[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-679":3,"related-679":70},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":64,"slug":67,"view_count":68,"doi":8,"paper":8,"created_at":69},679,"Tea Shoot Category Detection Based on UAV Remote Sensing(MDPI Agronomy 2026-08-17)","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576","基于无人机遥感的茶梢类别检测。研究利用UAV遥感图像对茶树新梢进行自动分类检测,支持茶园精细化管理与产量预测。通过深度学习方法识别不同生长阶段的茶梢,助力茶叶采摘决策与品质分级,为茶产业数字化转型提供技术支撑。",null,"## 1. Introduction\n\nAs one of the pillar industries of Yunnan’s modern agricultural system with distinctive plateau characteristics, the tea industry plays an important role in regional economic growth, rural revitalization, ecological civilization construction, ethnic tea culture inheritance, and sustainable agricultural development. During the optimization and upgrading of Yunnan’s agricultural industrial structure and the promotion of high-quality modern agricultural development, the tea industry has shown prominent economic, ecological, and social value [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B1-agronomy-16-01576)]. The detection efficiency and overall development level of the three tea shoot categories are closely related to the stable operation of the regional tea industry chain and supply chain, while also influencing the continuous improvement of industrial competitiveness. As core indicators for evaluating tea garden productivity and management efficiency, the distribution and abundance of the three tea shoot categories are shaped by multiple physiological and ecological factors, such as photosynthetic efficiency, nutrient absorption and transport capacity, growth and developmental status, and responses to pest and disease stress [[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B2-agronomy-16-01576),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B3-agronomy-16-01576)]. The distribution and abundance of the three tea shoot categories not only reflect the production potential of tea gardens but also provide an important basis for evaluating tea plant health, growth vigor, and ecological adaptability. Therefore, achieving efficient and accurate UAV remote sensing-based detection of three tea shoot categories in Yunnan has become a key requirement for accelerating the digital transformation of the tea industry, improving refined tea garden management, and supporting the high-quality development of the modern tea industry [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B4-agronomy-16-01576)].\n\nIn recent years, the tea industry in Yunnan has continued to develop toward large-scale operation, standardized production, and refined management, making the accurate detection of three tea shoot categories an important basis for supporting high-quality industrial development. At present, however, the detection of three tea shoot categories still depends largely on manual sampling surveys and empirical identification methods. These approaches are not only labor-intensive and limited in timeliness but also susceptible to subjective judgment, making it difficult to satisfy the practical needs of modern tea plantation management for real-time, precise, and full-coverage monitoring [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B5-agronomy-16-01576)]. With the rapid development of digital agriculture and smart agriculture, UAV remote sensing technology has gradually become an important technical means for agricultural resource investigation and crop growth monitoring. Its advantages, including operational flexibility, high spatial resolution, efficient data acquisition, and relatively controllable cost, provide a feasible technical pathway for improving the detection of three tea shoot categories [[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B6-agronomy-16-01576)].\n\nTo address the limitations of small-object detection in unmanned aerial vehicle (UAV) remote-sensing scenarios caused by rapid viewpoint changes and motion blur, Kıratlı et al. proposed the YOLOv11-EFAC network based on the nano version of YOLOv11n [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B7-agronomy-16-01576)]. The network adopts a multistage optimization strategy in which EfficientNet-B0 is used as the backbone, and a Feature Pyramid Network (FPN) and Path Aggregation Network (PANet) are combined for multiscale feature fusion. In addition, a Squeeze-and-Excitation (SE) attention module is introduced to enhance small-object feature representation, while the loss function is optimized to improve small-object localization. Experimental results showed that YOLOv11-EFAC achieved a mean Average Precision at an intersection-over-union threshold of 0.5 (mAP@0.5) of 83.7% in UAV-based detection, representing a 21.4% improvement in small-object detection performance over its baseline model. These findings indicate the potential of this method for real-time small-object detection and tracking in UAV imagery.\n\nTo improve feature extraction, detection accuracy, and deployment efficiency in crop-yield detection, Wang et al. proposed a lightweight YOLOv11-based model named RSWD-YOLO [[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B8-agronomy-16-01576)]. The model combines a hierarchical-scale feature pyramid with lightweight feature-extraction modules to enhance multiscale feature fusion while reducing computational complexity. It achieved an mAP of 86.1% on the evaluated crop dataset, indicating its potential for efficient crop-yield monitoring.\n\nTo improve multiscale small-object detection in UAV imagery with complex backgrounds, Yu et al. proposed OTB-YOLO, which integrates full-dimensional dynamic convolution, triplet attention, and a bidirectional feature pyramid network [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B9-agronomy-16-01576)]. On the evaluated UAV dataset, the model achieved a Precision of 95.6%, a Recall of 92.1%, and an mAP@0.5 of 96.6%, outperforming its baseline by 3.2, 2.5, and 3.1 percentage points, respectively.\n\nIn our previous study, we developed a YOLOv10-based tea-target detector for low-resolution and heavily occluded UAV imagery [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B10-agronomy-16-01576)]. That model combined an improved Shape-IoU loss, wavelet-transform convolution, and a Histogram Transformer to enhance fine-grained feature extraction and multiscale feature fusion. In contrast, the present study focuses primarily on target-scale variation, complex background interference, and inaccurate boundary localization. The proposed HR-YOLOv10-S replaces the wavelet-transform convolution used in our previous work with a Rectangular Self-Calibrated Module for directional spatial-context and boundary modeling. It also introduces Shape Weights and Scale Adjustment Factors into bounding-box regression to achieve shape- and scale-adaptive localization. Although the Histogram Transformer is retained, it is integrated with these newly designed components to form a different joint optimization framework. Therefore, the main novelty of the present work lies in the shape–scale adaptive regression strategy, the rectangular self-calibrated spatial modeling module, and their complementary integration for tea-target detection under complex UAV imaging conditions.\n\nDeep learning-based object detectors have been increasingly applied to agricultural and UAV remote-sensing tasks, including crop recognition, tea-plant monitoring, small-object detection, and yield-related assessment [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B4-agronomy-16-01576),[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B7-agronomy-16-01576)]. Recent studies have improved detection performance through multiscale feature pyramids, attention mechanisms, lightweight convolutional structures, bounding-box regression optimization, and local–global feature fusion [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B11-agronomy-16-01576),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B12-agronomy-16-01576)]. Nevertheless, UAV imagery acquired in complex agricultural environments remains challenging because of substantial target-scale variation, dense occlusion, uneven illumination, background clutter, and high visual similarity between crops and surrounding vegetation [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B7-agronomy-16-01576),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B8-agronomy-16-01576),[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B9-agronomy-16-01576),[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B10-agronomy-16-01576)]. These difficulties are particularly evident in mountainous tea plantations, where variations in canopy morphology, overlapping branches and leaves, and changing viewpoints can weaken boundary features and reduce small-target detection accuracy. Although existing methods have improved either multiscale representation, contextual modeling, or localization accuracy, their ability to simultaneously model target geometry, directional spatial context, and global background statistics remains limited. Therefore, further research is needed to develop a tea-target detector that combines scale-adaptive regression, enhanced spatial-structure modeling, and robust multiscale feature fusion for complex UAV imaging conditions.\n\nTo address the above limitations, this study proposes an improved object detection network, HR-YOLOv10-S, for UAV remote sensing-based detection of three tea shoot categories in complex tea plantation environments [[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B13-agronomy-16-01576)]. The network is based on the YOLOv10 architecture and is designed to improve detection under conditions involving substantial target-scale variation, complex background interference, and inaccurate boundary localization. During bounding-box regression, shape weights and scale adjustment factors are introduced to improve the matching between predicted and ground-truth boxes. These factors assign adaptive weights and scale adjustments to candidate boxes according to their aspect ratios and scale characteristics, thereby improving localization accuracy. A rectangular self-calibrated module is incorporated into the backbone network. This module combines horizontal and vertical separable convolutions with a lightweight self-calibration mechanism to enhance the modeling of spatial structures and target boundaries. In addition, a histogram transformer is introduced to exploit global statistical information and suppress interference from non-target regions, including soil, weeds, and shadows. This design facilitates multiscale feature fusion and semantic enhancement. The study aims to develop an accurate and robust model for detecting and extracting information on three tea shoot categories from UAV remote-sensing imagery and to provide technical support for refined tea plantation management and the digital development of the tea industry [[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B14-agronomy-16-01576)]. It also provides a new theoretical basis and practical paradigm for the deep integration of deep learning and agricultural remote sensing technologies, as well as for the design and optimization of intelligent perception models in complex agricultural scenarios.\n\n## 2. Materials and Methods\n\n### 2.1. Dataset Construction\n\nMenghai County in Xishuangbanna Dai Autonomous Prefecture and Fengqing County in Lincang City, Yunnan Province, were selected as the data-acquisition areas. These two plateau mountainous tea-producing regions contain substantial variation in terrain, canopy structure, planting density, background composition, and illumination. Such variation is particularly relevant to the present detection task because it produces changes in target size, appearance, occlusion, and the visual similarity between tea targets and surrounding vegetation. In addition, the dataset includes multitemporal and multiview UAV images acquired at different tea growth stages and times of day. Consequently, the dataset contains pronounced variations in target scale, morphology, viewing angle, and lighting conditions, which directly correspond to the detection challenges addressed by HR-YOLOv10-S [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B15-agronomy-16-01576)].\n\nThe data acquisition strategy covered the key growth stages of tea trees, including the young bud stage, the one bud and one leaf stage, and the one bud and two leaves stage [[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B16-agronomy-16-01576)]. Several rounds of aerial imaging were conducted at different times and from different viewing angles to increase sample diversity under low-altitude UAV remote-sensing conditions. Images were acquired at vertical distances of 1.0, 2.0, and 3.0 m above the tea canopy. It should be emphasized that these values represent the relative vertical distance between the UAV and the upper surface of the tea canopy, rather than the absolute flight height above the local ground surface. The objects investigated in this study were mature Yunnan large-leaf tea plants, which generally have plant heights of approximately 2.0–3.5 m, while some old tea trees can exceed 5 m. Therefore, the corresponding absolute UAV height above the local ground surface was approximately 3.0–6.5 m in conventional mature tea plantations and generally approximately 6.0–8.0 m or higher in old-tea-tree plantations, depending on the actual tree height and local terrain. Data collection was carried out in three time periods. These periods were 7:00 to 9:00 in the morning, 12:00 to 14:00 at noon, and 16:00 to 18:00 in the evening. This strategy enabled the collection of tea plantation image features under different sun heights and lighting conditions. It also made the dataset more adaptable to complex lighting environments.\n\nImages were acquired using a DJI Mavic 2 Pro UAV equipped with a Hasselblad L1D-20c camera featuring a 1-inch sensor and a resolution of 20 megapixels. Autofocus was disabled during image acquisition. To reduce variability caused by occasional focusing errors, all images were visually inspected during quality screening, and images showing noticeable defocus or motion blur were excluded from the dataset. Before model training and evaluation and before being fed into the networks, all images were resized and padded using letterbox preprocessing to an input size of 640 × 640 pixels. The same preprocessing procedure and input resolution were applied to all compared models.\n\nThis study collected a total of 1364 UAV images of tea plantations in Yunnan. Three tea-shoot categories were annotated: tea bud, one bud and one leaf, and one bud and two leaves. The data preprocessing procedure included image denoising, lens-distortion correction, redundancy screening, and image-quality screening. Images with blurred focus, abnormal exposure, severe occlusion, or excessive duplicated spatial content were removed, resulting in 1247 high-quality images for construction of the original dataset. The resulting dataset contained approximately 4380 annotated instances, including about 1100 tea buds, about 1700 one-bud-and-one-leaf targets, and about 1600 one-bud-and-two-leaves targets. As shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#fig_body_display_agronomy-16-01576-f001)A, the three categories exhibited a moderate class imbalance, with one-bud-and-one-leaf targets being the most frequent. After image acquisition, a manual redundancy-screening procedure was performed before dataset partitioning. Images containing more than 5% duplicated spatial content relative to another retained image were regarded as redundant samples, and the corresponding duplicated images were manually removed. This procedure was conducted on the original image pool prior to the division of the dataset in order to reduce the presence of highly similar samples caused by repeated UAV observations of the same tea-canopy regions. Therefore, the 5% criterion refers to the threshold used for manual redundancy screening rather than to an overlap ratio calculated among the training, validation, and test subsets. The study accurately annotated three target categories. These categories were tea buds, one bud and one leaf, and mature one bud and two leaves. The data preprocessing stage mainly included image denoising, lens distortion correction, and quality screening. Low-quality samples with blurred focus, abnormal exposure, or serious occlusion were removed, and 1247 high-quality images were finally obtained to build the original dataset. As shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#fig_body_display_agronomy-16-01576-f001), [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#fig_body_display_agronomy-16-01576-f001)A shows the quantity distribution histogram of tea buds, one bud and one leaf, and mature one bud and two leaves. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#fig_body_display_agronomy-16-01576-f001)B shows the size distribution of labels in the width and height directions, with the center points of all labels normalized to the image center. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#fig_body_display_agronomy-16-01576-f001)C shows the spatial distribution features of labels in the image coordinate system. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#fig_body_display_agronomy-16-01576-f001)D shows the aspect ratio distribution of the marked bounding boxes. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#fig_body_display_agronomy-16-01576-f001)E further shows the detailed features of the labels in the images [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B17-agronomy-16-01576)]. After redundancy screening and image-quality control, 1247 high-quality images were retained. These images were subsequently divided into training, validation, and test sets at a ratio of 6:2:2, resulting in 748, 249, and 250 images, respectively.\n\n**Figure 1.** Visualization of dataset labels.\n\n### 2.2. Data Augmentation\n\nIn UAV-based tea plantation remote sensing images, the original data are often affected by several factors. These factors include changes in natural illumination, fluctuations in UAV flight attitude, and differences in imaging conditions. These factors can cause problems such as imbalanced sample distribution, large target scale variation, and limited scene diversity. These problems not only increase the difficulty of target recognition and feature extraction, but also tend to cause overfitting during the training of deep learning models, thereby limiting their generalization ability and robustness in complex environments and unknown scenarios. To improve the adaptability and robustness of the model in complex environments and enhance the sufficiency of feature learning and the diversity of data distribution, this study applied multiple data augmentation strategies to the training set based on the original dataset.\n\nMean blur was therefore applied by replacing each pixel value with the average value of its local neighborhood, thereby producing uniformly smoothed images and simulating the loss of local texture details. Gaussian blur was implemented by assigning Gaussian-distributed weights to neighboring pixels, which generated gradually smoothed image regions and simulated more natural optical or motion-related blur. Median blur replaced As shown in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#fig_body_display_agronomy-16-01576-f002), multiple data augmentation techniques were applied to the training set, including HSV color-space perturbation, mean blur, Gaussian blur, median blur, random cropping, and random scaling along the x- and y-axes. To simulate the imaging differences in UAV remote-sensing images acquired at different times and under different weather and illumination conditions, random perturbations were applied to the original samples in the HSV color space, namely hue, saturation, and value. By randomly adjusting the hue, saturation, and value parameters, training samples with diverse color and illumination characteristics were generated [[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B18-agronomy-16-01576)]. Considering that UAV image acquisition can be affected by platform vibration, changes in flight motion, and focusing variability, the collected images may exhibit different degrees and forms of blur degradation [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1576#B19-agronomy-16-01576)]. each pixel value with the median value within its local neighborhood, there","MDPI Agronomy 2026-08-17","2026-08-17T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},15,20,18,14,1,"基于无人机遥感的茶叶嫩梢分类研究，方法新颖，数据详实，对智慧农业有实质贡献。",[24],{"name":10,"url":6},[26,27,28,29],"无人机","茶叶","茶梢检测","遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,57],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},12,true,[47],"remote sensing",[29],[29],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},"B3",7,[],[27,55],"茶树",[27],{"code":58,"score":59,"supported":45,"title":60,"abstract":61,"tags":63},"I2",3,[],[62],"深度学习",[],[65,66],"茶梢检测 无人机 茶叶 遥感","茶梢检测 无人机","茶梢检测无人机茶叶遥感-679",2,"2026-08-17T23:54:56.121772Z",{"total":71,"page":21,"page_size":71,"items":72},6,[73,178,259,315,387,438],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":78,"content":8,"source_name":79,"source_url":76,"published_at":80,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":86,"tags":88,"primary_topic":92,"secondary_topics":98,"classification":100,"search_phrases":127,"slug":130,"view_count":131,"doi":132,"paper":133,"created_at":177},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":19,"substance":83,"depth":19,"authority":44,"freshness":84,"relevant":21,"comment":85},22,9,"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[87],{"name":79,"url":76},[89,26,90,29,91],"智慧农业","育种","紫花苜蓿",{"code":93,"slug":94,"name":95,"path":96,"parent_code":97},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[99],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":101},{"candidates":102,"agricultural_evidence":45},[103,109,114,121],{"code":93,"score":17,"supported":45,"title":104,"abstract":106,"tags":108},[105],"alfalfa",[107,105],"苜蓿",[91],{"code":31,"score":44,"supported":45,"title":110,"abstract":112,"tags":113},[111],"multispectral",[111],[29],{"code":115,"score":52,"supported":45,"title":116,"abstract":117,"tags":120},"I1",[],[118,119],"精准农业","precision agriculture",[89],{"code":122,"score":123,"supported":45,"title":124,"abstract":125,"tags":126},"C4",4,[],[107],[91],[128,129],"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",0,"10.64898\u002F2026.10.14.759249",{"doi":132,"openalex_id":134,"authors":135,"venue":79,"cited_by_count":131,"oa_url":76,"card":170,"direction":174,"ingested_from":176},"W7223083966",[136,139,142,145,148,150,152,155,158,161,164,167],{"name":137,"orcid":138},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":140,"orcid":141},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":143,"orcid":144},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":146,"orcid":147},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":149,"orcid":8},"Julie Hansen",{"name":151,"orcid":8},"Christopher Pierce",{"name":153,"orcid":154},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":156,"orcid":157},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":159,"orcid":160},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":162,"orcid":163},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":165,"orcid":166},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":168,"orcid":169},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":171,"method":172,"finding":173,"direction":174,"opportunity":175},"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。","农业遥感与作物表型","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。","openalex","2026-10-22T11:20:32.655394Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":8,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":186,"score_detail":187,"sources":191,"tags":193,"primary_topic":196,"secondary_topics":197,"classification":202,"search_phrases":232,"slug":235,"view_count":131,"doi":236,"paper":237,"created_at":258},4505,"Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23232412","Developments in unmanned aerial vehicle (UAV) technology and the rise of Deep Learning (DL) have drastically boosted the capacities of precision viticulture to monitor the environments of farms in a scalable and detailed manner. Vineyard mappings and extraction of vine-specific features are crucial for yield estimation, plant health assessment, and management decisions. This study examines the application of Deep Learning-based image analysis techniques for vineyard mapping and characterization using multispectral UAV image data (Deep Learning). The proposed approach integrates spatial and spectral information to delineate vineyard structures and capture key canopy characteristics relevant to precision agriculture. Multimodal datasets, including RGB and multispectral bands, facilitate improved discrimination between vines and surrounding elements. Geospatial analysis and data processing are further performed through ArcGIS Pro, enabling spatial validation of results, identifying extracted features, and creating appropriate interpretative thematic layers. The combination of UAV imagery, DL tools and GIS based analysis offers a strong and flexible workflow that can be adjusted for different grape cultivars, growth conditions and circumstances. Overall, the study demonstrates the potential benefits of the rigid union of UAV-based remote sensing with Deep Learning and GIS techniques to create a flexible, operational, and data-driven framework for vineyard management. Based on this, sustainable and fruitful management practices can be elaborated, thus optimizing resources and vineyard management decisions in precision viticulture,underlining the importance of such a technological integration for monitoring and managing modern vineyards.","无人机（UAV）技术的发展与深度学习（DL）的兴起，极大地提升了精准葡萄栽培以可扩展且精细的方式监测农场环境的能力。葡萄园制图与葡萄植株特异性特征的提取对于产量估算、植株健康评估和管理决策至关重要。本研究探讨了基于深度学习的图像分析技术在多光谱无人机影像数据（深度学习）用于葡萄园制图与表征中的应用。所提出的方法整合了空间与光谱信息，以 delineate 葡萄园结构并捕捉与精准农业相关的关键冠层特征。多模态数据集（包括RGB和多光谱波段）有助于更好地区分葡萄藤与周围元素。通过ArcGIS Pro进一步进行地理空间分析与数据处理，从而实现结果的空间验证、提取特征的识别以及适当的解释性专题图层的创建。无人机影像、深度学习工具与基于GIS的分析相结合，提供了一套强大且灵活的工作流程，可针对不同葡萄品种、生长条件和环境进行调整。总体而言，本研究展示了将基于无人机的遥感与深度学习和GIS技术 rigidly 结合，以创建灵活、可操作且数据驱动的葡萄园管理框架的潜在优势。在此基础上，可制定可持续且富有成效的管理实践，从而优化精准葡萄栽培中的资源与葡萄园管理决策，凸显了这种技术整合对现代葡萄园监测与管理的重要性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",68,{"impact":44,"substance":19,"depth":188,"authority":189,"freshness":84,"relevant":21,"comment":190},16,13,"希腊北部葡萄园的多光谱无人机深度学习制图案例，方法新颖但属区域性研究，对国内智慧农业有参考价值。",[192],{"name":184,"url":181},[89,26,194,29,195],"农业人工智能","葡萄园管理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[198],{"code":58,"slug":199,"name":200,"path":201,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":14,"version":39,"method":40,"evidence":203},{"candidates":204,"agricultural_evidence":45},[205,209,214,218,225],{"code":31,"score":19,"supported":45,"title":206,"abstract":207,"tags":208},[111],[29,47,111],[29],{"code":58,"score":17,"supported":45,"title":210,"abstract":212,"tags":213},[211],"deep learning",[62,211],[194],{"code":115,"score":52,"supported":45,"title":215,"abstract":216,"tags":217},[],[118,119],[89],{"code":219,"score":71,"supported":45,"title":220,"abstract":221,"tags":224},"B2",[],[222,223],"葡萄","grape",[],{"code":226,"score":71,"supported":45,"title":227,"abstract":228,"tags":231},"I4",[],[229,230],"空间分析","GIS",[],[233,234],"希腊 葡萄园 无人机 深度学习","多光谱 UAV 葡萄园 制图","希腊葡萄园无人机深度学习-4505","10.5281\u002Fzenodo.23232412",{"doi":236,"openalex_id":238,"authors":239,"venue":184,"cited_by_count":131,"oa_url":181,"card":253,"direction":174,"ingested_from":176},"W7220993218",[240,242,245,247,250],{"name":241,"orcid":8},"Dimitra Passa",{"name":243,"orcid":244},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":246,"orcid":8},"Georgios Nektarios Tselos",{"name":248,"orcid":249},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":251,"orcid":252},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":254,"method":255,"finding":256,"direction":174,"opportunity":257},"用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。","多光谱无人机影像、RGB与多光谱融合、深度学习、ArcGIS Pro空间分析。","UAV+DL+GIS可灵活构建葡萄园精准管理的数据驱动框架。","可探索跨品种、跨生长阶段的迁移学习与轻量化模型，提升葡萄园实时监测泛化能力。","2026-10-09T23:30:25.912664Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":8,"source_name":265,"source_url":262,"published_at":266,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":267,"score_detail":268,"sources":272,"tags":274,"primary_topic":276,"secondary_topics":277,"classification":278,"search_phrases":290,"slug":293,"view_count":68,"doi":294,"paper":295,"created_at":314},3982,"Direct surface reflectance retrieval from UAV multispectral imagery using onboard upward-looking spectrometer with MLR under variable illuminations","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112470","Direct surface reflectance retrieval from UAV multispectral imagery using onboard upward-looking spectrometer with MLR under variable illuminations。Computers and Electronics in Agriculture","基于机载向上观测光谱仪与多元线性回归（MLR）在变化光照条件下从无人机多光谱影像直接反演地表反射率。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",71,{"impact":44,"substance":18,"depth":269,"authority":20,"freshness":270,"relevant":21,"comment":271},17,8,"发表于农业信息领域核心期刊的方法类论文，提出机载上行光谱仪结合多元线性回归在多变光照下直接反演地表反射率，对无人机遥感定量监测有实用价值，但属细分技术进展，公共影响有限。",[273],{"name":265,"url":262},[89,26,29,275],"多光谱",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":279},{"candidates":280,"agricultural_evidence":45},[281,286],{"code":31,"score":17,"supported":45,"title":282,"abstract":283,"tags":285},[111],[284,111],"无人机多光谱",[29],{"code":115,"score":21,"supported":14,"title":287,"abstract":288,"tags":289},[],[],[89],[291,292],"无人机 多光谱 地表反射率","机载 光谱仪 反射率反演","无人机多光谱地表反射率-3982","10.1016\u002Fj.compag.2026.112470",{"doi":294,"openalex_id":296,"authors":297,"venue":265,"cited_by_count":131,"oa_url":8,"card":309,"direction":174,"ingested_from":176},"W7215128624",[298,301,304,306],{"name":299,"orcid":300},"Jiayang Xie","https:\u002F\u002Forcid.org\u002F0000-0001-5711-2351",{"name":302,"orcid":303},"Yutao Shen","https:\u002F\u002Forcid.org\u002F0009-0001-4348-125X",{"name":305,"orcid":8},"Wenjuan Li",{"name":307,"orcid":308},"Haiyan Cen","https:\u002F\u002Forcid.org\u002F0000-0003-0266-2123",{"tldr":310,"method":311,"finding":312,"direction":174,"opportunity":313},"利用无人机搭载向上光谱仪与多元线性回归，从多光谱影像直接反演地表反射率。","无人机多光谱影像、机载向上光谱仪、多元线性回归（MLR）。","在光照变化条件下，该方法能有效直接反演地表反射率。","可探索复杂光照下多光谱辐射校正的普适模型，并验证对作物表型参数反演的提升。","2026-10-02T23:30:03.513918Z",{"id":316,"title":317,"url":318,"summary":319,"summary_zh":8,"content":8,"source_name":320,"source_url":318,"published_at":321,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":322,"score_detail":323,"sources":325,"tags":327,"primary_topic":330,"secondary_topics":335,"classification":337,"search_phrases":353,"slug":356,"view_count":357,"doi":358,"paper":359,"created_at":386},3619,"Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10767247\u002Fv1","Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee。Research Square","Research Square","2026-09-24T00:00:00Z",51,{"impact":270,"substance":20,"depth":17,"authority":71,"freshness":270,"relevant":21,"comment":324},"咖啡园NDVI主动传感器与无人机多光谱对比研究，方法有参考价值但属细分作物技术验证，影响面有限。",[326],{"name":320,"url":318},[89,26,29,328,329],"NDVI","咖啡种植",{"code":51,"slug":331,"name":332,"path":333,"parent_code":334},"tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[336],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":338},{"candidates":339,"agricultural_evidence":45},[340,344,349],{"code":31,"score":189,"supported":45,"title":341,"abstract":342,"tags":343},[111],[111],[328,29],{"code":51,"score":44,"supported":45,"title":345,"abstract":347,"tags":348},[346],"coffee",[346],[329],{"code":115,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[89],[354,355],"咖啡 NDVI 无人机 多光谱","Research Square 咖啡 遥感","咖啡NDVI无人机多光谱-3619",5,"10.21203\u002Frs.3.rs-10767247\u002Fv1",{"doi":358,"openalex_id":360,"authors":361,"venue":320,"cited_by_count":131,"oa_url":385,"card":8,"direction":174,"ingested_from":176},"W7214220732",[362,364,366,368,370,373,375,377,380,383],{"name":363,"orcid":8},"Gabriel de Morais Campos",{"name":365,"orcid":8},"Aline Bhering Silva",{"name":367,"orcid":8},"Cileimar Aparecida da Silva",{"name":369,"orcid":8},"Vanda Maria Salles Andrade",{"name":371,"orcid":372},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":374,"orcid":8},"Andressa Barcellos Silva",{"name":376,"orcid":8},"Marco Thúlio Gonçalves Vieira",{"name":378,"orcid":379},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":381,"orcid":382},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":384,"orcid":8},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":8,"source_name":393,"source_url":390,"published_at":321,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":402,"secondary_topics":403,"classification":404,"search_phrases":415,"slug":418,"view_count":84,"doi":419,"paper":420,"created_at":437},3503,"Adversarial Patch and Camouflage Attacks on Aerial Object Detection: A Geometry-Aware Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26196057","Uncrewed aerial vehicles increasingly rely on on-board deep object detectors for applications such as surveillance, delivery, agriculture, and traffic monitoring. The vulnerability of these detectors to adversarial patches has therefore become a practical safety concern. Research on adversarial patch and camouflage attacks against aerial object detection has expanded rapidly, yet existing reviews consider aerial systems only as one application domain among many and do not examine the unique geometric conditions of aerial imagery. This review surveys adversarial patch and camouflage attacks, together with the corresponding defense mechanisms, for aerial, UAV, and remote sensing object detection. The literature is organized using a taxonomy based on two dimensions: the physical medium of the perturbation and the application domain it targets. Existing methods are then compared with respect to physical robustness, evaluation datasets and detectors, threat models, and their treatment of viewpoint variation. A recurring observation across the literature is that viewpoint variation is represented primarily through object scale, while explicit modeling of viewing geometry remains limited. This trend is closely linked to the characteristics of the aerial datasets used for evaluation, which typically provide realistic imagery but limited geometric information. Consequently, questions concerning viewpoint-dependent placement, degradation, and naturalness remain only partially explored. The review concludes by identifying the major research gaps, future directions, and deployment challenges that are likely to shape the next generation of aerial adversarial attack and defense research.","无人驾驶飞行器（UAV）在监视、配送、农业和交通监测等应用中日益依赖机载深度目标检测器。因此，这些检测器对对抗性补丁的脆弱性已成为一个实际的安全问题。针对空中目标检测的对抗性补丁与伪装攻击研究迅速扩展，然而现有综述仅将空中系统视为众多应用领域之一，并未考察航空影像独特的几何条件。本综述考察了针对空中、无人机及遥感目标检测的对抗性补丁与伪装攻击以及相应的防御机制。文献按照基于两个维度的分类体系进行组织：扰动的物理介质及其所针对的应用领域。随后，现有方法在物理鲁棒性、评估数据集与检测器、威胁模型以及其对视角变化的处理方面进行了比较。文献中反复出现的一个观察是，视角变化主要通过目标尺度来表示，而对观察几何的显式建模仍然有限。这一趋势与用于评估的航空数据集的特征密切相关，这些数据集通常提供真实影像但几何信息有限。因此，关于视角依赖的放置、退化及自然性的问题仅得到部分探讨。本综述最后指出了可能塑造下一代空中对抗攻击与防御研究的主要研究空白、未来方向及部署挑战。","Sensors",67,{"impact":270,"substance":18,"depth":269,"authority":189,"freshness":84,"relevant":21,"comment":396},"系统梳理航空与无人机目标检测的对抗补丁与伪装攻击及防御，指出视角几何建模不足的研究空白，对农业遥感与无人机应用安全有参考价值。",[398],{"name":393,"url":390},[26,194,400,29,401],"目标检测","对抗攻击",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":405},{"candidates":406,"agricultural_evidence":45},[407,411],{"code":31,"score":52,"supported":45,"title":408,"abstract":409,"tags":410},[],[29,47],[29],{"code":58,"score":357,"supported":45,"title":412,"abstract":413,"tags":414},[],[400],[194,400],[416,417],"无人机 对抗补丁 目标检测","航空图像 对抗攻击","无人机对抗补丁目标检测-3503","10.3390\u002Fs26196057",{"doi":419,"openalex_id":421,"authors":422,"venue":393,"cited_by_count":131,"oa_url":390,"card":432,"direction":174,"ingested_from":176},"W7214217753",[423,426,429],{"name":424,"orcid":425},"Sandesh Shrestha","https:\u002F\u002Forcid.org\u002F0000-0001-5298-4468",{"name":427,"orcid":428},"K. T. Y. Mahima","https:\u002F\u002Forcid.org\u002F0000-0003-4975-9408",{"name":430,"orcid":431},"Asanka G. Perera","https:\u002F\u002Forcid.org\u002F0000-0003-4021-3943",{"tldr":433,"method":434,"finding":435,"direction":174,"opportunity":436},"综述无人机与遥感目标检测中的对抗补丁与伪装攻击及防御，并提出几何感知分类体系。","基于扰动介质与应用领域的分类法，比较物理鲁棒性、数据集、威胁模型与视角处理。","现有研究多以目标尺度代替视角变化，缺乏显式视角几何建模，因数据集几何信息有限。","可构建含视角几何标注的农业航拍数据集，研究视角依赖的对抗补丁放置与自然性防御。","2026-09-25T23:30:31.219576Z",{"id":439,"title":440,"url":441,"summary":442,"summary_zh":443,"content":8,"source_name":444,"source_url":441,"published_at":445,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":446,"sources":448,"tags":450,"primary_topic":453,"secondary_topics":457,"classification":459,"search_phrases":487,"slug":490,"view_count":59,"doi":491,"paper":492,"created_at":539},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",{"impact":19,"substance":83,"depth":19,"authority":189,"freshness":270,"relevant":21,"comment":447},"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。",[449],{"name":444,"url":441},[89,26,451,29,452],"小麦","氮素监测",{"code":454,"slug":455,"name":451,"path":456,"parent_code":97},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat",[458],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":460},{"candidates":461,"agricultural_evidence":45},[462,466,471,475,481],{"code":454,"score":44,"supported":45,"title":463,"abstract":464,"tags":465},[455],[455],[451],{"code":31,"score":44,"supported":45,"title":467,"abstract":469,"tags":470},[468],"hyperspectral",[468],[29],{"code":115,"score":52,"supported":45,"title":472,"abstract":473,"tags":474},[],[118,119],[89],{"code":58,"score":71,"supported":45,"title":476,"abstract":477,"tags":480},[],[478,479],"机器学习","machine learning",[],{"code":482,"score":59,"supported":45,"title":483,"abstract":484,"tags":486},"F3",[],[485],"养分管理",[],[488,489],"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":491,"openalex_id":493,"authors":494,"venue":444,"cited_by_count":131,"oa_url":8,"card":533,"direction":538,"ingested_from":176},"W7214104831",[495,497,499,502,505,507,509,512,515,518,520,523,525,527,530],{"name":496,"orcid":8},"R. G. Rejith",{"name":498,"orcid":8},"Rabi N. Sahoo",{"name":500,"orcid":501},"Shalini Gakhar","https:\u002F\u002Forcid.org\u002F0000-0001-5717-1714",{"name":503,"orcid":504},"Jochem Verrelst","https:\u002F\u002Forcid.org\u002F0000-0002-6313-2081",{"name":506,"orcid":8},"Amrita Bhandari",{"name":508,"orcid":8},"Tarun Kondraju",{"name":510,"orcid":511},"Rajeev Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-2233-9147",{"name":513,"orcid":514},"Mahesh C. Meena","https:\u002F\u002Forcid.org\u002F0000-0001-5386-883X",{"name":516,"orcid":517},"Abir Dey","https:\u002F\u002Forcid.org\u002F0000-0002-5009-9518",{"name":519,"orcid":8},"Joydeep Mukherjee",{"name":521,"orcid":522},"Sudhir Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-1089-7435",{"name":524,"orcid":8},"Mahesh Kumar",{"name":526,"orcid":8},"Raju Dhandapani",{"name":528,"orcid":529},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":531,"orcid":532},"Viswanathan Chinnusamy","https:\u002F\u002Forcid.org\u002F0000-0003-2174-9064",{"tldr":534,"method":535,"finding":536,"direction":174,"opportunity":537},"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"]