[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2647":3,"related-2647":86},{"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":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":63,"slug":66,"view_count":21,"doi":67,"paper":68,"created_at":85},2647,"Agricultural drone use, pesticide reduction, and biodiversity restoration: evidence from China","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10109-026-00522-6","Agricultural drone use, pesticide reduction, and biodiversity restoration: evidence from China。Journal of Geographical Systems",null,"Journal of Geographical Systems","2026-09-15T00:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,17,14,8,1,"基于中国证据的学术论文，探讨无人机施药与农药减量、生物多样性恢复的关系，方法新颖且结论具政策参考价值，但属细分领域研究，影响层级有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业无人机","遥感","农药减量","生物多样性",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":62},[38,46,52,57],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"F4",12,true,[43],"pesticide",[43],[29],{"code":47,"score":40,"supported":41,"title":48,"abstract":50,"tags":51},"H6",[49],"biodiversity",[49],[30],{"code":53,"score":21,"supported":13,"title":54,"abstract":55,"tags":56},"I1",[],[],[26],{"code":58,"score":21,"supported":13,"title":59,"abstract":60,"tags":61},"I3",[],[],[28],"多个产业证据相当，需要确认主归属",[64,65],"农业无人机 生物多样性 农药减量 智慧农业","农业无人机 生物多样性","农业无人机生物多样性农药减量智慧农业-2647","10.1007\u002Fs10109-026-00522-6",{"doi":67,"openalex_id":69,"authors":70,"venue":9,"cited_by_count":82,"oa_url":8,"card":8,"direction":83,"ingested_from":84},"W7213342483",[71,74,77,80],{"name":72,"orcid":73},"Jianjun Tang","https:\u002F\u002Forcid.org\u002F0000-0002-6994-2093",{"name":75,"orcid":76},"Jingru Chen","https:\u002F\u002Forcid.org\u002F0009-0009-5773-5482",{"name":78,"orcid":79},"Jingyang Yan","https:\u002F\u002Forcid.org\u002F0000-0001-8330-6272",{"name":81,"orcid":8},"Fei Li",0,"智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:12.279104Z",{"total":87,"page":21,"page_size":87,"items":88},6,[89,159,229,292,342,440],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":8,"source_name":95,"source_url":92,"published_at":96,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":97,"score_detail":98,"sources":101,"tags":103,"primary_topic":8,"secondary_topics":106,"classification":107,"search_phrases":126,"slug":129,"view_count":130,"doi":131,"paper":132,"created_at":158},2908,"An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112447","An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization。Computers and Electronics in Agriculture","一种信息驱动的空地协同框架，用于基于无人机耕作缺陷识别与再耕作路径优化。《农业计算机与电子》","Computers and Electronics in Agriculture","2026-09-18T00:00:00Z",79,{"impact":16,"substance":99,"depth":16,"authority":19,"freshness":20,"relevant":21,"comment":100},21,"发表于农业信息领域核心期刊，提出空地协同的无人机耕地缺陷识别与再耕路径优化框架，方法新颖且面向精准农业实际需求，具备较高参考价值。",[102],{"name":95,"url":92},[26,104,27,105,28],"农业人工智能","精准农业",[],{"status":108,"locked":13,"version":34,"method":35,"evidence":109},"unclassified",{"candidates":110,"agricultural_evidence":41,"reason":125},[111,116,121],{"code":53,"score":112,"supported":13,"title":113,"abstract":114,"tags":115},2,[],[],[26,105],{"code":117,"score":21,"supported":13,"title":118,"abstract":119,"tags":120},"I2",[],[],[104],{"code":58,"score":21,"supported":13,"title":122,"abstract":123,"tags":124},[],[],[28],"缺少足够的主题证据",[127,128],"无人机 耕地缺陷 识别","再耕路径 优化","无人机耕地缺陷识别-2908",3,"10.1016\u002Fj.compag.2026.112447",{"doi":131,"openalex_id":133,"authors":134,"venue":95,"cited_by_count":82,"oa_url":8,"card":153,"direction":83,"ingested_from":84},"W7213547466",[135,137,139,141,144,146,148,151],{"name":136,"orcid":8},"Chenshuo Xie",{"name":138,"orcid":8},"Yejun Zhu",{"name":140,"orcid":8},"Dongfang Li",{"name":142,"orcid":143},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"name":145,"orcid":8},"Le Yang",{"name":147,"orcid":8},"Yuxuan Wan",{"name":149,"orcid":150},"Weihua Wei","https:\u002F\u002Forcid.org\u002F0000-0001-5333-4707",{"name":152,"orcid":8},"Mingfeng Wang",{"tldr":154,"method":155,"finding":156,"direction":83,"opportunity":157},"提出空地协同框架，用无人机识别耕整缺陷并优化补耕路径。","无人机遥感影像与信息驱动算法，识别缺陷并规划重耕路径。","框架能有效识别耕整缺陷并生成优化补耕路径，提升作业质量。","可探索多机协同与实时动态重规划，结合土壤传感器提升缺陷识别精度。","2026-09-19T23:30:02.081036Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":8,"source_name":165,"source_url":162,"published_at":166,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":167,"score_detail":168,"sources":171,"tags":173,"primary_topic":176,"secondary_topics":181,"classification":182,"search_phrases":211,"slug":214,"view_count":82,"doi":215,"paper":216,"created_at":228},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":40,"substance":169,"depth":16,"authority":40,"freshness":12,"relevant":21,"comment":170},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。",[172],{"name":165,"url":162},[26,104,174,28,175],"粮食安全","气候分析",{"code":117,"slug":177,"name":178,"path":179,"parent_code":180},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":183,"locked":13,"version":34,"method":35,"evidence":184},"classified",{"candidates":185,"agricultural_evidence":41},[186,192,198,203,207],{"code":117,"score":187,"supported":41,"title":188,"abstract":190,"tags":191},9,[189],"artificial intelligence",[],[104],{"code":193,"score":130,"supported":41,"title":194,"abstract":195,"tags":197},"I5",[],[196],"农业传感",[],{"code":199,"score":21,"supported":13,"title":200,"abstract":201,"tags":202},"A8",[],[],[174],{"code":53,"score":21,"supported":13,"title":204,"abstract":205,"tags":206},[],[],[26],{"code":58,"score":21,"supported":13,"title":208,"abstract":209,"tags":210},[],[],[28],[212,213],"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":215,"openalex_id":217,"authors":218,"venue":165,"cited_by_count":82,"oa_url":162,"card":221,"direction":227,"ingested_from":84},"W7223111766",[219],{"name":220,"orcid":8},"Fatima-Zahra El Khattabi",{"tldr":222,"method":223,"finding":224,"direction":225,"opportunity":226},"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。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":234,"content":8,"source_name":235,"source_url":232,"published_at":166,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":236,"score_detail":237,"sources":239,"tags":241,"primary_topic":244,"secondary_topics":248,"classification":249,"search_phrases":267,"slug":270,"view_count":82,"doi":271,"paper":272,"created_at":291},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy",73,{"impact":40,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":21,"comment":238},"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[240],{"name":235,"url":232},[26,242,28,243],"作物制图","SAR",{"code":58,"slug":245,"name":246,"path":247,"parent_code":180},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":183,"locked":13,"version":34,"method":35,"evidence":250},{"candidates":251,"agricultural_evidence":41},[252,258,263],{"code":58,"score":12,"supported":41,"title":253,"abstract":254,"tags":257},[],[28,255,256],"remote sensing","multispectral",[28],{"code":199,"score":87,"supported":41,"title":259,"abstract":260,"tags":262},[],[174,261],"food security",[],{"code":53,"score":21,"supported":13,"title":264,"abstract":265,"tags":266},[],[],[26],[268,269],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":271,"openalex_id":273,"authors":274,"venue":235,"cited_by_count":82,"oa_url":232,"card":286,"direction":225,"ingested_from":84},"W7223117203",[275,278,280,283],{"name":276,"orcid":277},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":279,"orcid":8},"Wentao Lv",{"name":281,"orcid":282},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":284,"orcid":285},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":287,"method":288,"finding":289,"direction":225,"opportunity":290},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":8,"source_name":165,"source_url":295,"published_at":166,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":298,"score_detail":299,"sources":301,"tags":303,"primary_topic":306,"secondary_topics":307,"classification":309,"search_phrases":326,"slug":329,"view_count":82,"doi":330,"paper":331,"created_at":341},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":20,"substance":17,"depth":18,"authority":40,"freshness":12,"relevant":21,"comment":300},"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。",[302],{"name":165,"url":295},[26,104,304,28,305],"产量预测","可解释性",{"code":117,"slug":177,"name":178,"path":179,"parent_code":180},[308],{"code":58,"slug":245,"name":246,"path":247,"parent_code":180},{"status":183,"locked":13,"version":34,"method":35,"evidence":310},{"candidates":311,"agricultural_evidence":41},[312,318,322],{"code":117,"score":40,"supported":41,"title":313,"abstract":315,"tags":317},[314],"computer vision",[316],"计算机视觉",[104],{"code":58,"score":20,"supported":41,"title":319,"abstract":320,"tags":321},[],[28,255],[304,28],{"code":53,"score":21,"supported":13,"title":323,"abstract":324,"tags":325},[],[],[26],[327,328],"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":330,"openalex_id":332,"authors":333,"venue":165,"cited_by_count":82,"oa_url":295,"card":336,"direction":225,"ingested_from":84},"W7223114002",[334],{"name":335,"orcid":8},"Kadiatou Barry",{"tldr":337,"method":338,"finding":339,"direction":225,"opportunity":340},"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":343,"title":344,"url":345,"summary":346,"summary_zh":347,"content":8,"source_name":348,"source_url":345,"published_at":349,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":97,"score_detail":350,"sources":352,"tags":354,"primary_topic":358,"secondary_topics":364,"classification":366,"search_phrases":392,"slug":395,"view_count":82,"doi":396,"paper":397,"created_at":439},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",{"impact":16,"substance":169,"depth":16,"authority":40,"freshness":187,"relevant":21,"comment":351},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[353],{"name":348,"url":345},[26,355,356,28,357],"无人机","育种","紫花苜蓿",{"code":359,"slug":360,"name":361,"path":362,"parent_code":363},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[365],{"code":58,"slug":245,"name":246,"path":247,"parent_code":180},{"status":183,"locked":13,"version":34,"method":35,"evidence":367},{"candidates":368,"agricultural_evidence":41},[369,376,380,386],{"code":359,"score":370,"supported":41,"title":371,"abstract":373,"tags":375},15,[372],"alfalfa",[374,372],"苜蓿",[357],{"code":58,"score":40,"supported":41,"title":377,"abstract":378,"tags":379},[256],[256],[28],{"code":53,"score":381,"supported":41,"title":382,"abstract":383,"tags":385},7,[],[105,384],"precision agriculture",[26],{"code":387,"score":388,"supported":41,"title":389,"abstract":390,"tags":391},"C4",4,[],[374],[357],[393,394],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":396,"openalex_id":398,"authors":399,"venue":348,"cited_by_count":82,"oa_url":345,"card":434,"direction":225,"ingested_from":84},"W7223083966",[400,403,406,409,412,414,416,419,422,425,428,431],{"name":401,"orcid":402},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":404,"orcid":405},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":407,"orcid":408},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":410,"orcid":411},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":413,"orcid":8},"Julie Hansen",{"name":415,"orcid":8},"Christopher Pierce",{"name":417,"orcid":418},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":420,"orcid":421},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":423,"orcid":424},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":426,"orcid":427},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":429,"orcid":430},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":432,"orcid":433},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":435,"method":436,"finding":437,"direction":225,"opportunity":438},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":8,"source_name":446,"source_url":443,"published_at":166,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":97,"score_detail":447,"sources":449,"tags":451,"primary_topic":454,"secondary_topics":460,"classification":466,"search_phrases":524,"slug":527,"view_count":82,"doi":528,"paper":529,"created_at":552},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",{"impact":16,"substance":169,"depth":16,"authority":40,"freshness":187,"relevant":21,"comment":448},"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。",[450],{"name":446,"url":443},[26,104,28,452,453],"作物水分胁迫","精准灌溉",{"code":455,"slug":456,"name":457,"path":458,"parent_code":459},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[461,462],{"code":117,"slug":177,"name":178,"path":179,"parent_code":180},{"code":193,"slug":463,"name":464,"path":465,"parent_code":180},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":183,"locked":13,"version":34,"method":35,"evidence":467},{"candidates":468,"agricultural_evidence":41},[469,474,480,487,493,497,504,511,518],{"code":455,"score":370,"supported":41,"title":470,"abstract":471,"tags":473},[456],[472,456],"灌溉",[453],{"code":117,"score":12,"supported":41,"title":475,"abstract":476,"tags":479},[],[477,478,189],"机器学习","machine learning",[104],{"code":193,"score":187,"supported":41,"title":481,"abstract":482,"tags":486},[],[483,484,485],"物联网","internet of things","IoT",[],{"code":53,"score":381,"supported":41,"title":488,"abstract":489,"tags":492},[],[490,491],"数字孪生","digital twin",[26],{"code":58,"score":381,"supported":41,"title":494,"abstract":495,"tags":496},[],[28,255],[28],{"code":498,"score":87,"supported":41,"title":499,"abstract":500,"tags":503},"A2",[],[501,502],"小麦","wheat",[],{"code":505,"score":87,"supported":41,"title":506,"abstract":507,"tags":510},"A3",[],[508,509],"玉米","maize",[],{"code":512,"score":87,"supported":41,"title":513,"abstract":514,"tags":517},"A5",[],[515,516],"马铃薯","potato",[],{"code":519,"score":130,"supported":41,"title":520,"abstract":521,"tags":523},"B2",[],[522],"葡萄",[],[525,526],"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":528,"openalex_id":530,"authors":531,"venue":446,"cited_by_count":82,"oa_url":443,"card":547,"direction":83,"ingested_from":84},"W7223088921",[532,535,538,541,544],{"name":533,"orcid":534},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":536,"orcid":537},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":539,"orcid":540},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":542,"orcid":543},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":545,"orcid":546},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":548,"method":549,"finding":550,"direction":225,"opportunity":551},"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"]