[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5385":3,"related-5385":89},{"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":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":88},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。",null,"International Journal of Drug Delivery Technology","2026-10-18T00:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,16,6,1,"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。",[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","深度学习","水果品质检测",{"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},[44,56,64],{"code":32,"score":45,"supported":46,"title":47,"abstract":50,"tags":55},28,true,[48,49],"deep learning","computer vision",[51,29,52,28,53,48,54,49],"目标检测","机器学习","machine learning","artificial intelligence",[27,29,28],{"code":57,"score":58,"supported":46,"title":59,"abstract":60,"tags":63},"I1",7,[],[61,62],"精准农业","precision agriculture",[26],{"code":65,"score":66,"supported":46,"title":67,"abstract":68,"tags":70},"B2",3,[],[69],"orchard",[],[72,73],"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",0,"10.25258\u002Fijddt.16.75s.151",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":75,"oa_url":6,"card":80,"direction":86,"ingested_from":87},"W7222892831",[],{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"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。","农业遥感与作物表型","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","openalex","2026-10-20T23:30:10.066042Z",{"total":20,"page":21,"page_size":20,"items":90},[91,165,221,274,336,389],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":105,"tags":107,"primary_topic":109,"secondary_topics":114,"classification":116,"search_phrases":134,"slug":137,"view_count":75,"doi":138,"paper":139,"created_at":164},4744,"An empirical performance comparison of deep learning models for apple leaf disease recognition","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10573-4","Currently, Biotic and abiotic factors, in agriculture, are estimated to be responsible for around 22% of the total losses in crop production. It is therefore very important, but at the same time very difficult for farmers to identify these constraints at the earliest possible stage. The most recent developments in computer vision have made it possible to identify as well as classify plant lesions at a primary level. Various deep learning techniques have made it possible to detect biotic stress in apple plant leaves with a very high degree of accuracy. Hence, automated segmentation of images of diseased leaves has become a leading research theme all over the world. In this paper, a dataset from FGVC7, which is an open, source dataset and contains four different classes of leaf diseases of the apple crop is used to measure the performance of the proposed model. A three, step image preprocessing procedure is conducted, which involves image enhancement, removal of background noise, and object identification through a variety of approaches. Using UNet, PSPNet, and UNet++, disease areas are identified. UNet + + delivering the most precise object segmentation. The segmented patch data are then fed into a fusion, based classification model as input. Patch segmentation makes it possible to separate the various disease areas, thus increasing the classification accuracy. The proposed framework combines several CNN architectures, namely, VGG16, InceptionV3, AlexNet, and GoogleNet, in order to take advantage of their complementary strengths, then a Vision Transformer (ViT) is used to identify distinct local features via early feature fusion. After this, the efficiency of the proposed model is check in comparison with the pre-trained CNN models individually. Comparing with all other models the proposed model has shown the accuracy of highest order i.e. 98.85%, clearly out performing individual CNN models.","目前，农业中的生物和非生物因素估计约占作物生产总损失的22%。因此，农民尽早识别这些限制因素非常重要，但同时也非常困难。计算机视觉的最新发展使得在初级水平上识别和分类植物病变成为可能。各种深度学习技术使得以极高准确度检测苹果植株叶片的生物胁迫成为可能。因此，患病叶片图像的自动分割已成为世界范围内的前沿研究主题。本文使用来自FGVC7的数据集来衡量所提出模型的性能，该数据集是一个开放源数据集，包含苹果作物的四类不同叶片病害。进行了三步图像预处理，包括图像增强、背景噪声去除以及通过多种方法进行目标识别。使用UNet、PSPNet和UNet++识别病害区域，其中UNet++提供了最精确的目标分割。然后将分割后的图像块数据作为输入馈入基于融合的分类模型。图像块分割使得能够分离不同的病害区域，从而提高分类准确率。所提出的框架结合了多种CNN架构，即VGG16、InceptionV3、AlexNet和GoogleNet，以利用它们互补的优势，然后使用视觉Transformer（ViT）通过早期特征融合来识别不同的局部特征。此后，将所提出模型的效率与各个预训练CNN模型进行比较。与所有其他模型相比，所提出的模型显示出最高阶的准确率，即98.85%，明显优于各个单独的CNN模型。","Discover Computing","2026-10-10T00:00:00Z",69,{"impact":101,"substance":102,"depth":19,"authority":103,"freshness":17,"relevant":21,"comment":104},12,20,13,"方法组合（UNet++分割+CNN与ViT融合）与98.85%准确率有参考价值，但属实验室数据集验证，产业落地影响有限，可作为技术前沿资讯收录。",[106],{"name":97,"url":94},[26,27,28,29,108],"苹果病害识别",{"code":65,"slug":110,"name":111,"path":112,"parent_code":113},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[115],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":117},{"candidates":118,"agricultural_evidence":46},[119,123,130],{"code":32,"score":102,"supported":46,"title":120,"abstract":121,"tags":122},[48],[29,28,48,49],[27,29,28],{"code":65,"score":124,"supported":46,"title":125,"abstract":127,"tags":129},14,[126],"apple",[128,126],"苹果",[],{"code":57,"score":21,"supported":14,"title":131,"abstract":132,"tags":133},[],[],[26],[135,136],"苹果叶片病害 深度学习","FGVC7 苹果病害 数据集","苹果叶片病害深度学习-4744","10.1007\u002Fs10791-026-10573-4",{"doi":138,"openalex_id":140,"authors":141,"venue":97,"cited_by_count":75,"oa_url":157,"card":158,"direction":86,"ingested_from":87},"W7221353656",[142,144,147,149,151,154],{"name":143,"orcid":9},"Apoorva Arora",{"name":145,"orcid":146},"Vinay Gautam","https:\u002F\u002Forcid.org\u002F0000-0002-0258-5132",{"name":148,"orcid":9},"Anand Muni Mishra",{"name":150,"orcid":9},"Santar Pal Singh",{"name":152,"orcid":153},"Yadvendra Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-6396-3355",{"name":155,"orcid":156},"Mukund Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-5877-9510","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10791-026-10573-4.pdf",{"tldr":159,"method":160,"finding":161,"direction":162,"opportunity":163},"用UNet++分割苹果病叶并融合多种CNN与ViT分类，实现98.85%识别准确率。","FGVC7数据集，UNet\u002FPSPNet\u002FUNet++分割，VGG16等CNN与","UNet++分割最准，融合模型准确率达98.85%，优于单个CNN模型。","农业人工智能与决策模型","可探索轻量化分割-分类联合模型在田间移动端的实时部署与多作物泛化。","2026-10-12T23:30:09.091475Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":177,"tags":179,"primary_topic":182,"secondary_topics":183,"classification":184,"search_phrases":201,"slug":204,"view_count":75,"doi":205,"paper":206,"created_at":220},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":17,"substance":18,"depth":175,"authority":101,"freshness":13,"relevant":21,"comment":176},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。",[178],{"name":171,"url":168},[26,27,28,180,181],"产量预测","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":185},{"candidates":186,"agricultural_evidence":46},[187,192,196],{"code":32,"score":188,"supported":46,"title":189,"abstract":190,"tags":191},24,[54,49],[28,49],[27,28],{"code":57,"score":21,"supported":14,"title":193,"abstract":194,"tags":195},[],[],[26],{"code":197,"score":21,"supported":14,"title":198,"abstract":199,"tags":200},"I3",[],[],[180],[202,203],"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":205,"openalex_id":207,"authors":208,"venue":171,"cited_by_count":75,"oa_url":168,"card":215,"direction":162,"ingested_from":87},"W7223104380",[209,211,213],{"name":210,"orcid":9},"Tigist Alemu Gebremariam",{"name":212,"orcid":9},"Hawi Tesfaye Desta",{"name":214,"orcid":9},"Mulugeta Bekele Wolde",{"tldr":216,"method":217,"finding":218,"direction":162,"opportunity":219},"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":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":171,"source_url":224,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":231,"tags":233,"primary_topic":236,"secondary_topics":237,"classification":238,"search_phrases":254,"slug":257,"view_count":75,"doi":258,"paper":259,"created_at":273},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":101,"substance":229,"depth":18,"authority":103,"freshness":13,"relevant":21,"comment":230},22,"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。",[232],{"name":171,"url":224},[26,27,28,234,235],"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":239},{"candidates":240,"agricultural_evidence":46},[241,245,250],{"code":32,"score":188,"supported":46,"title":242,"abstract":243,"tags":244},[54,49],[28,49],[27,28],{"code":246,"score":21,"supported":14,"title":247,"abstract":248,"tags":249},"H7",[],[],[235],{"code":57,"score":21,"supported":14,"title":251,"abstract":252,"tags":253},[],[],[26],[255,256],"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":258,"openalex_id":260,"authors":261,"venue":171,"cited_by_count":75,"oa_url":224,"card":268,"direction":162,"ingested_from":87},"W7223087892",[262,264,266],{"name":263,"orcid":9},"Kossi Afeli",{"name":265,"orcid":9},"Akouvi Amegashie",{"name":267,"orcid":9},"Komlan Sélom Dossou",{"tldr":269,"method":270,"finding":271,"direction":162,"opportunity":272},"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":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":9,"source_name":280,"source_url":277,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":281,"score_detail":282,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":294,"classification":296,"search_phrases":316,"slug":319,"view_count":75,"doi":320,"paper":321,"created_at":335},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations",64,{"impact":101,"substance":18,"depth":19,"authority":13,"freshness":17,"relevant":21,"comment":283},"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[285],{"name":280,"url":277},[26,27,29,287,288],"番茄病害","图像识别",{"code":290,"slug":291,"name":292,"path":293,"parent_code":113},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",[295],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":297},{"candidates":298,"agricultural_evidence":46},[299,306,311],{"code":290,"score":300,"supported":46,"title":301,"abstract":303,"tags":305},15,[302],"tomato",[304,302],"番茄",[287],{"code":32,"score":307,"supported":46,"title":308,"abstract":309,"tags":310},9,[],[29,48],[27,288,29],{"code":57,"score":58,"supported":46,"title":312,"abstract":313,"tags":315},[],[26,314],"smart agriculture",[26],[317,318],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":320,"openalex_id":322,"authors":323,"venue":280,"cited_by_count":75,"oa_url":329,"card":330,"direction":86,"ingested_from":87},"W7222977034",[324,326],{"name":325,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":327,"orcid":328},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":331,"method":332,"finding":333,"direction":162,"opportunity":334},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","2026-10-20T23:30:10.424511Z",{"id":337,"title":338,"url":339,"summary":340,"summary_zh":9,"content":9,"source_name":341,"source_url":9,"published_at":342,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":343,"score_detail":344,"sources":346,"tags":348,"primary_topic":351,"secondary_topics":357,"classification":359,"search_phrases":376,"slug":379,"view_count":75,"doi":9,"paper":380,"created_at":388},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":101,"substance":102,"depth":175,"authority":103,"freshness":20,"relevant":21,"comment":345},"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":341,"url":339},[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},[362,367,372],{"code":32,"score":188,"supported":46,"title":363,"abstract":365,"tags":366},[28,364],"YOLO",[28,364],[27,28],{"code":352,"score":101,"supported":46,"title":368,"abstract":370,"tags":371},[369],"肉牛",[369],[349],{"code":57,"score":21,"supported":14,"title":373,"abstract":374,"tags":375},[],[],[26],[377,378],"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":381,"venue":9,"cited_by_count":75,"oa_url":9,"card":382,"direction":84,"ingested_from":387},[],{"tldr":383,"method":384,"finding":385,"direction":84,"opportunity":386},"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":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":9,"source_name":395,"source_url":392,"published_at":396,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":400,"tags":402,"primary_topic":405,"secondary_topics":406,"classification":412,"search_phrases":429,"slug":432,"view_count":75,"doi":433,"paper":434,"created_at":457},5236,"A cross-domain review of artificial intelligence techniques for iot security, computer networks, image processing and smart agriculture in connected environments","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02500-9","The rapid digitization of modern systems has increased the demand for intelligent, adaptive, and data-driven solutions across connected systems. However, these domains face persistent challenges such as heterogeneous datasets, limited labeled data, real-time processing constraints, privacy concerns, model interpretability, and poor generalization across environments. This review examines the application of major Artificial Intelligence (AI) techniques for solving classification, prediction, optimization, anomaly detection, routing, image analysis, and decision-support problems. The empirical patterns across the literature are summarized cautiously across studies. General lessons that emerged across the reviewed domains were also presented. Overall, the review provides researchers with a concise foundation for understanding current AI methods, their practical limitations, and future research directions in the aspect of connected environments. This article has tried to clarify various aspects and overall issues related to the use of AI techniques in computer networks, image processing and computer vision, smart agriculture and related intelligent systems. The authors believe that this is a comprehensive and integrated survey of these AI techniques in a number of application domains, with an emphasis on their practical suitability, their current empirical limitations, and future research directions. Thus, the article is informative to researchers in the field and may be a guide to future research on the development of robust, interpretable, scalable, and context-aware AI-based connected systems.","现代系统的快速数字化增加了对互联系统中智能、自适应和数据驱动解决方案的需求。然而，这些领域面临诸如异构数据集、标注数据有限、实时处理约束、隐私问题、模型可解释性以及跨环境泛化能力差等持续挑战。本综述考察了主要人工智能（Artificial Intelligence, AI）技术在解决分类、预测、优化、异常检测、路由、图像分析和决策支持问题中的应用。本文谨慎地总结了文献中的实证规律。文中还提出了在所综述领域中浮现的一般性经验教训。总体而言，本综述为研究人员提供了一个简明的基础，以理解当前AI方法、其实际局限性以及互联环境方面的未来研究方向。本文试图阐明与AI技术在计算机网络、图像处理与计算机视觉、智慧农业及相关智能系统中的应用有关的各个方面和总体问题。作者认为，这是对若干应用领域中这些AI技术的全面且综合的调查，重点在于其实际适用性、当前实证局限性和未来研究方向。因此，本文对该领域的研究人员具有参考价值，并可为开发稳健、可解释、可扩展且具有情境感知能力的基于AI的互联系统的未来研究提供指导。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",51,{"impact":17,"substance":101,"depth":13,"authority":101,"freshness":307,"relevant":21,"comment":399},"跨领域AI综述，涉及智慧农业但仅为其一分支，方法层面无突破性新结论，公共价值有限，不宜进入每日精选。",[401],{"name":395,"url":392},[26,27,28,403,404],"物联网安全","智能系统",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[407],{"code":408,"slug":409,"name":410,"path":411,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":413},{"candidates":414,"agricultural_evidence":46},[415,420,424],{"code":32,"score":416,"supported":46,"title":417,"abstract":418,"tags":419},19,[54],[28,54,49],[27,28],{"code":57,"score":300,"supported":46,"title":421,"abstract":422,"tags":423},[314],[26,314],[26],{"code":408,"score":17,"supported":46,"title":425,"abstract":427,"tags":428},[426],"IoT",[],[],[430,431],"AI 物联网安全 智慧农业","农业人工智能 物联网安全 计算机视觉 智慧农业","AI物联网安全智慧农业-5236","10.1007\u002Fs44163-026-02500-9",{"doi":433,"openalex_id":435,"authors":436,"venue":395,"cited_by_count":75,"oa_url":392,"card":452,"direction":86,"ingested_from":87},"W7222847048",[437,440,443,446,449],{"name":438,"orcid":439},"Bashir Olaniyi Sadiq","https:\u002F\u002Forcid.org\u002F0000-0001-7868-4076",{"name":441,"orcid":442},"Mohammed Dahiru Buhari","https:\u002F\u002Forcid.org\u002F0000-0002-1076-216X",{"name":444,"orcid":445},"IBRAHIM ADABARA","https:\u002F\u002Forcid.org\u002F0000-0001-8949-7540",{"name":447,"orcid":448},"Enerst Edozie","https:\u002F\u002Forcid.org\u002F0000-0002-7616-0962",{"name":450,"orcid":451},"Asaba Shabiluh","https:\u002F\u002Forcid.org\u002F0000-0001-6342-9099",{"tldr":453,"method":454,"finding":455,"direction":86,"opportunity":456},"综述AI在物联网安全、计算机网络、图像处理与智慧农业中的应用与局限。","跨领域文献综述，归纳分类、预测、优化、异常检测等方法。","各领域普遍受限于异构数据、标注少、实时性与可解释性不足。","可聚焦农业物联网中少标注、跨环境泛化的可解释实时AI模型研究。","2026-10-18T23:30:07.908595Z"]