[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5608":3,"related-5608":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":18,"tags":20,"primary_topic":24,"secondary_topics":30,"classification":31,"search_phrases":55,"slug":58,"view_count":15,"doi":59,"paper":60,"created_at":88},5608,"Object Detection in Computer Vision: A Comprehensive Review of Algorithms, Datasets, and Evaluation Techniques","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206663","Object detection has evolved dramatically over three decades, transitioning from handcrafted features to deep learning, transformers, and now large language models. This paper provides a comprehensive systematic review of this evolution, covering traditional methods (SIFT, HOG, Viola-Jones), CNN-based single- and two-stage detectors (R-CNN family, YOLO v1–v12, SSD), transformer-based architectures (DETR, ViT, Swin), and emerging LLM-driven approaches (GPT-4V, Grounding DINO, YOLO-World). We benchmark and compare these methods using key metrics—mAP, IoU, FPS—and analyze over 50 datasets across generic, autonomous driving, medical, remote sensing, and agricultural domains. Our critical synthesis identifies persistent research gaps: lightweight edge deployment, sensor fusion for real-time systems, bias and fairness in AI, and multimodal vision–language integration. By consolidating three decades of progress and outlining future challenges, this survey serves as a valuable resource for researchers and practitioners seeking to develop accurate, efficient, and reliable object detection systems.","目标检测在三十年间经历了显著演变，从手工设计特征发展到深度学习、Transformer，再到如今的大语言模型。本文对这一演进过程进行了全面系统的综述，涵盖传统方法（SIFT、HOG、Viola-Jones）、基于CNN的单阶段与两阶段检测器（R-CNN系列、YOLO v1–v12、SSD）、基于Transformer的架构（DETR、ViT、Swin）以及新兴的LLM驱动方法（GPT-4V、Grounding DINO、YOLO-World）。我们采用关键指标——mAP、IoU、FPS——对这些方法进行基准测试与比较，并分析了涵盖通用、自动驾驶、医学、遥感与农业领域的50余个数据集。通过批判性综合，我们识别出持续存在的研究空白：轻量化边缘部署、面向实时系统的传感器融合、AI中的偏见与公平性，以及多模态视觉—语言集成。通过整合三十年的进展并勾勒未来挑战，本综述为致力于开发准确、高效且可靠的目标检测系统的研究人员与实践者提供了有价值的参考资源。",null,"Sensors","2026-10-21T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"通用计算机视觉综述，仅在数据集部分涉及农业领域，不属于三农或农业信息化主题，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"农业人工智能","计算机视觉","目标检测",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"classified","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":39},[37,47],{"code":25,"score":38,"supported":39,"title":40,"abstract":42,"tags":46},20,true,[41],"computer vision",[23,43,44,45],"深度学习","deep learning","YOLO",[21,23,22],{"code":48,"score":49,"supported":39,"title":50,"abstract":51,"tags":54},"I3",6,[],[52,53],"遥感","remote sensing",[],[56,57],"农业人工智能 计算机视觉 目标检测","农业人工智能 计算机视觉","农业人工智能计算机视觉目标检测-5608","10.3390\u002Fs26206663",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":15,"oa_url":6,"card":80,"direction":86,"ingested_from":87},"W7223169730",[63,66,69,72,75,78],{"name":64,"orcid":65},"Md. Faishal Rahaman","https:\u002F\u002Forcid.org\u002F0000-0001-5656-1542",{"name":67,"orcid":68},"Xueyuan Li","https:\u002F\u002Forcid.org\u002F0000-0003-1112-5610",{"name":70,"orcid":71},"Md Nagib Mahfuz Sunny","https:\u002F\u002Forcid.org\u002F0009-0007-2875-1147",{"name":73,"orcid":74},"Muhammad Amjad","https:\u002F\u002Forcid.org\u002F0009-0008-0068-1439",{"name":76,"orcid":77},"Xin Gao","https:\u002F\u002Forcid.org\u002F0000-0002-7317-8059",{"name":79,"orcid":9},"Sakib Hasan",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"系统综述目标检测三十年演进，从传统方法到深度学习、Transformer与大模型。","综述SIFT\u002FHOG、R-CNN\u002FYOLO\u002FSSD、DETR\u002FViT及GPT-4","梳理算法与数据集进展，指出轻量边缘部署、传感器融合、公平性与多模态融合等空白。","农业人工智能与决策模型","可将轻量边缘部署与多模态视觉语言模型结合，用于农业场景实时目标检测与决策。","农业遥感与作物表型","openalex","2026-10-22T23:30:40.380327Z",{"total":49,"page":90,"page_size":49,"items":91},1,[92,185,240,294,342,399],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":105,"tags":107,"primary_topic":111,"secondary_topics":112,"classification":118,"search_phrases":151,"slug":153,"view_count":15,"doi":154,"paper":155,"created_at":184},5603,"AI-Enabled Smart and Precision Agriculture: Smart Sensors, IoT, Deep Learning, Computer Vision, and Decision Support: A Comprehensive Survey","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.2112.v1","Artificial intelligence (AI) is increasingly integrated with smart sensors, Internet of Things (IoT) networks, machine learning, deep learning, and computer vision to support site-specific agricultural management. However, strong performance on computational benchmarks does not necessarily translate into reliable decisions, effective field interventions, or measurable improvements in agricultural productivity and resource efficiency. This comprehensive narrative survey critically synthesizes advances in agricultural sensing, IoT architectures, data processing, multimodal sensor fusion, predictive modeling, computer vision, decision optimization, and automated actuation. Particular emphasis is placed on the relationships between measurement reliability, model generalization, decision feasibility, intervention accuracy, and verified agricultural outcomes. The reviewed literature demonstrates promising capabilities in crop monitoring, yield prediction, disease and weed recognition, precision irrigation, and selected automated operations. Nevertheless, heterogeneous evaluation protocols, limited independent field validation, sensor calibration challenges, distribution shifts, interoperability barriers, and deployment costs constrain operational reliability and generalizability. To address these interconnected challenges, the survey proposes a literature-informed reference architecture linking agricultural observations, data integration, AI inference, constrained decision-making, physical interventions, and outcome feedback, supported by uncertainty assessment, human oversight, and cross-layer assurance. The proposed architecture is conceptual and has not been empirically validated. Future research priorities include reproducible field evaluations, representative agricultural datasets, uncertainty-aware and explainable AI, resource-efficient computing, interoperable infrastructures, and economically inclusive deployment. The review concludes that the practical value of AI-enabled precision agriculture should be evaluated through reliable interventions, sustainable resource utilization, and demonstrated agricultural outcomes rather than predictive performance alone.","人工智能（AI）正日益与智能传感器、物联网（IoT）网络、机器学习、深度学习及计算机视觉相融合，以支撑因地制宜的精准农业管理。然而，在计算基准上的优异表现并不必然转化为可靠的决策、有效的田间干预，或农业生产率与资源效率方面可衡量的提升。本综述性研究系统梳理并批判性综合了农业传感、物联网架构、数据处理、多模态传感器融合、预测建模、计算机视觉、决策优化及自动执行等方面的进展，重点探讨测量可靠性、模型泛化能力、决策可行性、干预精度与经核实的农业成效之间的关系。所综述文献表明，上述技术在作物监测、产量预测、病害与杂草识别、精准灌溉及部分自动化作业中展现出良好前景。然而，评估方案异质、独立田间验证有限、传感器校准难题、分布偏移、互操作性障碍及部署成本等因素制约了运行可靠性与泛化能力。为应对这些相互关联的挑战，本综述提出一种基于文献的参考架构，将农业观测、数据集成、AI推理、受约束决策、物理干预与成效反馈相衔接，并以不确定性评估、人工监督与跨层保障为支撑。该架构为概念性框架，尚未经实证验证。未来研究重点包括可复现的田间评估、具有代表性的农业数据集、不确定性感知与可解释AI、资源高效计算、可互操作基础设施及经济包容性部署。本综述认为，AI赋能精准农业的实际价值应通过可靠的干预措施、可持续的资源利用及经证实的农业成效来评估，而非仅凭预测性能。","Preprints.org",70,{"impact":101,"substance":38,"depth":102,"authority":49,"freshness":103,"relevant":90,"comment":104},18,17,9,"这是一篇系统性综述，梳理了AI与传感器、物联网、深度学习在精准农业中的融合进展，并提出参考架构，但尚未经实证验证；来源为预印本平台，权威性有限，适合作为主题聚合素材，而非每日精选头条。",[106],{"name":98,"url":95},[108,21,22,109,110],"智慧农业","物联网","精准农业",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[113],{"code":114,"slug":115,"name":116,"path":117,"parent_code":29},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":32,"locked":14,"version":33,"method":34,"evidence":119},{"candidates":120,"agricultural_evidence":39},[121,130,137,144],{"code":25,"score":122,"supported":39,"title":123,"abstract":125,"tags":129},35,[44,41,124],"decision support",[43,126,22,127,44,128,41],"机器学习","machine learning","artificial intelligence",[21,22],{"code":114,"score":101,"supported":39,"title":131,"abstract":133,"tags":136},[132],"IoT",[109,134,135,132],"农业传感","internet of things",[109],{"code":138,"score":139,"supported":39,"title":140,"abstract":142,"tags":143},"I1",16,[141],"precision agriculture",[110,141],[108,110],{"code":145,"score":49,"supported":39,"title":146,"abstract":147,"tags":150},"H3",[],[148,149],"灌溉","irrigation",[],[152,57],"农业人工智能 计算机视觉 智慧农业 精准农业","农业人工智能计算机视觉智慧农业精准农业-5603","10.20944\u002Fpreprints202610.2112.v1",{"doi":154,"openalex_id":156,"authors":157,"venue":98,"cited_by_count":15,"oa_url":95,"card":178,"direction":182,"ingested_from":87},"W7223175252",[158,161,164,167,169,171,174,176],{"name":159,"orcid":160},"IBRAHIM ADABARA","https:\u002F\u002Forcid.org\u002F0000-0001-8949-7540",{"name":162,"orcid":163},"Bashir Olaniyi Sadiq","https:\u002F\u002Forcid.org\u002F0000-0001-7868-4076",{"name":165,"orcid":166},"Aliyu Nuhu Shuaibu","https:\u002F\u002Forcid.org\u002F0000-0002-5171-675X",{"name":168,"orcid":9},"Yale Ibrahim Danjuma",{"name":170,"orcid":9},"Ainamukama Anold",{"name":172,"orcid":173},"Mutebi Joe","https:\u002F\u002Forcid.org\u002F0000-0002-2757-3875",{"name":175,"orcid":9},"Elhadi Abdalla A. Arbab",{"name":177,"orcid":9},"Brian Mugisha",{"tldr":179,"method":180,"finding":181,"direction":182,"opportunity":183},"综述AI赋能精准农业的传感、物联网、深度学习与决策支持，并提出参考架构。","叙述性综述，整合传感器、IoT、多模态融合、计算机视觉与决策优化文献。","基准性能不等于田间可靠决策，评估协议异质、独立验证不足制约落地。","智慧农业 \u002F 农业物联网","可研究不确定性感知与可解释AI、可复现田间评估及跨层保障架构的实证验证。","2026-10-22T23:30:19.310160Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":192,"score_detail":193,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":220,"slug":223,"view_count":15,"doi":224,"paper":225,"created_at":239},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)",65,{"impact":194,"substance":101,"depth":102,"authority":195,"freshness":13,"relevant":90,"comment":196},8,12,"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。",[198],{"name":191,"url":188},[108,21,22,200,201],"产量预测","传感器校准",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":205},{"candidates":206,"agricultural_evidence":39},[207,212,216],{"code":25,"score":208,"supported":39,"title":209,"abstract":210,"tags":211},24,[128,41],[22,41],[21,22],{"code":138,"score":90,"supported":14,"title":213,"abstract":214,"tags":215},[],[],[108],{"code":48,"score":90,"supported":14,"title":217,"abstract":218,"tags":219},[],[],[200],[221,222],"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":224,"openalex_id":226,"authors":227,"venue":191,"cited_by_count":15,"oa_url":188,"card":234,"direction":84,"ingested_from":87},"W7223104380",[228,230,232],{"name":229,"orcid":9},"Tigist Alemu Gebremariam",{"name":231,"orcid":9},"Hawi Tesfaye Desta",{"name":233,"orcid":9},"Mulugeta Bekele Wolde",{"tldr":235,"method":236,"finding":237,"direction":84,"opportunity":238},"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":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":9,"source_name":191,"source_url":243,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":246,"score_detail":247,"sources":251,"tags":253,"primary_topic":256,"secondary_topics":257,"classification":258,"search_phrases":274,"slug":277,"view_count":15,"doi":278,"paper":279,"created_at":293},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":195,"substance":248,"depth":101,"authority":249,"freshness":13,"relevant":90,"comment":250},22,13,"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。",[252],{"name":191,"url":243},[108,21,22,254,255],"作物病害","气候变化",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":259},{"candidates":260,"agricultural_evidence":39},[261,265,270],{"code":25,"score":208,"supported":39,"title":262,"abstract":263,"tags":264},[128,41],[22,41],[21,22],{"code":266,"score":90,"supported":14,"title":267,"abstract":268,"tags":269},"H7",[],[],[255],{"code":138,"score":90,"supported":14,"title":271,"abstract":272,"tags":273},[],[],[108],[275,276],"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":278,"openalex_id":280,"authors":281,"venue":191,"cited_by_count":15,"oa_url":243,"card":288,"direction":84,"ingested_from":87},"W7223087892",[282,284,286],{"name":283,"orcid":9},"Kossi Afeli",{"name":285,"orcid":9},"Akouvi Amegashie",{"name":287,"orcid":9},"Komlan Sélom Dossou",{"tldr":289,"method":290,"finding":291,"direction":84,"opportunity":292},"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":295,"title":296,"url":297,"summary":298,"summary_zh":9,"content":9,"source_name":299,"source_url":9,"published_at":300,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":246,"score_detail":301,"sources":304,"tags":306,"primary_topic":310,"secondary_topics":316,"classification":318,"search_phrases":329,"slug":332,"view_count":15,"doi":9,"paper":333,"created_at":341},5534,"DC-YOLO：应对遮挡与光照变化的湖羊行为检测模型","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-19\u002Fdoc-inivumaf7730137.shtml","中国农业大学团队针对集约化养殖中多目标严重遮挡、光照多变与类别不均问题，引入自适应细节—上下文注意力机制构建模块，并采用边界框相似度软抑制防止误删遮挡目标，预训练光照编码器迁移参数应对光照变化。数据集来自湖羊养殖场全天候监控，含505张标注图像、5类行为。","智慧农业（中英文）2026-10-19","2026-10-19T00:00:00Z",{"impact":101,"substance":302,"depth":102,"authority":249,"freshness":49,"relevant":90,"comment":303},21,"论文提出面向湖羊行为检测的DC-YOLO模型，方法有新意、数据来自真实养殖场，属智慧养殖细分领域实质进展，值得精选。",[305],{"name":299,"url":297},[21,307,23,308,309],"智慧养殖","湖羊","动物行为检测",{"code":311,"slug":312,"name":313,"path":314,"parent_code":315},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[317],{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},{"status":32,"locked":14,"version":33,"method":34,"evidence":319},{"candidates":320,"agricultural_evidence":39},[321,325],{"code":311,"score":195,"supported":39,"title":322,"abstract":323,"tags":324},[308],[308],[308],{"code":25,"score":13,"supported":39,"title":326,"abstract":327,"tags":328},[45],[],[21,23],[330,331],"中国农业大学 湖羊 行为检测","DC-YOLO 遮挡 光照","中国农业大学湖羊行为检测-5534",{"doi":9,"openalex_id":9,"authors":334,"venue":9,"cited_by_count":15,"oa_url":9,"card":335,"direction":182,"ingested_from":340},[],{"tldr":336,"method":337,"finding":338,"direction":182,"opportunity":339},"提出DC-YOLO模型，用于湖羊行为检测，解决遮挡与光照变化问题。","自适应细节-上下文注意力、边界框相似度软抑制、光照编码器迁移，505张标注图像。","模型在遮挡和光照变化下有效检测湖羊行为，减少误删。","可探索多模态融合与轻量化部署，提升复杂养殖场景下的实时行为监测能力。","agent","2026-10-22T00:07:09.998247Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":9,"content":9,"source_name":347,"source_url":9,"published_at":348,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":349,"score_detail":350,"sources":352,"tags":354,"primary_topic":357,"secondary_topics":363,"classification":365,"search_phrases":387,"slug":390,"view_count":15,"doi":9,"paper":391,"created_at":398},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",69,{"impact":139,"substance":302,"depth":102,"authority":103,"freshness":49,"relevant":90,"comment":351},"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[353],{"name":347,"url":345},[108,21,23,355,356],"无人机遥感","棉花病害",{"code":358,"slug":359,"name":360,"path":361,"parent_code":362},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[364],{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},{"status":32,"locked":14,"version":33,"method":34,"evidence":366},{"candidates":367,"agricultural_evidence":39},[368,372,379,383],{"code":25,"score":101,"supported":39,"title":369,"abstract":370,"tags":371},[43,45],[],[21,23],{"code":358,"score":373,"supported":39,"title":374,"abstract":376,"tags":378},11,[375],"棉花",[377],"cotton",[],{"code":138,"score":90,"supported":14,"title":380,"abstract":381,"tags":382},[],[],[108],{"code":48,"score":90,"supported":14,"title":384,"abstract":385,"tags":386},[],[],[355],[388,389],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":9,"openalex_id":9,"authors":392,"venue":9,"cited_by_count":15,"oa_url":9,"card":393,"direction":86,"ingested_from":340},[],{"tldr":394,"method":395,"finding":396,"direction":86,"opportunity":397},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","2026-10-22T00:07:09.816790Z",{"id":400,"title":401,"url":402,"summary":403,"summary_zh":404,"content":9,"source_name":405,"source_url":402,"published_at":406,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":407,"score_detail":408,"sources":411,"tags":413,"primary_topic":414,"secondary_topics":415,"classification":416,"search_phrases":432,"slug":435,"view_count":90,"doi":436,"paper":437,"created_at":460},5393,"IMPLEMENTATION OF META-HEURISTIC OPTIMIZATION ALGORITHMS IN CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION AND CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr251214006i","CNNs have grown to form the backbone of modern computer vision systems, producing outstanding object detection and classification performance within diverse application domains. However, the manual development of suitable CNN architectures and, correspondingly, the selection of optimal hyperparameter sets are still inconvenient and time-consuming processes. The aim of this survey is to contribute a comprehensive review regarding how meta-heuristic optimization algorithms such as Genetic Algorithms, Particle Swarm Optimization, and other swarm intelligence-based methods are embedded in the CNN-based pipelines with the intent to achieve better detection and classification results. We gather recent progress regarding automated architecture search, hyperparameter tuning, model compression, and ensemble learning. We identify how hybrid optimization approaches consistently yield superior accuracy with computational efficiency by systematically reviewing applications in medical imaging, agriculture, vehicular recognition, and remote sensing. Optimized CNN models outperform their baseline versions many times, especially in health diagnostics, where several fine-tuned architectures reach accuracies greater than 99%. This survey provides a structured and updated overview of methodological developments, implementation practices, and emerging research directions for those practitioners and researchers who will apply meta-heuristic optimization within deep learning-based computer vision systems.","卷积神经网络（CNNs）已发展成为现代计算机视觉系统的核心骨干，在多个应用领域中展现出卓越的目标检测与分类性能。然而，人工设计合适的CNN架构并相应地选择最优超参数集仍然是繁琐且耗时的过程。本综述旨在全面回顾遗传算法、粒子群优化及其他群体智能方法等元启发式优化算法如何嵌入基于CNN的流程中以获得更优的检测与分类结果。我们汇集了自动化架构搜索、超参数调优、模型压缩和集成学习方面的最新进展。通过系统梳理在医学影像、农业、车辆识别和遥感领域的应用，我们揭示了混合优化方法如何以计算效率持续实现更优的精度。优化后的CNN模型在多数情况下远超其基线版本，尤其在健康诊断领域，若干精细调优的架构达到了超过99%的准确率。本综述为将在基于深度学习的计算机视觉系统中应用元启发式优化的从业者和研究者，提供了关于方法学进展、实施实践和新兴研究方向的结构化且更新的概述。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",61,{"impact":194,"substance":101,"depth":409,"authority":195,"freshness":194,"relevant":90,"comment":410},15,"综述系统梳理元启发式优化在CNN视觉任务中的嵌入方法，含农业应用，但属通用方法综述，非农业专属突破。",[412],{"name":405,"url":402},[108,21,23,52],{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":417},{"candidates":418,"agricultural_evidence":39},[419,423,428],{"code":25,"score":373,"supported":39,"title":420,"abstract":421,"tags":422},[],[23,43,22,44,41],[21,23],{"code":48,"score":424,"supported":39,"title":425,"abstract":426,"tags":427},7,[],[52,53],[52],{"code":138,"score":90,"supported":14,"title":429,"abstract":430,"tags":431},[],[],[108],[433,434],"元启发式优化 卷积神经网络","CNN 目标检测","元启发式优化卷积神经网络-5393","10.22190\u002Ffuacr251214006i",{"doi":436,"openalex_id":438,"authors":439,"venue":405,"cited_by_count":15,"oa_url":402,"card":455,"direction":86,"ingested_from":87},"W7222920697",[440,443,446,449,452],{"name":441,"orcid":442},"Nikola Ivačko","https:\u002F\u002Forcid.org\u002F0000-0003-4367-119X",{"name":444,"orcid":445},"Ivan S Ciric","https:\u002F\u002Forcid.org\u002F0000-0003-0430-8937",{"name":447,"orcid":448},"Milica Tasić","https:\u002F\u002Forcid.org\u002F0009-0001-9160-6379",{"name":450,"orcid":451},"Ljiljana Radović","https:\u002F\u002Forcid.org\u002F0000-0002-4099-1289",{"name":453,"orcid":454},"Žarko Ćojbašić","https:\u002F\u002Forcid.org\u002F0000-0002-4581-1048",{"tldr":456,"method":457,"finding":458,"direction":84,"opportunity":459},"综述元启发式算法嵌入CNN流程以优化目标检测与分类的研究进展。","系统综述遗传算法、粒子群等群智能方法用于CNN架构搜索与超参调优。","混合优化方法在医学影像、农业等领域兼顾精度与效率，部分模型准确率超99%。","农业场景下元启发式优化CNN的架构搜索与轻量化部署仍缺乏系统基准与田间验证。","2026-10-20T23:30:19.233167Z"]