[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1275":3,"related-1275":136},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":135},1275,"Combining 3D-multispectral and hyperspectral imaging to identify environmental stress treatments imposed during plant growth","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.08.28.747774","Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments - including different growth irradiances, heat treatment and drought stress - with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.",null,"bioRxiv (Cold Spring Harbor Laboratory)","2026-08-28T00:00:00Z","论文",10,false,74,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,12,4,1,"研究融合3D多光谱与高光谱成像及机器学习，实现非破坏性高通量植物表型分析，对精准农业有参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","表型分析","遥感","环境胁迫",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":13,"reason":64},[37,46,52,59],{"code":38,"score":39,"supported":40,"title":41,"abstract":44,"tags":45},"I3",23,true,[42,43],"multispectral","hyperspectral",[42,43],[28],{"code":47,"score":19,"supported":40,"title":48,"abstract":49,"tags":51},"I2",[],[50],"machine learning",[26],{"code":53,"score":54,"supported":40,"title":55,"abstract":56,"tags":58},"H7",3,[],[57],"drought",[],{"code":60,"score":20,"supported":13,"title":61,"abstract":62,"tags":63},"I1",[],[],[25],"农业相关性需要复核",[66,67],"农业人工智能 智慧农业 环境胁迫 表型分析","农业人工智能 智慧农业","农业人工智能智慧农业环境胁迫表型分析-1275",6,"10.64898\u002F2026.08.28.747774",{"doi":70,"openalex_id":72,"authors":73,"venue":9,"cited_by_count":125,"oa_url":126,"card":127,"direction":133,"ingested_from":134},"W7204524092",[74,77,80,82,84,86,89,92,95,98,100,102,104,107,110,112,114,117,120,122],{"name":75,"orcid":76},"Frederike Stock","https:\u002F\u002Forcid.org\u002F0000-0001-8923-4273",{"name":78,"orcid":79},"Saswat Panda","https:\u002F\u002Forcid.org\u002F0000-0002-7635-2753",{"name":81,"orcid":8},"Richard Poire",{"name":83,"orcid":8},"Timothy Brown",{"name":85,"orcid":8},"Ayesha Akram",{"name":87,"orcid":88},"Liang Zheng","https:\u002F\u002Forcid.org\u002F0000-0002-1464-9500",{"name":90,"orcid":91},"Huan Lei","https:\u002F\u002Forcid.org\u002F0000-0002-2945-8934",{"name":93,"orcid":94},"Ruyi Zha","https:\u002F\u002Forcid.org\u002F0009-0005-0410-1807",{"name":96,"orcid":97},"Mingrui Zhao","https:\u002F\u002Forcid.org\u002F0009-0006-3882-8526",{"name":99,"orcid":8},"Sébastien Isabelle",{"name":101,"orcid":8},"Michèle Martel",{"name":103,"orcid":8},"Marc‐André Comeau",{"name":105,"orcid":106},"Louis‐Philippe Hamel","https:\u002F\u002Forcid.org\u002F0000-0001-8289-4498",{"name":108,"orcid":109},"Pierre‐Olivier Lavoie","https:\u002F\u002Forcid.org\u002F0000-0001-9994-2417",{"name":111,"orcid":8},"Marc Andre D'Aoust",{"name":113,"orcid":8},"Hannah Reithinger",{"name":115,"orcid":116},"Pooja Saxena","https:\u002F\u002Forcid.org\u002F0000-0002-0977-5721",{"name":118,"orcid":119},"Eric A. Stone","https:\u002F\u002Forcid.org\u002F0000-0002-2725-4209",{"name":121,"orcid":8},"Hongdong Li",{"name":123,"orcid":124},"Danielle A. Way","https:\u002F\u002Forcid.org\u002F0000-0003-4801-5319",0,"https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F08\u002F28\u002F2026.08.28.747774.full.pdf",{"tldr":128,"method":129,"finding":130,"direction":131,"opportunity":132},"结合3D多光谱和2D高光谱成像及机器学习，识别植物生长期间的环境胁迫处理。","融合3D多光谱扫描仪和VNIR高光谱相机数据，提取形态和光谱特征，训练ML模型。","融合3D+HSI模型优于单一模态，能准确区分不同环境胁迫处理。","农业人工智能与决策模型","可探索在田间复杂环境下融合多模态成像与深度学习，实现作物胁迫的实时诊断与预警。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:15.198826Z",{"total":69,"page":20,"page_size":69,"items":137},[138,205,262,377,448,511],{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":8,"source_name":144,"source_url":141,"published_at":145,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":146,"sources":148,"tags":150,"primary_topic":153,"secondary_topics":158,"classification":159,"search_phrases":188,"slug":191,"view_count":125,"doi":192,"paper":193,"created_at":204},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",{"impact":18,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":20,"comment":147},"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。",[149],{"name":144,"url":141},[25,26,151,28,152],"粮食安全","气候分析",{"code":47,"slug":154,"name":155,"path":156,"parent_code":157},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":160,"locked":13,"version":33,"method":34,"evidence":161},"classified",{"candidates":162,"agricultural_evidence":40},[163,169,175,180,184],{"code":47,"score":164,"supported":40,"title":165,"abstract":167,"tags":168},9,[166],"artificial intelligence",[],[26],{"code":170,"score":54,"supported":40,"title":171,"abstract":172,"tags":174},"I5",[],[173],"农业传感",[],{"code":176,"score":20,"supported":13,"title":177,"abstract":178,"tags":179},"A8",[],[],[151],{"code":60,"score":20,"supported":13,"title":181,"abstract":182,"tags":183},[],[],[25],{"code":38,"score":20,"supported":13,"title":185,"abstract":186,"tags":187},[],[],[28],[189,190],"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":192,"openalex_id":194,"authors":195,"venue":144,"cited_by_count":125,"oa_url":141,"card":198,"direction":131,"ingested_from":134},"W7223111766",[196],{"name":197,"orcid":8},"Fatima-Zahra El Khattabi",{"tldr":199,"method":200,"finding":201,"direction":202,"opportunity":203},"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。","农业遥感与作物表型","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":206,"title":207,"url":208,"summary":209,"summary_zh":210,"content":8,"source_name":144,"source_url":208,"published_at":145,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":211,"score_detail":212,"sources":217,"tags":219,"primary_topic":222,"secondary_topics":223,"classification":228,"search_phrases":246,"slug":249,"view_count":125,"doi":250,"paper":251,"created_at":261},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":213,"substance":214,"depth":215,"authority":18,"freshness":12,"relevant":20,"comment":216},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。",[218],{"name":144,"url":208},[25,26,220,28,221],"产量预测","可解释性",{"code":47,"slug":154,"name":155,"path":156,"parent_code":157},[224],{"code":38,"slug":225,"name":226,"path":227,"parent_code":157},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":160,"locked":13,"version":33,"method":34,"evidence":229},{"candidates":230,"agricultural_evidence":40},[231,237,242],{"code":47,"score":18,"supported":40,"title":232,"abstract":234,"tags":236},[233],"computer vision",[235],"计算机视觉",[26],{"code":38,"score":213,"supported":40,"title":238,"abstract":239,"tags":241},[],[28,240],"remote sensing",[220,28],{"code":60,"score":20,"supported":13,"title":243,"abstract":244,"tags":245},[],[],[25],[247,248],"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":250,"openalex_id":252,"authors":253,"venue":144,"cited_by_count":125,"oa_url":208,"card":256,"direction":202,"ingested_from":134},"W7223114002",[254],{"name":255,"orcid":8},"Kadiatou Barry",{"tldr":257,"method":258,"finding":259,"direction":202,"opportunity":260},"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":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":8,"source_name":268,"source_url":265,"published_at":145,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":269,"score_detail":270,"sources":272,"tags":274,"primary_topic":277,"secondary_topics":283,"classification":289,"search_phrases":348,"slug":351,"view_count":125,"doi":352,"paper":353,"created_at":376},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":16,"substance":17,"depth":16,"authority":18,"freshness":164,"relevant":20,"comment":271},"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。",[273],{"name":268,"url":265},[25,26,28,275,276],"作物水分胁迫","精准灌溉",{"code":278,"slug":279,"name":280,"path":281,"parent_code":282},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[284,285],{"code":47,"slug":154,"name":155,"path":156,"parent_code":157},{"code":170,"slug":286,"name":287,"path":288,"parent_code":157},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":160,"locked":13,"version":33,"method":34,"evidence":290},{"candidates":291,"agricultural_evidence":40},[292,298,303,310,317,321,328,335,342],{"code":278,"score":293,"supported":40,"title":294,"abstract":295,"tags":297},15,[279],[296,279],"灌溉",[276],{"code":47,"score":12,"supported":40,"title":299,"abstract":300,"tags":302},[],[301,50,166],"机器学习",[26],{"code":170,"score":164,"supported":40,"title":304,"abstract":305,"tags":309},[],[306,307,308],"物联网","internet of things","IoT",[],{"code":60,"score":311,"supported":40,"title":312,"abstract":313,"tags":316},7,[],[314,315],"数字孪生","digital twin",[25],{"code":38,"score":311,"supported":40,"title":318,"abstract":319,"tags":320},[],[28,240],[28],{"code":322,"score":69,"supported":40,"title":323,"abstract":324,"tags":327},"A2",[],[325,326],"小麦","wheat",[],{"code":329,"score":69,"supported":40,"title":330,"abstract":331,"tags":334},"A3",[],[332,333],"玉米","maize",[],{"code":336,"score":69,"supported":40,"title":337,"abstract":338,"tags":341},"A5",[],[339,340],"马铃薯","potato",[],{"code":343,"score":54,"supported":40,"title":344,"abstract":345,"tags":347},"B2",[],[346],"葡萄",[],[349,350],"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":352,"openalex_id":354,"authors":355,"venue":268,"cited_by_count":125,"oa_url":265,"card":371,"direction":133,"ingested_from":134},"W7223088921",[356,359,362,365,368],{"name":357,"orcid":358},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":360,"orcid":361},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":363,"orcid":364},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":366,"orcid":367},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":369,"orcid":370},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":372,"method":373,"finding":374,"direction":202,"opportunity":375},"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。","2026-10-22T11:20:24.526683Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":8,"source_name":383,"source_url":380,"published_at":384,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":385,"score_detail":386,"sources":388,"tags":390,"primary_topic":392,"secondary_topics":397,"classification":399,"search_phrases":429,"slug":432,"view_count":125,"doi":433,"paper":434,"created_at":447},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":213,"relevant":20,"comment":387},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[389],{"name":383,"url":380},[25,26,391,220,28],"水稻",{"code":393,"slug":394,"name":391,"path":395,"parent_code":396},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[398],{"code":38,"slug":225,"name":226,"path":227,"parent_code":157},{"status":160,"locked":13,"version":33,"method":34,"evidence":400},{"candidates":401,"agricultural_evidence":40},[402,406,410,415,421,425],{"code":393,"score":293,"supported":40,"title":403,"abstract":404,"tags":405},[394],[391,394],[391],{"code":38,"score":213,"supported":40,"title":407,"abstract":408,"tags":409},[],[28,240],[220,28],{"code":176,"score":69,"supported":40,"title":411,"abstract":412,"tags":414},[],[151,413],"food security",[],{"code":416,"score":54,"supported":40,"title":417,"abstract":418,"tags":420},"J1",[],[419],"农业规划",[],{"code":60,"score":20,"supported":13,"title":422,"abstract":423,"tags":424},[],[],[25],{"code":47,"score":20,"supported":13,"title":426,"abstract":427,"tags":428},[],[],[26],[430,431],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":433,"openalex_id":435,"authors":436,"venue":383,"cited_by_count":125,"oa_url":380,"card":442,"direction":202,"ingested_from":134},"W7222942064",[437,439],{"name":438,"orcid":8},"Nayana Venkatesh",{"name":440,"orcid":441},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":443,"method":444,"finding":445,"direction":202,"opportunity":446},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":449,"title":450,"url":451,"summary":452,"summary_zh":453,"content":8,"source_name":454,"source_url":451,"published_at":384,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":462,"secondary_topics":463,"classification":464,"search_phrases":482,"slug":485,"view_count":125,"doi":486,"paper":487,"created_at":510},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",61,{"impact":213,"substance":16,"depth":293,"authority":18,"freshness":213,"relevant":20,"comment":457},"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。",[459],{"name":454,"url":451},[25,26,461,28],"目标检测",{"code":47,"slug":154,"name":155,"path":156,"parent_code":157},[],{"status":160,"locked":13,"version":33,"method":34,"evidence":465},{"candidates":466,"agricultural_evidence":40},[467,474,478],{"code":47,"score":468,"supported":40,"title":469,"abstract":470,"tags":473},11,[],[461,471,235,472,233],"深度学习","deep learning",[26,461],{"code":38,"score":311,"supported":40,"title":475,"abstract":476,"tags":477},[],[28,240],[28],{"code":60,"score":20,"supported":13,"title":479,"abstract":480,"tags":481},[],[],[25],[483,484],"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":486,"openalex_id":488,"authors":489,"venue":454,"cited_by_count":125,"oa_url":451,"card":505,"direction":202,"ingested_from":134},"W7222920697",[490,493,496,499,502],{"name":491,"orcid":492},"Nikola Ivačko","https:\u002F\u002Forcid.org\u002F0000-0003-4367-119X",{"name":494,"orcid":495},"Ivan S Ciric","https:\u002F\u002Forcid.org\u002F0000-0003-0430-8937",{"name":497,"orcid":498},"Milica Tasić","https:\u002F\u002Forcid.org\u002F0009-0001-9160-6379",{"name":500,"orcid":501},"Ljiljana Radović","https:\u002F\u002Forcid.org\u002F0000-0002-4099-1289",{"name":503,"orcid":504},"Žarko Ćojbašić","https:\u002F\u002Forcid.org\u002F0000-0002-4581-1048",{"tldr":506,"method":507,"finding":508,"direction":131,"opportunity":509},"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":512,"title":513,"url":514,"summary":515,"summary_zh":516,"content":8,"source_name":517,"source_url":514,"published_at":518,"category":11,"cover_url":8,"hotness":12,"is_selected":40,"score":519,"score_detail":520,"sources":525,"tags":527,"primary_topic":530,"secondary_topics":531,"classification":532,"search_phrases":558,"slug":561,"view_count":19,"doi":562,"paper":563,"created_at":589},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":521,"substance":522,"depth":523,"authority":293,"freshness":164,"relevant":20,"comment":524},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。",[526],{"name":517,"url":514},[25,26,528,28,529],"精准农业","全球评估",{"code":47,"slug":154,"name":155,"path":156,"parent_code":157},[],{"status":160,"locked":13,"version":33,"method":34,"evidence":533},{"candidates":534,"agricultural_evidence":40},[535,540,544,548,552],{"code":60,"score":468,"supported":40,"title":536,"abstract":537,"tags":539},[],[25,528,538],"precision agriculture",[25,528],{"code":47,"score":311,"supported":40,"title":541,"abstract":542,"tags":543},[],[301,50],[26],{"code":278,"score":69,"supported":40,"title":545,"abstract":546,"tags":547},[],[296,279],[],{"code":38,"score":19,"supported":40,"title":549,"abstract":550,"tags":551},[],[28],[28],{"code":553,"score":54,"supported":40,"title":554,"abstract":555,"tags":557},"F4",[],[556],"除草",[],[559,560],"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":562,"openalex_id":564,"authors":565,"venue":517,"cited_by_count":125,"oa_url":514,"card":584,"direction":133,"ingested_from":134},"W7222974188",[566,569,572,575,578,581],{"name":567,"orcid":568},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":570,"orcid":571},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":573,"orcid":574},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":576,"orcid":577},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":579,"orcid":580},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":582,"orcid":583},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":585,"method":586,"finding":587,"direction":133,"opportunity":588},"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"]