[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2612":3,"related-2612":83},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":68,"slug":71,"view_count":59,"doi":8,"paper":72,"created_at":82},2612,"AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification","https:\u002F\u002Fzhichai.net\u002Ftopic\u002F178634732","Mandal等发表在arXiv:2609.10469。提出AgroVisNet一种从头训练的紧凑卷积网络和BD-PlantDX专家验证基准(12,432张田间图像，涵盖孟加拉国Bogura和Nilphamari地区收集的萝卜、马铃薯和尖瓜12类健康和病害状态)。AgroVisNet仅290,572个可训练参数在BD-PlantDX上达到99.52%测试准确率和加权F1，超过所有六个ImageNet预训练轻量级主干网络，同时参数量少8.7到16.8倍。",null,"arXiv 2609.10469","2026-09-09T01:00:00Z","论文",10,false,73,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,22,18,12,6,1,"提出仅29万参数的轻量卷积网络与专家验证的12类作物病害基准，准确率99.52%且显著优于预训练主干，方法新颖、数据规模扎实，对农业AI病害识别有实用参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30,31],"智慧农业","农业人工智能","马铃薯","萝卜","病害识别","轻量卷积网络",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[],{"status":40,"locked":13,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,51,57,63],{"code":33,"score":19,"supported":46,"title":47,"abstract":49,"tags":50},true,[48],"potato",[28],[28],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":56},"B1",4,[],[29],[29],{"code":58,"score":59,"supported":13,"title":60,"abstract":61,"tags":62},"I2",2,[],[],[27,30],{"code":64,"score":21,"supported":13,"title":65,"abstract":66,"tags":67},"I1",[],[],[26],[69,70],"农业人工智能 轻量卷积网络 智慧农业 病害识别","农业人工智能 轻量卷积网络","农业人工智能轻量卷积网络智慧农业病害识别-2612",{"doi":8,"openalex_id":8,"authors":73,"venue":8,"cited_by_count":74,"oa_url":8,"card":75,"direction":79,"ingested_from":81},[],0,{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"提出轻量卷积网络AgroVisNet及专家验证的萝卜、马铃薯和尖瓜病害图像基准BD-PlantDX。","从头训练紧凑CNN，使用12,432张田间图像、12类病害，与6个预训练轻量主干","仅29万参数即达99.52%准确率，超越所有ImageNet预训练轻量模型且参数少8.7-16.8倍","农业人工智能与决策模型","可探索跨地区跨作物泛化、田间复杂背景鲁棒性及模型轻量化部署到移动端的研究。","agent","2026-09-16T00:03:52.515327Z",{"total":20,"page":21,"page_size":20,"items":84},[85,143,218,287,371,436],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":8,"content":8,"source_name":90,"source_url":8,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":93,"sources":98,"tags":100,"primary_topic":102,"secondary_topics":103,"classification":104,"search_phrases":131,"slug":134,"view_count":113,"doi":8,"paper":135,"created_at":142},5064,"Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.20544","Haimanot Bitew Alemayehu、Simachew Melaku Ayalew、Chilotie Desalew Mengesha、Ayisheshim Almaw Bogale、Anteneh Birhan Yenew、Abrham Tegegne Bogale在arXiv 2026-10-15上传的预印本,面向埃塞俄比亚粮食安全作物马铃薯,提出可部署于边缘设备的轻量卷积神经网络,完成健康、早疫病、晚疫病三类分类。模型在PlantVillage(2,215张图像)训练,测试准确率98.66%;在埃塞俄比亚Banja镇田间独立采集的247张图像测试中准确率为90.28%,体现独立田间验证相较受控基准更接近真实部署条件。模型仅含446,787个可训练参数、约1.70MB,训练周期较VGG16和MobileNetV2更短。【预印本未审稿;仅在埃塞俄比亚一地进行田间验证,跨地区泛化能力未测】","arXiv preprint 2026-10-15","2026-10-15T00:00:00Z",71,{"impact":16,"substance":94,"depth":95,"authority":96,"freshness":12,"relevant":21,"comment":97},20,17,9,"预印本提出仅1.7MB的轻量CNN实现马铃薯早疫病、晚疫病识别，含独立田间验证，方法扎实但尚未审稿、泛化性待验证，属细分领域技术进展。",[99],{"name":90,"url":88},[26,27,28,101,30],"边缘计算",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":13,"version":41,"method":42,"evidence":105},{"candidates":106,"agricultural_evidence":46},[107,111,118,122,126],{"code":33,"score":19,"supported":46,"title":108,"abstract":109,"tags":110},[48],[28],[28],{"code":112,"score":113,"supported":46,"title":114,"abstract":115,"tags":117},"A8",3,[],[116],"粮食安全",[],{"code":58,"score":59,"supported":13,"title":119,"abstract":120,"tags":121},[],[],[27,30],{"code":64,"score":21,"supported":13,"title":123,"abstract":124,"tags":125},[],[],[26],{"code":127,"score":21,"supported":13,"title":128,"abstract":129,"tags":130},"I5",[],[],[101],[132,133],"埃塞俄比亚 马铃薯 晚疫病 检测","轻量CNN 马铃薯 叶片病害","埃塞俄比亚马铃薯晚疫病检测-5064",{"doi":8,"openalex_id":8,"authors":136,"venue":8,"cited_by_count":74,"oa_url":8,"card":137,"direction":79,"ingested_from":81},[],{"tldr":138,"method":139,"finding":140,"direction":79,"opportunity":141},"提出轻量CNN实现马铃薯早疫病与晚疫病三分类，并在埃塞俄比亚田间验证。","轻量卷积神经网络，PlantVillage 2215张训练，Banja镇247张","测试集准确率98.66%，独立田间测试降至90.28%，模型仅1.70MB。","可研究跨地区\u002F多作物泛化、田间复杂背景鲁棒性及边缘设备实时部署优化。","2026-10-16T00:14:01.369956Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":148,"content":8,"source_name":149,"source_url":146,"published_at":150,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":151,"score_detail":152,"sources":157,"tags":159,"primary_topic":162,"secondary_topics":163,"classification":169,"search_phrases":187,"slug":190,"view_count":74,"doi":191,"paper":192,"created_at":217},4818,"Hybrid Deep Feature Extraction and Ensemble Machine Learning for Potato Leaf Disease Classification in Uncontrolled Field Conditions","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1837896","Early and accurate diagnosis of potato leaf diseases is critical for preventing yield losses under real field conditions. This study proposes a hybrid deep feature–based machine learning framework that combines transfer learning and ensemble classifiers to automatically identify seven potato leaf disease classes from images. A total of 4,260 images from the publicly available Potato Leaf Disease Dataset were organized into seven categories: Bacteria, Virus, Pest, Fungi, Nematode, Phytophthora, and Healthy. Real-time image augmentation was performed using Keras ImageDataGenerator to improve strength to field variability. Deep feature vectors were extracted from three pre-trained convolutional neural networks (ResNet50, EfficientNetB0, and MobileNetV2), each followed by a lightweight dense head, and concatenated into 768-dimensional feature representations. After standardization, dimensionality reduction was applied via principal component analysis, and class imbalance in the training set was addressed using the SMOTE-ENN hybrid resampling method. On these deep features, 32 classical machine learning and ensemble models including SVMs, tree-based methods, boosting algorithms, multilayer perceptrons, and four ensemble schemes, were trained. Model performance was assessed by three-fold cross-validation on the training set and final evaluation on the held-out validation set using accuracy, precision, recall, and macro F1-score. Ensemble models outperformed individual classifiers. The best performance was obtained with the StackingClassifier_L2 model, which combines XGBoost, LightGBM, Random Forest, SVM with RBF kernel, and Extra Trees within a stacking framework. This model achieved an accuracy of 81.34% and a macro F1-score of 81.74% on the validation set. The proposed hybrid system, which integrates multi-backbone deep feature extraction with comprehensive classical and ensemble classifiers, provides a robust and practical solution for automatic potato leaf disease classification in uncontrolled field conditions. The results suggest that stacking-based ensembles on top of deep features can capture complex visual patterns more effectively than single models and may support decision-making in precision agriculture.","在马铃薯实际田间条件下，早期准确地诊断叶片病害对于防止产量损失至关重要。本研究提出了一种基于混合深度特征的机器学习框架，该框架结合迁移学习和集成分类器，从图像中自动识别七类马铃薯叶片病害。来自公开可用的马铃薯叶片病害数据集的4,260张图像被分为七个类别：细菌（Bacteria）、病毒（Virus）、害虫（Pest）、真菌（Fungi）、线虫（Nematode）、疫霉（Phytophthora）和健康（Healthy）。使用Keras ImageDataGenerator进行实时图像增强，以提高对田间变异的鲁棒性。从三个预训练的卷积神经网络（ResNet50、EfficientNetB0和MobileNetV2）中提取深度特征向量，每个网络后接一个轻量级密集头，并将其拼接为768维特征表示。经过标准化后，通过主成分分析进行降维，并使用SMOTE-ENN混合重采样方法解决训练集中的类别不平衡问题。在这些深度特征上，训练了32种经典机器学习和集成模型，包括支持向量机、基于树的方法、提升算法、多层感知机以及四种集成方案。通过训练集上的三折交叉验证和保留验证集上的最终评估，使用准确率、精确率、召回率和宏F1分数评估模型性能。集成模型优于单个分类器。最佳性能由StackingClassifier_L2模型获得，该模型在堆叠框架内结合了XGBoost、LightGBM、随机森林、带RBF核的支持向量机和极端随机树。该模型在验证集上达到了81.34%的准确率和81.74%的宏F1分数。所提出的混合系统将多骨干深度特征提取与全面的经典分类器和集成分类器相结合，为无控制田间条件下的马铃薯叶片病害自动分类提供了一种稳健且实用的解决方案。结果表明，基于深度特征的堆叠集成能够比单一模型更有效地捕捉复杂的视觉模式，并可能支持精准农业中的决策。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",65,{"impact":153,"substance":94,"depth":154,"authority":155,"freshness":153,"relevant":21,"comment":156},8,16,13,"方法组合较全面、数据规模明确，但验证集准确率仅81.34%，属细分领域技术进展，可作主题聚合素材而非头条精选。",[158],{"name":149,"url":146},[26,27,28,160,161],"病虫害识别","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[164],{"code":58,"slug":165,"name":166,"path":167,"parent_code":168},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":13,"version":41,"method":42,"evidence":170},{"candidates":171,"agricultural_evidence":46},[172,178,182],{"code":58,"score":154,"supported":46,"title":173,"abstract":175,"tags":177},[174],"machine learning",[176,174],"机器学习",[27,160],{"code":33,"score":16,"supported":46,"title":179,"abstract":180,"tags":181},[48],[28,48],[28],{"code":64,"score":153,"supported":46,"title":183,"abstract":184,"tags":186},[],[161,185],"precision agriculture",[26,161],[188,189],"马铃薯 叶片病害 深度学习","Potato Leaf Disease Dataset 集成学习","马铃薯叶片病害深度学习-4818","10.15832\u002Fankutbd.1837896",{"doi":191,"openalex_id":193,"authors":194,"venue":149,"cited_by_count":74,"oa_url":209,"card":210,"direction":215,"ingested_from":216},"W7221546496",[195,198,200,203,206],{"name":196,"orcid":197},"Cihan Ünal","https:\u002F\u002Forcid.org\u002F0000-0002-5255-4078",{"name":199,"orcid":8},"Cengizhan Tumer",{"name":201,"orcid":202},"Elham Tahsin Yasin","https:\u002F\u002Forcid.org\u002F0000-0003-3246-6000",{"name":204,"orcid":205},"İlkay Çınar","https:\u002F\u002Forcid.org\u002F0000-0003-0611-3316",{"name":207,"orcid":208},"Murat Köklü","https:\u002F\u002Forcid.org\u002F0000-0002-2737-2360","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5487015",{"tldr":211,"method":212,"finding":213,"direction":79,"opportunity":214},"融合三种CNN深度特征与集成学习，实现田间马铃薯叶片七类病害分类。","ResNet50\u002FEfficientNetB0\u002FMobileNetV2特征拼接+","StackingClassifier_L2最优，验证集准确率81.34%、宏F1为81.74%。","可探索轻量化模型与多模态数据融合，提升非受控田间小样本病害识别鲁棒性。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:16.021066Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":8,"source_name":224,"source_url":221,"published_at":225,"category":11,"cover_url":8,"hotness":226,"is_selected":13,"score":227,"score_detail":228,"sources":231,"tags":239,"primary_topic":243,"secondary_topics":244,"classification":246,"search_phrases":271,"slug":274,"view_count":113,"doi":275,"paper":276,"created_at":286},3912,"An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054047","An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction presents a technology-driven approach for automated crop health monitoring and yield estimation in sorghum cultivation. The study integrates computer vision techniques and machine learning algorithms to identify and quantify disease severity from crop images and to predict potential yield based on relevant visual and agronomic characteristics. The framework aims to reduce the limitations of conventional manual disease assessment, which can be time-consuming and subject to human variability. Image processing and feature extraction techniques are employed to capture disease-related characteristics, while machine learning models support classification, severity estimation, and yield prediction. By combining disease assessment with yield forecasting, the proposed framework provides a comprehensive approach to precision agriculture and data-driven crop management. The work demonstrates the potential of artificial intelligence and computer vision to support timely decision-making, improve crop monitoring, and contribute to sustainable and efficient sorghum production.","一个集成的计算机视觉与机器学习框架用于高粱病害严重度评估与产量预测，提出了一种技术驱动的方法，用于高粱种植中的自动化作物健康监测与产量估算。该研究整合了计算机视觉技术和机器学习算法，从作物图像中识别和量化病害严重度，并基于相关视觉和农艺特征预测潜在产量。该框架旨在减少传统人工病害评估的局限性，传统方法可能耗时且受人为变异影响。研究采用图像处理和特征提取技术捕捉病害相关特征，同时机器学习模型支持分类、严重度估计和产量预测。通过将病害评估与产量预测相结合，所提出的框架为精准农业和数据驱动的作物管理提供了一种综合方法。该工作展示了人工智能和计算机视觉在支持及时决策、改善作物监测以及促进可持续和高效高粱生产方面的潜力。","International Journal of Emerging Technologies and Innovative Research","2026-09-30T00:00:00Z",55,56,{"impact":19,"substance":154,"depth":229,"authority":20,"freshness":153,"relevant":21,"comment":230},14,"论文提出计算机视觉与机器学习结合的高粱病害严重度评估与产量预测框架，方法有一定新意但期刊权威性一般，属细分领域技术进展。",[232,233,235,237],{"name":224,"url":221},{"name":224,"url":234},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054048",{"name":224,"url":236},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053752",{"name":224,"url":238},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053753",[26,27,240,241,242,30],"计算机视觉","产量预测","高粱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[245],{"code":58,"slug":165,"name":166,"path":167,"parent_code":168},{"status":40,"locked":13,"version":41,"method":42,"evidence":247},{"candidates":248,"agricultural_evidence":46},[249,256,261,266],{"code":58,"score":250,"supported":46,"title":251,"abstract":253,"tags":255},28,[174,252],"computer vision",[176,240,174,254,252],"artificial intelligence",[27,30,240],{"code":33,"score":16,"supported":46,"title":257,"abstract":259,"tags":260},[258],"sorghum",[242,258],[242],{"code":64,"score":262,"supported":46,"title":263,"abstract":264,"tags":265},7,[],[161,185],[26],{"code":267,"score":21,"supported":13,"title":268,"abstract":269,"tags":270},"I3",[],[],[241],[272,273],"高粱 病害识别 产量预测","计算机视觉 作物病害评估","高粱病害识别产量预测-3912","10.5281\u002Fzenodo.23054047",{"doi":275,"openalex_id":277,"authors":278,"venue":224,"cited_by_count":74,"oa_url":221,"card":281,"direction":79,"ingested_from":216},"W7214900332",[279],{"name":280,"orcid":8},"Anantjit Publication",{"tldr":282,"method":283,"finding":284,"direction":79,"opportunity":285},"融合计算机视觉与机器学习，实现高粱病害严重度评估与产量预测。","图像处理与特征提取结合机器学习分类回归模型。","该框架可自动量化病害严重度并预测产量，减少人工评估偏差。","可探索多作物迁移学习与田间实时轻量化部署，提升泛化性。","2026-10-01T23:30:48.742698Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":8,"source_name":293,"source_url":290,"published_at":294,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":295,"score_detail":296,"sources":298,"tags":300,"primary_topic":303,"secondary_topics":304,"classification":306,"search_phrases":339,"slug":342,"view_count":21,"doi":343,"paper":344,"created_at":370},3153,"Explainable optimized deep learning and generative AI based framework for finger millet disease detection in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02222-y","Abstract Eleusine coracana, locally known as finger millet (ragi), is a wholesome, climate-resilient crop. It is a staple food in semi-dry and dry areas of Asia and Africa. The productivity of this crop can be severely reduced by diseases such as downy, mottle, seedling, smut, and wilt. Conventional diagnosis methods are often time-consuming and unsuitable for large-scale farming. Therefore,Deep Learning (DL) offers an alternative for smart commercial farming that can streamline disease screening and improve productivity. This work offers an AI-integrated framework for early disease intervention combining DL, Explainable AI (XAI), and an image-based decision-support interface. The finger millet (ragi) dataset from Kaggle was used to train and evaluate custom CNN, VGG16, and ResNet50 models. To reduce feature redundancy and improve generalization, Grey Wolf Optimizer (GWO) based feature selection was applied to the penultimate-layer features of each model. Custom CNN, VGG16, and ResNet50 achieved accuracies of 89.40%, 83.11%, and 91.83%, respectively. Further, the use of GWO improved the accuracies to 97.71%, 90.84%, and 98.36%, respectively. ResNet50 achieved the highest accuracy 98.36% and was selected as the final backbone. The final ResNet50 model is integrated with the Gemini API to provide disease-specific recommendations based on the predicted class and user queries. To increase prediction transparency, Grad-CAM was used to highlight image regions that influenced the model output. The integration of DL, XAI, and generative AI provides a scalable approach for detecting and managing finger millet diseases. The proposed work boosts precision agriculture by providing AI-based and XAI-supported decision-making, while fostering sustainable agricultural practices.","摘要 穇子（Eleusine coracana），当地称为指黍（ragi），是一种有益健康且气候适应性强的作物。它是亚洲和非洲半干旱及干旱地区的主食。霜霉病、斑驳病、苗枯病、黑穗病和枯萎病等病害可严重降低该作物的产量。传统诊断方法往往耗时且不适合大规模种植。因此，深度学习（DL）为智能商业农业提供了一种替代方案，可简化病害筛查并提高生产力。本研究提出了一种人工智能集成框架，用于早期病害干预，结合了深度学习、可解释人工智能（XAI）和基于图像的决策支持界面。使用来自Kaggle的指黍（ragi）数据集训练和评估了自定义CNN、VGG16和ResNet50模型。为减少特征冗余并提高泛化能力，将灰狼优化器（GWO）基于特征选择应用于每个模型的倒数第二层特征。自定义CNN、VGG16和ResNet50分别达到了89.40%、83.11%和91.83%的准确率。此外，使用GWO将准确率分别提高到97.71%、90.84%和98.36%。ResNet50达到了最高准确率98.36%，并被选为最终骨干网络。最终的ResNet50模型与Gemini API集成，根据预测类别和用户查询提供针对特定病害的建议。为提高预测透明度，使用Grad-CAM突出显示影响模型输出的图像区域。深度学习、可解释人工智能和生成式人工智能的集成提供了一种可扩展的方法，用于检测和管理指黍病害。所提出的工作通过提供基于人工智能和可解释人工智能支持的决策，促进了精准农业，同时推动了可持续农业实践。","Discover Artificial Intelligence","2026-09-22T00:00:00Z",75,{"impact":16,"substance":94,"depth":18,"authority":19,"freshness":12,"relevant":21,"comment":297},"将深度学习、可解释AI与生成式AI结合用于指状粟病害识别，方法新颖、数据充分，对智慧农业有参考价值。",[299],{"name":293,"url":290},[26,27,301,30,302],"可解释AI","小米作物",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[305],{"code":58,"slug":165,"name":166,"path":167,"parent_code":168},{"status":40,"locked":13,"version":41,"method":42,"evidence":307},{"candidates":308,"agricultural_evidence":46},[309,315,320,326,333],{"code":58,"score":95,"supported":46,"title":310,"abstract":312,"tags":314},[311],"deep learning",[313,311],"深度学习",[27,301,30],{"code":64,"score":16,"supported":46,"title":316,"abstract":318,"tags":319},[317],"smart agriculture",[161,185],[26],{"code":33,"score":321,"supported":46,"title":322,"abstract":324,"tags":325},11,[323],"millet",[323],[],{"code":327,"score":20,"supported":46,"title":328,"abstract":329,"tags":332},"H7",[],[330,331],"气候适应","干旱",[],{"code":334,"score":113,"supported":46,"title":335,"abstract":336,"tags":338},"H8",[],[337],"可持续农业",[],[340,341],"ResNet50 GWO 病害识别","农业人工智能 小米作物 智慧农业 病害识别","ResNet50GWO病害识别-3153","10.1007\u002Fs44163-026-02222-y",{"doi":343,"openalex_id":345,"authors":346,"venue":293,"cited_by_count":74,"oa_url":290,"card":365,"direction":215,"ingested_from":216},"W7213978567",[347,350,352,354,356,359,362],{"name":348,"orcid":349},"Sunil Kumar Mohapatra","https:\u002F\u002Forcid.org\u002F0000-0002-5865-095X",{"name":351,"orcid":8},"A. Sanjib Kumar Patro",{"name":353,"orcid":8},"Lulen Kumar Sahu",{"name":355,"orcid":8},"Chinmaye Dora",{"name":357,"orcid":358},"Sujata Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-1293-5378",{"name":360,"orcid":361},"Kshira Sagar Sahoo","https:\u002F\u002Forcid.org\u002F0000-0002-6435-5738",{"name":363,"orcid":364},"Byomakesh Mahapatra","https:\u002F\u002Forcid.org\u002F0000-0002-9126-1729",{"tldr":366,"method":367,"finding":368,"direction":79,"opportunity":369},"提出融合深度学习、可解释AI与生成式AI的指状粟病害检测框架，实现高精度识别与决策支持。","用Kaggle指状粟图像训练CNN、VGG16、ResNet50，并用灰狼优化做","GWO特征选择显著提升精度，ResNet50达98.36%，结合Grad-CAM与Gemini AP","可探索轻量化模型与多作物泛化，并将XAI与生成式建议在田间移动端实时验证。","2026-09-22T23:30:10.255790Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":376,"content":8,"source_name":377,"source_url":374,"published_at":378,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":295,"score_detail":379,"sources":381,"tags":383,"primary_topic":385,"secondary_topics":386,"classification":387,"search_phrases":407,"slug":410,"view_count":59,"doi":411,"paper":412,"created_at":435},1709,"EOL-DQN: An intelligent cutting decision of potato seed tubers based on improved deep reinforcement learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112372","EOL-DQN: An intelligent cutting decision of potato seed tubers based on improved deep reinforcement learning。Computers and Electronics in Agriculture","EOL-DQN：基于改进深度强化学习的马铃薯种薯智能切块决策。Computers and Electronics in Agriculture","Computers and Electronics in Agriculture","2026-09-05T00:00:00Z",{"impact":16,"substance":94,"depth":18,"authority":229,"freshness":153,"relevant":21,"comment":380},"提出基于改进深度强化学习的马铃薯种薯切块决策方法，属于智慧农业细分领域的技术创新，对种业智能化有参考价值。",[382],{"name":377,"url":374},[26,27,28,384,313],"种业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":13,"version":41,"method":42,"evidence":388},{"candidates":389,"agricultural_evidence":46},[390,394,398,403],{"code":33,"score":16,"supported":46,"title":391,"abstract":392,"tags":393},[48],[28,48],[28],{"code":58,"score":59,"supported":13,"title":395,"abstract":396,"tags":397},[],[],[27,313],{"code":399,"score":21,"supported":13,"title":400,"abstract":401,"tags":402},"F1",[],[],[384],{"code":64,"score":21,"supported":13,"title":404,"abstract":405,"tags":406},[],[],[26],[408,409],"农业人工智能 智慧农业 深度学习 马铃薯","农业人工智能 智慧农业","农业人工智能智慧农业深度学习马铃薯-1709","10.1016\u002Fj.compag.2026.112372",{"doi":411,"openalex_id":413,"authors":414,"venue":377,"cited_by_count":74,"oa_url":8,"card":430,"direction":79,"ingested_from":216},"W7208757680",[415,417,419,421,423,425,427],{"name":416,"orcid":8},"Xuyang Wang",{"name":418,"orcid":8},"Yun Liu",{"name":420,"orcid":8},"Tianshuo Su",{"name":422,"orcid":8},"Yulu Sun",{"name":424,"orcid":8},"Binheng Huang",{"name":426,"orcid":8},"Yangqian Zhang",{"name":428,"orcid":429},"Wanzhi Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6196-8037",{"tldr":431,"method":432,"finding":433,"direction":79,"opportunity":434},"提出EOL-DQN改进深度强化学习模型，用于马铃薯种薯切块智能决策。","改进深度强化学习（DQN），结合视觉识别与决策模型。","EOL-DQN能有效提高切块决策的准确性和效率。","可探索将强化学习应用于其他农产品加工或农业机器人操作，优化决策策略。","2026-09-05T23:30:01.927676Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":8,"content":8,"source_name":441,"source_url":439,"published_at":442,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":443,"score_detail":444,"sources":446,"tags":448,"primary_topic":450,"secondary_topics":451,"classification":453,"search_phrases":468,"slug":471,"view_count":53,"doi":472,"paper":473,"created_at":486},1404,"Explainable Deep Learning-Based Potato Leaf Disease Detection and Severity Assessment for Smart Agriculture in Bangladesh","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10879535\u002Fv1","Explainable Deep Learning-Based Potato Leaf Disease Detection and Severity Assessment for Smart Agriculture in Bangladesh。Research Square","Research Square","2026-09-01T00:00:00Z",53,{"impact":153,"substance":16,"depth":16,"authority":153,"freshness":262,"relevant":21,"comment":445},"针对孟加拉国马铃薯病害的可解释深度学习检测研究，方法新颖但地域性强，影响有限。",[447],{"name":441,"url":439},[26,27,28,449],"作物病害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[452],{"code":58,"slug":165,"name":166,"path":167,"parent_code":168},{"status":40,"locked":13,"version":41,"method":42,"evidence":454},{"candidates":455,"agricultural_evidence":46},[456,460,464],{"code":33,"score":19,"supported":46,"title":457,"abstract":458,"tags":459},[48],[48],[28],{"code":64,"score":19,"supported":46,"title":461,"abstract":462,"tags":463},[317],[317],[26],{"code":58,"score":19,"supported":46,"title":465,"abstract":466,"tags":467},[311],[311],[27],[469,470],"农业人工智能 作物病害 智慧农业 马铃薯","农业人工智能 作物病害","农业人工智能作物病害智慧农业马铃薯-1404","10.21203\u002Frs.3.rs-10879535\u002Fv1",{"doi":472,"openalex_id":474,"authors":475,"venue":441,"cited_by_count":74,"oa_url":485,"card":8,"direction":215,"ingested_from":216},"W7204884709",[476,479,482],{"name":477,"orcid":478},"Kazi Abdul Mannan","https:\u002F\u002Forcid.org\u002F0000-0002-7123-132X",{"name":480,"orcid":481},"Sinha Nafiz Hasan","https:\u002F\u002Forcid.org\u002F0009-0007-8906-4586",{"name":483,"orcid":484},"Ahmed Shafin","https:\u002F\u002Forcid.org\u002F0009-0000-4162-6913","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10879535\u002Flatest.pdf","2026-09-02T23:30:10.900822Z"]