[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1522":3,"related-1522":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":58,"slug":61,"view_count":62,"doi":63,"paper":64,"created_at":79},1522,"Can agricultural finance mitigate supply chain vulnerability? Evidence from the cotton industry in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1912590","Introduction Improving the resilience of the cotton supply chain is critical for advancing high-quality industrial development and securing the supply chain of the textile industry. As a core instrument for optimizing resource allocation and easing financing constraints, agricultural financial services may foster supply chain resilience. Methods Based on panel data from 12 major cotton-producing provinces in China covering 2014-2023, this study employs fixed-effects models and mediation models to examine the impact of agricultural financial service development on cotton supply chain resilience and its underlying mechanisms, with resilience and financial service levels measured by the entropy method. Results (1) The development of agricultural financial services significantly improves cotton supply chain resilience. (2) These services strengthen resilience primarily by enhancing total factor productivity (TFP) in cotton production. (3) Positive effects are observed across all four sub-dimensions of resilience (resistance, recovery, restructuring, and renewal capacity); the facilitating effect on renewal capacity ranks the highest, while that on recovery capacity is the weakest. Discussion The findings reveal a TFP-based transmission channel and heterogeneous effects across resilience sub-dimensions, providing targeted policy implications for optimizing financial support for the cotton industry.","引言 提升棉花供应链韧性对于推动产业高质量发展、保障纺织业供应链安全至关重要。农业金融服务作为优化资源配置、缓解融资约束的核心工具，可能对供应链韧性产生促进作用。方法 基于2014—2023年中国12个棉花主产省份的面板数据，本研究采用固定效应模型和中介效应模型，考察农业金融服务发展对棉花供应链韧性的影响及其内在机制，其中韧性与金融服务水平通过熵值法测度。结果 （1）农业金融服务的发展显著提升了棉花供应链韧性。（2）这些服务主要通过提高棉花生产的全要素生产率（TFP）来增强供应链韧性。（3）韧性四个子维度（抵抗力、恢复力、重构力和更新力）均受到正向影响；其中对更新力的促进作用最强，对恢复力的促进作用最弱。讨论 研究结果揭示了基于全要素生产率的传导渠道以及韧性各子维度间的异质性效应，为优化棉花产业金融支持提供了有针对性的政策启示。",null,"Frontiers in Sustainable Food Systems","2026-09-02T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,7,1,"基于省级面板数据，揭示农业金融通过提升全要素生产率增强棉花供应链韧性，为产业政策提供实证依据。",[24],{"name":10,"url":6},[26,27,28,29],"供应链韧性","棉花产业","农业金融","全要素生产率",{"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},[43,51],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":50},14,true,[47],"cotton",[49,47],"棉花",[],{"code":52,"score":53,"supported":45,"title":54,"abstract":55,"tags":57},"A4",3,[],[56],"花生",[],[59,60],"全要素生产率 供应链韧性 农业金融 棉花产业","全要素生产率 供应链韧性","全要素生产率供应链韧性农业金融棉花产业-1522",2,"10.3389\u002Ffsufs.2026.1912590",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":71,"oa_url":6,"card":72,"direction":76,"ingested_from":78},"W7206157843",[67,69],{"name":68,"orcid":9},"Jingzhou Wei",{"name":70,"orcid":9},"Minghui Liu",0,{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"研究农业金融服务对中国棉花供应链韧性的影响及机制。","基于2014-2023年12省面板数据，用固定效应和中介模型，熵值法测度。","农业金融显著提升供应链韧性，主要通过提高全要素生产率，对更新能力促进最大。","农业人工智能与决策模型","可探索农业金融对不同作物供应链韧性的异质性影响，或结合数字金融与农业保险的协同机制。","openalex","2026-09-03T23:30:07.952630Z",{"total":81,"page":21,"page_size":81,"items":82},6,[83,129,192,241,289,346],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":9,"content":9,"source_name":88,"source_url":9,"published_at":89,"category":90,"cover_url":9,"hotness":13,"is_selected":45,"score":91,"score_detail":92,"sources":96,"tags":98,"primary_topic":102,"secondary_topics":103,"classification":104,"search_phrases":124,"slug":127,"view_count":53,"doi":9,"paper":9,"created_at":128},1162,"中国农业科学院棉花研究所发布全球首个棉花专属大语言模型棉芯1.0 中英文维吾尔语乌兹别克语四语服务","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F8\u002F570344.shtm","8月24日,中国农业科学院棉花研究所联合中国农业科学院西部农业研究中心正式发布全球首个棉花领域专属知识大语言模型棉芯1.0(CottonMind1.0),标志着我国棉花产业迈入AI+科研+生产深度融合新阶段。棉芯1.0以网页端+小程序端双端设计,网页端为科研人员打造云端智研工作台集成AI智能检索、文献加速阅读、知识图谱可视化等功能;小程序端为棉农打造随身农技专家,提供AI问答、拍照诊断、专家在线咨询等服务。已整合全球41680篇中外文科研文献、2000余项专利与国家标准、559部专业著作、1806条审定品种信息。采用混合检索增强生成与多智能体协同问答方案,有效破解通用AI大模型在农业场景中专业知识不足、幻觉频出等短板。","科学网\u002F中国科学报 2026-08-24","2026-08-24T08:00:00Z","报道",87,{"impact":93,"substance":94,"depth":17,"authority":44,"freshness":53,"relevant":21,"comment":95},28,24,"全球首个棉花专属大模型，四语服务，整合海量数据，标志棉花产业AI深度融合，影响深远。",[97],{"name":88,"url":86},[99,100,101,27],"智慧农业","农业人工智能","大语言模型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":105},{"candidates":106,"agricultural_evidence":45},[107,112,119],{"code":31,"score":108,"supported":45,"title":109,"abstract":110,"tags":111},11,[49],[49],[],{"code":113,"score":114,"supported":45,"title":115,"abstract":116,"tags":118},"I2",5,[],[117],"大模型",[100,101],{"code":120,"score":21,"supported":14,"title":121,"abstract":122,"tags":123},"I1",[],[],[99],[125,126],"农业人工智能 大语言模型 智慧农业 棉花产业","农业人工智能 大语言模型","农业人工智能大语言模型智慧农业棉花产业-1162","2026-09-01T00:03:36.434610Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":9,"content":9,"source_name":134,"source_url":9,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":143,"tags":145,"primary_topic":149,"secondary_topics":150,"classification":156,"search_phrases":178,"slug":181,"view_count":71,"doi":9,"paper":182,"created_at":191},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",69,{"impact":138,"substance":139,"depth":140,"authority":141,"freshness":81,"relevant":21,"comment":142},16,21,17,9,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[144],{"name":134,"url":132},[99,100,146,147,148],"目标检测","无人机遥感","棉花病害",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[151],{"code":113,"slug":152,"name":153,"path":154,"parent_code":155},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":157},{"candidates":158,"agricultural_evidence":45},[159,165,169,173],{"code":113,"score":17,"supported":45,"title":160,"abstract":163,"tags":164},[161,162],"深度学习","YOLO",[],[100,146],{"code":31,"score":108,"supported":45,"title":166,"abstract":167,"tags":168},[49],[47],[],{"code":120,"score":21,"supported":14,"title":170,"abstract":171,"tags":172},[],[],[99],{"code":174,"score":21,"supported":14,"title":175,"abstract":176,"tags":177},"I3",[],[],[147],[179,180],"Cotton-Vision 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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 2026-10-19","2026-10-19T00:00:00Z",60,{"impact":138,"substance":44,"depth":202,"authority":202,"freshness":81,"relevant":21,"comment":203},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。",[205],{"name":198,"url":195},[207,208,209,210,211],"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":215},{"candidates":216,"agricultural_evidence":45},[217,221,226,231],{"code":31,"score":108,"supported":45,"title":218,"abstract":219,"tags":220},[211],[211],[],{"code":222,"score":114,"supported":45,"title":223,"abstract":224,"tags":225},"F1",[],[209],[210,209],{"code":227,"score":21,"supported":14,"title":228,"abstract":229,"tags":230},"G1",[],[],[208],{"code":232,"score":21,"supported":14,"title":233,"abstract":234,"tags":235},"J1",[],[],[207],[237,238],"安徽农科院 蚕桑所 十五五规划","蚕桑 主推技术 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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":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":9,"source_name":295,"source_url":292,"published_at":296,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":297,"sources":300,"tags":302,"primary_topic":305,"secondary_topics":306,"classification":307,"search_phrases":325,"slug":328,"view_count":21,"doi":329,"paper":330,"created_at":345},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":202,"substance":298,"depth":138,"authority":202,"freshness":141,"relevant":21,"comment":299},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。",[301],{"name":295,"url":292},[99,100,303,161,304],"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":308},{"candidates":309,"agricultural_evidence":45},[310,316,321],{"code":31,"score":311,"supported":45,"title":312,"abstract":314,"tags":315},15,[313],"sugarcane",[303,313],[303],{"code":120,"score":311,"supported":45,"title":317,"abstract":319,"tags":320},[318],"smart agriculture",[99,318],[99],{"code":113,"score":62,"supported":14,"title":322,"abstract":323,"tags":324},[],[],[100,161],[326,327],"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":329,"openalex_id":331,"authors":332,"venue":295,"cited_by_count":71,"oa_url":9,"card":339,"direction":344,"ingested_from":78},"W7222832245",[333,335,337],{"name":334,"orcid":9},"Bala Subramanian Chokkalingam",{"name":336,"orcid":9},"Uvarani Meenrajan",{"name":338,"orcid":9},"Shanmugapriya Sankaran",{"tldr":340,"method":341,"finding":342,"direction":76,"opportunity":343},"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。","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","2026-10-18T23:30:07.777036Z",{"id":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":9,"source_name":352,"source_url":349,"published_at":296,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":353,"score_detail":354,"sources":357,"tags":359,"primary_topic":363,"secondary_topics":364,"classification":365,"search_phrases":389,"slug":392,"view_count":21,"doi":393,"paper":394,"created_at":402},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":355,"substance":17,"depth":44,"authority":81,"freshness":141,"relevant":21,"comment":356},8,"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。",[358],{"name":352,"url":349},[99,360,49,361,362],"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":366},{"candidates":367,"agricultural_evidence":45},[368,372,378,383],{"code":31,"score":311,"supported":45,"title":369,"abstract":370,"tags":371},[47],[49,47],[49],{"code":120,"score":20,"supported":45,"title":373,"abstract":374,"tags":377},[],[375,376],"精准农业","precision agriculture",[99],{"code":113,"score":20,"supported":45,"title":379,"abstract":380,"tags":382},[],[360,381],"machine learning",[360],{"code":384,"score":385,"supported":45,"title":386,"abstract":387,"tags":388},"H7",4,[],[361],[361],[390,391],"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":393,"openalex_id":395,"authors":396,"venue":352,"cited_by_count":71,"oa_url":349,"card":397,"direction":344,"ingested_from":78},"W7222826491",[],{"tldr":398,"method":399,"finding":400,"direction":76,"opportunity":401},"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"]