[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3070":3,"related-3070":68},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":46,"slug":49,"view_count":41,"doi":50,"paper":51,"created_at":67},3070,"Deep neural network-driven adaptive fusion recognition method for radar-optical heterogeneous remote sensing imagery","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70090-8","Abstract The fusion of radar and optical remote sensing imagery presents significant challenges due to fundamental differences in imaging mechanisms and feature representations. This paper proposes an adaptive fusion recognition framework based on deep neural networks for heterogeneous radar-optical imagery. The framework comprises three core components: a heterogeneous feature extraction and alignment module that projects SAR and optical features into a shared semantic space through contrastive learning, a cross-modal adaptive fusion mechanism employing dual-attention architecture with dynamic weight generation to enable context-sensitive modality integration, and an end-to-end recognition network that jointly optimizes all processing stages. To probe the framework from multiple angles, we ran a comprehensive experimental campaign on the SEN1-2 benchmark, which spans head-to-head comparisons against ten representative fusion baselines, ablations isolating each module as well as the GLCM texture branch, a sensitivity sweep over the contrastive temperature parameter, stress tests under additive speckle noise and single-modality missing scenarios, and an efficiency audit reporting parameter count and inference latency. The proposed method reaches 91.38% overall accuracy with a Kappa coefficient of 0.892 and macro F1 score of 90.45%, surpassing competitive baselines while holding inference cost to 11.4 ms per sample. Ablation studies confirm the effectiveness of each component, with feature alignment contributing 3.45% accuracy improvement and the complete adaptive fusion mechanism providing additional 5.46% gains over baseline concatenation. The dynamic weighting strategy exhibits robust performance under degraded input conditions by automatically adjusting modality contributions based on estimated reliability.","摘要 雷达与光学遥感影像的融合面临显著挑战，根源在于二者成像机理与特征表示存在本质差异。本文提出了一种基于深度神经网络的异源雷达—光学影像自适应融合识别框架。该框架包含三个核心组件：异源特征提取与对齐模块，通过对比学习将合成孔径雷达（SAR）与光学特征投影至共享语义空间；跨模态自适应融合机制，采用双注意力架构与动态权重生成，实现上下文敏感模态集成；以及端到端识别网络，联合优化所有处理阶段。为从多角度探究该框架，我们在SEN1-2基准上开展了系统性实验，包括与十种代表性融合基线的直接对比、分别隔离各模块及GLCM纹理分支的消融实验、对比温度参数的敏感性扫描、加性散斑噪声与单模态缺失场景下的压力测试，以及报告参数量与推理延迟的效率审计。所提方法总体精度达91.38%，Kappa系数为0.892，宏F1分数为90.45%，在超越竞争性基线的同时将推理开销控制在每样本11.4 ms。消融研究证实了各组成部分的有效性，其中特征对齐贡献了3.45%的精度提升，完整的自适应融合机制在基线拼接基础上额外带来5.46%的增益。动态加权策略在退化输入条件下通过基于估计可靠性自动调整模态贡献，表现出稳健的性能。",null,"Scientific Reports","2026-09-19T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"雷达与光学异构遥感影像融合识别方法研究，属遥感与深度学习交叉领域，与三农、农业信息化无直接关联，不纳入每日精选。",[19],{"name":10,"url":6},[21,22,23],"农业人工智能","遥感","雷达光学融合",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":45},[31,39],{"code":32,"score":33,"supported":34,"title":35,"abstract":37,"tags":38},"I3",15,true,[36],"remote sensing",[22,36],[22],{"code":40,"score":41,"supported":14,"title":42,"abstract":43,"tags":44},"I2",1,[],[],[21],"农业相关性需要复核",[47,48],"SEN1-2 雷达光学 融合识别","农业人工智能 雷达光学融合 遥感","SEN1-2雷达光学融合识别-3070","10.1038\u002Fs41598-026-70090-8",{"doi":50,"openalex_id":52,"authors":53,"venue":10,"cited_by_count":15,"oa_url":59,"card":60,"direction":64,"ingested_from":66},"W7213614235",[54,56],{"name":55,"orcid":9},"Binlu Liu",{"name":57,"orcid":58},"Zijing Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1935-7048","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-70090-8_reference.pdf",{"tldr":61,"method":62,"finding":63,"direction":64,"opportunity":65},"提出深度神经网络自适应融合框架，实现雷达与光学异构遥感影像的高精度识别。","基于对比学习特征对齐、双注意力动态权重融合，在SEN1-2数据集上端到端训练。","总体精度91.38%，Kappa 0.892，特征对齐与自适应融合分别提升3.45%和5.46%。","农业遥感与作物表型","可探索该融合框架在农业地块分类、作物长势监测及多模态缺失场景下的迁移应用。","openalex","2026-09-21T23:30:24.192758Z",{"total":69,"page":41,"page_size":69,"items":70},6,[71,145,201,317,381,449],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":9,"source_name":77,"source_url":74,"published_at":78,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":79,"score_detail":80,"sources":85,"tags":87,"primary_topic":91,"secondary_topics":96,"classification":97,"search_phrases":128,"slug":131,"view_count":15,"doi":132,"paper":133,"created_at":144},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":81,"substance":82,"depth":83,"authority":81,"freshness":13,"relevant":41,"comment":84},12,22,18,"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。",[86],{"name":77,"url":74},[88,21,89,22,90],"智慧农业","粮食安全","气候分析",{"code":40,"slug":92,"name":93,"path":94,"parent_code":95},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":98,"locked":14,"version":27,"method":28,"evidence":99},"classified",{"candidates":100,"agricultural_evidence":34},[101,107,114,119,124],{"code":40,"score":102,"supported":34,"title":103,"abstract":105,"tags":106},9,[104],"artificial intelligence",[],[21],{"code":108,"score":109,"supported":34,"title":110,"abstract":111,"tags":113},"I5",3,[],[112],"农业传感",[],{"code":115,"score":41,"supported":14,"title":116,"abstract":117,"tags":118},"A8",[],[],[89],{"code":120,"score":41,"supported":14,"title":121,"abstract":122,"tags":123},"I1",[],[],[88],{"code":32,"score":41,"supported":14,"title":125,"abstract":126,"tags":127},[],[],[22],[129,130],"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":132,"openalex_id":134,"authors":135,"venue":77,"cited_by_count":15,"oa_url":74,"card":138,"direction":143,"ingested_from":66},"W7223111766",[136],{"name":137,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":139,"method":140,"finding":141,"direction":64,"opportunity":142},"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":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":9,"source_name":77,"source_url":148,"published_at":78,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":151,"score_detail":152,"sources":157,"tags":159,"primary_topic":162,"secondary_topics":163,"classification":168,"search_phrases":185,"slug":188,"view_count":15,"doi":189,"paper":190,"created_at":200},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":153,"substance":154,"depth":155,"authority":81,"freshness":13,"relevant":41,"comment":156},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。",[158],{"name":77,"url":148},[88,21,160,22,161],"产量预测","可解释性",{"code":40,"slug":92,"name":93,"path":94,"parent_code":95},[164],{"code":32,"slug":165,"name":166,"path":167,"parent_code":95},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":98,"locked":14,"version":27,"method":28,"evidence":169},{"candidates":170,"agricultural_evidence":34},[171,177,181],{"code":40,"score":81,"supported":34,"title":172,"abstract":174,"tags":176},[173],"computer vision",[175],"计算机视觉",[21],{"code":32,"score":153,"supported":34,"title":178,"abstract":179,"tags":180},[],[22,36],[160,22],{"code":120,"score":41,"supported":14,"title":182,"abstract":183,"tags":184},[],[],[88],[186,187],"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":189,"openalex_id":191,"authors":192,"venue":77,"cited_by_count":15,"oa_url":148,"card":195,"direction":64,"ingested_from":66},"W7223114002",[193],{"name":194,"orcid":9},"Kadiatou Barry",{"tldr":196,"method":197,"finding":198,"direction":64,"opportunity":199},"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":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":78,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":208,"score_detail":209,"sources":211,"tags":213,"primary_topic":216,"secondary_topics":222,"classification":228,"search_phrases":287,"slug":290,"view_count":15,"doi":291,"paper":292,"created_at":316},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":83,"substance":82,"depth":83,"authority":81,"freshness":102,"relevant":41,"comment":210},"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。",[212],{"name":207,"url":204},[88,21,22,214,215],"作物水分胁迫","精准灌溉",{"code":217,"slug":218,"name":219,"path":220,"parent_code":221},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[223,224],{"code":40,"slug":92,"name":93,"path":94,"parent_code":95},{"code":108,"slug":225,"name":226,"path":227,"parent_code":95},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":98,"locked":14,"version":27,"method":28,"evidence":229},{"candidates":230,"agricultural_evidence":34},[231,236,242,249,256,260,267,274,281],{"code":217,"score":33,"supported":34,"title":232,"abstract":233,"tags":235},[218],[234,218],"灌溉",[215],{"code":40,"score":13,"supported":34,"title":237,"abstract":238,"tags":241},[],[239,240,104],"机器学习","machine learning",[21],{"code":108,"score":102,"supported":34,"title":243,"abstract":244,"tags":248},[],[245,246,247],"物联网","internet of things","IoT",[],{"code":120,"score":250,"supported":34,"title":251,"abstract":252,"tags":255},7,[],[253,254],"数字孪生","digital twin",[88],{"code":32,"score":250,"supported":34,"title":257,"abstract":258,"tags":259},[],[22,36],[22],{"code":261,"score":69,"supported":34,"title":262,"abstract":263,"tags":266},"A2",[],[264,265],"小麦","wheat",[],{"code":268,"score":69,"supported":34,"title":269,"abstract":270,"tags":273},"A3",[],[271,272],"玉米","maize",[],{"code":275,"score":69,"supported":34,"title":276,"abstract":277,"tags":280},"A5",[],[278,279],"马铃薯","potato",[],{"code":282,"score":109,"supported":34,"title":283,"abstract":284,"tags":286},"B2",[],[285],"葡萄",[],[288,289],"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":291,"openalex_id":293,"authors":294,"venue":207,"cited_by_count":15,"oa_url":204,"card":310,"direction":315,"ingested_from":66},"W7223088921",[295,298,301,304,307],{"name":296,"orcid":297},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":299,"orcid":300},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":302,"orcid":303},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":305,"orcid":306},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":308,"orcid":309},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":311,"method":312,"finding":313,"direction":64,"opportunity":314},"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":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":77,"source_url":320,"published_at":78,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":327,"tags":329,"primary_topic":333,"secondary_topics":334,"classification":337,"search_phrases":365,"slug":368,"view_count":15,"doi":369,"paper":370,"created_at":380},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":83,"substance":154,"depth":325,"authority":81,"freshness":102,"relevant":41,"comment":326},16,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[328],{"name":77,"url":320},[21,330,22,331,332],"农业物联网","智慧灌溉","节水农业",{"code":217,"slug":218,"name":219,"path":220,"parent_code":221},[335,336],{"code":108,"slug":225,"name":226,"path":227,"parent_code":95},{"code":32,"slug":165,"name":166,"path":167,"parent_code":95},{"status":98,"locked":14,"version":27,"method":28,"evidence":338},{"candidates":339,"agricultural_evidence":34},[340,346,350,354,358],{"code":217,"score":341,"supported":34,"title":342,"abstract":343,"tags":345},19,[218],[234,344,218],"水资源",[331,332],{"code":108,"score":83,"supported":34,"title":347,"abstract":348,"tags":349},[247],[245,246,247],[330],{"code":32,"score":33,"supported":34,"title":351,"abstract":352,"tags":353},[36],[22,36],[22],{"code":40,"score":81,"supported":34,"title":355,"abstract":356,"tags":357},[104],[104],[21],{"code":359,"score":69,"supported":34,"title":360,"abstract":361,"tags":364},"H8",[],[362,363],"可持续农业","sustainable agriculture",[],[366,367],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":369,"openalex_id":371,"authors":372,"venue":77,"cited_by_count":15,"oa_url":320,"card":375,"direction":315,"ingested_from":66},"W7223090389",[373],{"name":374,"orcid":9},"Patil",{"tldr":376,"method":377,"finding":378,"direction":315,"opportunity":379},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":9,"source_name":77,"source_url":384,"published_at":387,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":388,"score_detail":389,"sources":391,"tags":393,"primary_topic":397,"secondary_topics":401,"classification":402,"search_phrases":429,"slug":432,"view_count":15,"doi":433,"paper":434,"created_at":448},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","2026-10-20T00:00:00Z",70,{"impact":81,"substance":154,"depth":155,"authority":81,"freshness":102,"relevant":41,"comment":390},"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[392],{"name":77,"url":384},[21,22,394,395,396],"GIS","野生动物保护","人兽冲突",{"code":95,"slug":398,"name":399,"path":400,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":403,"locked":14,"version":27,"method":28,"evidence":404},"partial",{"candidates":405,"agricultural_evidence":34,"reason":428},[406,411,418,423],{"code":40,"score":13,"supported":34,"title":407,"abstract":408,"tags":410},[],[239,240,409],"decision support",[21],{"code":412,"score":13,"supported":34,"title":413,"abstract":414,"tags":417},"I4",[],[415,394,416],"地理信息","geographic information",[394],{"code":32,"score":419,"supported":34,"title":420,"abstract":421,"tags":422},4,[],[22],[22],{"code":424,"score":41,"supported":14,"title":425,"abstract":426,"tags":427},"H6",[],[],[395],"一级领域明确，二级方向需要细分",[430,431],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":433,"openalex_id":435,"authors":436,"venue":77,"cited_by_count":15,"oa_url":384,"card":443,"direction":143,"ingested_from":66},"W7222999966",[437,439,441],{"name":438,"orcid":9},"Bhutia Dechen Doma",{"name":440,"orcid":9},"Swarup Das",{"name":442,"orcid":9},"Mandal Rakesh Kumar",{"tldr":444,"method":445,"finding":446,"direction":64,"opportunity":447},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","2026-10-21T23:30:43.936453Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":455,"source_url":452,"published_at":456,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":457,"score_detail":458,"sources":460,"tags":462,"primary_topic":464,"secondary_topics":469,"classification":471,"search_phrases":501,"slug":504,"view_count":15,"doi":505,"paper":506,"created_at":519},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":83,"substance":82,"depth":83,"authority":81,"freshness":153,"relevant":41,"comment":459},"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。",[461],{"name":455,"url":452},[88,21,463,160,22],"水稻",{"code":465,"slug":466,"name":463,"path":467,"parent_code":468},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[470],{"code":32,"slug":165,"name":166,"path":167,"parent_code":95},{"status":98,"locked":14,"version":27,"method":28,"evidence":472},{"candidates":473,"agricultural_evidence":34},[474,478,482,487,493,497],{"code":465,"score":33,"supported":34,"title":475,"abstract":476,"tags":477},[466],[463,466],[463],{"code":32,"score":153,"supported":34,"title":479,"abstract":480,"tags":481},[],[22,36],[160,22],{"code":115,"score":69,"supported":34,"title":483,"abstract":484,"tags":486},[],[89,485],"food security",[],{"code":488,"score":109,"supported":34,"title":489,"abstract":490,"tags":492},"J1",[],[491],"农业规划",[],{"code":120,"score":41,"supported":14,"title":494,"abstract":495,"tags":496},[],[],[88],{"code":40,"score":41,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[21],[502,503],"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":505,"openalex_id":507,"authors":508,"venue":455,"cited_by_count":15,"oa_url":452,"card":514,"direction":64,"ingested_from":66},"W7222942064",[509,511],{"name":510,"orcid":9},"Nayana Venkatesh",{"name":512,"orcid":513},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":515,"method":516,"finding":517,"direction":64,"opportunity":518},"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"]