidates":202,"agricultural_evidence":45},[203,207,213,219],{"code":31,"score":98,"supported":45,"title":204,"abstract":205,"tags":206},[48],[48],[],{"code":208,"score":44,"supported":45,"title":209,"abstract":210,"tags":212},"F3",[],[211],"肥料",[],{"code":214,"score":44,"supported":45,"title":215,"abstract":216,"tags":218},"F4",[],[217],"农药",[],{"code":167,"score":22,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[193],[224,225],"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":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":234,"source_url":231,"published_at":235,"category":65,"cover_url":8,"hotness":13,"is_selected":14,"score":66,"score_detail":236,"sources":239,"tags":241,"primary_topic":244,"secondary_topics":245,"classification":246,"search_phrases":264,"slug":267,"view_count":22,"doi":268,"paper":269,"created_at":286},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":141,"substance":237,"depth":18,"authority":141,"freshness":21,"relevant":22,"comment":238},20,"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。",[240],{"name":234,"url":231},[74,75,242,93,243],"甘蔗","病害诊断",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":247},{"candidates":248,"agricultural_evidence":45},[249,254,259],{"code":31,"score":20,"supported":45,"title":250,"abstract":252,"tags":253},[251],"sugarcane",[242,251],[242],{"code":105,"score":20,"supported":45,"title":255,"abstract":257,"tags":258},[256],"smart agriculture",[74,256],[74],{"code":82,"score":260,"supported":14,"title":261,"abstract":262,"tags":263},2,[],[],[75,93],[265,266],"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":268,"openalex_id":270,"authors":271,"venue":234,"cited_by_count":118,"oa_url":8,"card":278,"direction":284,"ingested_from":285},"W7222832245",[272,274,276],{"name":273,"orcid":8},"Bala Subramanian Chokkalingam",{"name":275,"orcid":8},"Uvarani Meenrajan",{"name":277,"orcid":8},"Shanmugapriya Sankaran",{"tldr":279,"method":280,"finding":281,"direction":282,"opportunity":283},"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":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":8,"source_name":293,"source_url":290,"published_at":235,"category":65,"cover_url":8,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":297,"tags":299,"primary_topic":303,"secondary_topics":304,"classification":305,"search_phrases":330,"slug":333,"view_count":22,"doi":334,"paper":335,"created_at":343},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":19,"substance":91,"depth":140,"authority":56,"freshness":21,"relevant":22,"comment":296},"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。",[298],{"name":293,"url":290},[74,300,100,301,302],"机器学习","农业气象","病虫害预警",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":306},{"candidates":307,"agricultural_evidence":45},[308,312,319,324],{"code":31,"score":20,"supported":45,"title":309,"abstract":310,"tags":311},[102],[100,102],[100],{"code":105,"score":313,"supported":45,"title":314,"abstract":315,"tags":318},7,[],[316,317],"精准农业","precision agriculture",[74],{"code":82,"score":313,"supported":45,"title":320,"abstract":321,"tags":323},[],[300,322],"machine learning",[300],{"code":325,"score":326,"supported":45,"title":327,"abstract":328,"tags":329},"H7",4,[],[301],[301],[331,332],"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":334,"openalex_id":336,"authors":337,"venue":293,"cited_by_count":118,"oa_url":290,"card":338,"direction":284,"ingested_from":285},"W7222826491",[],{"tldr":339,"method":340,"finding":341,"direction":282,"opportunity":342},"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",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":349,"content":8,"source_name":350,"source_url":347,"published_at":64,"category":65,"cover_url":8,"hotness":13,"is_selected":14,"score":351,"score_detail":352,"sources":355,"tags":357,"primary_topic":359,"secondary_topics":360,"classification":362,"search_phrases":379,"slug":382,"view_count":22,"doi":383,"paper":384,"created_at":418},5150,"MSFANet: a multi-scale feature aggregation network for tobacco pest recognition","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871587","Introduction Tobacco is a globally significant economic crop, and pest infestation represents one of the primary factors contributing to declines in both yield and quality. However, tobacco pests encompass a wide variety of species that differ substantially in morphology and scale. Field-captured images are frequently subject to complex background interference and class imbalance, presenting considerable challenges for automated recognition. Existing deep learning approaches remain limited in their capacity for multi-scale feature extraction and discriminative feature representation under such conditions. To address these challenges, this study proposes a Multi-Scale Feature Aggregation Network (MSFANet) for tobacco pest recognition, formulated here as a single-label image classification task. Methods To address scale variation and complex field backgrounds, two task-oriented modules were developed. The Multi-Scale Pyramid Pooling (MSPP) module aggregates contextual information across multiple receptive fields through parallel feature extraction and adaptive cross-branch fusion. The Hierarchical Feature Fusion Block (HFFB) subsequently refines the feature representations through spatial gating and partial convolution. An auxiliary classification head and Focal Loss were incorporated during training to provide intermediate supervision and address class imbalance, respectively. Results The effectiveness of MSFANet was evaluated on a dataset comprising 13 tobacco-associated pest and natural-enemy categories. The model achieved accuracy, precision, recall, and F1-score of 97.63%, 97.89%, 97.59%, and 97.71%, respectively, with a parameter count of 1.21 M. Among the models trained from random initialization, MSFANet shared the highest accuracy while using the fewest parameters. Discussion By combining multi-scale contextual aggregation with hierarchical feature refinement, MSFANet achieved accurate classification across the 13 tobacco-associated categories under complex field conditions. The proposed method provides effective technical support for automated and precision-oriented pest monitoring in tobacco fields, offering practical value for advancing precise pest management and the broader development of smart agriculture.","引言 烟草是全球重要的经济作物，虫害侵染是导致其产量和品质下降的主要因素之一。然而，烟草害虫种类繁多，形态和尺度差异显著。田间采集的图像常受到复杂背景干扰和类别不平衡的影响，给自动化识别带来了相当大的挑战。现有深度学习方法在此类条件下进行多尺度特征提取和判别性特征表示的能力仍然有限。为应对这些挑战，本研究提出了一种用于烟草害虫识别的多尺度特征聚合网络（Multi-Scale Feature Aggregation Network，MSFANet），并将其形式化为单标签图像分类任务。方法 针对尺度变化和复杂田间背景，开发了两个面向任务的模块。多尺度金字塔池化（Multi-Scale Pyramid Pooling，MSPP）模块通过并行特征提取和自适应跨分支融合，聚合多个感受野的上下文信息。分层特征融合模块（Hierarchical Feature Fusion Block，HFFB）随后通过空间门控和部分卷积对特征表示进行细化。训练过程中引入辅助分类头和Focal Loss，分别提供中间监督和处理类别不平衡。结果 在包含13个烟草相关害虫及天敌类别的数据集上评估了MSFANet的有效性。模型的准确率、精确率、召回率和F1分数分别为97.63%、97.89%、97.59%和97.71%，参数量为1.21 M。在随机初始化训练的模型中，MSFANet以最少的参数量取得了最高准确率。讨论 通过结合多尺度上下文聚合与分层特征细化，MSFANet在复杂田间条件下实现了对13个烟草相关类别的准确分类。所提方法为烟草田间害虫的自动化和精准监测提供了有效的技术支持，对推进精准害虫管理和智慧农业的更广泛发展具有实用价值。","Frontiers in Plant Science",76,{"impact":18,"substance":68,"depth":69,"authority":353,"freshness":21,"relevant":22,"comment":354},13,"该论文提出轻量化多尺度特征聚合网络，在烟草害虫识别上取得97%以上精度且参数量仅1.21M，方法新颖、数据扎实，对智慧农业虫情监测有实用价值，值得进入每日精选。",[356],{"name":350,"url":347},[74,75,93,358,48],"病虫害识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[361],{"code":82,"slug":83,"name":84,"path":85,"parent_code":86},{"status":38,"locked":14,"version":39,"method":40,"evidence":363},{"candidates":364,"agricultural_evidence":45},[365,370,375],{"code":31,"score":20,"supported":45,"title":366,"abstract":368,"tags":369},[367],"tobacco",[48,367],[48],{"code":82,"score":21,"supported":45,"title":371,"abstract":372,"tags":374},[],[93,373],"deep learning",[75,93,358],{"code":105,"score":313,"supported":45,"title":376,"abstract":377,"tags":378},[],[74,256],[74],[380,381],"MSFANet 烟草害虫识别","多尺度特征聚合 害虫识别","MSFANet烟草害虫识别-5150","10.3389\u002Ffpls.2026.1871587",{"doi":383,"openalex_id":385,"authors":386,"venue":350,"cited_by_count":118,"oa_url":412,"card":413,"direction":284,"ingested_from":285},"W7222603139",[387,389,391,393,395,397,399,401,404,407,410],{"name":388,"orcid":8},"Shun He",{"name":390,"orcid":8},"Sifan Li",{"name":392,"orcid":8},"Xinjian Li",{"name":394,"orcid":8},"Qianqian Luo",{"name":396,"orcid":8},"Dong Sufen",{"name":398,"orcid":8},"Lu Lei",{"name":400,"orcid":8},"Xingjiang Chen",{"name":402,"orcid":403},"Liuti Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9151-8002",{"name":405,"orcid":406},"Jian‐Yu Meng","https:\u002F\u002Forcid.org\u002F0000-0002-5796-1086",{"name":408,"orcid":409},"Ning Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0214-5038",{"name":411,"orcid":8},"Hancheng Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1871587\u002Fpdf",{"tldr":414,"method":415,"finding":416,"direction":282,"opportunity":417},"提出MSFANet多尺度特征聚合网络，实现复杂田间背景下烟草害虫的自动识别。","多尺度金字塔池化MSPP、分层特征融合HFFB、辅助分类头与Focal Loss","模型准确率97.63%、参数量仅1.21M，在随机初始化模型中精度最高且参数最少。","可探索轻量多尺度网络在边缘设备部署及跨作物害虫迁移识别，结合类不平衡与复杂背景的鲁棒性研究。","2026-10-17T23:30:08.695455Z"]