[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1534":3,"related-1534":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":24,"tags":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":88},1534,"Deep Learning Based Semantic Segmentation of Satellite Imagery for Land Cover Classification","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22251467","Land cover classification from remote sensing imagery underpins agricultural monitoring, urban planning, disaster response, and environmental assessment, and deep learning has become the dominant paradigm for extracting land cover information from satellite scenes at pixel-level granularity. This survey reviews recent advances in deep learning-based land cover classification and semantic segmentation, organizing the literature into convolutional neural network (CNN)-based encoder–decoder architectures, attention and self-attention mechanisms, transformer and hybrid CNN–Transformer architectures, and optimization-augmented and foundation-model-adapted approaches. We summarize the datasets, evaluation metrics, and comparative performance reported across the reviewed studies, and use a representative hybrid EfficientNet–Vision Transformer framework as a case study to illustrate current design trends. Persistent challenges are identified, including class imbalance for narrow structures such as roads, the computational cost of hybrid transformer-based models, limited cross-dataset generalization, and the scarcity of densely annotated data. The survey concludes by outlining promising future directions, including lightweight hybrid architectures, self-supervised and weakly supervised learning, adaptation of large vision foundation models, multi-sensor data fusion, and explainable land cover mapping.","基于遥感影像的土地覆盖分类支撑着农业监测、城市规划、灾害响应和环境评估，而深度学习已成为从卫星场景中提取像素级土地覆盖信息的主导范式。本综述回顾了基于深度学习的土地覆盖分类与语义分割的最新进展，将相关文献组织为基于卷积神经网络（CNN）的编码器-解码器架构、注意力与自注意力机制、Transformer及CNN-Transformer混合架构，以及优化增强和基础模型适配方法等类别。我们总结了所评述研究中报告的数据集、评估指标和对比性能，并以一个具有代表性的混合EfficientNet-Vision Transformer框架作为案例研究，以说明当前的设计趋势。本文指出了持续存在的挑战，包括道路等狭窄结构的类别不平衡问题、基于混合Transformer模型的计算成本、跨数据集泛化能力有限，以及密集标注数据的稀缺性。综述最后概述了有前景的未来方向，包括轻量级混合架构、自监督和弱监督学习、大型视觉基础模型的适配、多传感器数据融合以及可解释的土地覆盖制图。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-02T00:00:00Z","论文",25,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,18,10,7,1,"综述深度学习用于卫星影像土地覆盖分类，对农业监测有参考价值，但偏学术，影响有限。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22251468",[29,30,31,32,33],"智慧农业","农业人工智能","深度学习","遥感","土地覆盖分类",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,54,60],{"code":35,"score":48,"supported":49,"title":50,"abstract":52,"tags":53},16,true,[51],"deep learning",[31,51],[30,31],{"code":55,"score":21,"supported":49,"title":56,"abstract":57,"tags":59},"I3",[],[32,58],"remote sensing",[32],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"I1",[],[],[29],[66,67],"农业人工智能 土地覆盖分类 智慧农业 深度学习","农业人工智能 土地覆盖分类","农业人工智能土地覆盖分类智慧农业深度学习-1534",3,"10.5281\u002Fzenodo.22251467",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":80,"oa_url":6,"card":81,"direction":85,"ingested_from":87},"W7206172334",[74,76,78],{"name":75,"orcid":9},"Antony Daniel Rex. J",{"name":77,"orcid":9},"R. Vidya",{"name":79,"orcid":9},"A. Martin",0,{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"综述深度学习用于卫星影像土地覆盖分类的语义分割方法，并分析挑战与未来方向。","综述CNN、Transformer及混合架构，案例为EfficientNet-V","混合架构性能优，但存在类别不平衡、计算成本高、泛化差等问题。","农业遥感与作物表型","可探索轻量级混合架构或自监督学习，以提升农业土地覆盖分类的精度与泛化能力。","openalex","2026-09-03T23:30:30.230250Z",{"total":90,"page":22,"page_size":90,"items":91},6,[92,155,209,325,387,448],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":10,"source_url":95,"published_at":98,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":99,"score_detail":100,"sources":103,"tags":105,"primary_topic":108,"secondary_topics":109,"classification":110,"search_phrases":138,"slug":141,"view_count":80,"doi":142,"paper":143,"created_at":154},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","2026-10-21T00:00:00Z",74,{"impact":17,"substance":101,"depth":19,"authority":17,"freshness":20,"relevant":22,"comment":102},22,"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[104],{"name":10,"url":95},[29,30,106,32,107],"粮食安全","气候分析",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":111},{"candidates":112,"agricultural_evidence":49},[113,119,125,130,134],{"code":35,"score":114,"supported":49,"title":115,"abstract":117,"tags":118},9,[116],"artificial intelligence",[],[30],{"code":120,"score":69,"supported":49,"title":121,"abstract":122,"tags":124},"I5",[],[123],"农业传感",[],{"code":126,"score":22,"supported":14,"title":127,"abstract":128,"tags":129},"A8",[],[],[106],{"code":61,"score":22,"supported":14,"title":131,"abstract":132,"tags":133},[],[],[29],{"code":55,"score":22,"supported":14,"title":135,"abstract":136,"tags":137},[],[],[32],[139,140],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":142,"openalex_id":144,"authors":145,"venue":10,"cited_by_count":80,"oa_url":95,"card":148,"direction":153,"ingested_from":87},"W7223111766",[146],{"name":147,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":149,"method":150,"finding":151,"direction":85,"opportunity":152},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":9,"source_name":10,"source_url":158,"published_at":98,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":15,"score_detail":161,"sources":165,"tags":167,"primary_topic":170,"secondary_topics":171,"classification":176,"search_phrases":193,"slug":196,"view_count":80,"doi":197,"paper":198,"created_at":208},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",{"impact":162,"substance":18,"depth":163,"authority":17,"freshness":20,"relevant":22,"comment":164},8,17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[166],{"name":10,"url":158},[29,30,168,32,169],"产量预测","可解释性",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[172],{"code":55,"slug":173,"name":174,"path":175,"parent_code":39},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":42,"locked":14,"version":43,"method":44,"evidence":177},{"candidates":178,"agricultural_evidence":49},[179,185,189],{"code":35,"score":17,"supported":49,"title":180,"abstract":182,"tags":184},[181],"computer vision",[183],"计算机视觉",[30],{"code":55,"score":162,"supported":49,"title":186,"abstract":187,"tags":188},[],[32,58],[168,32],{"code":61,"score":22,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[29],[194,195],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":197,"openalex_id":199,"authors":200,"venue":10,"cited_by_count":80,"oa_url":158,"card":203,"direction":85,"ingested_from":87},"W7223114002",[201],{"name":202,"orcid":9},"Kadiatou Barry",{"tldr":204,"method":205,"finding":206,"direction":85,"opportunity":207},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":98,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":216,"score_detail":217,"sources":219,"tags":221,"primary_topic":224,"secondary_topics":230,"classification":236,"search_phrases":295,"slug":298,"view_count":80,"doi":299,"paper":300,"created_at":324},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":19,"substance":101,"depth":19,"authority":17,"freshness":114,"relevant":22,"comment":218},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[220],{"name":215,"url":212},[29,30,32,222,223],"作物水分胁迫","精准灌溉",{"code":225,"slug":226,"name":227,"path":228,"parent_code":229},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[231,232],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"code":120,"slug":233,"name":234,"path":235,"parent_code":39},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":42,"locked":14,"version":43,"method":44,"evidence":237},{"candidates":238,"agricultural_evidence":49},[239,245,251,258,264,268,275,282,289],{"code":225,"score":240,"supported":49,"title":241,"abstract":242,"tags":244},15,[226],[243,226],"灌溉",[223],{"code":35,"score":20,"supported":49,"title":246,"abstract":247,"tags":250},[],[248,249,116],"机器学习","machine learning",[30],{"code":120,"score":114,"supported":49,"title":252,"abstract":253,"tags":257},[],[254,255,256],"物联网","internet of things","IoT",[],{"code":61,"score":21,"supported":49,"title":259,"abstract":260,"tags":263},[],[261,262],"数字孪生","digital twin",[29],{"code":55,"score":21,"supported":49,"title":265,"abstract":266,"tags":267},[],[32,58],[32],{"code":269,"score":90,"supported":49,"title":270,"abstract":271,"tags":274},"A2",[],[272,273],"小麦","wheat",[],{"code":276,"score":90,"supported":49,"title":277,"abstract":278,"tags":281},"A3",[],[279,280],"玉米","maize",[],{"code":283,"score":90,"supported":49,"title":284,"abstract":285,"tags":288},"A5",[],[286,287],"马铃薯","potato",[],{"code":290,"score":69,"supported":49,"title":291,"abstract":292,"tags":294},"B2",[],[293],"葡萄",[],[296,297],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":299,"openalex_id":301,"authors":302,"venue":215,"cited_by_count":80,"oa_url":212,"card":318,"direction":323,"ingested_from":87},"W7223088921",[303,306,309,312,315],{"name":304,"orcid":305},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":307,"orcid":308},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":310,"orcid":311},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":313,"orcid":314},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":316,"orcid":317},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":319,"method":320,"finding":321,"direction":85,"opportunity":322},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":9,"source_name":331,"source_url":328,"published_at":332,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":333,"score_detail":334,"sources":336,"tags":338,"primary_topic":340,"secondary_topics":341,"classification":342,"search_phrases":358,"slug":361,"view_count":80,"doi":362,"paper":363,"created_at":386},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":162,"substance":19,"depth":240,"authority":17,"freshness":162,"relevant":22,"comment":335},"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。",[337],{"name":331,"url":328},[29,30,339,32],"目标检测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":343},{"candidates":344,"agricultural_evidence":49},[345,350,354],{"code":35,"score":346,"supported":49,"title":347,"abstract":348,"tags":349},11,[],[339,31,183,51,181],[30,339],{"code":55,"score":21,"supported":49,"title":351,"abstract":352,"tags":353},[],[32,58],[32],{"code":61,"score":22,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[29],[359,360],"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":362,"openalex_id":364,"authors":365,"venue":331,"cited_by_count":80,"oa_url":328,"card":381,"direction":85,"ingested_from":87},"W7222920697",[366,369,372,375,378],{"name":367,"orcid":368},"Nikola Ivačko","https:\u002F\u002Forcid.org\u002F0000-0003-4367-119X",{"name":370,"orcid":371},"Ivan S Ciric","https:\u002F\u002Forcid.org\u002F0000-0003-0430-8937",{"name":373,"orcid":374},"Milica Tasić","https:\u002F\u002Forcid.org\u002F0009-0001-9160-6379",{"name":376,"orcid":377},"Ljiljana Radović","https:\u002F\u002Forcid.org\u002F0000-0002-4099-1289",{"name":379,"orcid":380},"Žarko Ćojbašić","https:\u002F\u002Forcid.org\u002F0000-0002-4581-1048",{"tldr":382,"method":383,"finding":384,"direction":153,"opportunity":385},"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",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":332,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":402,"secondary_topics":408,"classification":410,"search_phrases":428,"slug":431,"view_count":80,"doi":432,"paper":433,"created_at":447},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":17,"substance":19,"depth":48,"authority":20,"freshness":162,"relevant":22,"comment":396},"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。",[398],{"name":393,"url":390},[29,30,31,400,401],"番茄病害","图像识别",{"code":403,"slug":404,"name":405,"path":406,"parent_code":407},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[409],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":411},{"candidates":412,"agricultural_evidence":49},[413,419,423],{"code":403,"score":240,"supported":49,"title":414,"abstract":416,"tags":418},[415],"tomato",[417,415],"番茄",[400],{"code":35,"score":114,"supported":49,"title":420,"abstract":421,"tags":422},[],[31,51],[30,401,31],{"code":61,"score":21,"supported":49,"title":424,"abstract":425,"tags":427},[],[29,426],"smart agriculture",[29],[429,430],"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":432,"openalex_id":434,"authors":435,"venue":393,"cited_by_count":80,"oa_url":441,"card":442,"direction":323,"ingested_from":87},"W7222977034",[436,438],{"name":437,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":439,"orcid":440},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":443,"method":444,"finding":445,"direction":153,"opportunity":446},"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":449,"title":450,"url":451,"summary":452,"summary_zh":453,"content":9,"source_name":454,"source_url":451,"published_at":332,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":462,"secondary_topics":463,"classification":464,"search_phrases":483,"slug":486,"view_count":80,"doi":487,"paper":488,"created_at":496},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",56,{"impact":162,"substance":19,"depth":48,"authority":90,"freshness":162,"relevant":22,"comment":457},"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。",[459],{"name":454,"url":451},[29,30,183,31,461],"水果品质检测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":465},{"candidates":466,"agricultural_evidence":49},[467,472,478],{"code":35,"score":468,"supported":49,"title":469,"abstract":470,"tags":471},28,[51,181],[339,31,248,183,249,51,116,181],[30,31,183],{"code":61,"score":21,"supported":49,"title":473,"abstract":474,"tags":477},[],[475,476],"精准农业","precision agriculture",[29],{"code":290,"score":69,"supported":49,"title":479,"abstract":480,"tags":482},[],[481],"orchard",[],[484,485],"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":487,"openalex_id":489,"authors":490,"venue":454,"cited_by_count":80,"oa_url":451,"card":491,"direction":323,"ingested_from":87},"W7222892831",[],{"tldr":492,"method":493,"finding":494,"direction":85,"opportunity":495},"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。","2026-10-20T23:30:10.066042Z"]