[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4675":3,"related-4675":97},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":21,"doi":71,"paper":72,"created_at":96},4675,"Deep learning–based computer vision for rapid and accurate quantitative field assessment of powdery mildew severity: model comparison, validation, and agreement with human raters","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40858-026-00845-1","Abstract Quantifying foliar disease severity accurately and reproducibly remains a fundamental challenge in plant pathology, as traditional visual assessment is inherently subjective and prone to rater variability. Although computer vision has enabled automated disease detection, validated end-to-end frameworks that integrate reference generation, model training, and field-deployable percent severity estimation are lacking. Here, we developed and validated a computer vision pipeline for quantifying powdery mildew ( Oidium spp.) severity in pea ( Pisum sativum ) using smartphone-acquired images under field conditions, with tomato ( Solanum lycopersicum ) powdery mildew serving as a cross-species validation system. A semi-automated multichannel segmentation approach generated continuous percent severity estimates as reproducible training references. Among evaluated deep learning architectures, a YOLO-based model was selected for its superior balance of predictive accuracy, computational efficiency, and deployment flexibility and was integrated into PowderyComputerVision, a purpose-built web application enabling real-time inference. Under field conditions, the automated system showed substantially higher agreement with reference severity values than trained human raters (concordance correlation coefficient (CCC), 0.952 vs. 0.513). A linear mixed-effects model revealed that human evaluators produced absolute estimation errors 280.96 times greater than the automated system ( p \u003C 0.0001; 95% CI 120.34–655.91). Performance remained robust across variable lighting conditions, imaging angles, and plant structures, supporting applicability beyond controlled settings. The proposed four-stage pipeline comprising image capture, host and disease classification via YOLOv11m-cls, leaf localization via YOLOv11n-seg, and lesion quantification via multichannel segmentation offers a scalable, objective, and reproducible framework for continuous field-based disease severity estimation.","摘要 准确且可重复地量化叶部病害严重度仍是植物病理学的一项基本挑战，因为传统的视觉评估本质上具有主观性，且容易受到评估者间变异的影响。尽管计算机视觉已使病害自动检测成为可能，但仍缺乏经过验证的端到端框架，将参考标准生成、模型训练和可田间部署的百分比严重度估算整合在一起。在此，我们开发并验证了一套计算机视觉流程，用于在田间条件下利用智能手机采集的图像量化豌豆(Pisum sativum)白粉病(Oidium spp.)严重度，并以番茄(Solanum lycopersicum)白粉病作为跨物种验证系统。一种半自动多通道分割方法生成了连续的百分比严重度估算值，作为可重复的训练参考标准。在评估的深度学习架构中，基于YOLO的模型因其在预测准确性、计算效率和部署灵活性之间的优越平衡而被选中，并被整合到PowderyComputerVision这一专门构建的Web应用程序中，以实现实时推理。在田间条件下，该自动化系统与参考严重度值的一致性显著高于受过训练的人类评估者(一致性相关系数(CCC)，0.952 vs. 0.513)。线性混合效应模型显示，人类评估者产生的绝对估算误差是自动化系统的280.96倍(p \u003C 0.0001；95% CI 120.34–655.91)。其性能在多变的光照条件、成像角度和植物结构下均保持稳健，支持其在受控环境之外的适用性。所提出的四阶段流程——包括图像采集、通过YOLOv11m-cls进行寄主与病害分类、通过YOLOv11n-seg进行叶片定位，以及通过多通道分割进行病斑量化——为基于田间的连续病害严重度估算提供了一个可扩展、客观且可重复的框架。",null,"Tropical Plant Pathology","2026-10-09T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"该研究提出并验证了基于深度学习的白粉病严重度田间量化流水线，与人工评估对比数据扎实，对智慧植保具有较高参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","计算机视觉","病害识别","豌豆白粉病",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,54,62],{"code":32,"score":45,"supported":46,"title":47,"abstract":50,"tags":53},28,true,[48,49],"deep learning","computer vision",[51,28,48,49,52],"深度学习","YOLO",[27,29,28],{"code":55,"score":56,"supported":46,"title":57,"abstract":58,"tags":61},"B1",6,[],[59,60],"番茄","tomato",[],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[26],[68,69],"豌豆白粉病 计算机视觉","YOLO 病害严重度评估","豌豆白粉病计算机视觉-4675","10.1007\u002Fs40858-026-00845-1",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":87,"oa_url":88,"card":89,"direction":93,"ingested_from":95},"W7221108017",[75,77,80,82,84],{"name":76,"orcid":9},"Jesus Enrique Flores-Riera",{"name":78,"orcid":79},"William Fernando Cárdenas-Urrego","https:\u002F\u002Forcid.org\u002F0009-0006-4753-719X",{"name":81,"orcid":9},"Víctor Hugo Aguilar",{"name":83,"orcid":9},"Emerson Medeiros Del Ponte",{"name":85,"orcid":86},"Joaquín Guillermo Ramírez‐Gil","https:\u002F\u002Forcid.org\u002F0000-0002-0162-3598",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs40858-026-00845-1.pdf",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"开发并验证基于深度学习的计算机视觉流程，用手机图像快速定量评估豌豆白粉病严重度。","YOLO模型与多通道分割，手机田间图像，豌豆和番茄白粉病数据。","自动系统与参考值一致性CCC达0.952，远高于人工评估的0.513，误差显著更低。","农业遥感与作物表型","可扩展至多作物多病害的田间连续严重度估计，并探索轻量化模型与实时决策集成。","openalex","2026-10-11T23:30:21.770403Z",{"total":56,"page":21,"page_size":56,"items":98},[99,180,234,286,335,388],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":107,"is_selected":14,"score":108,"score_detail":109,"sources":115,"tags":123,"primary_topic":126,"secondary_topics":132,"classification":134,"search_phrases":162,"slug":165,"view_count":166,"doi":167,"paper":168,"created_at":179},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":110,"substance":111,"depth":112,"authority":56,"freshness":113,"relevant":21,"comment":114},12,16,14,8,"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。",[116,117,119,121],{"name":105,"url":102},{"name":105,"url":118},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054048",{"name":105,"url":120},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053752",{"name":105,"url":122},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053753",[26,27,28,124,125,29],"产量预测","高粱",{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[133],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":135},{"candidates":136,"agricultural_evidence":46},[137,144,150,157],{"code":32,"score":45,"supported":46,"title":138,"abstract":140,"tags":143},[139,49],"machine learning",[141,28,139,142,49],"机器学习","artificial intelligence",[27,29,28],{"code":127,"score":145,"supported":46,"title":146,"abstract":148,"tags":149},15,[147],"sorghum",[125,147],[125],{"code":63,"score":151,"supported":46,"title":152,"abstract":153,"tags":156},7,[],[154,155],"精准农业","precision agriculture",[26],{"code":158,"score":21,"supported":14,"title":159,"abstract":160,"tags":161},"I3",[],[],[124],[163,164],"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",3,"10.5281\u002Fzenodo.23054047",{"doi":167,"openalex_id":169,"authors":170,"venue":105,"cited_by_count":87,"oa_url":102,"card":173,"direction":177,"ingested_from":95},"W7214900332",[171],{"name":172,"orcid":9},"Anantjit Publication",{"tldr":174,"method":175,"finding":176,"direction":177,"opportunity":178},"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。","农业人工智能与决策模型","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":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":186,"source_url":183,"published_at":187,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":192,"tags":194,"primary_topic":196,"secondary_topics":197,"classification":198,"search_phrases":214,"slug":217,"view_count":87,"doi":218,"paper":219,"created_at":233},5587,"Calibration-Shift Inference for Computer Vision Agriculture Yield Models Under Retrofit Sensor Replacement: An Artificial Intelligence Post-Deployment Recalibration Guarantee","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544195","Computer vision yield models deployed in agricultural fields are routinely paired with retrofit sensor packages that replace or supplement the original imaging hardware, and the resulting calibration shift can silently invalidate the model's predictive guarantees. The analysis characterises the operating characteristics of the design under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, using 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design attains 80% power at a 5 percentage point effect with 54 clusters. The paper states a formal guarantee: under stated assumptions on the stability of the calibration map, the before-after-control-impact estimator yields a consistent and asymptotically normal estimate of the calibration shift, and the associated confidence interval covers the true shift at the nominal rate. The contribution is a design-theoretic account of how retrofit-induced calibration shift can be detected and bounded without observational data, together with the sample-size requirements that follow.","部署在农田中的计算机视觉产量模型通常与改装传感器组件配合使用，这些组件用于替换或补充原始成像硬件，由此产生的校准偏移可能悄然使模型的预测保证失效。该分析在声明的方差结构下刻画了设计的工作特性，其中组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64，每个设计单元采用2000次重复，聚类数分别为8、16、24和32，效应量为3、5和8个百分点。该设计在54个聚类、5个百分点效应下达到80%的检验力。论文给出了一项形式化保证：在关于校准映射稳定性的既定假设下，前后对照影响（before-after-control-impact）估计量给出校准偏移的一致且渐近正态的估计，且相关置信区间以名义覆盖率覆盖真实偏移。其贡献在于从设计理论角度说明了如何在没有观测数据的情况下检测并界定改装引起的校准偏移，以及随之而来的样本量要求。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",65,{"impact":113,"substance":17,"depth":190,"authority":110,"freshness":13,"relevant":21,"comment":191},17,"方法学扎实、统计设计严谨的预印本论文，但偏理论设计层面，缺乏田间实证数据，产业影响有限，适合作为技术参考而非每日精选头条。",[193],{"name":186,"url":183},[26,27,28,124,195],"传感器校准",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":199},{"candidates":200,"agricultural_evidence":46},[201,206,210],{"code":32,"score":202,"supported":46,"title":203,"abstract":204,"tags":205},24,[142,49],[28,49],[27,28],{"code":63,"score":21,"supported":14,"title":207,"abstract":208,"tags":209},[],[],[26],{"code":158,"score":21,"supported":14,"title":211,"abstract":212,"tags":213},[],[],[124],[215,216],"计算机视觉 产量预测 传感器校准","农业人工智能 传感器校准 计算机视觉 产量预测","计算机视觉产量预测传感器校准-5587","10.5281\u002Fzenodo.23544195",{"doi":218,"openalex_id":220,"authors":221,"venue":186,"cited_by_count":87,"oa_url":183,"card":228,"direction":177,"ingested_from":95},"W7223104380",[222,224,226],{"name":223,"orcid":9},"Tigist Alemu Gebremariam",{"name":225,"orcid":9},"Hawi Tesfaye Desta",{"name":227,"orcid":9},"Mulugeta Bekele Wolde",{"tldr":229,"method":230,"finding":231,"direction":177,"opportunity":232},"提出改造传感器替换后视觉产量模型校准漂移的检测与置信区间保证方法。","用前后对照影响估计量，基于方差结构做2000次重复的模拟设计分析。","5个百分点效应下54个集群可达80%检验力，估计量一致且渐近正态。","可将该设计理论扩展到多传感器异构替换与在线校准监测，填补真实田间验证空白。","2026-10-22T11:21:01.554995Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":9,"source_name":186,"source_url":237,"published_at":187,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":243,"tags":245,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":266,"slug":269,"view_count":87,"doi":270,"paper":271,"created_at":285},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。",75,{"impact":110,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":242},"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。",[244],{"name":186,"url":237},[26,27,28,246,247],"作物病害","气候变化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":251},{"candidates":252,"agricultural_evidence":46},[253,257,262],{"code":32,"score":202,"supported":46,"title":254,"abstract":255,"tags":256},[142,49],[28,49],[27,28],{"code":258,"score":21,"supported":14,"title":259,"abstract":260,"tags":261},"H7",[],[],[247],{"code":63,"score":21,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[26],[267,268],"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":270,"openalex_id":272,"authors":273,"venue":186,"cited_by_count":87,"oa_url":237,"card":280,"direction":177,"ingested_from":95},"W7223087892",[274,276,278],{"name":275,"orcid":9},"Kossi Afeli",{"name":277,"orcid":9},"Akouvi Amegashie",{"name":279,"orcid":9},"Komlan Sélom Dossou",{"tldr":281,"method":282,"finding":283,"direction":177,"opportunity":284},"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。","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":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":294,"sources":296,"tags":298,"primary_topic":300,"secondary_topics":301,"classification":302,"search_phrases":320,"slug":323,"view_count":87,"doi":324,"paper":325,"created_at":334},5385,"A Comprehensive Review of Deep Learning and Computer Vision Techniques for Fruit Quality Assessment and Classification","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.75s.151","Fruit image analysis has become an essential research area in precision agriculture, enabling automated fruit classification, quality grading, maturity estimation, disease diagnosis, yield prediction, and intelligent harvesting through advances in computer vision and artificial intelligence (AI).Recent progress in imaging technologies, large-scale benchmark datasets, and deep learning has substantially improved the accuracy, robustness, and scalability of automated fruit analysis systems.This survey presents a comprehensive and systematic review of image processing and AI techniques for fruit image analysis.It covers the complete image analysis pipeline, including image acquisition, preprocessing, enhancement, color space transformation, segmentation, feature extraction, and handcrafted texture descriptors.Furthermore, conventional machine learning algorithms are reviewed alongside state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Vision Mamba models, for applications such as fruit recognition, quality assessment, disease detection, ripeness estimation, and object detection.Publicly available benchmark datasets, evaluation metrics, and performance comparison strategies are also summarized to facilitate objective assessment of existing approaches.In addition, the survey discusses key research challenges, including illumination variations, occlusion, complex orchard environments, limited annotated datasets, computational complexity, and domain adaptation, while highlighting emerging research trends such as self-supervised learning, multimodal learning, explainable AI, lightweight deep networks, foundation models, and edge intelligence for real-time deployment.By providing a unified review of methodologies, datasets, evaluation protocols, challenges, and future research directions, this survey offers a comprehensive reference for researchers and practitioners developing intelligent and scalable fruit image analysis systems for precision agriculture.","水果图像分析已成为精准农业中的一个重要研究领域，借助计算机视觉和人工智能（AI）的进步，实现了果实自动分类、品质分级、成熟度估计、病害诊断、产量预测和智能采收。成像技术、大规模基准数据集和深度学习的最新进展显著提升了自动化水果分析系统的准确性、鲁棒性和可扩展性。本综述对用于水果图像分析的图像处理和AI技术进行了全面而系统的回顾。内容涵盖完整的图像分析流程，包括图像采集、预处理、增强、颜色空间变换、分割、特征提取和手工纹理描述符。此外，综述了传统机器学习算法以及最先进的深度学习架构，包括卷积神经网络（CNN）、视觉Transformer（ViT）和视觉Mamba模型，用于果实识别、品质评估、病害检测、成熟度估计和目标检测等应用。同时总结了公开可用的基准数据集、评价指标和性能比较策略，以便对现有方法进行客观评估。此外，本综述讨论了关键研究挑战，包括光照变化、遮挡、复杂果园环境、标注数据集有限、计算复杂度和域适应，并重点介绍了新兴研究趋势，如自监督学习、多模态学习、可解释AI、轻量级深度网络、基础模型和面向实时部署的边缘智能。通过提供方法、数据集、评价协议、挑战和未来研究方向的统一综述，本综述为开发面向精准农业的智能且可扩展的水果图像分析系统的研究人员和实践者提供了全面的参考。","International Journal of Drug Delivery Technology","2026-10-18T00:00:00Z",{"impact":113,"substance":17,"depth":111,"authority":56,"freshness":113,"relevant":21,"comment":295},"系统性综述覆盖CNN、ViT、Vision Mamba等前沿方法，信息量足，但期刊权威性一般且偏学术综述，公共价值有限，适合主题聚合而非每日精选头条。",[297],{"name":292,"url":289},[26,27,28,51,299],"水果品质检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":303},{"candidates":304,"agricultural_evidence":46},[305,310,314],{"code":32,"score":45,"supported":46,"title":306,"abstract":307,"tags":309},[48,49],[308,51,141,28,139,48,142,49],"目标检测",[27,51,28],{"code":63,"score":151,"supported":46,"title":311,"abstract":312,"tags":313},[],[154,155],[26],{"code":315,"score":166,"supported":46,"title":316,"abstract":317,"tags":319},"B2",[],[318],"orchard",[],[321,322],"水果品质 计算机视觉 深度学习","农业人工智能 水果品质检测 计算机视觉 智慧农业","水果品质计算机视觉深度学习-5385","10.25258\u002Fijddt.16.75s.151",{"doi":324,"openalex_id":326,"authors":327,"venue":292,"cited_by_count":87,"oa_url":289,"card":328,"direction":333,"ingested_from":95},"W7222892831",[],{"tldr":329,"method":330,"finding":331,"direction":93,"opportunity":332},"系统综述深度学习与计算机视觉在水果品质评估与分级中的全流程方法。","综述图像采集到分割、特征提取及CNN、ViT、Vision Mamba等模型与公","深度学习显著提升水果分析精度与鲁棒性，但光照、遮挡和小样本仍是主要瓶颈。","可探索自监督与轻量化模型在复杂果园遮挡、小样本场景下的边缘实时部署。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:10.066042Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":9,"content":9,"source_name":340,"source_url":9,"published_at":341,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":342,"score_detail":343,"sources":346,"tags":348,"primary_topic":351,"secondary_topics":357,"classification":359,"search_phrases":375,"slug":378,"view_count":87,"doi":9,"paper":379,"created_at":387},5363,"牛肉牛区域特征计算机视觉分析:基于 YOLO 与深度估计的牛体况自动评分","https:\u002F\u002Fbioengineer.org\u002Fcameras-learn-to-read-a-cows-spine-tail-head-and-body-shape-to-score-its-condition","Islam M.N., Schrick F.N., Edwards J.L., Burns R.T., Qi H., Gan H. (2026) 开发一种基于区域特定计算机视觉特征的牛肉牛体况自动评分(BCS)方法。研究通过 YOLO 检测 + 深度估计提取脊柱、尾根、体廓等区域特征,使用 SHAP 进行可解释 AI 分析,并以 XGBoost 融合。在 5 折交叉验证中两种模式均稳定。验证集仅 72 只动物且极端值样本少,深度图来自推断而非实测。框架可作为后续发展的方法学基础。DOI: 10.1016\u002Fj.atech.2026.102628。","Smart Agricultural Technology Vol.15 Article 102628 2026-10-15","2026-10-15T00:00:00Z",68,{"impact":110,"substance":344,"depth":190,"authority":19,"freshness":56,"relevant":21,"comment":345},20,"方法学上有新意，但验证样本仅72头且深度图为推断值，属细分领域论文进展，未达每日精选门槛。",[347],{"name":340,"url":338},[26,27,28,349,350],"肉牛养殖","体况评分",{"code":352,"slug":353,"name":354,"path":355,"parent_code":356},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[358],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":360},{"candidates":361,"agricultural_evidence":46},[362,366,371],{"code":32,"score":202,"supported":46,"title":363,"abstract":364,"tags":365},[28,52],[28,52],[27,28],{"code":352,"score":110,"supported":46,"title":367,"abstract":369,"tags":370},[368],"肉牛",[368],[349],{"code":63,"score":21,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[26],[376,377],"YOLO 深度估计 牛体况评分","牛肉牛 计算机视觉 BCS","YOLO深度估计牛体况评分-5363",{"doi":9,"openalex_id":9,"authors":380,"venue":9,"cited_by_count":87,"oa_url":9,"card":381,"direction":93,"ingested_from":386},[],{"tldr":382,"method":383,"finding":384,"direction":93,"opportunity":385},"用YOLO检测与深度估计提取牛体区域特征，实现肉牛体况自动评分。","YOLO检测、深度估计、SHAP可解释分析与XGBoost融合，5折交叉验证。","区域特征融合评分稳定，但验证集仅72头且深度图为推断值。","可引入实测深度传感器与更大样本，提升极端体况评分鲁棒性。","agent","2026-10-20T02:47:54.361149Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":391,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":396,"score_detail":397,"sources":399,"tags":401,"primary_topic":404,"secondary_topics":405,"classification":406,"search_phrases":439,"slug":442,"view_count":21,"doi":443,"paper":444,"created_at":452},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":113,"substance":112,"depth":110,"authority":56,"freshness":20,"relevant":21,"comment":398},"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[400],{"name":394,"url":391},[26,27,402,29,403],"决策支持系统","灌溉管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":407},{"candidates":408,"agricultural_evidence":46},[409,415,422,429,435],{"code":32,"score":410,"supported":46,"title":411,"abstract":413,"tags":414},11,[412],"decision support",[],[27,402,29],{"code":416,"score":151,"supported":46,"title":417,"abstract":418,"tags":421},"H3",[],[419,420],"灌溉","irrigation",[403],{"code":423,"score":56,"supported":46,"title":424,"abstract":425,"tags":428},"H2",[],[426,427],"土壤","soil",[],{"code":430,"score":166,"supported":46,"title":431,"abstract":432,"tags":434},"F3",[],[433],"肥料",[],{"code":63,"score":21,"supported":14,"title":436,"abstract":437,"tags":438},[],[],[26],[440,441],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":443,"openalex_id":445,"authors":446,"venue":394,"cited_by_count":87,"oa_url":391,"card":447,"direction":333,"ingested_from":95},"W7222785888",[],{"tldr":448,"method":449,"finding":450,"direction":177,"opportunity":451},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","2026-10-19T23:30:08.043132Z"]