[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5571":3,"related-5571":91},{"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":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":90},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。",null,"Agronomy","2026-10-21T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},12,20,17,14,1,"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。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","作物制图","遥感","SAR",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,50,58],{"code":31,"score":13,"supported":44,"title":45,"abstract":46,"tags":49},true,[],[28,47,48],"remote sensing","multispectral",[28],{"code":51,"score":52,"supported":44,"title":53,"abstract":54,"tags":57},"A8",6,[],[55,56],"粮食安全","food security",[],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"I1",[],[],[26],[64,65],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571",0,"10.3390\u002Fagronomy16202068",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":67,"oa_url":6,"card":83,"direction":87,"ingested_from":89},"W7223117203",[72,75,77,80],{"name":73,"orcid":74},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":76,"orcid":9},"Wentao Lv",{"name":78,"orcid":79},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":81,"orcid":82},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"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。","农业遥感与作物表型","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。","openalex","2026-10-22T11:20:39.359881Z",{"total":52,"page":21,"page_size":52,"items":92},[93,150,253,323,379,433],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":104,"tags":106,"primary_topic":110,"secondary_topics":115,"classification":117,"search_phrases":134,"slug":137,"view_count":67,"doi":138,"paper":139,"created_at":149},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。","Zenodo (CERN European Organization for Nuclear Research)",67,{"impact":102,"substance":18,"depth":19,"authority":17,"freshness":13,"relevant":21,"comment":103},8,"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。",[105],{"name":99,"url":96},[26,107,108,28,109],"农业人工智能","产量预测","可解释性",{"code":111,"slug":112,"name":113,"path":114,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[116],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":118},{"candidates":119,"agricultural_evidence":44},[120,126,130],{"code":111,"score":17,"supported":44,"title":121,"abstract":123,"tags":125},[122],"computer vision",[124],"计算机视觉",[107],{"code":31,"score":102,"supported":44,"title":127,"abstract":128,"tags":129},[],[28,47],[108,28],{"code":59,"score":21,"supported":14,"title":131,"abstract":132,"tags":133},[],[],[26],[135,136],"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":138,"openalex_id":140,"authors":141,"venue":99,"cited_by_count":67,"oa_url":96,"card":144,"direction":87,"ingested_from":89},"W7223114002",[142],{"name":143,"orcid":9},"Kadiatou Barry",{"tldr":145,"method":146,"finding":147,"direction":87,"opportunity":148},"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":151,"title":152,"url":153,"summary":154,"summary_zh":155,"content":9,"source_name":156,"source_url":153,"published_at":157,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":158,"score_detail":159,"sources":164,"tags":166,"primary_topic":170,"secondary_topics":176,"classification":178,"search_phrases":205,"slug":208,"view_count":67,"doi":209,"paper":210,"created_at":252},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",79,{"impact":160,"substance":161,"depth":160,"authority":17,"freshness":162,"relevant":21,"comment":163},18,22,9,"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。",[165],{"name":156,"url":153},[26,167,168,28,169],"无人机","育种","紫花苜蓿",{"code":171,"slug":172,"name":173,"path":174,"parent_code":175},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[177],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":179},{"candidates":180,"agricultural_evidence":44},[181,188,192,199],{"code":171,"score":182,"supported":44,"title":183,"abstract":185,"tags":187},15,[184],"alfalfa",[186,184],"苜蓿",[169],{"code":31,"score":17,"supported":44,"title":189,"abstract":190,"tags":191},[48],[48],[28],{"code":59,"score":193,"supported":44,"title":194,"abstract":195,"tags":198},7,[],[196,197],"精准农业","precision agriculture",[26],{"code":200,"score":201,"supported":44,"title":202,"abstract":203,"tags":204},"C4",4,[],[186],[169],[206,207],"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":209,"openalex_id":211,"authors":212,"venue":156,"cited_by_count":67,"oa_url":153,"card":247,"direction":87,"ingested_from":89},"W7223083966",[213,216,219,222,225,227,229,232,235,238,241,244],{"name":214,"orcid":215},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":217,"orcid":218},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":220,"orcid":221},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":223,"orcid":224},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":226,"orcid":9},"Julie Hansen",{"name":228,"orcid":9},"Christopher Pierce",{"name":230,"orcid":231},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":233,"orcid":234},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":236,"orcid":237},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":239,"orcid":240},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":242,"orcid":243},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":245,"orcid":246},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":248,"method":249,"finding":250,"direction":87,"opportunity":251},"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":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":9,"source_name":259,"source_url":256,"published_at":260,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":264,"tags":266,"primary_topic":268,"secondary_topics":272,"classification":274,"search_phrases":304,"slug":307,"view_count":67,"doi":308,"paper":309,"created_at":322},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":160,"substance":161,"depth":160,"authority":17,"freshness":102,"relevant":21,"comment":263},"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。",[265],{"name":259,"url":256},[26,107,267,108,28],"水稻",{"code":269,"slug":270,"name":267,"path":271,"parent_code":175},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[273],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":275},{"candidates":276,"agricultural_evidence":44},[277,281,285,289,296,300],{"code":269,"score":182,"supported":44,"title":278,"abstract":279,"tags":280},[270],[267,270],[267],{"code":31,"score":102,"supported":44,"title":282,"abstract":283,"tags":284},[],[28,47],[108,28],{"code":51,"score":52,"supported":44,"title":286,"abstract":287,"tags":288},[],[55,56],[],{"code":290,"score":291,"supported":44,"title":292,"abstract":293,"tags":295},"J1",3,[],[294],"农业规划",[],{"code":59,"score":21,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[26],{"code":111,"score":21,"supported":14,"title":301,"abstract":302,"tags":303},[],[],[107],[305,306],"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":308,"openalex_id":310,"authors":311,"venue":259,"cited_by_count":67,"oa_url":256,"card":317,"direction":87,"ingested_from":89},"W7222942064",[312,314],{"name":313,"orcid":9},"Nayana Venkatesh",{"name":315,"orcid":316},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":318,"method":319,"finding":320,"direction":87,"opportunity":321},"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":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":9,"source_name":329,"source_url":326,"published_at":330,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":331,"score_detail":332,"sources":334,"tags":336,"primary_topic":339,"secondary_topics":340,"classification":341,"search_phrases":352,"slug":355,"view_count":67,"doi":356,"paper":357,"created_at":378},5243,"Identificación de índices de vegetación relacionados con componentes de rendimiento de frijol común (Phaseolus vulgaris L.) mediante el uso de sensores multiespectrales","https:\u002F\u002Fdoi.org\u002F10.18845\u002Ftm.v39i4.8378","Bean production in Costa Rica supplies only 20 % of domestic consumption, requiring the importation of the remaining amount; this situation highlights the need to know in advance the actual productive capacity of local farmers to improve planning, agronomic management, and import regulation. Therefore, the objective of this study was to evaluate the use of multispectral data and vegetation indices to analyze their relationship with yield components in bean varieties cultivated in different production areas of Costa Rica and to identify the yield component with the greatest spectral association. The research was conducted at three sites located in Alajuela, Costa Rica. Multispectral reflectance data were collected during the R5 phenological stage using an airborne multispectral sensor. Subsequently, yield components were quantified per meter of crop row, including 100-grain weight, number of pods per meter, number of plants, number of grains, and total grain weight. The number of pods showed the highest association with spectral variables and was therefore identified as the yield component with the greatest potential for analysis through remote sensing. The SR (Simple Ratio) and GCI (Green Chlorophyll Index) indices showed correlations greater than 0.7 with this variable, being identified as the vegetation indices with the highest spectral association during the R5 stage of the crop.","哥斯达黎加的菜豆（bean）产量仅能满足国内消费量的20%，其余部分需要进口；这一状况凸显了提前了解当地农民实际生产能力的必要性，以改进生产规划、农艺管理和进口调控。因此，本研究旨在评估多光谱数据和植被指数（vegetation indices）的应用，分析其与哥斯达黎加不同产区菜豆品种产量构成因素（yield components）之间的关系，并确定与光谱关联性最强的产量构成因素。研究在哥斯达黎加阿拉胡埃拉（Alajuela）的三个地点开展。在R5物候期使用机载多光谱传感器采集多光谱反射率数据。随后，按每米作物行量化产量构成因素，包括百粒重、每米豆荚数、植株数、籽粒数和籽粒总重。豆荚数与光谱变量的关联性最高，因此被确定为最具遥感分析潜力的产量构成因素。SR（简单比值，Simple Ratio）和GCI（绿色叶绿素指数，Green Chlorophyll Index）与该变量的相关系数大于0.7，被确定为作物R5期光谱关联性最高的植被指数。","Revista Tecnología en Marcha","2026-10-16T00:00:00Z",62,{"impact":102,"substance":160,"depth":182,"authority":17,"freshness":162,"relevant":21,"comment":333},"该研究利用机载多光谱传感器识别与菜豆产量组分相关的植被指数，方法清晰、结论具体，但属于哥斯达黎加的小区域研究，公共影响有限，可作为遥感作物监测领域的技术性资讯收录。",[335],{"name":329,"url":326},[26,108,28,337,338],"植被指数","菜豆",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":342},{"candidates":343,"agricultural_evidence":44},[344,348],{"code":31,"score":17,"supported":44,"title":345,"abstract":346,"tags":347},[],[28,47,48],[108,337,28],{"code":59,"score":21,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[26],[353,354],"哥斯达黎加 菜豆 多光谱 遥感","Phaseolus vulgaris 植被指数 产量","哥斯达黎加菜豆多光谱遥感-5243","10.18845\u002Ftm.v39i4.8378",{"doi":356,"openalex_id":358,"authors":359,"venue":329,"cited_by_count":67,"oa_url":372,"card":373,"direction":87,"ingested_from":89},"W7222720635",[360,363,366,369],{"name":361,"orcid":362},"Guillermo Vargas-Hernández","https:\u002F\u002Forcid.org\u002F0000-0003-1410-3898",{"name":364,"orcid":365},"Eddison José Araya-Morales","https:\u002F\u002Forcid.org\u002F0009-0001-0550-414X",{"name":367,"orcid":368},"Juan Carlos Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1915-3213",{"name":370,"orcid":371},"Ignacio Mora-Cordero","https:\u002F\u002Forcid.org\u002F0009-0007-0925-0099","https:\u002F\u002Frevistas.tec.ac.cr\u002Findex.php\u002Ftec_marcha\u002Farticle\u002Fdownload\u002F8378\u002F8641",{"tldr":374,"method":375,"finding":376,"direction":87,"opportunity":377},"用机载多光谱传感器评估菜豆产量构成与植被指数的关系。","机载多光谱遥感获取R5期反射率，结合地面产量构成测定与相关分析。","豆荚数与光谱变量关联最强，SR和GCI指数与其相关系数超过0.7。","可探索将SR\u002FGCI用于菜豆产量早期预测，并扩展到多品种、多生育期与无人机高光谱验证。","2026-10-18T23:30:20.259545Z",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":9,"source_name":385,"source_url":382,"published_at":386,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":387,"score_detail":388,"sources":391,"tags":393,"primary_topic":396,"secondary_topics":397,"classification":398,"search_phrases":414,"slug":417,"view_count":67,"doi":418,"paper":419,"created_at":432},5156,"BGDNet: a boundary-guided deformable network for accurate farmland parcel segmentation in high-resolution remote sensing imagery","https:\u002F\u002Fdoi.org\u002F10.1080\u002F19479832.2026.2748607","To address the challenges of complex farmland parcel shapes, blurred boundaries, and frequent adhesion between adjacent parcels in high-resolution remote sensing imagery, this study proposes BGDNet, a boundary-guided and deformable convolution collaboratively driven network for farmland segmentation. First, DCN is introduced into the encoder to enhance the model’s ability to adaptively represent irregular farmland geometries. Second, a MSAM and a FEM are constructed for multi-scale contextual aggregation and cross-layer feature calibration, respectively, thereby improving semantic representation capability in complex scenes. On this basis, a BGDF is proposed, which generates spatial gating weights through collaborative learning between boundary and semantic branches, enabling dynamic constraint of decoder features by boundary information. This alleviates parcel adhesion and improves boundary localisation accuracy. Experimental results on the FGFD demonstrate that the BGDNet achieves an IoU of 85.57% and an F1-score of 92.22%, outperforming several mainstream semantic segmentation models in terms of segmentation accuracy, boundary integrity, and visual consistency. Qualitative visualisation and ablation studies validate the effectiveness of each module and their synergistic contributions. The proposed method provides an effective solution for high-precision farmland extraction under complex terrain conditions and offers technical support for agricultural resource investigation, cultivated land monitoring, and smart agriculture management.","针对高分辨率遥感影像中农田地块形状复杂、边界模糊以及相邻地块间频繁粘连等问题，本研究提出了一种边界引导与可变形卷积协同驱动的农田分割网络BGDNet。首先，在编码器中引入可变形卷积（DCN），增强模型对不规则农田几何形状的自适应表征能力。其次，构建多尺度注意力模块（MSAM）和特征增强模块（FEM），分别用于多尺度上下文聚合和跨层特征校准，从而提升复杂场景下的语义表征能力。在此基础上，提出边界引导动态融合模块（BGDF），通过边界分支与语义分支的协同学习生成空间门控权重，使边界信息能够动态约束解码器特征，从而缓解地块粘连问题并提高边界定位精度。在FGFD数据集上的实验结果表明，BGDNet的IoU达到85.57%，F1分数达到92.22%，在分割精度、边界完整性和视觉一致性方面优于多种主流语义分割模型。定性可视化与消融实验验证了各模块的有效性及其协同贡献。所提方法为复杂地形条件下的高精度农田提取提供了有效解决方案，并为农业资源调查、耕地监测和智慧农业管理提供了技术支撑。","International Journal of Image and Data Fusion","2026-10-15T00:00:00Z",71,{"impact":17,"substance":18,"depth":160,"authority":389,"freshness":102,"relevant":21,"comment":390},13,"提出边界引导可变形卷积网络用于高分辨率遥感农田地块分割，方法新颖、指标可靠，属细分领域技术进展，值得关注但影响面有限。",[392],{"name":385,"url":382},[26,394,28,395],"深度学习","农田地块分割",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":399},{"candidates":400,"agricultural_evidence":44},[401,405,410],{"code":31,"score":182,"supported":44,"title":402,"abstract":403,"tags":404},[47],[28,47],[28],{"code":59,"score":193,"supported":44,"title":406,"abstract":407,"tags":409},[],[26,408],"smart agriculture",[26],{"code":111,"score":21,"supported":14,"title":411,"abstract":412,"tags":413},[],[],[394],[415,416],"BGDNet 农田分割","高分辨率遥感 地块提取","BGDNet农田分割-5156","10.1080\u002F19479832.2026.2748607",{"doi":418,"openalex_id":420,"authors":421,"venue":385,"cited_by_count":67,"oa_url":9,"card":426,"direction":431,"ingested_from":89},"W7221931728",[422,424],{"name":423,"orcid":9},"Xianjun Du",{"name":425,"orcid":9},"Binda Bai",{"tldr":427,"method":428,"finding":429,"direction":87,"opportunity":430},"提出边界引导可变形网络BGDNet，实现高分辨率遥感耕地地块精准分割。","可变形卷积编码器+多尺度注意力+边界语义门控融合，在FGFD数据集验证。","IoU达85.57%、F1达92.22%，边界完整性与抗粘连优于主流分割模型。","可探索边界引导机制向地块实例化、跨区域迁移及多时相耕地变化监测延伸。","智慧农业 \u002F 农业物联网","2026-10-17T23:30:10.405075Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":438,"content":9,"source_name":439,"source_url":436,"published_at":440,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":441,"score_detail":442,"sources":445,"tags":447,"primary_topic":450,"secondary_topics":451,"classification":463,"search_phrases":511,"slug":514,"view_count":515,"doi":9,"paper":516,"created_at":526},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":443,"substance":18,"depth":19,"authority":389,"freshness":102,"relevant":21,"comment":444},16,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[446],{"name":439,"url":436},[26,196,28,448,449],"盐碱地治理","土壤监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[452,457],{"code":453,"slug":454,"name":455,"path":456,"parent_code":35},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":458,"slug":459,"name":460,"path":461,"parent_code":462},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":38,"locked":14,"version":39,"method":40,"evidence":464},{"candidates":465,"agricultural_evidence":44},[466,470,478,483,487,493,499,505],{"code":31,"score":160,"supported":44,"title":467,"abstract":468,"tags":469},[47],[28,47,48],[28],{"code":453,"score":19,"supported":44,"title":471,"abstract":473,"tags":477},[472],"GIS",[474,475,472,476],"地理信息","空间分析","geographic information",[],{"code":458,"score":182,"supported":44,"title":479,"abstract":480,"tags":482},[459],[481,459],"土壤",[448],{"code":59,"score":13,"supported":44,"title":484,"abstract":485,"tags":486},[197],[],[26,196],{"code":111,"score":52,"supported":44,"title":488,"abstract":489,"tags":492},[],[490,491],"机器学习","machine learning",[],{"code":494,"score":291,"supported":44,"title":495,"abstract":496,"tags":498},"H3",[],[497],"灌溉",[],{"code":500,"score":291,"supported":44,"title":501,"abstract":502,"tags":504},"H7",[],[503],"干旱",[],{"code":506,"score":291,"supported":44,"title":507,"abstract":508,"tags":510},"I7",[],[509],"methodological framework",[],[512,513],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",2,{"doi":9,"openalex_id":517,"authors":518,"venue":439,"cited_by_count":67,"oa_url":436,"card":521,"direction":87,"ingested_from":89},"W7221843179",[519],{"name":520,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":522,"method":523,"finding":524,"direction":87,"opportunity":525},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z"]