Search Results (114)

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has emerged as a promising tool for monitoring crop physiological status and supporting precision disease management. However, the capacity of multispectral vegetation indices to detect foliar diseases under commercial field conditions remains insufficiently understood, particularly in perennial crops grown in humid tropical environments. This study evaluated the potential of UAV-derived multispectral vegetation indices to assess the physiological response of avocado (Persea americana Mill.) canopies affected by anthracnose caused by Colletotrichum fructicola in Amazonas, Peru. Disease incidence and severity were assessed through field evaluations, while multispectral imagery was acquired using a UAV equipped with a MicaSense RedEdge-MX Dual sensor. Vegetation indices including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge Index (NDRE), Red Edge Chlorophyll Index (CIred-edge), Plant Senescence Reflectance Index (PSRI), and Visible Atmospherically Resistant Index (VARI) were calculated, and their relationships with anthracnose incidence were analyzed using Spearman’s rank correlation. Field observations confirmed a high incidence of foliar anthracnose with a heterogeneous spatial distribution across the orchard. Multispectral imagery successfully characterized spatial variability in the canopy physiological condition, revealing differences in vegetation vigor, chlorophyll-related reflectance, and senescence among experimental blocks. Nevertheless, none of the evaluated vegetation indices showed statistically significant correlations with anthracnose incidence (p > 0.05), indicating that spectral variability primarily reflected the general canopy physiological status rather than a disease-specific spectral response. These findings demonstrate that UAV-derived multispectral vegetation indices are valuable for monitoring spatial variability in the avocado canopy condition but have limited capability for independently detecting anthracnose under humid tropical field conditions. Future studies integrating multi-temporal UAV acquisitions, hyperspectral and thermal imagery, LiDAR, environmental variables, and machine-learning approaches are expected to improve the early detection and spatial prediction of anthracnose in avocado production systems. Full article

Nitrogen (N) is one of the most important nutrients influencing wheat growth, plant nutrition, and grain production. Appropriate timing of nitrogen application is essential to synchronize nutrient availability with crop demand. This study evaluated seven nitrogen management treatments, including a control (N0) and six split nitrogen application schedules (N1–N6), in durum wheat under Mediterranean conditions using an integrated approach combining ground-based hyperspectral sensing and unmanned aerial vehicle (UAV)-based multispectral imagery. Plant nutrient concentrations (N, P, K, Ca, and Mg), spectral reflectance, vegetation indices, plant height, and grain yield were evaluated at different phenological stages. Split nitrogen application significantly affected plant nutrient concentrations, spectral reflectance, vegetation indices, plant height, and grain yield. Plant nutrient concentrations generally declined with crop development, whereas spectral reflectance increased across the visible and near-infrared regions of the spectrum. Vegetation indices derived from both hyperspectral and UAV multispectral data successfully differentiated phenological stages and nitrogen treatments. UAV-derived plant height showed strong agreement with field measurements, confirming the reliability of photogrammetric measurements for monitoring crop development. Among the nitrogen treatments, the N3 split application schedule produced the most favorable overall crop response, with higher plant nitrogen concentration, stronger spectral responses, and the highest grain yield. In addition, UAV-derived NDVI measured at the booting stage showed the strongest relationship with grain yield (r = 0.717, p < 0.01). These findings demonstrate that integrating ground-based hyperspectral sensing with UAV multispectral imagery provides complementary information for evaluating crop development and plant nutritional responses under different split nitrogen application schedules. Full article

Accurate mapping of crop residue cover (CRC) is important for sustainable agricultural management because residue supports soil health, erosion control, and carbon sequestration. Traditional ground-based CRC methods are labor-intensive and spatially limited, increasing interest in UAV-based remote sensing. UAVs can carry RGB, multispectral, and hyperspectral sensors, which capture different spectral information for distinguishing crop residue from soil. This study compared four high-spatial-resolution (2.5 cm) UAV imagery types—RGB, multispectral, visible–near-infrared (VNIR) hyperspectral, and shortwave infrared (SWIR) hyperspectral—for fine-scale CRC classification. A fully connected neural network (FCNN) was developed to classify residue and soil pixels. Performance was evaluated using two complementary approaches: pixel-level accuracy assessment based on manually delineated image samples and plot-level validation against residue percentages derived from ground photos. Results showed that high pixel-level classification accuracy values were achieved across all imagery types, with overall accuracies above 94%. However, plot-level validation revealed that sensor performance depended on the evaluation metric considered. Multispectral imagery produced the highest R² with ground photo-derived reference CRC values (R² = 0.672). These results indicate that greater spectral dimensionality did not necessarily improve plot-level CRC estimation under the tested field conditions. More importantly, the findings show that high pixel-level classification accuracy does not necessarily translate into stronger plot-level CRC estimation, highlighting the importance of using complementary validation approaches when evaluating UAV-based CRC estimation. Full article

Alpine karst lakes in Jiuzhaigou exhibit spatial variations in optical properties associated with differences in water depth, underwater substrates, and travertine distribution. Conventional satellite observations may not provide sufficient spatial resolution to resolve the small-scale optical variations present in these shallow and heterogeneous lakes. In this study, UAV-based hyperspectral imagery was used to quantify spatial optical variations among three representative cascade lakes located in the “Y-shaped” valley network of Jiuzhaigou, China. Hyperspectral reflectance data were transformed into the CIE L*a*b* color space to quantify perceptual color variability, while the Euclidean color difference metric (ΔE) was used to assess relative spatial heterogeneity. In addition, seven empirical optical proxies were developed to describe spatial variations in apparent optical properties without attempting absolute retrieval of water constituents. A tiered unsupervised K-means clustering approach was applied to resolve optical heterogeneity across multiple spatial scales. Global-scale clustering separated the major optical differences among lakes, whereas lake-specific clustering revealed distinct intra-lake optical zones, including areas with stronger benthic influence, transitional optical conditions, and zones dominated by water-column signals. Non-parametric Kruskal–Wallis H tests showed significant differences among optical zones, indicating that the selected proxies can distinguish spatial optical variability. The results show that UAV hyperspectral observations combined with color metrics and empirical optical proxies can effectively describe fine-scale spatial optical variations in alpine karst lakes. Rather than retrieving absolute biogeochemical or bathymetric parameters, this approach provides a reference description of relative optical variability that may assist future repeated observations and spatial monitoring of protected aquatic ecosystems. Full article

Reported accuracy in small unmanned aerial vehicle (UAV) hyperspectral studies depends strongly on how validation is designed, yet leakage-controlled and cross-site protocols are seldom reported. Using 177 plots in three maize fields at the heading stage in Qapqal County, Ili Valley, Xinjiang, China, we quantified this dependence for canopy nitrogen concentration (CNC; mass-based, mg g⁻¹) retrieval from UAV hyperspectral imagery (400–900 nm) with auxiliary LiDAR. A 1829-dimensional feature space served as a realistic testbed. Under a conventional mixed repeated GroupKFold benchmark, the model reached R² = 0.712, RMSE = 1.023 mg g⁻¹, and RPD = 1.86. Repeating feature ranking inside every training fold lowered this to R² = 0.612–0.627, and leave-one-field-out validation produced negative R² for every held-out field. Predictions collapsed toward the training mean with strong slope compression, consistent with a domain shift in which nitrogen level, cultivar, growth micro-stage, and irrigation regime all differ between fields. Feature optimization, not model complexity, drove within-campaign performance: top-140 selection raised R² from 0.613 to 0.682, whereas ensemble blending and LiDAR (2.7% of TreeSHAP importance) contributed marginally. We report this as a reproducible case study on validation design for small hyperspectral datasets. Full article

Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (${\mathrm{R}}^2$ = 0.82; MAE = 6.36 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (${\mathrm{R}}^2$ = 0.86; MAE = 6.56 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons. Full article

Reliable and scalable landmine detection technologies are essential for humanitarian mine action (HMA), yet standardized benchmarks for Unmanned Aerial Vehicle (UAV)-based sensing in operationally relevant environments remain limited. This study presents a comprehensive evaluation of 34 multimodal datasets acquired over a standardized seeded test site for landmine and unexploded ordnance detection. Nine sensing modalities, including RGB, thermal, multispectral, hyperspectral, LiDAR, and Synthetic Aperture Radar (SAR), are evaluated using the Anomaly, Identifiable Anomaly, Unique Identifiable Anomaly (AIU) index to establish a unified framework for quantifying detection fidelity. Results indicate that RGB imagery achieves the highest surface detection rate (94.8%), with 45.4% of targets classified as uniquely identifiable, reducing false-positive risk. For sub-surface detection, handheld electromagnetic induction (EMI) and magnetometry exceed 95% detection for ferrous items but fall below 10% for plastic ordnance. Ground-penetrating radar (GPR) is the only modality capable of detecting buried plastic targets (55.6% for cart-based systems), whereas UAV-mounted GPR remains limited (18.2%) at current operational flight heights. Based on the comparative analysis, we discuss the gaps in current detection capabilities, compare false-positive rates across modalities, and perform a cost–benefit analysis fitting contamination scenarios with the most cost-effective detection method. All datasets are publicly released, along with an interactive web-map, to support reproducible benchmarking and cross-modality comparison in UAV-enabled explosive hazard detection. Full article

Unmanned Aerial Vehicle (UAV)-based remote sensing using high-throughput spectral imaging has emerged as an effective non-destructive alternative for large-scale agricultural monitoring. This study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola. Field trials were conducted at irrigated and non-irrigated sites in southern and central Alberta, Canada, respectively, over three growing seasons (2023–2025). Coincident with ground-truth tissue sampling, aerial imagery was collected and processed to train and validate six machine learning models, using ~520 matchups per crop. All models successfully estimated nitrogen uptake across years and locations, although performance varied by sensor and data types. For canola, ANN produced the highest MSI-based accuracy (R² = 0.83, RMSE = 0.5%), whereas HSI data improved prediction performance, with SVR achieving the best results (R² = 0.90, RMSE = 0.40%). In wheat, ANN yielded the highest accuracy for both MSI and HSI data (R² = 0.77, RMSE = 0.54% for MSI; R² = 0.8, RMSE = 0.48% for HSI). These findings demonstrate that UAV-based spectral imaging combined with machine learning provides a reliable and scalable approach for non-destructive nitrogen uptake estimation. Although MSI sensors produced strong predictive performance, the enhanced spectral resolution of HSI data consistently improved estimation accuracy for both crops across varied growing conditions. Full article

Grassland foliar functional traits are closely linked to ecosystem functioning, biodiversity, and plant responses to environmental change. Hyperspectral remote sensing provides an efficient and non-destructive approach for mapping foliar traits, yet direct comparisons between UAV-based and airborne imaging spectroscopy remain limited. In this study, we evaluated the performance of UAV-based Nano and airborne Hyspex hyperspectral imagery for predicting ten foliar functional traits across experimental grassland plots at the Cedar Creek Ecosystem Science Reserve, USA. We further assessed the contributions of visible-to-near-infrared (VNIR) and shortwave infrared (SWIR) spectral regions, as well as the effects of spectral preprocessing approaches for minimizing confounding effects from canopy structure, illumination/viewing geometry, and soil background. Random Forest regression models were developed using plot-level average spectra derived from Nano and Hyspex imagery. Both UAV- and airborne-based imaging spectroscopy achieved moderate to high prediction accuracies for most foliar traits. High accuracies were obtained for non-structural carbohydrates (NSC), carotenoids, β-carotene, hemicellulose, and cellulose (R² = 0.66–0.82; NRMSE = 6–10%), while moderate accuracies were achieved for nitrogen, chlorophyll, and xanthophylls (R² = 0.51–0.74; NRMSE = 8–12%). In contrast, carbon and lignin consistently exhibited lower predictive performance (R² = 0.32–0.59; NRMSE = 9–15%). Despite covering only the VNIR spectral range, the UAV-based Nano imagery achieved accuracies comparable to those obtained using the airborne full-spectrum Hyspex imagery, indicating that high spatial resolution can partially compensate for limited spectral coverage by reducing soil background effects. The VNIR spectral region alone provided trait estimation accuracies comparable to those obtained using the full visible-to-shortwave infrared (VSWIR) spectrum, whereas SWIR wavelengths contributed only marginal improvements for a subset of structural traits. Among preprocessing approaches, vector normalization generally improved prediction performance by reducing the confounding effects of canopy structure and illumination/viewing geometry, whereas NIR_v-adjusted spectra provided limited benefits. Our findings demonstrate that UAV-based VNIR imaging spectroscopy can provide accurate and cost-effective estimation of grassland foliar functional traits. The results also highlight important trade-offs between spectral and spatial resolution in hyperspectral remote sensing and provide practical guidance for selecting imaging spectroscopy platforms and preprocessing approaches for grassland ecosystem monitoring. Full article

Destructive methods for monitoring stress responses remain a bottleneck in precision agriculture. This study presents a non-destructive methodological framework evaluating drought responses in 30 Opuntia ficus-indica plants over four months under five irrigation levels. Cladode traits (color, weight, and thickness) were measured alongside RGB imagery from a UAV and hyperspectral imaging (400–1000 nm). Partial least squares regression (PLSR) models showed high capability to model proline (R² = 0.91), chlorophyll a (R² = 0.97), and total chlorophyll (R² = 0.97) within the experimental dataset. Crucially, these models reflected continuous spectral–physiological variation across the irrigation gradient rather than discrete treatment separation, with key spectral regions identified at 530–600 nm and 550–750 nm. UAV-derived RGB imagery enabled the estimation of plant area and biomass (R² = 0.88). Under extreme drought, cladode thickness decreased by approximately 41%, accompanied by reduced biomass and increased soluble solids (°Brix). While no statistically significant differences were observed among irrigation treatments for biochemical variables, limiting treatment discrimination based on discrete classification, the hyperspectral data successfully captured the underlying continuous physiological variation. Consequently, this work demonstrates the methodological viability of integrating UAV structural phenotyping and hyperspectral analysis as a continuous monitoring tool rather than a rigid classification system. These findings provide a methodological baseline that highlights the need for continuous sensing in CAM plants, though further validation with independent datasets remains essential for wider operational application. Full article

Soil total nitrogen (STN) is a key indicator of soil fertility and plays a fundamental role in agricultural productivity and sustainable land management. However, achieving an accurate and spatially continuous estimate of STN at the field scale remains challenging due to inherent soil variability and the constraints of conventional sampling methods. In this study, we employed unmanned aerial vehicle (UAV)-based hyperspectral imagery to estimate STN by integrating spectral preprocessing, feature selection, and machine learning techniques. Multiple feature selection methods, including Pearson correlation analysis, variable importance in projection (VIP), and competitive adaptive reweighted sampling (CARS), were evaluated to identify the most informative spectral bands. Several regression models—support vector regression with radial basis function kernel (SVR-RBF), random forest (RF), Extra Trees, PCA-SVR-RBF, and XGBoost—were compared for STN prediction. Among these, the VIP-PCA-SVR-RBF model yielded the best performance, achieving a test R² of approximately 0.77 and an RMSE of 0.45 g kg⁻¹. The integration of VIP-based feature selection with PCA dimensionality reduction significantly enhanced predictive accuracy and generalization capability compared to the other models tested. Spatial prediction maps derived from the optimal model revealed considerable heterogeneity in STN distribution across the study area. These results underscore the potential of UAV hyperspectral remote sensing for high-resolution mapping of soil nitrogen and offer a promising framework for precision nutrient management in agriculture. Full article

The Yellow River Basin contains abundant coal resources; however, its ecological environment is inherently fragile, and vegetation degradation has been further intensified by extensive mining activities. Accurate classification of individual tree species in mining-affected areas is therefore essential for assessing ecological conditions and establishing a scientific foundation for targeted restoration and sustainable management. To address this need, an evaluated machine learning framework was developed and evaluated for individual tree species classification in a coal mining area of the Yellow River Basin using integrated unmanned aerial vehicle (UAV) data. A comprehensive feature set was constructed by extracting 278 attributes per tree. These attributes included 224 spectral bands and 29 hyperspectral indices derived from hyperspectral imagery, 24 textural metrics obtained from RGB orthophotos, and one canopy height feature generated from a LiDAR-derived model. Based on ground-truth data from 1095 individual trees, seven machine learning algorithms were trained and systematically compared: Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and XGBoost. Statistical significance testing using 5 × 5 repeated cross-validation, together with the Friedman test and post hoc Nemenyi test, and additional model stability analysis consistently identified XGBoost as the optimal classifier. On an independent test set, XGBoost achieved high accuracy (Overall Accuracy = 0.897, Kappa = 0.811) with an efficient training time of 2.36 s. Further analysis demonstrated the critical and complementary roles of hyperspectral and structural features in species discrimination. The optimized model was subsequently applied to generate a detailed wall-to-wall tree species map across the entire mining area. Overall, this study presents a statistically informed comparison of classifiers for multi-source feature-based species discrimination and delivers an evaluated and practical pipeline for effective vegetation monitoring. The proposed framework provides a scientific tool for assessing and managing ecological recovery in complex mining environments, particularly within ecologically sensitive regions such as the Yellow River Basin. Full article

This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation ${\mathrm{R}}^2$ of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation ${\mathrm{R}}^2$ was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s ${\mathrm{k}\mathrm{m}}^2$) using freely available satellite imagery and open-source machine learning frameworks. Full article

Rice undergoes rapid growth and exhibits a high demand for nutrients during the tillering and booting stages. SPAD readings, which reflect relative leaf chlorophyll status, and leaf nitrogen content (LNC) are key indicators of plant nutritional status, directly influencing photosynthetic efficiency and biomass accumulation, while plant height (PH) reflects canopy structure and nutrient availability. Establishing quantitative relationships among these traits at key growth stages is essential for stage-specific precision rice management. In this study, Unmanned Aerial Vehicle (UAV) hyperspectral imagery and ground-truth measurements of SPAD, LNC, and PH were collected from rice fields in Qingbaijiang District, Chengdu, China. Twelve vegetation indices (VIs) were calculated, and three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR)—were employed to develop stage-specific retrieval models. A stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages. The optimal models for SPAD readings and LNC achieved R² values of 0.916 and 0.936, respectively. The results indicate that integrating canopy structural information with hyperspectral features can improve the estimation accuracy of SPAD-related chlorophyll indicators and nitrogen status in rice. Under the controlled field conditions of this study, the proposed framework provides a plot-scale proof-of-concept demonstration for UAV-based stage-specific nitrogen monitoring. Full article

With the increasing availability of satellite imagery and the shortening revisit intervals, efficiently processing satellite hyperspectral images has become a critical task. However, in practice, a large portion of satellite hyperspectral data remains unlabeled, making it difficult to achieve satisfactory classification performance using satellite data alone. Meanwhile, UAV-based platforms offer acquisition flexibility, which facilitates the collection of rich and detailed information. To address these challenges, this paper proposes a method called Sharpness-Aware Minimization with Local-to-Global Feature Enhancement (SAMLFE), which uses UAV hyperspectral images for training to enhance the fine-grained classification performance of satellite hyperspectral images in large scenes. Specifically, a spectral dimension mapping model is first employed to unify UAV and satellite images into a common spectral dimension, thereby mitigating the impact of inconsistent feature representations. Next, a local-to-global feature extraction network is constructed to capture both local details and global semantics. Few-shot learning is applied to extract discriminative features from both the source and target domains within the shared feature space, thereby enhancing the model’s ability to utilize limited labeled data efficiently. Furthermore, a conditional adversarial domain adaptation strategy is adopted to align the feature distributions of the source and target domains, thereby alleviating spectral shift. Meanwhile, the integration of an improved Sharpness-Aware Minimization (ISAM) enhances the model’s robustness across domains. Finally, the K-Nearest Neighbor algorithm is employed to perform accurate classification. Experimental results on multiple datasets demonstrate that the proposed method achieves superior generalization and classification performance in cross-domain hyperspectral image classification. It also outperforms existing methods in terms of feature distribution alignment, robustness of feature extraction, and adaptability to small-sample scenarios. Full article