[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1709":3,"related-1709":99},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":67,"slug":70,"view_count":53,"doi":71,"paper":72,"created_at":98},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",null,"Computers and Electronics in Agriculture","2026-09-05T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,14,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","马铃薯","种业","深度学习",{"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":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,51,57,62],{"code":33,"score":17,"supported":46,"title":47,"abstract":49,"tags":50},true,[48],"potato",[29,48],[29],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":56},"I2",2,[],[],[28,31],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"F1",[],[],[30],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[27],[68,69],"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":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":90,"oa_url":9,"card":91,"direction":95,"ingested_from":97},"W7208757680",[75,77,79,81,83,85,87],{"name":76,"orcid":9},"Xuyang Wang",{"name":78,"orcid":9},"Yun Liu",{"name":80,"orcid":9},"Tianshuo Su",{"name":82,"orcid":9},"Yulu Sun",{"name":84,"orcid":9},"Binheng Huang",{"name":86,"orcid":9},"Yangqian Zhang",{"name":88,"orcid":89},"Wanzhi Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6196-8037",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"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。","农业人工智能与决策模型","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。","openalex","2026-09-05T23:30:01.927676Z",{"total":100,"page":22,"page_size":100,"items":101},6,[102,162,235,286,340,400],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":9,"content":9,"source_name":107,"source_url":9,"published_at":108,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":109,"score_detail":110,"sources":114,"tags":116,"primary_topic":119,"secondary_topics":120,"classification":121,"search_phrases":149,"slug":152,"view_count":131,"doi":9,"paper":153,"created_at":161},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":17,"substance":18,"depth":111,"authority":112,"freshness":13,"relevant":22,"comment":113},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。",[115],{"name":107,"url":105},[27,28,29,117,118],"边缘计算","病害识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":122},{"candidates":123,"agricultural_evidence":46},[124,129,136,140,144],{"code":33,"score":125,"supported":46,"title":126,"abstract":127,"tags":128},12,[48],[29],[29],{"code":130,"score":131,"supported":46,"title":132,"abstract":133,"tags":135},"A8",3,[],[134],"粮食安全",[],{"code":52,"score":53,"supported":14,"title":137,"abstract":138,"tags":139},[],[],[28,118],{"code":63,"score":22,"supported":14,"title":141,"abstract":142,"tags":143},[],[],[27],{"code":145,"score":22,"supported":14,"title":146,"abstract":147,"tags":148},"I5",[],[],[117],[150,151],"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":9,"openalex_id":9,"authors":154,"venue":9,"cited_by_count":90,"oa_url":9,"card":155,"direction":95,"ingested_from":160},[],{"tldr":156,"method":157,"finding":158,"direction":95,"opportunity":159},"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。","agent","2026-10-16T00:14:01.369956Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":168,"source_url":165,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":175,"tags":177,"primary_topic":180,"secondary_topics":181,"classification":187,"search_phrases":205,"slug":208,"view_count":90,"doi":209,"paper":210,"created_at":234},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":21,"substance":18,"depth":172,"authority":173,"freshness":21,"relevant":22,"comment":174},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。",[176],{"name":168,"url":165},[27,28,29,178,179],"病虫害识别","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[182],{"code":52,"slug":183,"name":184,"path":185,"parent_code":186},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":188},{"candidates":189,"agricultural_evidence":46},[190,196,200],{"code":52,"score":172,"supported":46,"title":191,"abstract":193,"tags":195},[192],"machine learning",[194,192],"机器学习",[28,178],{"code":33,"score":17,"supported":46,"title":197,"abstract":198,"tags":199},[48],[29,48],[29],{"code":63,"score":21,"supported":46,"title":201,"abstract":202,"tags":204},[],[179,203],"precision agriculture",[27,179],[206,207],"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":209,"openalex_id":211,"authors":212,"venue":168,"cited_by_count":90,"oa_url":227,"card":228,"direction":233,"ingested_from":97},"W7221546496",[213,216,218,221,224],{"name":214,"orcid":215},"Cihan Ünal","https:\u002F\u002Forcid.org\u002F0000-0002-5255-4078",{"name":217,"orcid":9},"Cengizhan Tumer",{"name":219,"orcid":220},"Elham Tahsin Yasin","https:\u002F\u002Forcid.org\u002F0000-0003-3246-6000",{"name":222,"orcid":223},"İlkay Çınar","https:\u002F\u002Forcid.org\u002F0000-0003-0611-3316",{"name":225,"orcid":226},"Murat Köklü","https:\u002F\u002Forcid.org\u002F0000-0002-2737-2360","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5487015",{"tldr":229,"method":230,"finding":231,"direction":95,"opportunity":232},"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","2026-10-13T23:30:16.021066Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":9,"content":9,"source_name":240,"source_url":9,"published_at":241,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":242,"score_detail":243,"sources":246,"tags":248,"primary_topic":251,"secondary_topics":252,"classification":253,"search_phrases":274,"slug":277,"view_count":53,"doi":9,"paper":278,"created_at":285},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。","arXiv 2609.10469","2026-09-09T01:00:00Z",73,{"impact":17,"substance":244,"depth":19,"authority":125,"freshness":100,"relevant":22,"comment":245},22,"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。",[247],{"name":240,"url":238},[27,28,29,249,118,250],"萝卜","轻量卷积网络",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":254},{"candidates":255,"agricultural_evidence":46},[256,260,266,270],{"code":33,"score":125,"supported":46,"title":257,"abstract":258,"tags":259},[48],[29],[29],{"code":261,"score":262,"supported":46,"title":263,"abstract":264,"tags":265},"B1",4,[],[249],[249],{"code":52,"score":53,"supported":14,"title":267,"abstract":268,"tags":269},[],[],[28,118],{"code":63,"score":22,"supported":14,"title":271,"abstract":272,"tags":273},[],[],[27],[275,276],"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":9,"openalex_id":9,"authors":279,"venue":9,"cited_by_count":90,"oa_url":9,"card":280,"direction":95,"ingested_from":160},[],{"tldr":281,"method":282,"finding":283,"direction":95,"opportunity":284},"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","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。","2026-09-16T00:03:52.515327Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":9,"content":9,"source_name":291,"source_url":289,"published_at":292,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":293,"score_detail":294,"sources":297,"tags":299,"primary_topic":301,"secondary_topics":302,"classification":304,"search_phrases":321,"slug":324,"view_count":262,"doi":325,"paper":326,"created_at":339},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":21,"substance":17,"depth":17,"authority":21,"freshness":295,"relevant":22,"comment":296},7,"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。",[298],{"name":291,"url":289},[27,28,29,300],"作物病害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[303],{"code":52,"slug":183,"name":184,"path":185,"parent_code":186},{"status":40,"locked":14,"version":41,"method":42,"evidence":305},{"candidates":306,"agricultural_evidence":46},[307,311,316],{"code":33,"score":125,"supported":46,"title":308,"abstract":309,"tags":310},[48],[48],[29],{"code":63,"score":125,"supported":46,"title":312,"abstract":314,"tags":315},[313],"smart agriculture",[313],[27],{"code":52,"score":125,"supported":46,"title":317,"abstract":319,"tags":320},[318],"deep learning",[318],[28],[322,323],"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":325,"openalex_id":327,"authors":328,"venue":291,"cited_by_count":90,"oa_url":338,"card":9,"direction":233,"ingested_from":97},"W7204884709",[329,332,335],{"name":330,"orcid":331},"Kazi Abdul Mannan","https:\u002F\u002Forcid.org\u002F0000-0002-7123-132X",{"name":333,"orcid":334},"Sinha Nafiz Hasan","https:\u002F\u002Forcid.org\u002F0009-0007-8906-4586",{"name":336,"orcid":337},"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",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":9,"content":9,"source_name":10,"source_url":343,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":242,"score_detail":346,"sources":348,"tags":350,"primary_topic":352,"secondary_topics":353,"classification":354,"search_phrases":370,"slug":372,"view_count":262,"doi":373,"paper":374,"created_at":399},1205,"Lightweight real-time detection of sweet potato virus disease by a hybrid CNN–Transformer network","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112319","Timely and precise detection of sweet potato virus disease (SPVD) is essential for ensuring crop health and improving yield quality in precision agriculture. However, conventional deep learning models often struggle to capture subtle lesion features under complex field conditions. This paper proposes Swin-SPVDNet, a hybrid CNN-Transformer detection framework with task-specific architectural enhancements that combines convolutional neural networks (CNNs) with a modified Swin Transformer to effectively integrate local spatial details and global contextual information. Unlike conventional hybrid models that simply stack convolutional and transformer blocks, the proposed network introduces three key structural designs: (1) A C2PSA-enhanced convolutional backbone for improved local feature sensitivity. (2) A C3STR module that tightly integrates convolutional operations with Swin Transformer blocks for joint local–global feature interaction. (3) A Residual Attention Block (RAB) that refines feature representations while stabilizing gradient propagation. A field-collected SPVD image dataset was constructed using handheld RGB imaging under real agricultural conditions. Extensive experiments and ablation studies demonstrate that Swin-SPVDNet achieves competitive performance compared with representative state-of-the-art detectors while providing a favorable balance between detection accuracy and deployment efficiency, attaining a mean Average Precision (mAP@0.5) of 86.8%, a precision of 85.1%, and a recall of 75.6%. Compared to Yolov5-v11 models, our approach demonstrates improved robustness in detecting small and irregular lesions, particularly under occlusion and lighting variability. These results confirm the practical value of Swin-SPVDNet for real-time field deployment and support its application in intelligent disease monitoring.","2026-08-25T00:00:00Z",{"impact":19,"substance":244,"depth":19,"authority":173,"freshness":53,"relevant":22,"comment":347},"提出轻量级混合CNN-Transformer模型用于甘薯病毒病实时检测，方法新颖且实验数据详实，对智能植保有参考价值。",[349],{"name":10,"url":343},[27,28,300,31,351],"甘薯",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":355},{"candidates":356,"agricultural_evidence":46},[357,361,366],{"code":33,"score":125,"supported":46,"title":358,"abstract":359,"tags":360},[48],[48],[351],{"code":52,"score":362,"supported":46,"title":363,"abstract":364,"tags":365},5,[],[318],[28,31],{"code":63,"score":262,"supported":46,"title":367,"abstract":368,"tags":369},[],[203],[27],[371,323],"农业人工智能 作物病害 智慧农业 深度学习","农业人工智能作物病害智慧农业深度学习-1205","10.1016\u002Fj.compag.2026.112319",{"doi":373,"openalex_id":375,"authors":376,"venue":10,"cited_by_count":90,"oa_url":343,"card":394,"direction":95,"ingested_from":97},"W7204154634",[377,380,383,386,388,390,392],{"name":378,"orcid":379},"Ru Han","https:\u002F\u002Forcid.org\u002F0009-0000-5128-1314",{"name":381,"orcid":382},"Lei Shu","https:\u002F\u002Forcid.org\u002F0000-0002-6700-9347",{"name":384,"orcid":385},"Grzegorz Cielniak","https:\u002F\u002Forcid.org\u002F0000-0002-6299-8465",{"name":387,"orcid":9},"Yan Zhu",{"name":389,"orcid":9},"Xing Yang",{"name":391,"orcid":9},"Fan Yang",{"name":393,"orcid":9},"Kai Huang",{"tldr":395,"method":396,"finding":397,"direction":95,"opportunity":398},"提出Swin-SPVDNet混合CNN-Transformer网络，实现甘薯病毒病轻量级实时检测。","结合C2PSA卷积骨干、C3STR模块和残差注意力块，使用手持RGB图像数据集。","mAP达86.8%，精度85.1%，召回75.6%，优于YOLOv5-v11，鲁棒性强。","可探索将混合网络应用于其他作物病害检测，或优化模型以适应更多复杂田间环境。","2026-09-01T04:03:02.220802Z",{"id":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":417,"secondary_topics":418,"classification":419,"search_phrases":443,"slug":446,"view_count":90,"doi":447,"paper":448,"created_at":463},5561,"Growth Stage and Cultivar Influence the Interpretation of UAV-Derived NDVI for Potato Yield Assessment","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11540-026-10161-5","Abstract Single-date UAV vegetation indices are widely used to assess potato crop performance and estimate yield. However, whether their biological interpretation remains consistent across cultivars and growth stages is rarely examined. This study investigated how the relationships between UAV-derived multispectral and thermal canopy indicators and potato yield changed during crop development under contrasting irrigation in two cultivars with contrasting maturity classes (Anouk and Celandine). UAV surveys were conducted in July and August, and canopy metrics were related to final yield and tuber traits. The relationship between NDVI and yield differed markedly between cultivars and growth stages. In the early-maturing cultivar Anouk, July NDVI showed a strong positive correlation with yield ( r = 0.87, p = 0.002), whereas August NDVI was not informative. In the late-maturing cultivar Celandine, the relationship reversed from a positive correlation in July ( r = 0.56) to a strong negative correlation in August ( r = −0.80, p = 0.009), indicating that greater late-season canopy greenness was associated with lower yield. Exploratory LOOCV Random Forest models achieved the highest yield prediction accuracy using July features for Anouk ( R 2 = 0.72) and August features for Celandine ( R 2 = 0.47), while multi-temporal features provided similar predictive performance. Greater temporal declines in NDVI were associated with higher yield in both cultivars, consistent with the change in canopy greenness acting as a phenological indicator of the transition from vegetative growth to tuber bulking. These results demonstrate that the interpretation of UAV-derived vegetation indices is strongly dependent on cultivar maturity and growth stage. A single UAV acquisition may therefore provide misleading assessments of crop performance when canopy phenology is not considered. Rather than serving as fixed proxies for yield, canopy vegetation indices should be interpreted within cultivar-specific and temporal contexts when used for potato monitoring and agronomic decision-making.","摘要 单期无人机植被指数被广泛用于评估马铃薯作物表现和估算产量。然而，其生物学解释在不同品种和生育期之间是否保持一致，鲜有研究加以检验。本研究探讨了在两种成熟性类别不同的品种（Anouk和Celandine）中，不同灌溉条件下，无人机获取的多光谱与热红外冠层指标与马铃薯产量之间的关系在作物发育过程中如何变化。无人机调查于7月和8月进行，并将冠层指标与最终产量及块茎性状进行关联分析。NDVI与产量之间的关系在品种和生育期之间差异显著。在早熟品种Anouk中，7月NDVI与产量呈强正相关（r = 0.87，p = 0.002），而8月NDVI则不具有指示意义。在晚熟品种Celandine中，该关系由7月的正相关（r = 0.56）转变为8月的强负相关（r = −0.80，p = 0.009），表明后期冠层绿度越高与产量越低相关。探索性留一交叉验证（LOOCV）随机森林模型在使用Anouk的7月特征（R² = 0.72）和Celandine的8月特征（R² = 0.47）时取得了最高的产量预测精度，而多时相特征提供了相似的预测表现。两个品种中NDVI随时间下降幅度越大均与产量越高相关，这与冠层绿度变化作为从营养生长向块茎膨大转变的物候指示相一致。这些结果表明，无人机获取的植被指数的解释强烈依赖于品种成熟性和生育期。因此，在未考虑冠层物候的情况下，单次无人机采集可能对作物表现作出误导性评估。冠层植被指数不应作为产量的固定替代指标，而应在品种特异性和时间背景下加以解释，方可用于马铃薯监测和农艺决策。","Potato Research","2026-10-20T00:00:00Z",79,{"impact":172,"substance":244,"depth":19,"authority":20,"freshness":112,"relevant":22,"comment":410},"该研究揭示UAV-NDVI与马铃薯产量的关系随品种熟期和生育阶段而反转，对智慧农业遥感监测具有实质方法学与决策参考价值。",[412],{"name":406,"url":403},[27,29,414,415,416],"产量预测","遥感","作物表型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":420},{"candidates":421,"agricultural_evidence":46},[422,426,433,439],{"code":33,"score":17,"supported":46,"title":423,"abstract":424,"tags":425},[48],[29,48],[29],{"code":427,"score":100,"supported":46,"title":428,"abstract":429,"tags":432},"H3",[],[430,431],"灌溉","irrigation",[],{"code":434,"score":100,"supported":46,"title":435,"abstract":436,"tags":438},"I3",[],[437],"multispectral",[414,416,415],{"code":63,"score":22,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[27],[444,445],"UAV NDVI 马铃薯 产量","马铃薯 品种 生育期 遥感","UAVNDVI马铃薯产量-5561","10.1007\u002Fs11540-026-10161-5",{"doi":447,"openalex_id":449,"authors":450,"venue":406,"cited_by_count":90,"oa_url":456,"card":457,"direction":233,"ingested_from":97},"W7223067378",[451,454],{"name":452,"orcid":453},"Jiangsan Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-3916-7388",{"name":455,"orcid":9},"Jaroslaw S. Grodek","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11540-026-10161-5.pdf",{"tldr":458,"method":459,"finding":460,"direction":461,"opportunity":462},"研究不同品种和生育期下无人机NDVI与马铃薯产量的关系差异。","两个熟期品种、多时相无人机多光谱与热红外冠层指标，结合随机森林建模。","NDVI与产量的关系随品种和时期反转，晚熟品种8月NDVI与产量强负相关。","农业遥感与作物表型","可构建融合品种熟期与冠层物候期的时相自适应产量估算模型，避免单次飞行误判。","2026-10-22T11:20:21.975818Z"]