[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4321":3,"related-4321":126},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":95,"slug":98,"view_count":84,"doi":99,"paper":100,"created_at":125},4321,"Improving early season crop type mapping using machine learning algorithms with combined SAR and optical time series","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04839-6","Timely and reliable crop type mapping is essential for sustainable agricultural management and food security. This study investigates the potential of integrating multi-temporal Synthetic Aperture Radar (SAR) data from Sentinel-1 and optical imagery from Sentinel-2 for early-season crop classification in Udham Singh Nagar district, Uttarakhand, India. Leveraging the Google Earth Engine (GEE) cloud platform, two ensemble machine learning algorithms, Random Forest (RF) and Gradient Boosted Trees (GBT), were implemented and evaluated for their ability to classify major land cover and crop types using combined SAR-optical datasets acquired during November–December 2022. A total of 1141 ground truth samples were collected through field surveys. The integration of structural (SAR) and spectral (optical) information substantially enhanced classification performance compared with single-sensor approaches. Among the models, GBT achieved the highest accuracy, with an overall accuracy of 93% and a Kappa coefficient of 0.90. Wheat, forest, and water bodies were mapped with high precision (F1 ≥ 0.90), whereas mustard and sugarcane showed lower accuracy due to spectral similarity and limited training data. Feature importance analysis identified late-season SAR (VV, VH) and optical (red-edge and SWIR) bands as the most influential variables. The results demonstrate that the synergy between SAR and optical time-series data, coupled with ensemble machine learning, provides a robust and scalable framework for near real-time crop monitoring. This approach offers valuable insights for precision agriculture and supports data-driven policy planning in India’s agricultural sector.","及时可靠的作物类型制图对可持续农业管理和粮食安全至关重要。本研究探讨了整合Sentinel-1多时相合成孔径雷达（SAR）数据与Sentinel-2光学影像在印度北阿坎德邦乌达姆辛格纳加尔地区进行早季作物分类的潜力。依托Google Earth Engine（GEE）云平台，研究采用随机森林（RF）和梯度提升树（GBT）两种集成机器学习算法，利用2022年11月至12月获取的SAR-光学联合数据集，对主要土地覆盖和作物类型进行分类，并评估其分类能力。通过实地调查共采集1141个地面真实样本。与单一传感器方法相比，结构信息（SAR）与光谱信息（光学）的融合显著提升了分类性能。在各模型中，GBT取得了最高精度，总体精度达93%，Kappa系数为0.90。小麦、森林和水体的制图精度较高（F1 ≥ 0.90），而芥菜和甘蔗因光谱相似及训练数据有限，精度较低。特征重要性分析表明，晚季SAR（VV、VH）和光学（红边和短波红外）波段是最具影响力的变量。结果表明，SAR与光学时间序列数据的协同结合集成机器学习，为近实时作物监测提供了稳健且可扩展的框架。该方法为精准农业提供了有价值的见解，并支持印度农业领域的数据驱动政策规划。",null,"Discover Sustainability","2026-10-05T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,9,1,"该研究将SAR与光学时序数据结合集成机器学习，实现93%早期作物分类精度，方法可扩展、结论可靠，对农业遥感监测有实质参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","粮食安全","遥感","作物分类",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,60,67,75,82,89],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},15,true,[49],"machine learning",[51,49],"机器学习",[28],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":59},"A8",7,[],[29,58],"food security",[29],{"code":61,"score":55,"supported":47,"title":62,"abstract":63,"tags":66},"I1",[],[64,65],"精准农业","precision agriculture",[27],{"code":68,"score":69,"supported":47,"title":70,"abstract":71,"tags":74},"A2",6,[],[72,73],"小麦","wheat",[],{"code":76,"score":69,"supported":47,"title":77,"abstract":78,"tags":81},"A6",[],[79,80],"甘蔗","sugarcane",[],{"code":83,"score":84,"supported":47,"title":85,"abstract":86,"tags":88},"H8",3,[],[87],"可持续农业",[],{"code":90,"score":91,"supported":14,"title":92,"abstract":93,"tags":94},"I3",2,[],[],[31,30],[96,97],"Sentinel-1 Sentinel-2 作物分类","Google Earth Engine 机器学习 作物制图","Sentinel-1Sentinel-2作物分类-4321","10.1007\u002Fs43621-026-04839-6",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":116,"oa_url":6,"card":117,"direction":123,"ingested_from":124},"W7219792303",[103,106,109,111,114],{"name":104,"orcid":105},"Rajeev Ranjan Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-2670-6189",{"name":107,"orcid":108},"Jayanta Das","https:\u002F\u002Forcid.org\u002F0000-0003-0995-9114",{"name":110,"orcid":9},"Jadhav Rajaram Anna",{"name":112,"orcid":113},"Abhishek Danodia","https:\u002F\u002Forcid.org\u002F0000-0002-5724-8790",{"name":115,"orcid":9},"Dipanwita Haldar",0,{"tldr":118,"method":119,"finding":120,"direction":121,"opportunity":122},"融合Sentinel-1 SAR与Sentinel-2光学时序，用集成机器学习实现印度早季作物类型高","GEE平台，RF与GBT集成学习，1141个地面样本，SAR-光学多时相数据。","GBT精度最高（总体93%，Kappa 0.90），SAR与光学结合显著优于单传感器。","农业遥感与作物表型","可探索更早物候期、小样本作物（如芥菜、甘蔗）的SAR-光学特征优化与迁移学习。","农业人工智能与决策模型","openalex","2026-10-06T23:30:38.288174Z",{"total":69,"page":22,"page_size":69,"items":127},[128,189,246,356,420,498],{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":141,"tags":143,"primary_topic":145,"secondary_topics":146,"classification":147,"search_phrases":173,"slug":176,"view_count":116,"doi":177,"paper":178,"created_at":188},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":138,"substance":139,"depth":19,"authority":138,"freshness":13,"relevant":22,"comment":140},12,22,"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。",[142],{"name":134,"url":131},[27,28,29,30,144],"气候分析",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":148},{"candidates":149,"agricultural_evidence":47},[150,155,161,165,169],{"code":33,"score":21,"supported":47,"title":151,"abstract":153,"tags":154},[152],"artificial intelligence",[],[28],{"code":156,"score":84,"supported":47,"title":157,"abstract":158,"tags":160},"I5",[],[159],"农业传感",[],{"code":54,"score":22,"supported":14,"title":162,"abstract":163,"tags":164},[],[],[29],{"code":61,"score":22,"supported":14,"title":166,"abstract":167,"tags":168},[],[],[27],{"code":90,"score":22,"supported":14,"title":170,"abstract":171,"tags":172},[],[],[30],[174,175],"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":177,"openalex_id":179,"authors":180,"venue":134,"cited_by_count":116,"oa_url":131,"card":183,"direction":123,"ingested_from":124},"W7223111766",[181],{"name":182,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":184,"method":185,"finding":186,"direction":121,"opportunity":187},"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。","2026-10-22T11:21:02.682497Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":134,"source_url":192,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":207,"classification":212,"search_phrases":230,"slug":233,"view_count":116,"doi":234,"paper":235,"created_at":245},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。",67,{"impact":197,"substance":198,"depth":199,"authority":138,"freshness":13,"relevant":22,"comment":200},8,20,17,"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。",[202],{"name":134,"url":192},[27,28,204,30,205],"产量预测","可解释性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[208],{"code":90,"slug":209,"name":210,"path":211,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":213},{"candidates":214,"agricultural_evidence":47},[215,221,226],{"code":33,"score":138,"supported":47,"title":216,"abstract":218,"tags":220},[217],"computer vision",[219],"计算机视觉",[28],{"code":90,"score":197,"supported":47,"title":222,"abstract":223,"tags":225},[],[30,224],"remote sensing",[204,30],{"code":61,"score":22,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[27],[231,232],"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":234,"openalex_id":236,"authors":237,"venue":134,"cited_by_count":116,"oa_url":192,"card":240,"direction":121,"ingested_from":124},"W7223114002",[238],{"name":239,"orcid":9},"Kadiatou Barry",{"tldr":241,"method":242,"finding":243,"direction":121,"opportunity":244},"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":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":9,"source_name":252,"source_url":249,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":267,"classification":273,"search_phrases":326,"slug":329,"view_count":116,"doi":330,"paper":331,"created_at":355},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":139,"depth":19,"authority":138,"freshness":21,"relevant":22,"comment":255},"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。",[257],{"name":252,"url":249},[27,28,30,259,260],"作物水分胁迫","精准灌溉",{"code":262,"slug":263,"name":264,"path":265,"parent_code":266},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[268,269],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":156,"slug":270,"name":271,"path":272,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":274},{"candidates":275,"agricultural_evidence":47},[276,281,285,292,298,302,306,313,320],{"code":262,"score":46,"supported":47,"title":277,"abstract":278,"tags":280},[263],[279,263],"灌溉",[260],{"code":33,"score":13,"supported":47,"title":282,"abstract":283,"tags":284},[],[51,49,152],[28],{"code":156,"score":21,"supported":47,"title":286,"abstract":287,"tags":291},[],[288,289,290],"物联网","internet of things","IoT",[],{"code":61,"score":55,"supported":47,"title":293,"abstract":294,"tags":297},[],[295,296],"数字孪生","digital twin",[27],{"code":90,"score":55,"supported":47,"title":299,"abstract":300,"tags":301},[],[30,224],[30],{"code":68,"score":69,"supported":47,"title":303,"abstract":304,"tags":305},[],[72,73],[],{"code":307,"score":69,"supported":47,"title":308,"abstract":309,"tags":312},"A3",[],[310,311],"玉米","maize",[],{"code":314,"score":69,"supported":47,"title":315,"abstract":316,"tags":319},"A5",[],[317,318],"马铃薯","potato",[],{"code":321,"score":84,"supported":47,"title":322,"abstract":323,"tags":325},"B2",[],[324],"葡萄",[],[327,328],"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":330,"openalex_id":332,"authors":333,"venue":252,"cited_by_count":116,"oa_url":249,"card":349,"direction":354,"ingested_from":124},"W7223088921",[334,337,340,343,346],{"name":335,"orcid":336},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":338,"orcid":339},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":341,"orcid":342},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":344,"orcid":345},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":347,"orcid":348},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":350,"method":351,"finding":352,"direction":121,"opportunity":353},"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":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":362,"source_url":359,"published_at":363,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":364,"score_detail":365,"sources":367,"tags":369,"primary_topic":371,"secondary_topics":372,"classification":373,"search_phrases":391,"slug":394,"view_count":116,"doi":395,"paper":396,"created_at":419},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":197,"substance":19,"depth":46,"authority":138,"freshness":197,"relevant":22,"comment":366},"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。",[368],{"name":362,"url":359},[27,28,370,30],"目标检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":374},{"candidates":375,"agricultural_evidence":47},[376,383,387],{"code":33,"score":377,"supported":47,"title":378,"abstract":379,"tags":382},11,[],[370,380,219,381,217],"深度学习","deep learning",[28,370],{"code":90,"score":55,"supported":47,"title":384,"abstract":385,"tags":386},[],[30,224],[30],{"code":61,"score":22,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[27],[392,393],"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":395,"openalex_id":397,"authors":398,"venue":362,"cited_by_count":116,"oa_url":359,"card":414,"direction":121,"ingested_from":124},"W7222920697",[399,402,405,408,411],{"name":400,"orcid":401},"Nikola Ivačko","https:\u002F\u002Forcid.org\u002F0000-0003-4367-119X",{"name":403,"orcid":404},"Ivan S Ciric","https:\u002F\u002Forcid.org\u002F0000-0003-0430-8937",{"name":406,"orcid":407},"Milica Tasić","https:\u002F\u002Forcid.org\u002F0009-0001-9160-6379",{"name":409,"orcid":410},"Ljiljana Radović","https:\u002F\u002Forcid.org\u002F0000-0002-4099-1289",{"name":412,"orcid":413},"Žarko Ćojbašić","https:\u002F\u002Forcid.org\u002F0000-0002-4581-1048",{"tldr":415,"method":416,"finding":417,"direction":123,"opportunity":418},"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":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":428,"score_detail":429,"sources":434,"tags":436,"primary_topic":438,"secondary_topics":439,"classification":440,"search_phrases":466,"slug":469,"view_count":456,"doi":470,"paper":471,"created_at":497},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":430,"substance":431,"depth":432,"authority":46,"freshness":21,"relevant":22,"comment":433},26,24,19,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[435],{"name":426,"url":423},[27,28,64,30,437],"全球评估",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":441},{"candidates":442,"agricultural_evidence":47},[443,447,451,455,460],{"code":61,"score":377,"supported":47,"title":444,"abstract":445,"tags":446},[],[27,64,65],[27,64],{"code":33,"score":55,"supported":47,"title":448,"abstract":449,"tags":450},[],[51,49],[28],{"code":262,"score":69,"supported":47,"title":452,"abstract":453,"tags":454},[],[279,263],[],{"code":90,"score":456,"supported":47,"title":457,"abstract":458,"tags":459},4,[],[30],[30],{"code":461,"score":84,"supported":47,"title":462,"abstract":463,"tags":465},"F4",[],[464],"除草",[],[467,468],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":470,"openalex_id":472,"authors":473,"venue":426,"cited_by_count":116,"oa_url":423,"card":492,"direction":354,"ingested_from":124},"W7222974188",[474,477,480,483,486,489],{"name":475,"orcid":476},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":478,"orcid":479},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":481,"orcid":482},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":484,"orcid":485},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":487,"orcid":488},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":490,"orcid":491},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":493,"method":494,"finding":495,"direction":354,"opportunity":496},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":499,"title":500,"url":501,"summary":502,"summary_zh":503,"content":9,"source_name":504,"source_url":501,"published_at":505,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":506,"score_detail":507,"sources":509,"tags":511,"primary_topic":514,"secondary_topics":515,"classification":522,"search_phrases":549,"slug":552,"view_count":116,"doi":553,"paper":554,"created_at":574},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":138,"substance":18,"depth":199,"authority":20,"freshness":21,"relevant":22,"comment":508},"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[510],{"name":504,"url":501},[27,28,30,512,513],"土壤盐渍化","新疆农业",{"code":90,"slug":209,"name":210,"path":211,"parent_code":37},[516,521],{"code":517,"slug":518,"name":519,"path":520,"parent_code":266},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":523},{"candidates":524,"agricultural_evidence":47},[525,529,535,539,545],{"code":90,"score":46,"supported":47,"title":526,"abstract":527,"tags":528},[224],[30,224],[30],{"code":517,"score":530,"supported":47,"title":531,"abstract":532,"tags":534},14,[518],[533,518],"土壤",[],{"code":33,"score":21,"supported":47,"title":536,"abstract":537,"tags":538},[381],[],[28],{"code":540,"score":84,"supported":47,"title":541,"abstract":542,"tags":544},"H7",[],[543],"干旱",[],{"code":61,"score":22,"supported":14,"title":546,"abstract":547,"tags":548},[],[],[27],[550,551],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":553,"openalex_id":555,"authors":556,"venue":504,"cited_by_count":116,"oa_url":501,"card":569,"direction":121,"ingested_from":124},"W7221816183",[557,559,562,564,566],{"name":558,"orcid":9},"Mingjie Qian",{"name":560,"orcid":561},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":563,"orcid":9},"Peng Wang",{"name":565,"orcid":9},"Hangyuan Liu",{"name":567,"orcid":568},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":570,"method":571,"finding":572,"direction":121,"opportunity":573},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z"]