[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3488":3,"related-3488":56},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":55},3488,"NDAVI Improves Genotypic Discrimination in Dense Bread Wheat Canopies under Moderate Nitrogen Contrast","https:\u002F\u002Fdoi.org\u002F10.29278\u002Fazd.1972563","Objective: Rapid and non-destructive assessment of wheat canopy status is important for improving nitrogen management and field-based phenotyping. This study evaluated the ability of UAV-derived multispectral vegetation indices to detect canopy spectral variation associated with a moderate, non-zero nitrogen contrast and to discriminate among commercial bread wheat genotypes under dense canopy conditions during the reproductive stage. Materials and Methods: A field experiment was conducted during the 2024–2025 growing season in Bornova, İzmir, Türkiye, using ten commercial bread wheat cultivars grown under low-nitrogen (LN) and high-nitrogen (HN) treatments in a split-plot randomized complete block design with four replications. Multispectral imagery was acquired after heading and before anthesis using a DJI Matrice 350 RTK UAV equipped with a MicaSense RedEdge-P sensor. Four canopy vegetation indices, NDVI, GNDVI, CLRED, and NDAVI, were calculated from plot-level reflectance data. Each index was analyzed using linear mixed models, and index consistency was further evaluated using coefficient of variation, repeatability, and genotype-specific plasticity.Conclusion: Nitrogen treatment significantly affected NDVI, GNDVI, and NDAVI, with higher index values under HN than LN, whereas CLRED was not significantly affected by nitrogen. Among the evaluated indices, NDVI showed the strongest nitrogen response, indicating its usefulness for detecting overall nitrogen-related canopy differences. However, genotype effects were significant only for NDAVI, while NDVI, GNDVI, and CLRED did not provide reliable genotypic discrimination. NDAVI also showed the highest repeatability (0.791) and the lowest coefficient of variation (1.14%), supporting its superior consistency and discriminatory ability. UAV-derived multispectral indices successfully detected canopy spectral responses to moderate nitrogen differences in bread wheat, but nitrogen sensitivity and genotype discrimination differed substantially among indices. Among the evaluated indices, NDAVI appears to be the most promising index for detecting differences among wheat genotypes, particularly when conventional indices such as NDVI may be constrained by saturation.","目的：快速、无损地评估小麦冠层状态对于改进氮素管理和田间表型分析具有重要意义。本研究评估了无人机多光谱植被指数在生殖阶段密集冠层条件下检测与中等非零氮素差异相关的冠层光谱变异以及区分商业面包小麦基因型的能力。材料与方法：田间试验于2024—2025生长季在土耳其伊兹密尔博尔诺瓦进行，采用10个商业面包小麦品种，设置低氮（LN）和高氮（HN）处理，采用裂区随机完全区组设计，4次重复。在抽穗后至开花前，使用搭载MicaSense RedEdge-P传感器的DJI Matrice 350 RTK无人机获取多光谱影像。基于小区尺度反射率数据计算了4个冠层植被指数：NDVI、GNDVI、CLRED和NDAVI。各指数采用线性混合模型进行分析，并通过变异系数、重复性和基因型特异性可塑性进一步评估指数一致性。结论：氮素处理显著影响NDVI、GNDVI和NDAVI，高氮处理下指数值高于低氮处理，而CLRED未受氮素的显著影响。在评估的指数中，NDVI表现出最强的氮素响应，表明其可用于检测整体氮素相关的冠层差异。然而，基因型效应仅对NDAVI显著，而NDVI、GNDVI和CLRED未能提供可靠的基因型区分。NDAVI还表现出最高的重复性（0.791）和最低的变异系数（1.14%），支持其更优的一致性和区分能力。无人机多光谱指数成功检测了面包小麦对中等氮素差异的冠层光谱响应，但不同指数的氮素敏感性和基因型区分能力存在显著差异。在评估的指数中，NDAVI似乎是检测小麦基因型差异最有前景的指数，尤其是当NDVI等常规指数可能受到饱和限制时。",null,"Akademik Ziraat Dergisi","2026-09-22T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"基于无人机多光谱的田间试验，提出NDAVI在密植小麦冠层中优于NDVI的基因型判别能力，方法新颖、数据扎实，对智慧农业表型与氮肥精准管理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","小麦","遥感","氮肥管理","无人机表型",[33,34],"NDAVI 小麦 基因型判别","无人机 多光谱 小麦 氮素","NDAVI小麦基因型判别-3488",0,"10.29278\u002Fazd.1972563",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":47,"card":48,"direction":52,"ingested_from":54},"W7214030909",[41,44],{"name":42,"orcid":43},"Deniz İştipliler","https:\u002F\u002Forcid.org\u002F0000-0002-0887-1121",{"name":45,"orcid":46},"Aliye YILDIRIM","https:\u002F\u002Forcid.org\u002F0000-0002-8101-0803","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6117047",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"评估无人机多光谱植被指数在密植小麦中区分氮处理和基因型的能力。","无人机多光谱影像，四种指数，线性混合模型与重复性分析。","NDAVI重复性最高且能区分基因型，NDVI对氮响应最强但无法区分基因型。","农业遥感与作物表型","可探索NDAVI在其它作物或更高密度冠层中的基因型区分能力及抗饱和机制。","openalex","2026-09-25T23:30:24.042515Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,131,162,207,240,265],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":73,"tags":75,"search_phrases":78,"slug":81,"view_count":36,"doi":82,"paper":83,"created_at":130},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":69,"substance":70,"depth":69,"authority":20,"freshness":71,"relevant":22,"comment":72},18,22,8,"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。",[74],{"name":65,"url":62},[27,76,28,29,77],"无人机","氮素监测",[79,80],"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":82,"openalex_id":84,"authors":85,"venue":65,"cited_by_count":36,"oa_url":9,"card":124,"direction":129,"ingested_from":54},"W7214104831",[86,88,90,93,96,98,100,103,106,109,111,114,116,118,121],{"name":87,"orcid":9},"R. G. Rejith",{"name":89,"orcid":9},"Rabi N. Sahoo",{"name":91,"orcid":92},"Shalini Gakhar","https:\u002F\u002Forcid.org\u002F0000-0001-5717-1714",{"name":94,"orcid":95},"Jochem Verrelst","https:\u002F\u002Forcid.org\u002F0000-0002-6313-2081",{"name":97,"orcid":9},"Amrita Bhandari",{"name":99,"orcid":9},"Tarun Kondraju",{"name":101,"orcid":102},"Rajeev Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-2233-9147",{"name":104,"orcid":105},"Mahesh C. Meena","https:\u002F\u002Forcid.org\u002F0000-0001-5386-883X",{"name":107,"orcid":108},"Abir Dey","https:\u002F\u002Forcid.org\u002F0000-0002-5009-9518",{"name":110,"orcid":9},"Joydeep Mukherjee",{"name":112,"orcid":113},"Sudhir Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-1089-7435",{"name":115,"orcid":9},"Mahesh Kumar",{"name":117,"orcid":9},"Raju Dhandapani",{"name":119,"orcid":120},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":122,"orcid":123},"Viswanathan Chinnusamy","https:\u002F\u002Forcid.org\u002F0000-0003-2174-9064",{"tldr":125,"method":126,"finding":127,"direction":52,"opportunity":128},"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":132,"title":133,"url":134,"summary":135,"summary_zh":9,"content":9,"source_name":136,"source_url":9,"published_at":137,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":142,"tags":144,"search_phrases":148,"slug":151,"view_count":36,"doi":9,"paper":152,"created_at":161},3324,"Diag-STFN：全球收获前作物产量预测的诊断时空多模态融合网络——覆盖38国玉米29国小麦（Ecological Informatics 2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37354974?&&labelLang=hun","《Ecological Informatics》2026年第96期：Zhuang等提出Diag-STFN——一种诊断时空多模态融合网络，用于全球收获前作物产量预测。该网络基于数据集特征选择模型结构，以确定是否需要时间趋势耦合和空间模块激活。在三种前置期（早、中、晚季）下，基于覆盖38国玉米和29国小麦的CY-Bench基准数据集进行评估。结果表明，所提方法在所有前置期均实现了两种作物的最低汇总NRMSE，并在MAPE和KGE等补充指标上保持领先。消融研究表明诊断模块选择提供了主要的性能提升；方差分解显示性能差异在国家之间大于模型之间。","《Ecological Informatics》96 (2026) 103860","2026-09-17T00:00:00Z",78,{"impact":69,"substance":70,"depth":69,"authority":140,"freshness":57,"relevant":22,"comment":141},14,"方法新颖、覆盖38国玉米与29国小麦的全球收获前产量预测研究，学术价值突出但产业落地尚早，适合作为前沿技术资讯收录。",[143],{"name":136,"url":134},[27,145,146,28,147,29],"农业人工智能","产量预测","玉米",[149,150],"Diag-STFN 作物产量预测","CY-Bench 玉米 小麦","Diag-STFN作物产量预测-3324",{"doi":9,"openalex_id":9,"authors":153,"venue":9,"cited_by_count":36,"oa_url":9,"card":154,"direction":158,"ingested_from":160},[],{"tldr":155,"method":156,"finding":157,"direction":158,"opportunity":159},"提出诊断式时空多模态融合网络Diag-STFN，实现全球收获前玉米小麦产量预测。","基于CY-Bench基准，按数据特征诊断选择时间趋势与空间模块，覆盖38国玉米2","各前置期均取得最低NRMSE，诊断模块选择贡献最大，国家间差异大于模型间差异。","农业人工智能与决策模型","可探索自适应诊断机制迁移至其他作物，并针对国家间差异开展区域化建模与不确定性量化。","agent","2026-09-24T00:04:02.684732Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":168,"source_url":165,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":173,"tags":175,"search_phrases":177,"slug":180,"view_count":36,"doi":181,"paper":182,"created_at":206},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":69,"substance":70,"depth":69,"authority":140,"freshness":71,"relevant":22,"comment":172},"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。",[174],{"name":168,"url":165},[27,145,28,29,176],"精准灌溉",[178,179],"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":181,"openalex_id":183,"authors":184,"venue":168,"cited_by_count":36,"oa_url":165,"card":201,"direction":52,"ingested_from":54},"W7213246708",[185,188,190,192,195,198],{"name":186,"orcid":187},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":189,"orcid":9},"Qian Cheng",{"name":191,"orcid":9},"Fuyi Duan",{"name":193,"orcid":194},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":196,"orcid":197},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":199,"orcid":200},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":202,"method":203,"finding":204,"direction":52,"opportunity":205},"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":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":214,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":219,"tags":221,"search_phrases":224,"slug":227,"view_count":22,"doi":228,"paper":229,"created_at":239},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":17,"substance":69,"depth":217,"authority":13,"freshness":57,"relevant":22,"comment":218},15,"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。",[220],{"name":213,"url":210},[27,222,28,29,223],"精准施肥","NDVI",[225,226],"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":228,"openalex_id":230,"authors":231,"venue":213,"cited_by_count":36,"oa_url":9,"card":234,"direction":52,"ingested_from":54},"W7212289620",[232],{"name":233,"orcid":9},"Mohammed A. Naser",{"tldr":235,"method":236,"finding":237,"direction":52,"opportunity":238},"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":241,"title":242,"url":243,"summary":244,"summary_zh":9,"content":9,"source_name":245,"source_url":9,"published_at":246,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":247,"sources":249,"tags":251,"search_phrases":253,"slug":256,"view_count":22,"doi":9,"paper":257,"created_at":264},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",{"impact":69,"substance":70,"depth":69,"authority":17,"freshness":71,"relevant":22,"comment":248},"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。",[250],{"name":245,"url":243},[27,145,252,28,29],"多光谱无人机",[254,255],"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":258,"venue":9,"cited_by_count":36,"oa_url":9,"card":259,"direction":52,"ingested_from":160},[],{"tldr":260,"method":261,"finding":262,"direction":52,"opportunity":263},"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。","2026-09-05T00:04:40.858350Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":9,"source_name":271,"source_url":268,"published_at":272,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":278,"tags":280,"search_phrases":282,"slug":285,"view_count":36,"doi":286,"paper":287,"created_at":311},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。","Frontiers in Plant Science","2026-09-03T00:00:00Z",76,{"impact":69,"substance":275,"depth":69,"authority":20,"freshness":276,"relevant":22,"comment":277},20,7,"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。",[279],{"name":271,"url":268},[27,28,281,29,77],"多模态融合",[283,284],"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","10.3389\u002Ffpls.2026.1909856",{"doi":286,"openalex_id":288,"authors":289,"venue":271,"cited_by_count":36,"oa_url":268,"card":306,"direction":52,"ingested_from":54},"W7207858118",[290,292,294,296,298,301,304],{"name":291,"orcid":9},"Qingqing Hong",{"name":293,"orcid":9},"Siqi Cao",{"name":295,"orcid":9},"Bohan Hu",{"name":297,"orcid":9},"Changwei Tan",{"name":299,"orcid":300},"Zhenghua Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0880-0240",{"name":302,"orcid":303},"Bin Li","https:\u002F\u002Forcid.org\u002F0009-0000-0898-7864",{"name":305,"orcid":9},"Hongwei Zhang",{"tldr":307,"method":308,"finding":309,"direction":52,"opportunity":310},"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。","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。","2026-09-04T23:30:33.457309Z"]