[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5147":3,"related-5147":104},{"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":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":103},5147,"FDUNet-LRSA: efficient frequency-adaptive segmentation for automated crop disease inspection","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1963106","The transition toward industrialized and automated agriculture requires reliable machine vision for high-throughput crop health monitoring and field decision support. Automated sweetpotato disease inspection, however, remains challenging because field images exhibit substantial variations in leaf scale, appearance, illumination, occlusion, and background complexity. Conventional U-shaped networks rely primarily on fixed convolutional kernels and local feature aggregation, whereas Transformer-based alternatives often introduce considerable computational overhead. This article proposes FDUNet-LRSA, a frequency-adaptive segmentation network that integrates frequency-domain dynamic convolution, low-resolution self-attention, and boundary-aware refinement. Frequency-domain dynamic convolution reconstructs input-dependent spatial kernels to improve feature adaptation under changing imaging conditions. Low-resolution self-attention captures long-range contextual dependencies in a fixed compact feature space, while boundary-aware refinement preserves irregular leaf contours during mask reconstruction. Under the reported evaluation protocol, FDUNet-LRSA achieved a mean test foreground IoU of 0.94 and a Dice coefficient of 0.97, with 3.72 GFLOPs. Additional experiments indicate that task-oriented moderate augmentation improves generalization compared with stronger augmentation. The proposed network provides an efficient visual perception approach for automated crop inspection systems.","向工业化与自动化农业转型，需要可靠的机器视觉技术，以支持高通量作物健康监测与田间决策。然而，甘薯病害自动检测仍具挑战性，因为田间图像在叶片尺度、外观、光照、遮挡和背景复杂度方面存在显著变化。传统U形网络主要依赖固定卷积核和局部特征聚合，而基于Transformer的替代方案往往带来较大的计算开销。本文提出FDUNet-LRSA，一种频率自适应分割网络，融合了频域动态卷积、低分辨率自注意力与边界感知细化。频域动态卷积重建依赖输入的空间卷积核，以提升成像条件变化下的特征适应性。低分辨率自注意力在固定的紧凑特征空间中捕获长程上下文依赖，而边界感知细化在掩码重建过程中保留不规则叶片轮廓。在所报告的评估协议下，FDUNet-LRSA取得了0.94的平均测试前景IoU和0.97的Dice系数，计算量为3.72 GFLOPs。额外实验表明，与更强的增强相比，面向任务的中等增强可提升泛化能力。所提出的网络为自动化作物检测系统提供了一种高效的视觉感知方法。",null,"Frontiers in Plant Science","2026-10-16T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,9,1,"提出频域自适应分割网络，在甘薯病害检测上取得高精度且计算量低，方法有新意、结论可靠，属细分领域实质进展，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","作物病害","甘薯","图像分割",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},5,true,[],[50],"decision support",[28,31],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":57},"A5",4,[],[30],[30],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"I1",[],[],[27],[64,65],"FDUNet-LRSA 甘薯病害","甘薯病害 图像分割","FDUNet-LRSA甘薯病害-5147",0,"10.3389\u002Ffpls.2026.1963106",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":67,"oa_url":94,"card":95,"direction":101,"ingested_from":102},"W7222502318",[72,75,78,81,83,86,88,90,92],{"name":73,"orcid":74},"Ru Han","https:\u002F\u002Forcid.org\u002F0009-0000-5128-1314",{"name":76,"orcid":77},"Lei Shu","https:\u002F\u002Forcid.org\u002F0000-0002-6700-9347",{"name":79,"orcid":80},"Grzegorz Cielniak","https:\u002F\u002Forcid.org\u002F0000-0002-6299-8465",{"name":82,"orcid":9},"Yan Zhu",{"name":84,"orcid":85},"Jucheng Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5046-2663",{"name":87,"orcid":9},"Fan Yang",{"name":89,"orcid":9},"Weihan Song",{"name":91,"orcid":9},"Qian Zhang",{"name":93,"orcid":9},"Kai Huang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1963106\u002Fpdf",{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"提出频域自适应分割网络FDUNet-LRSA，用于甘薯病害自动巡检。","频域动态卷积、低分辨率自注意力与边界感知细化，结合适度数据增强。","测试前景IoU达0.94、Dice达0.97，仅3.72 GFLOPs，轻量高效。","农业遥感与作物表型","可探索频域动态卷积在多种作物病害与复杂田间光照下的跨域泛化与边缘部署。","智慧农业 \u002F 农业物联网","openalex","2026-10-17T23:30:08.487857Z",{"total":105,"page":22,"page_size":105,"items":106},6,[107,168,258,312,365,406],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":9,"source_name":113,"source_url":110,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":121,"tags":123,"primary_topic":126,"secondary_topics":127,"classification":128,"search_phrases":147,"slug":150,"view_count":67,"doi":151,"paper":152,"created_at":167},5584,"Distributionally-Robust Evaluation of Computer Vision Agriculture Disease Classifiers Under Climate-Driven Pathogen Range Shifts: An Artificial Intelligence Worst-Case Generalization Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544150","Computer vision classifiers for crop disease are evaluated almost exclusively on held-out samples drawn from the same distribution as their training data, yet climate-driven pathogen range shifts mean that deployment environments are systematically out of distribution. The framework treats the deployment distribution as an adversarial perturbation of the training distribution within a Wasserstein ball whose radius is calibrated to observed climate displacement. Using NASA POWER monthly reanalysis for Harare, Zimbabwe (2000-2023, 312 monthly observations), the analysis estimates a mean air temperature of 18.914 C, a standard deviation of 3.107 C, and a warming trend of 0.2001 C per decade. These estimates anchor the radius of the ambiguity set and the resulting bound. The bound decomposes worst-case risk into an empirical risk term, a Wasserstein radius term scaled by the Lipschitz constant of the classifier, and a concentration term. Under plausible Lipschitz constants for convolutional backbones, the radius term dominates the empirical risk term once the climate displacement exceeds a threshold that the Harare record already approaches. The contribution is a formal evaluation criterion that replaces the i. i. d. assumption with a climate-calibrated ambiguity set, together with the analytical machinery needed to compute it.","作物病害的计算机视觉分类器几乎完全在与训练数据同分布的留出样本上进行评估，然而气候驱动的病原体分布范围变化意味着部署环境系统性地处于分布之外。该框架将部署分布视为训练分布在Wasserstein球内的对抗性扰动，球的半径根据观测到的气候位移进行校准。利用NASA POWER对津巴布韦哈拉雷的月度再分析数据（2000—2023年，312个月度观测值），分析估计平均气温为18.914°C，标准差为3.107°C，每十年升温趋势为0.2001°C。这些估计值锚定了模糊集的半径及由此得到的界。该界将最坏情况风险分解为经验风险项、由分类器Lipschitz常数缩放后的Wasserstein半径项以及集中项。在卷积骨干网络合理的Lipschitz常数下，一旦气候位移超过哈拉雷记录已接近的阈值，半径项便主导经验风险项。本文的贡献在于提出了一种形式化评估准则，以气候校准的模糊集替代独立同分布（i.i.d.）假设，并提供了计算该准则所需的分析工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",75,{"impact":117,"substance":118,"depth":119,"authority":20,"freshness":13,"relevant":22,"comment":120},12,22,18,"方法新颖、数据具体，但属方法论预印论文，产业影响有限，适合作为专业精选而非头条。",[122],{"name":113,"url":110},[27,28,124,29,125],"计算机视觉","气候变化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":129},{"candidates":130,"agricultural_evidence":47},[131,138,143],{"code":33,"score":132,"supported":47,"title":133,"abstract":136,"tags":137},24,[134,135],"artificial intelligence","computer vision",[124,135],[28,124],{"code":139,"score":22,"supported":14,"title":140,"abstract":141,"tags":142},"H7",[],[],[125],{"code":59,"score":22,"supported":14,"title":144,"abstract":145,"tags":146},[],[],[27],[148,149],"NASA POWER 哈拉雷 气温","Wasserstein 分布鲁棒 病害分类","NASAPOWER哈拉雷气温-5584","10.5281\u002Fzenodo.23544150",{"doi":151,"openalex_id":153,"authors":154,"venue":113,"cited_by_count":67,"oa_url":110,"card":161,"direction":165,"ingested_from":102},"W7223087892",[155,157,159],{"name":156,"orcid":9},"Kossi Afeli",{"name":158,"orcid":9},"Akouvi Amegashie",{"name":160,"orcid":9},"Komlan Sélom Dossou",{"tldr":162,"method":163,"finding":164,"direction":165,"opportunity":166},"提出气候校准的分布鲁棒评估准则，替代i.i.d.假设评价作物病害视觉分类器。","用Wasserstein球建模部署分布偏移，以NASA POWER哈拉雷气温数据","气候位移超阈值后，半径项主导经验风险，现有评估会高估模型泛化能力。","农业人工智能与决策模型","可将气候校准的鲁棒评估扩展到多作物多区域，并研究适应分布偏移的鲁棒训练方法。","2026-10-22T11:21:00.036937Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":179,"tags":181,"primary_topic":184,"secondary_topics":185,"classification":186,"search_phrases":217,"slug":220,"view_count":54,"doi":221,"paper":222,"created_at":257},4010,"Intelligent Automated Crop Monitoring System Based on Unmanned Aerial Vehicles and Deep Learning for Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16199747","The rapid development of precision agriculture technologies requires intelligent systems capable of automatically analyzing unmanned aerial vehicle (UAV) imagery and transforming image-processing results into structured information for crop-monitoring decision support. This study develops and computationally validates an integrated framework combining deep learning-based semantic segmentation, RGB-based spatial interpretation, image-space vectorization of candidate RGB heterogeneity zones, and monitoring-priority assessment. Experimental validation was performed using the open-access dataset “High-Resolution RGB Images and Corresponding Masks of Agricultural Fields”, comprising mixed agricultural vegetation scenes. Following data-integrity verification, 326 valid RGB image–reference mask pairs were retained and divided into 21,324 non-overlapping 512 × 512-pixel patches. U-Net-ResNet50, DeepLabV3-ResNet50, and FCN-ResNet50 were evaluated in three repeated source-image-grouped holdout experiments using the full dataset. U-Net-ResNet50 achieved the highest held-out performance, with mean Precision = 0.9946 ± 0.0008, Recall = 0.9931 ± 0.0021, vegetation IoU = 0.9877 ± 0.0020, and Dice = 0.9938 ± 0.0010, and was therefore selected for subsequent spatial analysis. Its segmentation outputs were combined with VARI, ExG, and GLI indices to identify candidate RGB heterogeneity zones, which were converted into image-space vector objects in source-image pixel coordinates. The revised full-data analysis generated 58,206 candidate RGB heterogeneity zones. These regions represent visible canopy heterogeneity rather than physiologically confirmed crop stress and are intended to support targeted field verification and monitoring prioritization. The results demonstrate the feasibility of an end-to-end UAV RGB processing workflow that transforms pixel-level segmentation outputs into structured spatial information for precision-agriculture monitoring and decision support.","精准农业技术的快速发展需要智能系统能够自动分析无人机（UAV）影像，并将图像处理结果转化为结构化信息，以支持作物监测决策。本研究开发并通过计算验证了一个集成框架，该框架结合了基于深度学习的语义分割、基于RGB的空间解译、候选RGB异质性区域的图像空间矢量化以及监测优先级评估。实验验证使用开放获取数据集“High-Resolution RGB Images and Corresponding Masks of Agricultural Fields”进行，该数据集包含混合农业植被场景。经数据完整性验证后，保留了326对有效RGB图像-参考掩膜对，并划分为21,324个不重叠的512 × 512像素图像块。使用完整数据集，在三次重复的以源图像分组的留出实验中评估了U-Net-ResNet50、DeepLabV3-ResNet50和FCN-ResNet50。U-Net-ResNet50取得了最高的留出性能，平均精确率 = 0.9946 ± 0.0008，召回率 = 0.9931 ± 0.0021，植被IoU = 0.9877 ± 0.0020，Dice = 0.9938 ± 0.0010，因此被选用于后续空间分析。其分割输出与VARI、ExG和GLI指数相结合，用于识别候选RGB异质性区域，并将其转换为源图像像素坐标下的图像空间矢量对象。修订后的全数据分析生成了58,206个候选RGB异质性区域。这些区域代表可见的冠层异质性，而非经生理学确认的作物胁迫，旨在支持有针对性的田间验证和监测优先级排序。结果表明，端到端无人机RGB处理工作流程具有可行性，该流程可将像素级分割输出转化为结构化空间信息，用于精准农业监测与决策支持。","Applied Sciences","2026-10-01T00:00:00Z",80,{"impact":119,"substance":118,"depth":119,"authority":20,"freshness":21,"relevant":22,"comment":178},"基于无人机RGB影像与深度学习语义分割的端到端作物监测框架，数据规模大、指标可靠，对精准农业决策支持有实质参考价值。",[180],{"name":174,"url":171},[27,28,182,183,31],"无人机遥感","作物监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":187},{"candidates":188,"agricultural_evidence":47},[189,196,204,211],{"code":33,"score":190,"supported":47,"title":191,"abstract":193,"tags":195},19,[192],"deep learning",[194,192,50],"深度学习",[28,31],{"code":59,"score":197,"supported":47,"title":198,"abstract":200,"tags":203},15,[199],"smart agriculture",[201,202],"精准农业","precision agriculture",[27],{"code":205,"score":105,"supported":47,"title":206,"abstract":207,"tags":210},"I4",[],[208,209],"空间分析","spatial analysis",[],{"code":212,"score":213,"supported":14,"title":214,"abstract":215,"tags":216},"I3",2,[],[],[183,182],[218,219],"UAV 作物监测 深度学习","U-Net 植被分割 无人机","UAV作物监测深度学习-4010","10.3390\u002Fapp16199747",{"doi":221,"openalex_id":223,"authors":224,"venue":174,"cited_by_count":67,"oa_url":171,"card":252,"direction":101,"ingested_from":102},"W7215074401",[225,228,231,234,236,238,241,243,246,249],{"name":226,"orcid":227},"Аnatoliy M. Тryhuba","https:\u002F\u002Forcid.org\u002F0000-0001-8014-5661",{"name":229,"orcid":230},"Paweł Kiełbasa","https:\u002F\u002Forcid.org\u002F0000-0003-0249-8626",{"name":232,"orcid":233},"Nazarii Ya. Koval","https:\u002F\u002Forcid.org\u002F0000-0001-7846-2924",{"name":235,"orcid":9},"Inna Tryhuba",{"name":237,"orcid":9},"Oleh Andrushkiv",{"name":239,"orcid":240},"Vitalij Grabovets","https:\u002F\u002Forcid.org\u002F0000-0002-0340-185X",{"name":242,"orcid":9},"Nataliia Kozak",{"name":244,"orcid":245},"Akinniyi Akinsunmade","https:\u002F\u002Forcid.org\u002F0000-0002-1602-1741",{"name":247,"orcid":248},"Anna Miernik","https:\u002F\u002Forcid.org\u002F0000-0003-0394-9185",{"name":250,"orcid":251},"Paweł Pysz","https:\u002F\u002Forcid.org\u002F0000-0001-7083-1078",{"tldr":253,"method":254,"finding":255,"direction":99,"opportunity":256},"构建无人机RGB影像深度学习分割与空间矢量化框架，实现作物监测优先级评估。","U-Net-ResNet50等模型在326张影像21,324个512×512块上","U-Net-ResNet50分割最优（IoU 0.9877），生成58,206个候选异质区用于田间验","可延伸研究：将RGB异质区与多光谱\u002F热红外数据融合，验证生理胁迫并构建实时决策模型。","2026-10-02T23:30:11.747839Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":271,"tags":273,"primary_topic":276,"secondary_topics":277,"classification":278,"search_phrases":293,"slug":296,"view_count":213,"doi":297,"paper":298,"created_at":311},3195,"Segmentation of Cactus Diseases Using Machine and Deep Learning)","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232018.2026.14.50","Artificial intelligence and machine learning play a critical role in plant disease detection and management. This study proposes an approach for diagnosing and segmenting cactus diseases using Random Forest (RF) and compares its performance with SVM, ANN, KNN, CNN, and U-Net models. A dataset of 20 cactus images (10 cochineal and 10 black bacterial soft rot), resized to 720×720 pixels, was used. Experimental results show that U-Net achieved the highest accuracy, reaching 98% for cochineal and 94% for black bacterial soft rot. RF also demonstrated strong performance, achieving 92% and 91%, respectively, outperforming several traditional machine learning models. These results confirm the effectiveness of U-Net for accurate segmentation and the suitability of RF for reliable disease detection with limited datasets, supporting their application in precision agriculture.","人工智能与机器学习在植物病害检测与管理中发挥着关键作用。本研究提出了一种利用随机森林（Random Forest, RF）诊断和分割仙人掌病害的方法，并将其性能与SVM、ANN、KNN、CNN和U-Net模型进行了比较。研究使用了20张仙人掌图像数据集（10张胭脂虫病害和10张黑细菌性软腐病），图像尺寸调整为720×720像素。实验结果表明，U-Net取得了最高准确率，胭脂虫病害达到98%，黑细菌性软腐病达到94%。随机森林同样表现出强劲性能，分别达到92%和91%，优于多种传统机器学习模型。这些结果证实了U-Net在精确分割方面的有效性，以及随机森林在有限数据集下进行可靠病害检测的适用性，支持其在精准农业中的应用。","WSEAS TRANSACTIONS ON COMPUTER RESEARCH","2026-09-21T00:00:00Z",62,{"impact":268,"substance":119,"depth":269,"authority":117,"freshness":13,"relevant":22,"comment":270},8,14,"方法对比扎实但数据集仅20张图像，属细分作物病害识别的技术验证，产业影响有限。",[272],{"name":264,"url":261},[27,28,274,31,275],"植物病害识别","仙人掌",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":279},{"candidates":280,"agricultural_evidence":47},[281,288],{"code":33,"score":282,"supported":47,"title":283,"abstract":284,"tags":287},20,[192],[285,286,134],"机器学习","machine learning",[28,31,274],{"code":59,"score":289,"supported":47,"title":290,"abstract":291,"tags":292},7,[],[201,202],[27],[294,295],"仙人掌 病害 图像分割","U-Net 胭脂虫 黑腐病","仙人掌病害图像分割-3195","10.37394\u002F232018.2026.14.50",{"doi":297,"openalex_id":299,"authors":300,"venue":264,"cited_by_count":67,"oa_url":261,"card":306,"direction":165,"ingested_from":102},"W7213945610",[301,303],{"name":302,"orcid":9},"Aissam El Ibrahimi",{"name":304,"orcid":305},"Nabil El Akchioui","https:\u002F\u002Forcid.org\u002F0000-0001-9808-932X",{"tldr":307,"method":308,"finding":309,"direction":165,"opportunity":310},"该研究用随机森林和多种深度学习模型对仙人掌病害进行分割诊断，并比较性能。","使用20张仙人掌图像，对比RF、SVM、ANN、KNN、CNN和U-Net模型。","U-Net分割准确率最高（胭脂虫98%，黑细菌软腐病94%），RF在有限数据下表现良好。","可探索小样本条件下传统机器学习与深度学习融合的病害分割方法，并扩展至更多作物和病害类型。","2026-09-22T23:30:39.786818Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":9,"content":9,"source_name":317,"source_url":9,"published_at":318,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":327,"secondary_topics":328,"classification":329,"search_phrases":352,"slug":355,"view_count":213,"doi":9,"paper":356,"created_at":364},3119,"CropRowSeg: A Lightweight Model for Seedling Crop Row Image Segmentation Combining Multi-scale CNN and Transformer（融合多尺度 CNN 与 Transformer 的苗期作物行图像轻量化分割模型）","https:\u002F\u002Fnews.cau.edu.cn\u002Fkxyj\u002F9f1540e749e745f29f11355eac1c9444.htm","中国农业大学农机装备智能化设计与制造高水平创新团队翟志强副教授在国际学术期刊《Agriculture Communications》发表研究论文。中国农业大学为唯一署名单位，硕士研究生田永浩为论文第一作者、翟志强为通讯作者。研究面向农机对行作业和作物表型观测对作物行视觉感知的实际需求，提出从图像高效标注、模型架构设计到模型训练的系统解决方案：从数据标注、模型架构、损失函数三个维度构建完整的作物行图像分割技术体系；提出基于中心线引导的条带标注方法仅需人工标注冠层中心线两个端点即可自动生成分割掩码；CropRowSeg 模型采用编码器—解码器架构（编码器 CNN+ViT 双分支结构+通道-空间混合注意力机制，解码器残差连接与坐标注意力结合的渐进式解码架构）。研究可为大田作物感知提供视觉基础模型，为研发农业人工智能专用模型提供借鉴思路。","中国农业大学","2026-09-18T12:39:00Z",78,{"impact":119,"substance":18,"depth":19,"authority":269,"freshness":268,"relevant":22,"comment":321},"中国农业大学团队提出轻量化作物行分割模型CropRowSeg，方法体系完整、创新点明确，对农机对行作业与作物表型感知有实用价值，值得进入每日精选。",[323],{"name":317,"url":315},[27,28,325,326,31],"农机导航","作物行识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":330},{"candidates":331,"agricultural_evidence":47},[332,336,342,348],{"code":33,"score":46,"supported":47,"title":333,"abstract":334,"tags":335},[],[28],[28,31],{"code":337,"score":54,"supported":47,"title":338,"abstract":339,"tags":341},"F5",[],[340],"农机",[325],{"code":212,"score":343,"supported":47,"title":344,"abstract":345,"tags":347},3,[],[346],"作物表型",[],{"code":59,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[27],[353,354],"中国农业大学 翟志强 作物行分割","CropRowSeg 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Learning-Based Crop Disease Detection Using EfficientNet-B3 for Smart Agriculture。Research Square","Research Square","2026-09-04T00:00:00Z",56,{"impact":117,"substance":197,"depth":269,"authority":268,"freshness":289,"relevant":22,"comment":374},"基于EfficientNet-B3的深度学习作物病害检测研究，方法有一定新颖性，但缺乏具体数据规模与结论细节，权威性一般。",[376],{"name":370,"url":368},[27,28,29],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":381},{"candidates":382,"agricultural_evidence":47},[383,387],{"code":59,"score":117,"supported":47,"title":384,"abstract":385,"tags":386},[199],[199],[27],{"code":33,"score":117,"supported":47,"title":388,"abstract":389,"tags":390},[192],[192],[28],[392,393],"农业人工智能 作物病害 智慧农业","农业人工智能 作物病害","农业人工智能作物病害智慧农业-1727","10.21203\u002Frs.3.rs-10104203\u002Fv1",{"doi":395,"openalex_id":397,"authors":398,"venue":370,"cited_by_count":67,"oa_url":368,"card":9,"direction":101,"ingested_from":102},"W7208777285",[399,401,403],{"name":400,"orcid":9},"Gajanan Ankatwar",{"name":402,"orcid":9},"Chitra Dhawale",{"name":404,"orcid":9},"Supriya Narad","2026-09-05T23:30:09.738110Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":9,"content":9,"source_name":370,"source_url":409,"published_at":411,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":417,"primary_topic":419,"secondary_topics":424,"classification":426,"search_phrases":442,"slug":444,"view_count":54,"doi":445,"paper":446,"created_at":459},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","2026-09-01T00:00:00Z",53,{"impact":268,"substance":197,"depth":197,"authority":268,"freshness":289,"relevant":22,"comment":414},"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。",[416],{"name":370,"url":409},[27,28,418,29],"马铃薯",{"code":53,"slug":420,"name":421,"path":422,"parent_code":423},"tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[425],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":427},{"candidates":428,"agricultural_evidence":47},[429,434,438],{"code":53,"score":117,"supported":47,"title":430,"abstract":432,"tags":433},[431],"potato",[431],[418],{"code":59,"score":117,"supported":47,"title":435,"abstract":436,"tags":437},[199],[199],[27],{"code":33,"score":117,"supported":47,"title":439,"abstract":440,"tags":441},[192],[192],[28],[443,393],"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智慧农业马铃薯-1404","10.21203\u002Frs.3.rs-10879535\u002Fv1",{"doi":445,"openalex_id":447,"authors":448,"venue":370,"cited_by_count":67,"oa_url":458,"card":9,"direction":101,"ingested_from":102},"W7204884709",[449,452,455],{"name":450,"orcid":451},"Kazi Abdul Mannan","https:\u002F\u002Forcid.org\u002F0000-0002-7123-132X",{"name":453,"orcid":454},"Sinha Nafiz Hasan","https:\u002F\u002Forcid.org\u002F0009-0007-8906-4586",{"name":456,"orcid":457},"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"]