[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2962":3,"related-2962":58},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":57},2962,"A Multi-Modal Generative Model for Tomato Disease Leaves Understanding","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.19555","Artificial intelligence for plant disease analysis has advanced from task-specific classifiers to multi-modal models capable of jointly interpreting visual and textual information. However, practical deployment in precision agriculture remains limited because most existing approaches treat disease understanding as isolated prediction tasks, failing to capture the complementary relationships among symptom recognition, severity assessment, and question-driven diagnostic reasoning. In tomato pathology, accurate interpretation of diseased leaves requires more than label prediction; it demands integrating visual symptoms with semantic context to support a comprehensive and explainable understanding. Here, we present SOLAR, a multimodal generative model that understands tomato disease spanning six question-answering tasks. SOLAR learns to align visual features with task-aware language representations by Fusion Expert module based on mixture-of-expert, enabling it to generate contextually relevant answers across diverse diagnostic tasks. By formulating tomato disease analysis as a generative Visual Question Answering (VQA) task, SOLAR provides a flexible framework that supports multi-task inference within a single model while improving performance and cross-task knowledge sharing. We evaluate SOLAR on $41,677$ images, including $216,209$ Question-Answering (QA) pairs to understand tomato leaf disease under both closed and open-ended QA settings. Experimental results show that SOLAR consistently outperforms state-of-the-art vision-only, vision-language, and task-specific models across all tasks, demonstrating superior accuracy, robustness, and multimodal reasoning. These findings highlight the potential of generative multimodal modeling as an effective direction for understanding of plant disease. The code for this study is available at https:\u002F\u002Fgithub.com\u002FEnalisUs\u002FSOLAR.","人工智能用于植物病害分析已从任务专用分类器发展到能够联合解读视觉与文本信息的多模态模型。然而，在精准农业中的实际部署仍然有限，因为大多数现有方法将病害理解视为孤立的预测任务，未能捕捉症状识别、严重程度评估与问题驱动诊断推理之间的互补关系。在番茄病理学中，对病叶的准确解读需要的不仅仅是标签预测；它要求将视觉症状与语义上下文相结合，以支持全面且可解释的理解。在此，我们提出SOLAR，一种多模态生成模型，能够理解番茄病害并涵盖六项问答任务。SOLAR通过基于专家混合的融合专家模块，学习将视觉特征与任务感知的语言表示对齐，使其能够在多样化的诊断任务中生成上下文相关的答案。通过将番茄病害分析表述为生成式视觉问答（VQA）任务，SOLAR提供了一个灵活的框架，支持在单一模型内进行多任务推理，同时提升性能并促进跨任务知识共享。我们在$41,677$张图像上评估SOLAR，其中包括$216,209$个问答（QA）对，以在封闭式和开放式问答设置下理解番茄叶片病害。实验结果表明，SOLAR在所有任务上均持续优于最先进的纯视觉、视觉语言和任务专用模型，展现出更优的准确性、鲁棒性和多模态推理能力。这些发现凸显了生成式多模态建模作为理解植物病害的有效方向的潜力。本研究的代码可在https:\u002F\u002Fgithub.com\u002FEnalisUs\u002FSOLAR获取。",null,"arXiv (Cornell University)","2026-09-17T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"提出面向番茄病害的多模态生成式VQA模型SOLAR，在4万余张图像上验证多任务诊断性能，方法新颖、数据规模可观，对智慧植保有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","番茄病害","植物病害识别","多模态模型",[32,33],"SOLAR 番茄病害 多模态模型","番茄叶片病害 视觉问答","SOLAR番茄病害多模态模型-2962",0,"10.48550\u002Farxiv.2609.19555",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":50,"direction":54,"ingested_from":56},"W7213586905",[40,43,45,47],{"name":41,"orcid":42},"Khang Nguyen Quoc","https:\u002F\u002Forcid.org\u002F0000-0003-4927-4822",{"name":44,"orcid":9},"Minh-Phuoc Tran",{"name":46,"orcid":9},"Gia-Han Truong",{"name":48,"orcid":49},"Luyl-Da Quach","https:\u002F\u002Forcid.org\u002F0000-0002-5661-4250",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"提出多模态生成模型SOLAR，统一理解番茄病害叶片的六类问答任务。","基于混合专家融合模块对齐视觉与任务感知语言，用4万余图像和21万问答对训练。","SOLAR在闭集和开放问答中均超越视觉、视觉语言及任务专用模型，展现更强推理能力。","农业人工智能与决策模型","可探索将生成式多模态VQA扩展到更多作物和田间实时场景，并融合传感器数据。","openalex","2026-09-19T23:30:55.245800Z",{"total":59,"page":21,"page_size":59,"items":60},6,[61,99,126,153,186,223],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":69,"is_selected":14,"score":70,"score_detail":71,"sources":76,"tags":80,"search_phrases":82,"slug":85,"view_count":35,"doi":86,"paper":87,"created_at":98},2934,"A CNN-Based Approach for Leaf Disease Prediction in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22825329","Plants play a crucial role in sustaining life by serving as a primary source of energy and mitigating global warming. However, they are increasingly vulnerable to diseases such as bacterial spot, late blight, and Septoria leaf spot, which significantly impact crop yield and agricultural productivity. Early and accurate detection of these diseases is essential for effective disease management and improved agricultural outcomes. This project aims to develop a deep learning-based approach for detecting plant leaf diseases using Convolutional Neural Networks (CNN). By leveraging benchmark datasets, the proposed CNN model demonstrates superior performance compared to traditional machine learning techniques, achieving an accuracy of 92%, precision of 89%, F1-score of 93%, and recall of 92.47%. The results highlight the effectiveness of CNN in automating disease identification, enabling timely intervention, and promoting sustainable agricultural practices.","植物在维持生命方面发挥着至关重要的作用，既是主要的能量来源，又能缓解全球变暖。然而，植物日益受到细菌性斑点病、晚疫病和壳针孢叶斑病等病害的威胁，严重影响作物产量和农业生产率。早期准确地检测这些病害对于有效防控病害和改善农业成果至关重要。本项目旨在开发一种基于深度学习的方法，利用卷积神经网络（CNN）检测植物叶片病害。通过利用基准数据集，所提出的CNN模型展现出优于传统机器学习技术的性能，达到了92%的准确率、89%的精确率、93%的F1分数和92.47%的召回率。结果表明，CNN在自动化病害识别方面具有显著效果，能够实现及时干预并促进可持续农业实践。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-18T00:00:00Z",25,65,{"impact":72,"substance":17,"depth":73,"authority":72,"freshness":74,"relevant":21,"comment":75},12,14,9,"基于CNN的叶片病害识别研究，方法常规、数据集为公开基准，准确率92%属中等水平，对智慧农业植保场景有一定参考价值但缺乏突破性。",[77,78],{"name":67,"url":64},{"name":67,"url":79},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22825330",[26,27,81,29],"深度学习",[83,84],"CNN 植物叶片病害 识别","卷积神经网络 作物病害 检测","CNN植物叶片病害识别-2934","10.5281\u002Fzenodo.22825329",{"doi":86,"openalex_id":88,"authors":89,"venue":67,"cited_by_count":35,"oa_url":64,"card":92,"direction":97,"ingested_from":56},"W7213587327",[90],{"name":91,"orcid":9},"B.Yashmal Sai, K.Karthik, K.Neeraj, G. Mahabub Subhani",{"tldr":93,"method":94,"finding":95,"direction":54,"opportunity":96},"用CNN对植物叶片病害进行自动识别，在基准数据集上取得92%准确率。","基于卷积神经网络，使用植物叶片病害基准数据集训练与评估。","CNN优于传统机器学习方法，准确率92%、F1值93%，可支持及时干预。","可探索轻量化CNN在田间移动端实时检测，并结合多病害与早期症状识别。","智慧农业 \u002F 农业物联网","2026-09-19T23:30:11.855376Z",{"id":100,"title":101,"url":102,"summary":103,"summary_zh":9,"content":9,"source_name":104,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":108,"tags":110,"search_phrases":113,"slug":116,"view_count":35,"doi":9,"paper":117,"created_at":125},2901,"AgriScope:面向农业图像的像素级多模态理解统一框架,arXiv 2609.20325(预印本)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.20325","Boudiaf、Alanssari、Hussain、Javed提出AgriScope,一个统一的像素级多模态农业图像理解框架,联合支持图像级、区域级、像素级理解,可实现接地描述生成、指代表达分割、多轮多模态交互等任务。集成生物专用语义表征、密集空间表征与像素解码;引入大规模像素级农业多模态指令调优数据集AgriGround,包含50万+图像和1100万+指令跟随样本,涵盖植物病害分析、作物与杂草识别、昆虫识别、细粒度植物理解。实验表明AgriScope在多项农业视觉语言任务上有效。","arXiv (preprint)",75,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":74,"relevant":21,"comment":107},"提出统一像素级农业多模态理解框架并开源50万图像、1100万指令样本的大规模数据集，方法新颖、数据规模突出，但为arXiv预印本、未经同行评审，权威性有限，值得作为前沿技术动态精选。",[109],{"name":104,"url":102},[26,27,111,29,112],"农业遥感","多模态大模型",[114,115],"AgriScope 农业图像 多模态","AgriGround 像素级 农业数据集","AgriScope农业图像多模态-2901",{"doi":9,"openalex_id":9,"authors":118,"venue":9,"cited_by_count":35,"oa_url":9,"card":119,"direction":54,"ingested_from":124},[],{"tldr":120,"method":121,"finding":122,"direction":54,"opportunity":123},"提出AgriScope统一框架，实现农业图像像素级多模态理解与多任务交互。","构建AgriGround数据集（50万+图像、1100万+指令样本），融合语义与","AgriScope在接地描述、指代分割、多轮交互等农业视觉语言任务上有效。","可探索像素级多模态模型在田间实时病害诊断与精准施药决策中的落地与轻量化。","agent","2026-09-19T00:06:08.678379Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":9,"content":9,"source_name":131,"source_url":9,"published_at":132,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":134,"sources":136,"tags":138,"search_phrases":141,"slug":144,"view_count":35,"doi":9,"paper":145,"created_at":152},2274,"ArXiv 2609.10469 AgroVisNet:轻量化卷积网络及萝卜-马铃薯-葫芦病害诊断基准","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.10469","提出AgroVisNet紧凑型卷积网络及专家验证基准BD-PlantDX,包含孟加拉国Bogura和Nilphamari地区12类萝卜、土豆、葫芦共12432张田间图像。模型参数仅29万,测试精度99.52%,加权F1 99.52%,部署后量化0.46MB,CPU推理8.40ms\u002F张。","arXiv 2609.10469","2026-09-08T16:00:00Z",77,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":59,"relevant":21,"comment":135},"提出29万参数轻量卷积网络与万余张田间病害基准数据集，量化后仅0.46MB、CPU单张8.4ms，对低成本边缘部署的作物病害诊断有实用参考价值。",[137],{"name":131,"url":129},[26,27,139,29,140],"边缘计算","轻量化模型",[142,143],"农业人工智能 植物病害识别 轻量化模型 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别轻量化模型智慧农业-2274",{"doi":9,"openalex_id":9,"authors":146,"venue":9,"cited_by_count":35,"oa_url":9,"card":147,"direction":54,"ingested_from":124},[],{"tldr":148,"method":149,"finding":150,"direction":54,"opportunity":151},"提出轻量卷积网络AgroVisNet及萝卜、马铃薯、葫芦病害诊断基准BD-PlantDX。","构建12432张田间图像基准，设计29万参数紧凑CNN并量化部署。","测试精度99.52%，量化后仅0.46MB，CPU推理8.40ms\u002F张。","可探索跨地区跨作物泛化、田间复杂光照下的轻量模型鲁棒性与边缘部署。","2026-09-13T00:04:05.295687Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":164,"tags":166,"search_phrases":169,"slug":171,"view_count":35,"doi":172,"paper":173,"created_at":185},2178,"Hybrid explainable DeiT-based framework for plant disease classification and severity estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02191-2","The detection of plant diseases is essential for preserving agricultural productivity and food security; however, existing approaches often suffer from limited interpretability and generalization capability. This study proposes a hybrid deep learning framework based on Data-efficient Image Transformers (DeiT) for plant disease classification and severity estimation. The framework employs DeiT-Base, DeiT-Small, and DeiT-Tiny models to capture global contextual dependencies in plant leaf images. To improve interpretability, a hybrid Explainable Artificial Intelligence (XAI) module is introduced by combining Gradient-weighted Class Activation Mapping (Grad-CAM) for local feature attribution with Attention Rollout for global dependency visualization. In addition, HSV-based segmentation is applied after classification to isolate disease-relevant regions for damage ratio computation, severity estimation, and explanation refinement. The damage ratio is further integrated with Hybrid XAI attention maps to estimate disease severity. Experiments were conducted on the New Plant Diseases Dataset (Augmented), comprising 70,295 training images and 17,572 validation images across 38 disease classes and 14 plant species. The proposed DeiT-Base model achieved a maximum classification accuracy of 99.13%, outperforming several CNN architectures, including ResNet50, DenseNet121, MobileNetV3, EfficientNet-B4, and InceptionV3. Furthermore, the proposed Hybrid XAI framework demonstrated superior interpretability performance in terms of Focus Score, Background Noise, Signal-to-Noise Ratio (SNR), and Entropy compared with individual explanation methods. Overall, the proposed framework improves classification accuracy, enhances model transparency, and provides meaningful disease severity estimation, making it a promising solution for intelligent precision agriculture.","植物病害检测对于保障农业生产力和粮食安全至关重要，然而现有方法往往存在可解释性有限和泛化能力不足的问题。本研究提出了一种基于数据高效图像Transformer（Data-efficient Image Transformers，DeiT）的混合深度学习框架，用于植物病害分类和严重程度估计。该框架采用DeiT-Base、DeiT-Small和DeiT-Tiny模型来捕获植物叶片图像中的全局上下文依赖关系。为提高可解释性，引入了一种混合可解释人工智能（Explainable Artificial Intelligence，XAI）模块，将用于局部特征归因的梯度加权类激活映射（Gradient-weighted Class Activation Mapping，Grad-CAM）与用于全局依赖可视化的注意力展开（Attention Rollout）相结合。此外，在分类之后应用基于HSV的分割来分离病害相关区域，以进行损伤比率计算、严重程度估计和解释优化。损伤比率进一步与混合XAI注意力图相结合以估计病害严重程度。实验在新植物病害数据集（增强版）（New Plant Diseases Dataset (Augmented)）上进行，该数据集包含70,295张训练图像和17,572张验证图像，涵盖38个病害类别和14种植物物种。所提出的DeiT-Base模型达到了99.13%的最高分类准确率，优于多种CNN架构，包括ResNet50、DenseNet121、MobileNetV3、EfficientNet-B4和InceptionV3。此外，所提出的混合XAI框架在聚焦分数（Focus Score）、背景噪声（Background Noise）、信噪比（Signal-to-Noise Ratio，SNR）和熵（Entropy）方面表现出优于单一解释方法的可解释性性能。总体而言，所提出的框架提高了分类准确率，增强了模型透明度，并提供了有意义的病害严重程度估计，使其成为智能精准农业的一种有前景的解决方案。","Discover Artificial Intelligence","2026-09-10T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":72,"freshness":20,"relevant":21,"comment":163},"基于DeiT与混合可解释AI的植物病害分类与严重度估计研究，在7万张图像、38类病害上取得99.13%准确率，方法新颖、数据扎实，对智慧农业病害智能诊断有参考价值。",[165],{"name":159,"url":156},[26,27,167,168,29],"可解释AI","精准农业",[170,143],"农业人工智能 植物病害识别 智慧农业 精准农业","农业人工智能植物病害识别智慧农业精准农业-2178","10.1007\u002Fs44163-026-02191-2",{"doi":172,"openalex_id":174,"authors":175,"venue":159,"cited_by_count":35,"oa_url":156,"card":180,"direction":54,"ingested_from":56},"W7212179384",[176,178],{"name":177,"orcid":9},"Muskan Batra",{"name":179,"orcid":9},"Pooja Sharma",{"tldr":181,"method":182,"finding":183,"direction":54,"opportunity":184},"提出基于DeiT的混合可解释框架，实现植物病害分类与严重度估计。","DeiT-Base\u002FSmall\u002FTiny结合Grad-CAM与Attention","DeiT-Base分类准确率达99.13%，混合XAI可解释性指标优于单一方法。","可探索轻量化DeiT在边缘设备部署及多模态数据融合的病害严重度实时估计。","2026-09-11T23:30:47.756846Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":195,"sources":199,"tags":201,"search_phrases":202,"slug":204,"view_count":35,"doi":205,"paper":206,"created_at":222},1617,"MRC-Net: a reliable plant disease classification framework with multi-frequency state-space enhancement and conformal prediction","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1895287","Plant disease image classification is a key task in disease monitoring and precise prevention and control in smart agriculture. However, complex backgrounds, finegrained lesion differences, and model overconfidence still limit recognition performance and practical application reliability. To address these issues, this paper proposes a reliable plant disease recognition framework that integrates multi-frequency selective state-space feature enhancement with conformal-aware reliable prediction. The proposed method adopts Swin-Tiny as the backbone network and uses the MF-SSFE module to jointly model low-frequency leaf structures, high-frequency lesion textures, and cross-region state-space contexts, thereby enhancing disease-related evidence in complex scenarios. Meanwhile, the CARP module is introduced to incorporate the idea of conformal prediction into the classification output process, enabling the model to express uncertainty while providing class predictions. Experimental results show that the proposed method achieves Accuracy values of 0.9891, 0.3889, and 0.9800 on the NGLD, PlantDoc, and PlantVillage datasets, respectively, and AUC values of 0.9995, 0.8690, and 0.9989, respectively, outperforming the comparison methods. Ablation experiments and sensitivity analysis further verify the effectiveness and stability of each module. Model complexity analysis shows that the proposed method maintains acceptable computational overhead while achieving superior recognition performance.","植物病害图像分类是智慧农业中病害监测与精准防控的关键任务。然而，复杂背景、细粒度病斑差异以及模型过度自信等问题仍制约着识别性能与实际应用的可靠性。针对上述问题，本文提出了一种融合多频选择性状态空间特征增强与一致性感知可靠预测的可靠植物病害识别框架。该方法以Swin-Tiny为骨干网络，利用MF-SSFE模块联合建模低频叶片结构、高频病斑纹理及跨区域状态空间上下文，从而增强复杂场景下与病害相关的证据信息。同时，引入CARP模块，将一致性预测的思想融入分类输出过程，使模型在提供类别预测的同时能够表达不确定性。实验结果表明，该方法在NGLD、PlantDoc和PlantVillage数据集上的准确率分别达到0.9891、0.3889和0.9800，AUC值分别达到0.9995、0.8690和0.9989，优于对比方法。消融实验和敏感性分析进一步验证了各模块的有效性与稳定性。模型复杂度分析表明，该方法在保持可接受计算开销的同时，实现了优越的识别性能。","Frontiers in Plant Science","2026-09-03T00:00:00Z",70,{"impact":72,"substance":196,"depth":17,"authority":19,"freshness":197,"relevant":21,"comment":198},20,7,"提出结合多频状态空间与保形预测的植物病害分类框架，在多个数据集上表现优异，兼具可靠性与效率。",[200],{"name":192,"url":189},[26,27,81,29],[203,143],"农业人工智能 植物病害识别 智慧农业 深度学习","农业人工智能植物病害识别智慧农业深度学习-1617","10.3389\u002Ffpls.2026.1895287",{"doi":205,"openalex_id":207,"authors":208,"venue":192,"cited_by_count":35,"oa_url":189,"card":217,"direction":97,"ingested_from":56},"W7207549269",[209,211,214],{"name":210,"orcid":9},"Shiyao Xie",{"name":212,"orcid":213},"X. H. Zhang","https:\u002F\u002Forcid.org\u002F0009-0003-9957-0002",{"name":215,"orcid":216},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0002-3006-7420",{"tldr":218,"method":219,"finding":220,"direction":54,"opportunity":221},"提出可靠植物病害分类框架，结合多频状态空间增强与保形预测，提升复杂场景识别精度与可靠性。","Swin-Tiny骨干，MF-SSFE多频特征增强，CARP保形预测模块，在NG","在三个数据集上准确率分别达0.9891、0.3889、0.9800，AUC达0.9995、0.869","可探索将保形预测用于其他农业任务（如产量预测）以量化不确定性，或优化多频特征提取以应对更复杂田间场景。","2026-09-04T23:30:09.854906Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":9,"content":9,"source_name":228,"source_url":226,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":235,"tags":237,"search_phrases":238,"slug":240,"view_count":35,"doi":241,"paper":242,"created_at":258},1515,"From Laboratory to Field: Frozen Foundation-Model Features Toward Robust Plant Disease Recognition","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102531","From Laboratory to Field: Frozen Foundation-Model Features Toward Robust Plant Disease Recognition。Smart Agricultural Technology","Smart Agricultural Technology","2026-09-01T00:00:00Z",54,{"impact":72,"substance":232,"depth":73,"authority":13,"freshness":233,"relevant":21,"comment":234},15,3,"研究利用冻结基础模型特征提升田间植物病害识别鲁棒性，方法新颖但影响范围有限。",[236],{"name":228,"url":226},[26,27,29],[239,143],"农业人工智能 植物病害识别 智慧农业","农业人工智能植物病害识别智慧农业-1515","10.1016\u002Fj.atech.2026.102531",{"doi":241,"openalex_id":243,"authors":244,"venue":228,"cited_by_count":35,"oa_url":226,"card":9,"direction":9,"ingested_from":56},"W7206160003",[245,248,250,252,255],{"name":246,"orcid":247},"Thai Anh Nguyen","https:\u002F\u002Forcid.org\u002F0009-0005-5600-6510",{"name":249,"orcid":9},"Dung Son Nguyen",{"name":251,"orcid":9},"Quang Minh Dang",{"name":253,"orcid":254},"Lam Nguyen Phan Binh","https:\u002F\u002Forcid.org\u002F0009-0003-8303-4846",{"name":256,"orcid":257},"Nguyen Huu Loi","https:\u002F\u002Forcid.org\u002F0000-0001-7987-0348","2026-09-03T23:30:04.360545Z"]