[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3328":3,"related-3328":103},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":102},3328,"Yield ranking of spring wheat breeding lines absent from model training within two contrasting seasons in northern Kazakhstan","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102588","Reliable assessment of yield models in breeding trials requires evaluation on lines that have not contributed observations to model development. We examined this problem in an advanced spring wheat yield nursery in northern Kazakhstan during the 2024 and 2025 seasons. Of 300 plots, 176 from 22 entries formed the model development set, while 124 plots representing 31 breeding lines were completely excluded from training. Eleven variables were retained from 82 candidates using the training data alone; the candidates were derived from UAV multispectral imagery, ERA5-Land reanalysis, LiDAR-derived static plot microtopography, phenology and maturity group. LightGBM yielded a pooled R² of 0.587 for the excluded lines; resampling breeding lines within season gave a mean R² of 0.564 with a 95 % confidence interval from 0.252 to 0.776. The pooled value partly reflected the strong contrast between seasons rather than discrimination among lines within a season. Random division of plots produced an R² of 0.890, indicating that apparent predictive accuracy depended strongly on validation design. Agreement between observed and predicted line rankings was moderate, with Spearman correlations of 0.612 in 2024 and 0.650 in 2025, although uncertainty was wide because only 14 and 17 independent lines were available. Six modelling approaches produced overlapping uncertainty intervals, with no clear evidence of superiority, and the complete combination of data sources was not separable from simpler configurations retaining the multispectral block. A separate analysis in which screening, selection and fitting used 2024 data alone and evaluation used 2025 data showed a severe loss of performance, with R² = −8.81 and a Spearman correlation of −0.082 that was indistinguishable from zero. Poor transfer persisted among entries grown in both seasons, so genotype novelty alone was insufficient to explain the failure. Breeding lines absent from training could therefore be ranked with moderate consistency when both observed seasonal regimes were represented during model development, whereas prediction in an unobserved season remained unreliable. The study covers one nursery complex and two seasons, so transfer across locations and broader conditions remains to be established.","在育种试验中，要对产量模型进行可靠评估，必须在未参与模型开发的品系上进行评价。我们在2024年和2025年生长季于哈萨克斯坦北部的一个高级春小麦产量圃中考察了这一问题。在300个小区中，来自22个品系的176个小区构成模型开发集，而代表31个育种品系的124个小区则完全排除在训练之外。仅使用训练数据，从82个候选变量中保留了11个变量；这些候选变量来源于无人机多光谱影像、ERA5-Land再分析数据、LiDAR衍生的静态小区微地形、物候和成熟期组。LightGBM对被排除品系给出的合并R²为0.587；在生长季内对育种品系进行重采样得到的平均R²为0.564，95%置信区间为0.252至0.776。合并值部分反映了生长季之间的强烈差异，而非生长季内品系之间的区分能力。对小区进行随机划分得到的R²为0.890，表明表观预测精度在很大程度上取决于验证设计。观测品系排名与预测品系排名之间的一致性为中等，2024年和2025年的Spearman相关系数分别为0.612和0.650，但由于仅有14个和17个独立品系可用，不确定性范围较宽。六种建模方法产生了相互重叠的不确定性区间，没有明确证据表明哪一种更优，并且完整的数据源组合与保留多光谱模块的较简单配置无法区分。另一项分析中，筛选、选择和拟合仅使用2024年数据，而评估使用2025年数据，结果显示性能严重下降，R² = −8.81，Spearman相关系数为−0.082，与零无法区分。在两个生长季均种植的品系之间，较差的迁移性依然存在，因此仅用品系新颖性不足以解释这种失败。因此，当模型开发过程中涵盖了所观测到的两种生长季情形时，未参与训练的育种品系可以以中等一致性进行排名，而在未观测生长季中的预测仍然不可靠。本研究仅涵盖一个圃系复合体和两个生长季，因此跨地点和更广泛条件下的迁移性仍有待确立。",null,"Smart Agricultural Technology","2026-09-22T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"基于无人机多光谱与气象数据的春小麦育种品系产量预测研究，验证设计严谨、结论审慎，对智慧育种与遥感估产有参考价值，但属单点试验、地域性强，未达重大突破层级。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","产量预测","机器学习","小麦育种","遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,57,64,69],{"code":33,"score":52,"supported":53,"title":54,"abstract":55,"tags":56},15,true,[34],[35,34],[30],{"code":40,"score":58,"supported":53,"title":59,"abstract":60,"tags":63},8,[],[61,62],"无人机多光谱","multispectral",[28,31],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"I1",[],[],[27],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"I2",[],[],[29],[75,76],"哈萨克斯坦 春小麦 产量预测","UAV 多光谱 育种试验","哈萨克斯坦春小麦产量预测-3328",4,"10.1016\u002Fj.atech.2026.102588",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":94,"oa_url":6,"card":95,"direction":99,"ingested_from":101},"W7214044328",[83,86,89,92],{"name":84,"orcid":85},"Dastan Yelubayev","https:\u002F\u002Forcid.org\u002F0000-0001-5358-7982",{"name":87,"orcid":88},"TIMUR SAVIN","https:\u002F\u002Forcid.org\u002F0000-0002-3550-647X",{"name":90,"orcid":91},"Ismail Tokbergenov","https:\u002F\u002Forcid.org\u002F0000-0002-0656-9914",{"name":93,"orcid":9},"Bakhtiyar Zhanzakov",0,{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"评估春小麦育种品系产量模型在未参与训练品系上的跨季预测能力。","无人机多光谱、ERA5-Land、LiDAR与物候数据，LightGBM等六种模","两季均参与训练时品系排名中等一致，但预测未观测季节完全失效。","农业遥感与作物表型","需研究跨地点、跨年份可迁移的表型预测模型与验证设计，避免随机划分高估精度。","openalex","2026-09-24T23:30:03.201775Z",{"total":104,"page":22,"page_size":104,"items":105},6,[106,181,272,345,442,494],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":118,"tags":120,"primary_topic":121,"secondary_topics":122,"classification":124,"search_phrases":148,"slug":151,"view_count":116,"doi":152,"paper":153,"created_at":180},1307,"Estimation of the Comprehensive High Photosynthetic-Efficiency Phenotypic Index in Winter Wheat Based on UAV Multimodal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15172674","Accurate monitoring of photosynthetic phenotypes is fundamental for breeding high photosynthetic-efficiency wheat cultivars, and UAV remote sensing provides an effective approach for their large-scale identification. However, single photosynthetic parameters are limited in comprehensively evaluating crop photosynthetic efficiency. This study integrated multiple photosynthetic phenotypic parameters using principal component analysis (PCA) and the CRITIC objective weighting method (PCA-CRITIC) to construct a Comprehensive High Photosynthetic-Efficiency Phenotypic Index (CHPPI) for winter wheat. Concurrently, multispectral vegetation indices (MSVI), RGB vegetation indices (RGBVI), texture features (TF), and their combination, Comprehensive Multispectral-Visible-Texture Features (CMVTF), were extracted from UAV multimodal remote sensing data. Based on these features, four feature selection methods, namely PCC, VIP, SPA, and UVE, were evaluated in combination with five machine learning algorithms, including KNN, DT, SVR, RF, and XGBoost, to precisely estimate the CHPPI across key growth stages. The results demonstrated that the CHPPI exhibited significant cultivar variations across growth stages. Cultivars SH06144 and Liangxing19 consistently maintained high photosynthetic efficiency levels. The fused feature set CMVTF derived from multimodal remote sensing data outperformed individual feature categories, and the feature subset selected by the UVE method most significantly enhanced model accuracy. Regarding algorithms, the ensemble-based XGBoost and RF models markedly surpassed traditional machine learning algorithms. Specifically, the CMVTF-UVE-XGBoost model achieved the optimal comprehensive performance, yielding an R2 of 0.862, an RMSE of 0.032, and an MAE of 0.027 on the testing set, with its spatial distribution estimations highly consistent with measured values. This study provides robust technical support for the rapid and large-scale screening of high photosynthetic-efficiency wheat breeding materials.","光合表型精准监测是培育高光效小麦品种的基础，无人机遥感为其大规模鉴定提供了有效途径。然而，单一光合参数难以全面评价作物光合效率。本研究利用主成分分析(PCA)与CRITIC客观赋权法(PCA-CRITIC)整合多个光合表型参数，构建了冬小麦高光效综合表型指数(CHPPI)。同时，从无人机多模态遥感数据中提取多光谱植被指数(MSVI)、RGB植被指数(RGBVI)、纹理特征(TF)及其融合特征——多光谱-可见光-纹理综合特征(CMVTF)。基于上述特征，评估了PCC、VIP、SPA和UVE四种特征选择方法，并结合KNN、DT、SVR、RF和XGBoost五种机器学习算法，对关键生育期的CHPPI进行精准估算。结果表明，CHPPI在不同生育期表现出显著的品种间差异，品种SH06144和良星19始终保持较高的光合效率水平。基于多模态遥感数据构建的融合特征集CMVTF优于单一特征类别，且UVE方法筛选的特征子集对模型精度的提升最为显著。在算法方面，基于集成的XGBoost和RF模型明显优于传统机器学习算法。具体而言，CMVTF-UVE-XGBoost模型取得了最优的综合性能，在测试集上R²为0.862，RMSE为0.032，MAE为0.027，其空间分布估算结果与实测值高度一致。本研究为高光效小麦育种材料的快速大规模筛选提供了有力的技术支撑。","Plants","2026-08-31T00:00:00Z",73,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":116,"relevant":22,"comment":117},2,"基于无人机多模态遥感构建小麦光合效率综合指数，方法新颖且模型精度高，对育种筛选有实用价值。",[119],{"name":112,"url":109},[27,29,30,31],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[123],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":125},{"candidates":126,"agricultural_evidence":53},[127,133,138,144],{"code":33,"score":128,"supported":53,"title":129,"abstract":131,"tags":132},26,[130,34],"winter wheat",[35,130,34],[30],{"code":40,"score":19,"supported":53,"title":134,"abstract":136,"tags":137},[135],"remote sensing",[31,135,62],[31],{"code":70,"score":139,"supported":53,"title":140,"abstract":141,"tags":143},7,[],[29,142],"machine learning",[29],{"code":65,"score":22,"supported":14,"title":145,"abstract":146,"tags":147},[],[],[27],[149,150],"小麦育种 智慧农业 机器学习 遥感","小麦育种 智慧农业","小麦育种智慧农业机器学习遥感-1307","10.3390\u002Fplants15172674",{"doi":152,"openalex_id":154,"authors":155,"venue":112,"cited_by_count":94,"oa_url":109,"card":175,"direction":99,"ingested_from":101},"W7204845072",[156,158,160,162,164,166,168,170,172],{"name":157,"orcid":9},"Ning Yang",{"name":159,"orcid":9},"Dayong Cui",{"name":161,"orcid":9},"Songming Lin",{"name":163,"orcid":9},"Changliang Du",{"name":165,"orcid":9},"Liwen Wang",{"name":167,"orcid":9},"Fei Zhang",{"name":169,"orcid":9},"Zhu Shi",{"name":171,"orcid":9},"Bingqian Hou",{"name":173,"orcid":174},"Junke Zhu","https:\u002F\u002Forcid.org\u002F0009-0005-7778-9614",{"tldr":176,"method":177,"finding":178,"direction":99,"opportunity":179},"基于无人机多模态遥感数据，构建综合高光效表型指数并精准估算，用于小麦育种材料筛选。","PCA-CRITIC构建CHPPI，提取多模态特征，结合UVE特征选择与XGBo","CMVTF-UVE-XGBoost模型表现最优，测试集R²=0.862，可快速大规模筛选高光效小麦。","可探索将CHPPI估算模型推广至其他作物或不同环境，并引入深度学习或时序数据提升泛化能力。","2026-09-01T23:30:16.317443Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":194,"tags":196,"primary_topic":198,"secondary_topics":199,"classification":205,"search_phrases":239,"slug":242,"view_count":22,"doi":243,"paper":244,"created_at":271},3863,"Species identification of crops and weeds in wheat fields using hyperspectral data and machine learning algorithms","https:\u002F\u002Fdoi.org\u002F10.31489\u002F2026feb3\u002F137-151","Based on establishing the relationship between the morphological and anatomical properties of plant tissues and the recognition efficiency of the machine learning algorithm, a comprehensive assessment of the limitations of ML algorithms (using Random Forest as an example) in the multiclass identification of crops and weeds in the agroecosystems of northeastern Kazakhstan was carried out for the first time. The study substantiates the principal biological causes manifested in the optical overlap of classes in the hyperspectral feature space. Data collection was performed with a hyperspectral sensor in the VIS-NIR range. The analysis of spectral profiles was carried out based on variance distribution assessment using principal component analysis and the construction of reflectance curves. Classification of the extracted data was performed using the Random Forest algorithm. Spring wheat (Triticum aestivum) crops at different phenological phases and five dominant weed species (Euphorbia virgata, Descurainia sophia, Vicia hirsuta, Fallopia convolvulus, Echinochloa crus-galli) were selected as research objects. The algorithm demonstrated \"crop-weed\" segregation efficiency with an overall classification accuracy on test data of 72.94%. High identification accuracy was revealed for wheat (MCC=0.994) due to its spectral stability and pronounced red edge features (680-730 nm). Reduced accuracy in intra-class weed separation is driven by species-specific anatomy: thick cuticles in Euphorbia virgata increase reflectance, complex structure in Vicia hirsuta causes spectral dispersion, and mesophyll similarity between Echinochloa crus-galli and grasses leads to optical overlap with crop features. Thus, the model enables reliable crop-weed differentiation, a key requirement for precision agriculture and targeted agrochemical application.","在建立植物组织形态解剖特性与机器学习算法识别效率之间关系的基础上，首次对机器学习算法（以随机森林为例）在哈萨克斯坦东北部农业生态系统中作物与杂草多类识别方面的局限性进行了综合评估。研究证实了在高光谱特征空间中表现为类别光谱重叠的主要生物学成因。数据采集使用VIS-NIR波段的高光谱传感器完成。光谱剖面分析基于主成分分析的方差分布评估及反射率曲线构建进行。提取数据的分类采用随机森林算法完成。选取不同物候期的春小麦（Triticum aestivum）作物及五种优势杂草物种（Euphorbia virgata、Descurainia sophia、Vicia hirsuta、Fallopia convolvulus、Echinochloa crus-galli）作为研究对象。该算法在测试数据上实现了“作物-杂草”分离，总体分类精度为72.94%。小麦因其光谱稳定性及显著的红边特征（680-730 nm）而表现出较高的识别精度（MCC=0.994）。杂草类内分离精度降低由物种特异性解剖结构所致：Euphorbia virgata的厚角质层增加了反射率，Vicia hirsuta的复杂结构导致光谱离散，而Echinochloa crus-galli与禾本科植物之间的叶肉相似性导致其与作物特征发生光谱重叠。因此，该模型能够实现可靠的作物-杂草区分，这是精准农业和靶向农化施用的关键要求。","Fundamental and Experimental Biology","2026-09-29T00:00:00Z",70,{"impact":17,"substance":191,"depth":192,"authority":17,"freshness":21,"relevant":22,"comment":193},20,17,"高光谱结合机器学习实现麦田作物与杂草识别，方法新颖、结论具体，对精准农业有参考价值，但属细分领域研究，影响范围有限。",[195],{"name":187,"url":184},[27,29,35,197,31],"杂草识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[200,204],{"code":70,"slug":201,"name":202,"path":203,"parent_code":44},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":206},{"candidates":207,"agricultural_evidence":53},[208,212,216,221,227,234],{"code":33,"score":52,"supported":53,"title":209,"abstract":210,"tags":211},[34],[35,34],[35],{"code":70,"score":52,"supported":53,"title":213,"abstract":214,"tags":215},[142],[29,142],[29],{"code":40,"score":17,"supported":53,"title":217,"abstract":219,"tags":220},[218],"hyperspectral",[218],[31],{"code":65,"score":139,"supported":53,"title":222,"abstract":223,"tags":226},[],[224,225],"精准农业","precision agriculture",[27],{"code":228,"score":229,"supported":53,"title":230,"abstract":231,"tags":233},"H6",3,[],[232],"农业生态",[],{"code":235,"score":22,"supported":14,"title":236,"abstract":237,"tags":238},"F4",[],[],[197],[240,241],"高光谱 小麦 杂草 识别","随机森林 作物 杂草 分类","高光谱小麦杂草识别-3863","10.31489\u002F2026feb3\u002F137-151",{"doi":243,"openalex_id":245,"authors":246,"venue":187,"cited_by_count":94,"oa_url":9,"card":265,"direction":270,"ingested_from":101},"W7214771122",[247,250,253,256,259,262],{"name":248,"orcid":249},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":251,"orcid":252},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":254,"orcid":255},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":257,"orcid":258},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":260,"orcid":261},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":263,"orcid":264},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":266,"method":267,"finding":268,"direction":99,"opportunity":269},"利用高光谱数据与随机森林识别哈萨克斯坦东北部麦田作物与杂草，并分析误判的生物学原因。","VIS-NIR高光谱传感器采集数据，PCA分析光谱，随机森林分类春小麦与五种杂草","作物-杂草总体分类精度72.94%，小麦识别精度高（MCC=0.994），杂草种间分离受解剖结构影响","可探索融合形态解剖特征与深度学习，提升杂草种间分类精度，服务精准施药。","农业人工智能与决策模型","2026-09-30T23:30:54.952093Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":112,"source_url":275,"published_at":278,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":279,"score_detail":280,"sources":283,"tags":285,"primary_topic":288,"secondary_topics":289,"classification":291,"search_phrases":307,"slug":310,"view_count":116,"doi":311,"paper":312,"created_at":344},2522,"Estimation of Grain Yield and Quality in Awned and Awnless Wheat Genotypes Using UAV and Proximal Multispectral Sensors","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15182819","This study aimed to evaluate the complementary potential of UAV-based and proximal multispectral sensing using the Plant-O-Meter (POM) sensor for the assessment of wheat grain yield and quality across different phenological stages and two growing seasons. The analysis was based on three vegetation indices, the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Red Edge Index (NDRE), and included two morphologically distinct genotype groups, awned and awnless. The study included nine awned and nine awnless genotypes. Vegetation indices showed pronounced seasonal dynamics, with higher values during intensive vegetative development and a decline during later growth stages. The strongest relationship with grain yield was observed for GNDVI-A during the heading to beginning of flowering stage (BBCH 51–61) in Season I (r = 0.85), whereas in Season II, the strongest relationship was observed for NDRE-A during the flag leaf stage (BBCH 37–39) (r = 0.80). Awned genotypes generally showed stronger VI–yield relationships, while associations with grain quality parameters varied among genotype groups and seasons. Linear mixed-effects models showed that both VI-related effects and genotype variability contributed to the variation in grain yield and quality. For yield, marginal R2 was 0.43 in Season I and 0.50 in Season II, while conditional R2 was 0.78 and 0.64, respectively. For protein and wet gluten content, model performance was more variable, with marginal R2 values ranging from 0.30 to 0.39 for protein and from 0.31 to 0.33 for wet gluten. Genotype-level LOGO cross-validation further indicated variation in model performance when genotypes not included in model development were evaluated. Overall, the results indicate that relationships between multispectral vegetation indices and wheat grain yield and quality depend on sensing method, phenological stage, genotype characteristics, and growing season. The findings provide an exploratory basis for the application of multispectral sensing in wheat phenotyping and assessment of grain yield and quality, while further validation across broader genetic and environmental conditions is required.","本研究旨在评估基于无人机与近地多光谱传感（采用Plant-O-Meter（POM）传感器）在不同物候期和两个生长季中对小麦籽粒产量和品质评估的互补潜力。分析基于三个植被指数，即归一化差异植被指数（NDVI）、绿色归一化差异植被指数（GNDVI）和归一化差异红边指数（NDRE），并纳入两个形态学上不同的基因型组，即有芒和无芒。研究包括9个有芒基因型和9个无芒基因型。植被指数表现出明显的季节性动态变化，在旺盛营养发育期数值较高，在生长后期下降。与籽粒产量关系最强的是抽穗至开花初期（BBCH 51–61）第I季的GNDVI-A（r = 0.85），而在第II季，关系最强的是旗叶期（BBCH 37–39）的NDRE-A（r = 0.80）。有芒基因型总体上表现出更强的植被指数-产量关系，而与籽粒品质参数的关联因基因型组和季节而异。线性混合效应模型表明，植被指数相关效应和基因型变异均对籽粒产量和品质的变异有贡献。对于产量，第I季的边际R²为0.43，第II季为0.50，而条件R²分别为0.78和0.64。对于蛋白质和湿面筋含量，模型表现更为多变，蛋白质的边际R²范围为0.30至0.39，湿面筋为0.31至0.33。基因型水平的LOGO交叉验证进一步表明，在评估未纳入模型开发的基因型时，模型表现存在变异。总体而言，结果表明多光谱植被指数与小麦籽粒产量和品质之间的关系取决于传感方法、物候期、基因型特征和生长季。研究结果为多光谱传感在小麦表型分析及籽粒产量和品质评估中的应用提供了探索性依据，但仍需在更广泛的遗传和环境条件下进一步验证。","2026-09-14T00:00:00Z",75,{"impact":52,"substance":281,"depth":192,"authority":20,"freshness":21,"relevant":22,"comment":282},21,"该研究利用无人机与近地多光谱传感器评估有芒\u002F无芒小麦产量与品质，方法新颖、数据跨两季，对智慧农业遥感育种有参考价值，但属探索性论文，产业影响有限。",[284],{"name":112,"url":275},[27,28,30,286,287],"作物表型","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[290],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":292},{"candidates":293,"agricultural_evidence":53},[294,298,303],{"code":33,"score":52,"supported":53,"title":295,"abstract":296,"tags":297},[34],[35,34],[30],{"code":40,"score":299,"supported":53,"title":300,"abstract":301,"tags":302},14,[62],[62],[28,286,287],{"code":65,"score":22,"supported":14,"title":304,"abstract":305,"tags":306},[],[],[27],[308,309],"产量预测 作物表型 小麦育种 智慧农业","产量预测 作物表型","产量预测作物表型小麦育种智慧农业-2522","10.3390\u002Fplants15182819",{"doi":311,"openalex_id":313,"authors":314,"venue":112,"cited_by_count":94,"oa_url":275,"card":339,"direction":99,"ingested_from":101},"W7212560710",[315,318,321,324,327,330,333,336],{"name":316,"orcid":317},"Irina Marina","https:\u002F\u002Forcid.org\u002F0000-0002-5894-363X",{"name":319,"orcid":320},"Vesna Kandić","https:\u002F\u002Forcid.org\u002F0000-0003-1999-2030",{"name":322,"orcid":323},"Marko Kostić","https:\u002F\u002Forcid.org\u002F0000-0001-9446-994X",{"name":325,"orcid":326},"Nataša Ljubičić","https:\u002F\u002Forcid.org\u002F0000-0001-5982-9401",{"name":328,"orcid":329},"Biljana Bošković","https:\u002F\u002Forcid.org\u002F0000-0003-4977-3170",{"name":331,"orcid":332},"Kosta Gligorević","https:\u002F\u002Forcid.org\u002F0000-0001-5783-4637",{"name":334,"orcid":335},"Miloš Pajić","https:\u002F\u002Forcid.org\u002F0000-0002-8905-3293",{"name":337,"orcid":338},"Milan Dražić","https:\u002F\u002Forcid.org\u002F0000-0002-0416-5174",{"tldr":340,"method":341,"finding":342,"direction":99,"opportunity":343},"用无人机与近地多光谱传感器评估有芒\u002F无芒小麦产量与品质，比较不同生育期和年份的植被指数关系。","无人机与Plant-O-Meter多光谱传感器，NDVI\u002FGNDVI\u002FNDRE，","GNDVI与NDRE在抽穗至开花期和旗叶期与产量相关性最强，有芒基因型关系更强，模型对品质预测较弱。","可探索多源遥感融合与基因型分层建模，提升跨年份、跨环境的小麦产量与品质预测泛化能力。","2026-09-15T23:30:17.124343Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":9,"source_name":351,"source_url":348,"published_at":352,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":353,"score_detail":354,"sources":356,"tags":358,"primary_topic":361,"secondary_topics":362,"classification":364,"search_phrases":389,"slug":392,"view_count":229,"doi":393,"paper":394,"created_at":441},3354,"Optimizing canopy nitrogen retrieval in wheat from high-resolution UAV hyperspectral data using RTM-based Gaussian process regression","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01904167.2026.2736627","The canopy nitrogen content (CNC) is one of the vital crop health indicators directly influencing the crop growth, yield, and nutritional status. Unmanned aerial vehicles (UAVs) that acquired hyperspectral imagery opened a new path for nondestructive, accurate, and timely estimation of CNC, facilitating the adoption of smart agro practices in precision agriculture for site-specific nutrient management. With the purpose of processing such hyperspectral data into CNC maps, two hybrid retrieval approaches, protein-based (CNCprot) and chlorophyll-based (CNCchl) models, were built by combining a radiative transfer model with a machine learning Gaussian process regression (GPR) algorithm. The GPR retrieval models were trained using synthetic spectral data from the PROSAIL-PRO model, and the sampling was optimized using the Euclidean distance-based diversity (EBD) active learning (AL) technique. The CNC models were applied to a UAV-borne hyperspectral image in the spectral range of 400-1000 nm with an ultrahigh spatial resolution of 4 cm, acquired over the experimental wheat field of Indian Council of Agricultural Research (ICAR) -Indian Agricultural Research Institute (IARI), New Delhi, India. The GPR models yielded good prediction accuracies with R2 values of 0.74 and 0.56 and NRMSE of 13.62 and 23.82% for CNCchl and CNCprot, respectively. The consistent prediction accuracies and low associated uncertainties revealed that CNC was more accurately mapped using chlorophyll-based CNC retrieval applied to a Vis-NIR-based hyperspectral data. These promising results demonstrated the possibility of applying hybrid GPR models at ultrahigh resolution and the operational viability of UAV datasets for regular CNC monitoring.","冠层氮含量（CNC）是直接影响作物生长、产量和营养状况的重要作物健康指标之一。无人机（UAV）获取的高光谱影像为无损、精准、及时地估算CNC开辟了新途径，有助于在精准农业中采用智能农业实践进行定点养分管理。为了将此类高光谱数据处理为CNC分布图，本研究通过将辐射传输模型与机器学习高斯过程回归（GPR）算法相结合，构建了两种混合反演方法：基于蛋白质的模型（CNCprot）和基于叶绿素的模型（CNCchl）。GPR反演模型使用PROSAIL-PRO模型生成的合成光谱数据进行训练，并采用基于欧氏距离的多样性（EBD）主动学习（AL）技术优化采样。CNC模型被应用于一幅无人机载高光谱影像，光谱范围为400-1000 nm，空间分辨率高达4 cm，该影像获取自印度农业研究理事会（ICAR）-印度农业研究所（IARI）位于印度新德里的实验麦田。GPR模型取得了良好的预测精度，CNCchl和CNCprot的R²值分别为0.74和0.56，NRMSE分别为13.62%和23.82%。一致的预测精度和较低的相关不确定性表明，将基于叶绿素的CNC反演应用于基于可见光-近红外（Vis-NIR）的高光谱数据能够更准确地制图CNC。这些有前景的结果证明了在超高分辨率下应用混合GPR模型的可能性，以及无人机数据集用于常规CNC监测的可行性。","Journal of Plant Nutrition","2026-09-23T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":58,"relevant":22,"comment":355},"将辐射传输模型与高斯过程回归结合，用4厘米超高分辨率无人机高光谱实现小麦冠层氮素精准反演，方法新颖、结论可靠，对精准农业养分管理有实操参考价值。",[357],{"name":351,"url":348},[27,359,35,31,360],"无人机","氮素监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[363],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":365},{"candidates":366,"agricultural_evidence":53},[367,371,375,379,383],{"code":33,"score":17,"supported":53,"title":368,"abstract":369,"tags":370},[34],[34],[35],{"code":40,"score":17,"supported":53,"title":372,"abstract":373,"tags":374},[218],[218],[31],{"code":65,"score":139,"supported":53,"title":376,"abstract":377,"tags":378},[],[224,225],[27],{"code":70,"score":104,"supported":53,"title":380,"abstract":381,"tags":382},[],[29,142],[],{"code":384,"score":229,"supported":53,"title":385,"abstract":386,"tags":388},"F3",[],[387],"养分管理",[],[390,391],"无人机 高光谱 小麦 氮素","PROSAIL 高斯过程回归 小麦","无人机高光谱小麦氮素-3354","10.1080\u002F01904167.2026.2736627",{"doi":393,"openalex_id":395,"authors":396,"venue":351,"cited_by_count":94,"oa_url":9,"card":435,"direction":440,"ingested_from":101},"W7214104831",[397,399,401,404,407,409,411,414,417,420,422,425,427,429,432],{"name":398,"orcid":9},"R. G. Rejith",{"name":400,"orcid":9},"Rabi N. Sahoo",{"name":402,"orcid":403},"Shalini Gakhar","https:\u002F\u002Forcid.org\u002F0000-0001-5717-1714",{"name":405,"orcid":406},"Jochem Verrelst","https:\u002F\u002Forcid.org\u002F0000-0002-6313-2081",{"name":408,"orcid":9},"Amrita Bhandari",{"name":410,"orcid":9},"Tarun Kondraju",{"name":412,"orcid":413},"Rajeev Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-2233-9147",{"name":415,"orcid":416},"Mahesh C. Meena","https:\u002F\u002Forcid.org\u002F0000-0001-5386-883X",{"name":418,"orcid":419},"Abir Dey","https:\u002F\u002Forcid.org\u002F0000-0002-5009-9518",{"name":421,"orcid":9},"Joydeep Mukherjee",{"name":423,"orcid":424},"Sudhir Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-1089-7435",{"name":426,"orcid":9},"Mahesh Kumar",{"name":428,"orcid":9},"Raju Dhandapani",{"name":430,"orcid":431},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":433,"orcid":434},"Viswanathan Chinnusamy","https:\u002F\u002Forcid.org\u002F0000-0003-2174-9064",{"tldr":436,"method":437,"finding":438,"direction":99,"opportunity":439},"用PROSAIL-PRO与高斯过程回归从无人机高光谱数据反演小麦冠层氮含量。","PROSAIL-PRO合成光谱训练GPR，EBD主动学习采样，4cm无人机高光谱","叶绿素基模型精度更高，R²达0.74，NRMSE为13.62%，优于蛋白基模型。","可探索多生育期、多品种迁移能力及主动学习采样策略对反演精度的提升空间。","智慧农业 \u002F 农业物联网","2026-09-24T23:30:10.311254Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":9,"content":9,"source_name":447,"source_url":9,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":449,"score_detail":450,"sources":453,"tags":455,"primary_topic":459,"secondary_topics":460,"classification":462,"search_phrases":481,"slug":484,"view_count":229,"doi":9,"paper":485,"created_at":493},2996,"基于高光谱遥感和跨区域迁移学习的冬小麦叶绿素含量评估","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7686128266574430720","甘肃农业大学李怡悦等与中国农业科学院作物科学研究所肖永贵、孟亚雄研究员团队合作，针对高光谱遥感跨区域叶绿素评估中因\"域偏移\"导致的模型泛化性能下降问题，提出一种稳健自适应迁移学习框架（RATL），通过自适应特征选择、特征权重调整与域适应训练3模块协同实现跨区域知识有效迁移。基于2023—2024年冬小麦灌浆后期新乡与周口两地共1491份冠层高光谱与叶片SPAD数据，设置4种场景比较RATL与直接迁移、迁移成分分析和相关对齐之间的精度。","智慧农业(中英文)2026,8(4):47-59","2026-09-16T13:43:00Z",76,{"impact":451,"substance":18,"depth":19,"authority":299,"freshness":104,"relevant":22,"comment":452},16,"方法新颖、数据扎实的跨区域叶绿素遥感评估研究，对智慧农业遥感应用有参考价值。",[454],{"name":447,"url":445},[27,31,456,457,458],"冬小麦","迁移学习","叶绿素监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[461],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":463},{"candidates":464,"agricultural_evidence":53},[465,469,473,477],{"code":33,"score":17,"supported":53,"title":466,"abstract":467,"tags":468},[35],[35],[456],{"code":40,"score":17,"supported":53,"title":470,"abstract":471,"tags":472},[31],[31],[31],{"code":65,"score":22,"supported":14,"title":474,"abstract":475,"tags":476},[],[],[27],{"code":70,"score":22,"supported":14,"title":478,"abstract":479,"tags":480},[],[],[457],[482,483],"冬小麦 叶绿素 高光谱遥感","跨区域迁移学习 冬小麦","冬小麦叶绿素高光谱遥感-2996",{"doi":9,"openalex_id":9,"authors":486,"venue":9,"cited_by_count":94,"oa_url":9,"card":487,"direction":99,"ingested_from":492},[],{"tldr":488,"method":489,"finding":490,"direction":99,"opportunity":491},"提出RATL迁移学习框架，解决高光谱遥感跨区域冬小麦叶绿素评估的域偏移问题。","基于新乡与周口1491份冠层高光谱与SPAD数据，对比RATL与直接迁移、TCA","RATL通过自适应特征选择、权重调整与域适应训练，有效提升跨区域叶绿素评估泛化精度。","可探索多作物、多生育期及不同传感器间的迁移学习，构建通用跨域叶绿素评估模型。","agent","2026-09-20T00:03:07.685193Z",{"id":495,"title":496,"url":497,"summary":498,"summary_zh":499,"content":9,"source_name":500,"source_url":497,"published_at":501,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":502,"score_detail":503,"sources":505,"tags":507,"primary_topic":510,"secondary_topics":511,"classification":514,"search_phrases":542,"slug":545,"view_count":229,"doi":546,"paper":547,"created_at":571},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","Remote Sensing","2026-09-15T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":299,"freshness":58,"relevant":22,"comment":504},"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[506],{"name":500,"url":497},[27,508,35,31,509],"农业人工智能","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[512,513],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":70,"slug":201,"name":202,"path":203,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":515},{"candidates":516,"agricultural_evidence":53},[517,521,525,529,538],{"code":33,"score":128,"supported":53,"title":518,"abstract":519,"tags":520},[130,34],[35,130,34],[35],{"code":40,"score":19,"supported":53,"title":522,"abstract":523,"tags":524},[135],[31,135,62],[31],{"code":70,"score":52,"supported":53,"title":526,"abstract":527,"tags":528},[142],[29,142],[508],{"code":530,"score":13,"supported":53,"title":531,"abstract":532,"tags":537},"H3",[],[533,534,535,536],"灌溉","水资源","irrigation","water resource",[509],{"code":65,"score":139,"supported":53,"title":539,"abstract":540,"tags":541},[],[224,225],[27],[543,544],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":546,"openalex_id":548,"authors":549,"venue":500,"cited_by_count":94,"oa_url":497,"card":566,"direction":99,"ingested_from":101},"W7213246708",[550,553,555,557,560,563],{"name":551,"orcid":552},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":554,"orcid":9},"Qian Cheng",{"name":556,"orcid":9},"Fuyi Duan",{"name":558,"orcid":559},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":561,"orcid":562},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":564,"orcid":565},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":567,"method":568,"finding":569,"direction":99,"opportunity":570},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z"]