[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1644":3,"related-1644":115},{"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":36,"classification":43,"search_phrases":81,"slug":84,"view_count":85,"doi":86,"paper":87,"created_at":114},1644,"DMP-UNet: a multimodal fusion network for high-precision nitrogen status monitoring in wheat from UAV multispectral images","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1909856","Achieving high-precision monitoring of wheat nitrogen levels is essential for boosting yield and grain quality, yet the remote sensing community has largely confined itself to single-modal data sources, missing the synergistic benefits of multimodal fusion. To address this, we propose a multimodal feature fusion network that simultaneously utilizes RGB, multispectral, and vegetation index data. The network employs dual-branch encoders to extract modality‑specific features from fused images composed of NDVI, green, and near‑infrared bands; a deformable bidirectional cross‑attention module aligns pixel‑level features and enhances cross‑modal interactions; a modality‑aware pyramid attention feature pyramid network fuses semantic information across scales and modalities; and a dual‑path decoder separates background from wheat regions, with a foreground decoder guided by vegetation attention maps to classify nitrogen status into four levels: Enrichment, Optimum Level, Moderate Nitrogen Deficiency, and Severe Nitrogen Deficiency. Experimental results demonstrate that our method outperforms state‑of‑the‑art models such as DeepLabV3+, U‑Net, U‑Net++, and Attention UNet. Modality contribution analysis confirms the complementary roles of RGB and MS, while ablation studies validate the effectiveness of each key module. These findings confirm the practicality and efficiency of the proposed approach for precise nitrogen monitoring in wheat, highlighting its potential for optimizing fertilization strategies in precision agriculture.","实现对小麦氮素水平的高精度监测对于提高产量和籽粒品质至关重要，然而遥感领域长期局限于单一模态数据源，未能充分利用多模态融合的协同优势。为此，我们提出了一种多模态特征融合网络，同时利用RGB、多光谱和植被指数数据。该网络采用双分支编码器，从由NDVI、绿光和近红外波段组成的融合图像中提取模态特定特征；可变形双向交叉注意力模块用于对齐像素级特征并增强跨模态交互；模态感知金字塔注意力特征金字塔网络跨尺度和跨模态融合语义信息；双路径解码器将背景与小麦区域分离，其中前景解码器由植被注意力图引导，将氮素状态分为四个等级：富集、最适水平、中度缺氮和重度缺氮。实验结果表明，我们的方法优于DeepLabV3+、U-Net、U-Net++和Attention UNet等最先进模型。模态贡献分析证实了RGB和多光谱的互补作用，消融研究验证了各关键模块的有效性。这些发现证实了所提方法在小麦精确氮素监测中的实用性和高效性，凸显了其在精准农业中优化施肥策略的潜力。",null,"Frontiers in Plant Science","2026-09-03T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,13,7,1,"提出多模态融合网络提升小麦氮素监测精度，方法新颖，实验验证充分，对精准农业有实际价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","小麦","多模态融合","遥感","氮素监测",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":50},[49,56,61,68,76],{"code":38,"score":17,"supported":50,"title":51,"abstract":53,"tags":55},true,[52],"multispectral",[29,54,52],"remote sensing",[29],{"code":32,"score":57,"supported":50,"title":58,"abstract":59,"tags":60},15,[33],[27,33],[27],{"code":62,"score":20,"supported":50,"title":63,"abstract":64,"tags":67},"I1",[],[65,66],"精准农业","precision agriculture",[26],{"code":69,"score":70,"supported":50,"title":71,"abstract":72,"tags":75},"F3",6,[],[73,74],"施肥","fertilization",[],{"code":77,"score":21,"supported":14,"title":78,"abstract":79,"tags":80},"I2",[],[],[28],[82,83],"多模态融合 智慧农业 氮素监测 小麦","多模态融合 智慧农业","多模态融合智慧农业氮素监测小麦-1644",2,"10.3389\u002Ffpls.2026.1909856",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":111,"ingested_from":113},"W7207858118",[90,92,94,96,98,101,104],{"name":91,"orcid":9},"Qingqing Hong",{"name":93,"orcid":9},"Siqi Cao",{"name":95,"orcid":9},"Bohan Hu",{"name":97,"orcid":9},"Changwei Tan",{"name":99,"orcid":100},"Zhenghua Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0880-0240",{"name":102,"orcid":103},"Bin Li","https:\u002F\u002Forcid.org\u002F0009-0000-0898-7864",{"name":105,"orcid":9},"Hongwei Zhang",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"提出多模态融合网络DMP-UNet，利用无人机多光谱图像高精度监测小麦氮素水平。","双分支编码器、可变形双向交叉注意力、模态感知金字塔注意力FPN、双路径解码器。","该方法优于DeepLabV3+等模型，RGB与多光谱互补，各模块有效。","农业遥感与作物表型","可探索多模态融合在作物病害、产量预测等领域的应用，或引入时间序列数据提升动态监测能力。","openalex","2026-09-04T23:30:33.457309Z",{"total":70,"page":21,"page_size":70,"items":116},[117,219,306,387,447,510],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":132,"primary_topic":134,"secondary_topics":135,"classification":137,"search_phrases":166,"slug":169,"view_count":161,"doi":170,"paper":171,"created_at":218},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":17,"substance":127,"depth":17,"authority":19,"freshness":128,"relevant":21,"comment":129},22,8,"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。",[131],{"name":123,"url":120},[26,133,27,29,30],"无人机",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[136],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":138},{"candidates":139,"agricultural_evidence":50},[140,145,150,154,160],{"code":32,"score":141,"supported":50,"title":142,"abstract":143,"tags":144},12,[33],[33],[27],{"code":38,"score":141,"supported":50,"title":146,"abstract":148,"tags":149},[147],"hyperspectral",[147],[29],{"code":62,"score":20,"supported":50,"title":151,"abstract":152,"tags":153},[],[65,66],[26],{"code":77,"score":70,"supported":50,"title":155,"abstract":156,"tags":159},[],[157,158],"机器学习","machine learning",[],{"code":69,"score":161,"supported":50,"title":162,"abstract":163,"tags":165},3,[],[164],"养分管理",[],[167,168],"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":170,"openalex_id":172,"authors":173,"venue":123,"cited_by_count":106,"oa_url":9,"card":212,"direction":217,"ingested_from":113},"W7214104831",[174,176,178,181,184,186,188,191,194,197,199,202,204,206,209],{"name":175,"orcid":9},"R. G. Rejith",{"name":177,"orcid":9},"Rabi N. Sahoo",{"name":179,"orcid":180},"Shalini Gakhar","https:\u002F\u002Forcid.org\u002F0000-0001-5717-1714",{"name":182,"orcid":183},"Jochem Verrelst","https:\u002F\u002Forcid.org\u002F0000-0002-6313-2081",{"name":185,"orcid":9},"Amrita Bhandari",{"name":187,"orcid":9},"Tarun Kondraju",{"name":189,"orcid":190},"Rajeev Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-2233-9147",{"name":192,"orcid":193},"Mahesh C. Meena","https:\u002F\u002Forcid.org\u002F0000-0001-5386-883X",{"name":195,"orcid":196},"Abir Dey","https:\u002F\u002Forcid.org\u002F0000-0002-5009-9518",{"name":198,"orcid":9},"Joydeep Mukherjee",{"name":200,"orcid":201},"Sudhir Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-1089-7435",{"name":203,"orcid":9},"Mahesh Kumar",{"name":205,"orcid":9},"Raju Dhandapani",{"name":207,"orcid":208},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":210,"orcid":211},"Viswanathan Chinnusamy","https:\u002F\u002Forcid.org\u002F0000-0003-2174-9064",{"tldr":213,"method":214,"finding":215,"direction":111,"opportunity":216},"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":220,"title":221,"url":222,"summary":223,"summary_zh":224,"content":9,"source_name":225,"source_url":222,"published_at":226,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":232,"tags":234,"primary_topic":236,"secondary_topics":237,"classification":243,"search_phrases":273,"slug":276,"view_count":21,"doi":277,"paper":278,"created_at":305},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":141,"substance":18,"depth":229,"authority":141,"freshness":230,"relevant":21,"comment":231},17,9,"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。",[233],{"name":225,"url":222},[26,157,27,235,29],"杂草识别",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[238,242],{"code":77,"slug":239,"name":240,"path":241,"parent_code":42},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":244},{"candidates":245,"agricultural_evidence":50},[246,250,254,258,262,268],{"code":32,"score":57,"supported":50,"title":247,"abstract":248,"tags":249},[33],[27,33],[27],{"code":77,"score":57,"supported":50,"title":251,"abstract":252,"tags":253},[158],[157,158],[157],{"code":38,"score":141,"supported":50,"title":255,"abstract":256,"tags":257},[147],[147],[29],{"code":62,"score":20,"supported":50,"title":259,"abstract":260,"tags":261},[],[65,66],[26],{"code":263,"score":161,"supported":50,"title":264,"abstract":265,"tags":267},"H6",[],[266],"农业生态",[],{"code":269,"score":21,"supported":14,"title":270,"abstract":271,"tags":272},"F4",[],[],[235],[274,275],"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":277,"openalex_id":279,"authors":280,"venue":225,"cited_by_count":106,"oa_url":9,"card":299,"direction":304,"ingested_from":113},"W7214771122",[281,284,287,290,293,296],{"name":282,"orcid":283},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":285,"orcid":286},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":288,"orcid":289},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":291,"orcid":292},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":294,"orcid":295},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":297,"orcid":298},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":300,"method":301,"finding":302,"direction":111,"opportunity":303},"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":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":9,"source_name":312,"source_url":309,"published_at":313,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":314,"score_detail":315,"sources":318,"tags":320,"primary_topic":323,"secondary_topics":324,"classification":327,"search_phrases":357,"slug":360,"view_count":161,"doi":361,"paper":362,"created_at":386},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":17,"substance":127,"depth":17,"authority":316,"freshness":128,"relevant":21,"comment":317},14,"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。",[319],{"name":312,"url":309},[26,321,27,29,322],"农业人工智能","精准灌溉",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[325,326],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":77,"slug":239,"name":240,"path":241,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":328},{"candidates":329,"agricultural_evidence":50},[330,336,340,344,353],{"code":32,"score":331,"supported":50,"title":332,"abstract":334,"tags":335},26,[333,33],"winter wheat",[27,333,33],[27],{"code":38,"score":17,"supported":50,"title":337,"abstract":338,"tags":339},[54],[29,54,52],[29],{"code":77,"score":57,"supported":50,"title":341,"abstract":342,"tags":343},[158],[157,158],[321],{"code":345,"score":13,"supported":50,"title":346,"abstract":347,"tags":352},"H3",[],[348,349,350,351],"灌溉","水资源","irrigation","water resource",[322],{"code":62,"score":20,"supported":50,"title":354,"abstract":355,"tags":356},[],[65,66],[26],[358,359],"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":361,"openalex_id":363,"authors":364,"venue":312,"cited_by_count":106,"oa_url":309,"card":381,"direction":111,"ingested_from":113},"W7213246708",[365,368,370,372,375,378],{"name":366,"orcid":367},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":369,"orcid":9},"Qian Cheng",{"name":371,"orcid":9},"Fuyi Duan",{"name":373,"orcid":374},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":376,"orcid":377},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":379,"orcid":380},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":382,"method":383,"finding":384,"direction":111,"opportunity":385},"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",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":404,"classification":411,"search_phrases":431,"slug":434,"view_count":85,"doi":435,"paper":436,"created_at":446},2332,"Wheat Nitrogen Fertilizer Management Using GreenSeeker Handheld Crop Canopy Sensor","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no3.2025.pg12.22","One of the essential factors in increasing agricultural yields of cereal crops is increasing the grain yield without increasing production costs. Wheat (Triticum aestivum L.) is the main crop in the food basket of the entire world. Therefore, it is necessary to determine the appropriate nitrogen (N) requirements to obtain the optimal production of wheat crops by investigating the impact of different N levels on the wheat crop yield in the Al-Muthanna region, as well as determining the possibility of predicting grain yield using the GreenSeeker handheld proximal crop canopy sensorbased differences vegetative difference index (NDVI). Thus, there is a need to re-evaluate the previous recommendations using remote sensing techniques. The experimental treatments were five levels of N fertilizer including (0 Kg N ha-1 , 50 kg N ha-1 , 100 kg N ha-1 ,150 kg N ha-1, and 200 kg N ha-1, and each N level was divided into 100%, 70%, 50% percentages, respectively. This study was conducted at the Experiment Station and Agriculture Research of the College of AgricultureAl-Muthanna University. The NDVI measurements were obtained at FK5, FK7, and FK9 according to the Feekes scale growth stage. The results indicated significant differences in grain yield between different levels of N fertilizer and a 70% percentage outperformed on the 100% and 50% treatments for each of the two N levels of 200 and 150 kg ha-1 . The results also show significant differences between NDVI values for different N fertilizer levels. The NDVI readings and wheat yield values increased and followed a similar pattern with increasing N fertilizer levels. This suggests that NDVI can predict wheat grain yield when the NDVI is not saturated. This study showed the potential of using GreenSeeker proximal crop canopy sensor-based NDVI readings as a useful tool to predict wheat grain yield.","提高谷类作物农业产量的关键因素之一是在不增加生产成本的前提下提高谷物产量。小麦（Triticum aestivum L.）是全球粮食结构中的主要作物。因此，有必要通过研究不同氮水平对Al-Muthanna地区小麦作物产量的影响，确定获得小麦作物最佳产量所需的适宜氮（N）需求量，并确定利用基于GreenSeeker手持式近地作物冠层传感器的归一化植被差异指数（NDVI）预测谷物产量的可能性。因此，需要利用遥感技术重新评估以往的推荐方案。试验处理包括五个氮肥水平（0 kg N ha⁻¹、50 kg N ha⁻¹、100 kg N ha⁻¹、150 kg N ha⁻¹和200 kg N ha⁻¹），每个氮水平分别按100%、70%、50%的比例施用。本研究在Al-Muthanna大学农学院实验站与农业研究中心进行。NDVI测定根据Feekes尺度生育阶段在FK5、FK7和FK9时期进行。结果表明，不同氮肥水平间谷物产量存在显著差异，在200和150 kg ha⁻¹两个氮水平下，70%施用量处理均优于100%和50%处理。结果还显示，不同氮肥水平间NDVI值存在显著差异。随着氮肥水平的提高，NDVI读数和小麦产量值均增加并呈现相似的变化趋势。这表明在NDVI未饱和时，NDVI可以预测小麦谷物产量。本研究表明，利用基于GreenSeeker近地作物冠层传感器的NDVI读数作为预测小麦谷物产量的有用工具具有潜力。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-10T00:00:00Z",61,{"impact":141,"substance":17,"depth":57,"authority":13,"freshness":70,"relevant":21,"comment":397},"基于GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥用量，方法实用但属区域性田间试验，产业影响有限。",[399],{"name":393,"url":390},[26,401,27,29,402],"精准施肥","NDVI",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[405,410],{"code":69,"slug":406,"name":407,"path":408,"parent_code":409},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":412},{"candidates":413,"agricultural_evidence":50},[414,418,423,427],{"code":32,"score":57,"supported":50,"title":415,"abstract":416,"tags":417},[33],[27,33],[27],{"code":69,"score":141,"supported":50,"title":419,"abstract":421,"tags":422},[420],"fertilizer",[420],[401],{"code":38,"score":128,"supported":50,"title":424,"abstract":425,"tags":426},[],[29,54],[402,29],{"code":62,"score":21,"supported":14,"title":428,"abstract":429,"tags":430},[],[],[26],[432,433],"智慧农业 精准施肥 小麦 遥感","智慧农业 精准施肥","智慧农业精准施肥小麦遥感-2332","10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":435,"openalex_id":437,"authors":438,"venue":393,"cited_by_count":106,"oa_url":9,"card":441,"direction":111,"ingested_from":113},"W7212289620",[439],{"name":440,"orcid":9},"Mohammed A. Naser",{"tldr":442,"method":443,"finding":444,"direction":111,"opportunity":445},"用GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥管理。","伊拉克田间试验，5个氮水平，Feekes 5\u002F7\u002F9期测NDVI。","NDVI与产量随氮肥增加同步上升，未饱和时可预测产量，70%氮量表现更优。","可探索NDVI饱和条件下的替代植被指数及不同品种\u002F气候区的氮肥推荐模型迁移。","2026-09-13T23:30:26.260534Z",{"id":448,"title":449,"url":450,"summary":451,"summary_zh":9,"content":9,"source_name":452,"source_url":9,"published_at":453,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":454,"score_detail":455,"sources":457,"tags":459,"primary_topic":461,"secondary_topics":462,"classification":465,"search_phrases":497,"slug":500,"view_count":161,"doi":9,"paper":501,"created_at":509},1705,"VGG21 Deep Learning + Drone Multispectral Imagery Detects Wheat Waterlogging Stress and Silicon Fertilizer Effects","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F04\u002Fdrones-ai-spot-wheat-waterlogging-stress-in-real-time","江苏省农科院农业信息研究所梁万杰团队发表于《Smart Agriculture》。研究展示了一种基于多光谱相机的无人机快速检测小麦涝害并评估硅肥或氨基酸是否有助于作物恢复的方法。研究使用 DJI P4 多光谱无人机，在五个光谱波段捕获冠层图像。VGG21 模型识别胁迫和处理效应的整体准确率超过 91%，识别硅肥处理小麦的精确度达到 96.42%。研究指出早期涝害症状微妙多变导致健康与涝害小麦常被混淆，未来工作将量化胁迫检测与实际产量增益的相关性。","Agritech Insights","2026-09-04T00:00:00Z",78,{"impact":17,"substance":127,"depth":17,"authority":141,"freshness":128,"relevant":21,"comment":456},"省级农科院团队在核心期刊发表的研究，方法新颖且精度高，对小麦涝害监测有实际价值。",[458],{"name":452,"url":450},[26,321,460,27,29],"多光谱无人机",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[463,464],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":77,"slug":239,"name":240,"path":241,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":466},{"candidates":467,"agricultural_evidence":50},[468,472,476,481,485,491],{"code":32,"score":141,"supported":50,"title":469,"abstract":470,"tags":471},[33],[27],[27],{"code":38,"score":13,"supported":50,"title":473,"abstract":474,"tags":475},[52],[],[460,29],{"code":77,"score":230,"supported":50,"title":477,"abstract":479,"tags":480},[478],"deep learning",[],[321],{"code":69,"score":128,"supported":50,"title":482,"abstract":483,"tags":484},[420],[],[],{"code":486,"score":128,"supported":50,"title":487,"abstract":489,"tags":490},"H7",[488],"waterlogging",[],[],{"code":62,"score":492,"supported":50,"title":493,"abstract":494,"tags":496},4,[],[495],"smart agriculture",[26],[498,499],"农业人工智能 多光谱无人机 智慧农业 小麦","农业人工智能 多光谱无人机","农业人工智能多光谱无人机智慧农业小麦-1705",{"doi":9,"openalex_id":9,"authors":502,"venue":9,"cited_by_count":106,"oa_url":9,"card":503,"direction":111,"ingested_from":508},[],{"tldr":504,"method":505,"finding":506,"direction":111,"opportunity":507},"用无人机多光谱和VGG21深度学习检测小麦涝害并评估硅肥效果。","DJI P4多光谱无人机采集五波段图像，VGG21模型分类。","VGG21识别胁迫与处理效应准确率超91%，硅肥处理精确度96.42%。","早期涝害症状微妙，可研究多时相数据或结合产量数据量化胁迫与恢复效果。","agent","2026-09-05T00:04:40.858350Z",{"id":511,"title":512,"url":513,"summary":514,"summary_zh":515,"content":9,"source_name":516,"source_url":513,"published_at":517,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":518,"score_detail":519,"sources":521,"tags":523,"primary_topic":525,"secondary_topics":526,"classification":528,"search_phrases":543,"slug":546,"view_count":21,"doi":547,"paper":548,"created_at":579},1509,"Full-season wheat phenotyping using UAV-based multi-source remote sensing: dynamic time-series modeling and high-throughput trait extraction","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112383","Full-season wheat phenotyping using UAV-based multi-source remote sensing: dynamic time-series modeling and high-throughput trait extraction。Computers and Electronics in Agriculture","基于无人机多源遥感的全生育期小麦表型分析：动态时间序列建模与高通量性状提取。《Computers and Electronics in Agriculture》","Computers and Electronics in Agriculture","2026-09-02T00:00:00Z",73,{"impact":17,"substance":18,"depth":17,"authority":57,"freshness":85,"relevant":21,"comment":520},"基于无人机多源遥感的全生育期小麦表型研究，方法新颖，数据详实，对精准农业有参考价值。",[522],{"name":516,"url":513},[26,27,29,524],"高通量表型",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[527],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":529},{"candidates":530,"agricultural_evidence":50},[531,535,539],{"code":32,"score":57,"supported":50,"title":532,"abstract":533,"tags":534},[33],[27,33],[27],{"code":38,"score":57,"supported":50,"title":536,"abstract":537,"tags":538},[54],[29,54],[29],{"code":62,"score":21,"supported":14,"title":540,"abstract":541,"tags":542},[],[],[26],[544,545],"高通量表型 智慧农业 小麦 遥感","高通量表型 智慧农业","高通量表型智慧农业小麦遥感-1509","10.1016\u002Fj.compag.2026.112383",{"doi":547,"openalex_id":549,"authors":550,"venue":516,"cited_by_count":106,"oa_url":573,"card":574,"direction":111,"ingested_from":113},"W7154593787",[551,554,556,558,561,563,565,568,570],{"name":552,"orcid":553},"Honghao Zhou","https:\u002F\u002Forcid.org\u002F0009-0001-9850-5749",{"name":555,"orcid":9},"Chunmei Guo",{"name":557,"orcid":9},"Bingxi Qin",{"name":559,"orcid":560},"Wenlong Su","https:\u002F\u002Forcid.org\u002F0009-0002-0110-1754",{"name":562,"orcid":9},"Qi Sun",{"name":564,"orcid":9},"Zijun Pei",{"name":566,"orcid":567},"Qing Li","https:\u002F\u002Forcid.org\u002F0000-0002-5332-1401",{"name":569,"orcid":9},"Jiang Dong",{"name":571,"orcid":572},"Jiawei Chen","https:\u002F\u002Forcid.org\u002F0000-0002-5035-7804","https:\u002F\u002Fdoi.org\u002F10.2139\u002Fssrn.6589943",{"tldr":575,"method":576,"finding":577,"direction":111,"opportunity":578},"利用无人机多源遥感进行全季节小麦表型分析，通过动态时间序列建模提取高通量性状。","无人机多源遥感数据，动态时间序列建模，高通量性状提取。","实现了全季节小麦表型动态监测，提取了关键性状，支持育种评估。","可探索多源遥感数据融合与深度学习结合，提高复杂环境下作物性状预测精度和泛化能力。","2026-09-03T23:30:02.117716Z"]