[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2164":3,"related-2164":98},{"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":31,"secondary_topics":37,"classification":44,"search_phrases":68,"slug":71,"view_count":72,"doi":73,"paper":74,"created_at":97},2164,"Plastic Greenhouse Extraction Based on Index-Guided Three-Stage Large-Factor Remote Sensing Image Super-Resolution Reconstruction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183108","Fine-scale, large-area plastic greenhouse (PG) mapping generally relies on costly high-resolution imagery with limited spatial coverage. Although freely available Sentinel-2 data offer wide swaths and frequent revisits, their insufficient spatial resolution limits PG extraction accuracy. Direct super-resolution (SR) reconstruction from 10 m or coarser imagery to the submeter level is highly ill posed because PG structures must be inferred from severely mixed pixels. To address these challenges, we propose a three-stage 12× SR framework guided by a spatially enhanced Agricultural Plastic Greenhouse Index (APGI). The framework decomposes reconstruction into successive 4×, 2×, and 1.5×-scale transformations and progressively recovers PG spatial arrangements and local boundaries through an RRDB generator and learnable upsampling operations. APGI values, directional gradients, and gradient magnitude are encoded as structural priors to modulate shallow, backbone, and upsampling features. Intermediate-scale supervision, interstage residual refinement, and tone consistency constraints are further introduced to improve the structural stability and spectral consistency of the reconstructed imagery. The model is trained using strictly co-registered Sentinel-2 and Jilin-1 image pairs, and its practical value is further evaluated through a downstream PG segmentation task. Spatially blocked five-fold cross-validation is used, with four folds for training and one fold for testing in each run, to prevent overlapping or adjacent patches from entering different subsets. Experiments conducted in Weifang, China, and Almería, Spain, demonstrate that progressive reconstruction and APGI guidance improve PG boundary continuity, reduce extraction errors, and enhance downstream segmentation accuracy. Accordingly, the framework is positioned as task-driven conditional image generation that recovers a recognition-relevant PG structure, while textures below the Sentinel-2 information limit remain learned inferences rather than direct observations.","精细尺度、大范围的塑料大棚（PG）制图通常依赖成本高昂且空间覆盖有限的高分辨率影像。尽管免费可用的Sentinel-2数据具有宽幅覆盖和高重访频率，但其空间分辨率不足限制了塑料大棚提取精度。从10 m或更粗分辨率影像直接超分辨率（SR）重建至亚米级高度不适定，因为塑料大棚结构必须从严重混合像元中推断。为应对这些挑战，我们提出了一种由空间增强型农业塑料大棚指数（APGI）引导的三阶段12×超分辨率框架。该框架将重建分解为连续的4×、2×和1.5×尺度变换，并通过RRDB生成器和可学习上采样操作逐步恢复塑料大棚空间布局和局部边界。APGI值、方向梯度和梯度幅值被编码为结构先验，以调制浅层特征、主干特征和上采样特征。进一步引入中间尺度监督、阶段间残差细化和色调一致性约束，以提高重建影像的结构稳定性和光谱一致性。该模型使用严格配准的Sentinel-2和吉林一号影像对进行训练，并通过下游塑料大棚分割任务进一步评估其实际价值。采用空间分块五折交叉验证，每次运行使用四折训练、一折测试，以防止重叠或相邻图块进入不同子集。在中国潍坊和西班牙阿尔梅里亚开展的实验表明，渐进式重建和APGI引导改善了塑料大棚边界连续性，减少了提取误差，并提升了下游分割精度。据此，该框架被定位为任务驱动的条件图像生成，即恢复与识别相关的塑料大棚结构，而低于Sentinel-2信息极限的纹理仍为学习推断而非直接观测。",null,"Remote Sensing","2026-09-10T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"提出APGI指数引导的三阶段12倍超分辨率框架，用免费Sentinel-2数据实现亚米级塑料大棚制图，方法新颖、跨区域验证可靠，对设施农业遥感监测有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","设施农业","遥感","塑料大棚","图像超分辨率",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,63],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},11,true,[54],"greenhouse",[54],[],{"code":39,"score":58,"supported":52,"title":59,"abstract":61,"tags":62},9,[60],"remote sensing",[],[28],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"I1",[],[],[26],[69,70],"图像超分辨率 塑料大棚 智慧农业 设施农业","图像超分辨率 塑料大棚","图像超分辨率塑料大棚智慧农业设施农业-2164",0,"10.3390\u002Frs18183108",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":72,"oa_url":6,"card":90,"direction":94,"ingested_from":96},"W7212170849",[77,80,83,86,88],{"name":78,"orcid":79},"Linze Bai","https:\u002F\u002Forcid.org\u002F0000-0002-1384-5630",{"name":81,"orcid":82},"Xiaocan Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1513-4103",{"name":84,"orcid":85},"Cheng Su","https:\u002F\u002Forcid.org\u002F0000-0002-3540-0309",{"name":87,"orcid":9},"Bangyan Qiu",{"name":89,"orcid":9},"Shuyang Zheng",{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"提出APGI引导的三阶段12倍超分框架，从Sentinel-2影像重建塑料大棚结构并提升提取精度。","APGI指数引导三阶段超分，RRDB生成器，Sentinel-2与吉林一号配对训","渐进重建与APGI引导改善大棚边界连续性，降低提取误差，提升下游分割精度。","农业遥感与作物表型","可探索指数引导超分迁移至其他农业地物，并量化重建纹理的置信度与不确定性。","openalex","2026-09-11T23:30:29.918053Z",{"total":99,"page":21,"page_size":99,"items":100},6,[101,183,254,318,369,467],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":9,"source_name":107,"source_url":104,"published_at":108,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":109,"score_detail":110,"sources":114,"tags":116,"primary_topic":120,"secondary_topics":125,"classification":128,"search_phrases":156,"slug":159,"view_count":160,"doi":161,"paper":162,"created_at":182},1599,"Continual Learning Model Evaluation for Hyperspectral-Based Tomato Bacterial Spot Detection in Greenhouse","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102535","Detection of tomato bacterial spot (TBS) at canopy level is critical in greenhouse seedling production, yet most previous hyperspectral studies have been conducted at leaf scale under tightly controlled conditions. This study evaluated a domain-incremental continual-learning framework for automated canopy-scale TBS detection using hyperspectral sensing across five greenhouse experiments. Three spectral feature sets were evaluated, and Full-memory and Bounded-memory Gradient Episodic Memory multilayer perceptrons (GEM-MLP) were compared with Sequential and Joint-training MLP models under two conditions: a future unseen-experiment generalization test (Scenario 1) and a same-distribution benchmark (Scenario 2). Genetic-algorithm feature selection reduced input dimensionality but did not identify a single feature subset that was stable across all folds and scenarios. Under Scenario 1, Full-memory GEM-MLP using the fixed 18-VI feature set achieved 58.1% asymptomatic recall and 81.2% symptomatic recall. Its clearest advantage was knowledge retention: backward transfer (BWT) was -1.4%, compared with -7.2% for Sequential MLP, while forward transfer was similar (7.4% versus 7.0%). Under Scenario 2, Full-memory GEM-MLP achieved 46.5% asymptomatic recall and 86.2% symptomatic recall, while Joint-training MLP was highly competitive and reached the highest mean asymptomatic recall (53.4%). Full-memory GEM-MLP again showed better retention, with BWT of +1.8% compared with -3.2% for Sequential MLP. These results indicate that GEM did not provide universally superior classification performance, but it consistently reduced forgetting during sequential learning. The persistent difficulty of asymptomatic detection, even when all experimental domains were represented during model development, further suggests that early canopy-scale disease detection is limited by factors beyond cross-experiment distribution shift.","在温室育苗生产中，冠层尺度上的番茄细菌性斑点病（TBS）检测至关重要，然而以往大多数高光谱研究都是在严格控制条件下于叶片尺度进行的。本研究评估了一种基于域增量持续学习框架的自动化冠层尺度TBS检测方法，该方法利用跨五个温室实验的高光谱传感数据。研究评估了三组光谱特征集，并在两种条件下将全记忆与有界记忆梯度情景记忆多层感知器（GEM-MLP）与顺序训练和联合训练MLP模型进行了比较：一种是对未来未见实验的泛化测试（情景1），另一种是同分布基准测试（情景2）。遗传算法特征选择降低了输入维度，但未能识别出在所有折和情景下均稳定的单一特征子集。在情景1下，使用固定18-VI特征集的全记忆GEM-MLP实现了58.1%的无症状召回率和81.2%的有症状召回率。其最显著的优势在于知识保持能力：后向迁移（BWT）为-1.4%，而顺序MLP为-7.2%，同时前向迁移相近（7.4%对7.0%）。在情景2下，全记忆GEM-MLP实现了46.5%的无症状召回率和86.2%的有症状召回率，而联合训练MLP表现极具竞争力，达到了最高的平均无症状召回率（53.4%）。全记忆GEM-MLP再次展现出更好的保持能力，其BWT为+1.8%，而顺序MLP为-3.2%。这些结果表明，GEM并未提供普遍优越的分类性能，但在顺序学习过程中始终减少了遗忘。即使在模型开发阶段涵盖了所有实验域，无症状检测仍持续存在困难，这进一步表明早期冠层尺度病害检测受到跨实验分布偏移之外因素的限制。","Smart Agricultural Technology","2026-09-01T00:00:00Z",64,{"impact":111,"substance":17,"depth":17,"authority":111,"freshness":112,"relevant":21,"comment":113},12,4,"研究评估持续学习模型在温室番茄细菌性斑点病高光谱检测中的应用，虽为细分领域进展，但方法新颖且数据详实，对农业AI落地有参考价值。",[115],{"name":107,"url":104},[26,117,118,28,119],"农业人工智能","番茄","病害检测",{"code":121,"slug":122,"name":123,"path":124,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",[126,127],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":129},{"candidates":130,"agricultural_evidence":52},[131,137,142,147,151],{"code":121,"score":132,"supported":52,"title":133,"abstract":135,"tags":136},15,[134],"tomato",[118,134],[118],{"code":32,"score":19,"supported":52,"title":138,"abstract":139,"tags":141},[54],[140,54],"温室",[],{"code":39,"score":111,"supported":52,"title":143,"abstract":145,"tags":146},[144],"hyperspectral",[144],[28],{"code":64,"score":21,"supported":14,"title":148,"abstract":149,"tags":150},[],[],[26],{"code":152,"score":21,"supported":14,"title":153,"abstract":154,"tags":155},"I2",[],[],[117],[157,158],"农业人工智能 智慧农业 病害检测 番茄","农业人工智能 智慧农业","农业人工智能智慧农业病害检测番茄-1599",2,"10.1016\u002Fj.atech.2026.102535",{"doi":161,"openalex_id":163,"authors":164,"venue":107,"cited_by_count":72,"oa_url":104,"card":176,"direction":180,"ingested_from":96},"W7207729517",[165,167,169,172,174],{"name":166,"orcid":9},"Gaoshoutong Si",{"name":168,"orcid":9},"Peter Ling",{"name":170,"orcid":171},"Anna L. Testen","https:\u002F\u002Forcid.org\u002F0000-0002-6504-9599",{"name":173,"orcid":9},"Heping Zhu",{"name":175,"orcid":9},"Hongyoung Jeon",{"tldr":177,"method":178,"finding":179,"direction":180,"opportunity":181},"评估持续学习框架用于温室高光谱番茄细菌性斑点病冠层检测。","域增量持续学习，GEM-MLP与顺序\u002F联合训练MLP对比，遗传算法特征选择。","GEM-MLP减少遗忘但分类性能非最优，无症状检测仍困难。","智慧农业 \u002F 农业物联网","可探索结合多模态数据或更先进持续学习方法，提升早期无症状病害检测精度。","2026-09-04T23:30:04.578607Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":72,"score_detail":191,"sources":193,"tags":195,"primary_topic":196,"secondary_topics":197,"classification":203,"search_phrases":227,"slug":228,"view_count":72,"doi":229,"paper":230,"created_at":253},2002,"Identifying the influencing factors of lake surface greenhouse gas concentrations using sentinel-2 remote sensing and machine learning: A case study of bird Island, Qinghai Lake","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67206-5","Alpine lakes on the Qinghai-Tibet Plateau are important regions for studies of the regional carbon cycle and are highly sensitive to climate change. However, the variation patterns of lake surface greenhouse gases and their environmental regulation mechanisms remain unclear. In this study, Bird Island of Qinghai Lake was selected as the study area. Based on the measured concentrations of lake surface CO 2 , CH 4 , and H 2 O corresponding to Sentinel-2 satellite overpass periods throughout 2021, combined with Sentinel-2 remote sensing imagery and ERA5-Land meteorological data, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models, together with the SHAP method, were used to identify and predict the influencing factors and their contribution characteristics to the variations in lake surface CO 2 , CH 4 , and H 2 O. The results showed that CO 2 , CH 4 , and H 2 O exhibited distinct seasonal variations. The annual variation in CO 2 concentration generally showed a “V”-shaped pattern, with concentrations during the growing season being lower than those during the non-growing season. The annual variation in CH 4 concentration was relatively small and exhibited a “wave”-shaped pattern. The H 2 O concentration showed a distinct unimodal pattern, and its high-value period was generally consistent with the low-value period of CO 2 . Temperature and MNDWI were the main predictive factors affecting the variations in CO 2 , CH 4 , and H 2 O concentrations, while H 2 O concentration was also affected by Radiation. This study combined observational data, remote sensing imagery, and machine learning, providing new insights into the environmental predictive factors of greenhouse gas concentrations in lakes under climate change.","青藏高原高山湖泊是研究区域碳循环的重要区域，对气候变化高度敏感。然而，湖泊表层温室气体的变化规律及其环境调控机制仍不清楚。本研究以青海湖鸟岛为研究区，基于2021年全年与Sentinel-2卫星过境时段对应的湖泊表层CO₂、CH₄和H₂O实测浓度，结合Sentinel-2遥感影像和ERA5-Land气象数据，采用随机森林（Random Forest, RF）和极端梯度提升（Extreme Gradient Boosting, XGBoost）模型，并借助SHAP方法，识别和预测了影响湖泊表层CO₂、CH₄和H₂O变化的因素及其贡献特征。结果表明，CO₂、CH₄和H₂O表现出明显的季节变化。CO₂浓度的年变化总体呈“V”形模式，生长季浓度低于非生长季。CH₄浓度的年变化幅度相对较小，呈“波浪”形模式。H₂O浓度呈明显的单峰模式，其高值期与CO₂的低值期总体一致。温度和归一化差异水体指数（MNDWI）是影响CO₂、CH₄和H₂O浓度变化的主要预测因子，而H₂O浓度还受到辐射的影响。本研究将观测数据、遥感影像和机器学习相结合，为气候变化下湖泊温室气体浓度的环境预测因子提供了新的见解。","Scientific Reports","2026-09-07T00:00:00Z",{"impact":72,"substance":72,"depth":72,"authority":72,"freshness":72,"relevant":72,"comment":192},"研究青海湖温室气体，非农业领域，不相关。",[194],{"name":189,"url":186},[28],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[198,199],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":152,"slug":200,"name":201,"path":202,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":45,"locked":14,"version":46,"method":47,"evidence":204},{"candidates":205,"agricultural_evidence":52},[206,210,214,220],{"code":39,"score":132,"supported":52,"title":207,"abstract":208,"tags":209},[60],[28,60],[28],{"code":32,"score":19,"supported":52,"title":211,"abstract":212,"tags":213},[54],[140,54],[],{"code":152,"score":19,"supported":52,"title":215,"abstract":217,"tags":219},[216],"machine learning",[218,216],"机器学习",[],{"code":221,"score":99,"supported":52,"title":222,"abstract":223,"tags":226},"H7",[],[224,225],"气候变化","climate change",[],[],"Identifyingtheinfluencingfactorsoflakesu-2002","10.1038\u002Fs41598-026-67206-5",{"doi":229,"openalex_id":231,"authors":232,"venue":189,"cited_by_count":72,"oa_url":247,"card":248,"direction":94,"ingested_from":96},"W7211873544",[233,235,238,240,243,245],{"name":234,"orcid":9},"Xingyue Li",{"name":236,"orcid":237},"Yu Chen","https:\u002F\u002Forcid.org\u002F0000-0002-9910-6942",{"name":239,"orcid":9},"Zhen Chen",{"name":241,"orcid":242},"Lei Li","https:\u002F\u002Forcid.org\u002F0000-0003-4368-8637",{"name":244,"orcid":9},"Dong Han",{"name":246,"orcid":9},"Kelong Chen","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67206-5_reference.pdf",{"tldr":249,"method":250,"finding":251,"direction":94,"opportunity":252},"利用Sentinel-2遥感与机器学习识别青海湖鸟岛湖面温室气体浓度的影响因素。","结合Sentinel-2影像、ERA5-Land气象数据，使用RF和XGBoos","CO2呈V型季节变化，CH4呈波浪形，H2O呈单峰；温度和MNDWI是主要影响因素。","可延伸至其他高原湖泊或内陆水体，结合更多环境变量和深度学习模型，提高温室气体浓度预测精度。","2026-09-09T23:30:23.971965Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":263,"sources":267,"tags":269,"primary_topic":272,"secondary_topics":273,"classification":274,"search_phrases":293,"slug":296,"view_count":72,"doi":297,"paper":298,"created_at":317},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","2026-10-21T00:00:00Z",73,{"impact":111,"substance":264,"depth":265,"authority":19,"freshness":13,"relevant":21,"comment":266},20,17,"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。",[268],{"name":260,"url":257},[26,270,28,271],"作物制图","SAR",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":275},{"candidates":276,"agricultural_evidence":52},[277,282,289],{"code":39,"score":13,"supported":52,"title":278,"abstract":279,"tags":281},[],[28,60,280],"multispectral",[28],{"code":283,"score":99,"supported":52,"title":284,"abstract":285,"tags":288},"A8",[],[286,287],"粮食安全","food security",[],{"code":64,"score":21,"supported":14,"title":290,"abstract":291,"tags":292},[],[],[26],[294,295],"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":297,"openalex_id":299,"authors":300,"venue":260,"cited_by_count":72,"oa_url":257,"card":312,"direction":94,"ingested_from":96},"W7223117203",[301,304,306,309],{"name":302,"orcid":303},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":305,"orcid":9},"Wentao Lv",{"name":307,"orcid":308},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":310,"orcid":311},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":313,"method":314,"finding":315,"direction":94,"opportunity":316},"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":319,"title":320,"url":321,"summary":322,"summary_zh":323,"content":9,"source_name":324,"source_url":321,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":333,"secondary_topics":334,"classification":336,"search_phrases":353,"slug":356,"view_count":72,"doi":357,"paper":358,"created_at":368},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":20,"substance":264,"depth":265,"authority":111,"freshness":13,"relevant":21,"comment":327},"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。",[329],{"name":324,"url":321},[26,117,331,28,332],"产量预测","可解释性",{"code":152,"slug":200,"name":201,"path":202,"parent_code":43},[335],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":337},{"candidates":338,"agricultural_evidence":52},[339,345,349],{"code":152,"score":111,"supported":52,"title":340,"abstract":342,"tags":344},[341],"computer vision",[343],"计算机视觉",[117],{"code":39,"score":20,"supported":52,"title":346,"abstract":347,"tags":348},[],[28,60],[331,28],{"code":64,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[26],[354,355],"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":357,"openalex_id":359,"authors":360,"venue":324,"cited_by_count":72,"oa_url":321,"card":363,"direction":94,"ingested_from":96},"W7223114002",[361],{"name":362,"orcid":9},"Kadiatou Barry",{"tldr":364,"method":365,"finding":366,"direction":94,"opportunity":367},"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":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":372,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":380,"tags":382,"primary_topic":386,"secondary_topics":392,"classification":394,"search_phrases":419,"slug":422,"view_count":72,"doi":423,"paper":424,"created_at":466},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":17,"substance":18,"depth":17,"authority":111,"freshness":58,"relevant":21,"comment":379},"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。",[381],{"name":375,"url":372},[26,383,384,28,385],"无人机","育种","紫花苜蓿",{"code":387,"slug":388,"name":389,"path":390,"parent_code":391},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[393],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":395},{"candidates":396,"agricultural_evidence":52},[397,403,407,414],{"code":387,"score":132,"supported":52,"title":398,"abstract":400,"tags":402},[399],"alfalfa",[401,399],"苜蓿",[385],{"code":39,"score":111,"supported":52,"title":404,"abstract":405,"tags":406},[280],[280],[28],{"code":64,"score":408,"supported":52,"title":409,"abstract":410,"tags":413},7,[],[411,412],"精准农业","precision agriculture",[26],{"code":415,"score":112,"supported":52,"title":416,"abstract":417,"tags":418},"C4",[],[401],[385],[420,421],"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":423,"openalex_id":425,"authors":426,"venue":375,"cited_by_count":72,"oa_url":372,"card":461,"direction":94,"ingested_from":96},"W7223083966",[427,430,433,436,439,441,443,446,449,452,455,458],{"name":428,"orcid":429},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":431,"orcid":432},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":434,"orcid":435},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":437,"orcid":438},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":440,"orcid":9},"Julie Hansen",{"name":442,"orcid":9},"Christopher Pierce",{"name":444,"orcid":445},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":447,"orcid":448},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":450,"orcid":451},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":453,"orcid":454},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":456,"orcid":457},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":459,"orcid":460},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":462,"method":463,"finding":464,"direction":94,"opportunity":465},"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":468,"title":469,"url":470,"summary":471,"summary_zh":9,"content":9,"source_name":472,"source_url":9,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":473,"score_detail":474,"sources":479,"tags":481,"primary_topic":485,"secondary_topics":486,"classification":488,"search_phrases":503,"slug":506,"view_count":72,"doi":9,"paper":507,"created_at":515},5536,"日光温室番茄全生育期环境特征解析与智能调控决策支持方法","https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F10\u002F20261021145441802156759.shtm","研究解析日光温室番茄全生育期环境特征，发现该类温室气候缓冲能力较强、有效积温与番茄生理需求呈双峰匹配，但相对增益指标揭示单位时间热量供给失衡、深冬热量不足以及光能结构性不足、湿度调控昼夜失衡等问题。作者建议构建温度—光照—湿度耦合的智能闭环控制系统。","Agriculture 2026-10-21",77,{"impact":475,"substance":476,"depth":265,"authority":477,"freshness":13,"relevant":21,"comment":478},16,21,13,"论文解析日光温室番茄全生育期环境特征并提出温光湿耦合智能调控方案，结论有新意，对设施农业信息化有参考价值。",[480],{"name":472,"url":470},[26,27,482,483,484],"环境调控","温室番茄","智能决策",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[487],{"code":121,"slug":122,"name":123,"path":124,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":489},{"candidates":490,"agricultural_evidence":52},[491,495,499],{"code":32,"score":477,"supported":52,"title":492,"abstract":493,"tags":494},[140],[140],[483,482],{"code":121,"score":51,"supported":52,"title":496,"abstract":497,"tags":498},[118],[118],[],{"code":64,"score":21,"supported":14,"title":500,"abstract":501,"tags":502},[],[],[26],[504,505],"日光温室 番茄 环境调控","温室番茄 智能闭环控制","日光温室番茄环境调控-5536",{"doi":9,"openalex_id":9,"authors":508,"venue":9,"cited_by_count":72,"oa_url":9,"card":509,"direction":180,"ingested_from":514},[],{"tldr":510,"method":511,"finding":512,"direction":180,"opportunity":513},"解析日光温室番茄全生育期环境特征并提出智能调控决策支持方法。","基于全生育期环境监测数据，分析积温匹配与相对增益指标。","温室缓冲力强但深冬热量不足、光能结构性不足、湿度昼夜失衡。","可研发温度—光照—湿度耦合的智能闭环控制算法，并验证其对番茄产量与能耗的协同优化效果。","agent","2026-10-22T00:07:10.129353Z"]