Search Results (1,568)

The nutritional and commercial value of lettuce (Lactuca sativa L.) is determined by its foliar pigment and phenolic composition, which varies among cultivars. This study aimed to assess the capacity of hyperspectral and applied multispectral imaging, combined with machine learning algorithms, to predict and map key biochemical traits, such as chloroplastidic pigments (chlorophylls and carotenoids) and extrachloroplastidic pigments (anthocyanins, flavonoids, and phenolic compounds). Eleven cultivars exhibiting contrasting pigmentation profiles were grown under controlled greenhouse conditions, and their chlorophyll a and b, carotenoid, anthocyanin, flavonoid, and total phenolic contents were evaluated. Spectral reflectance data were acquired via a Headwall hyperspectral sensor and a MicaSense multispectral sensor, and the pigment contents were quantified via solvent extraction and a UV microplate reader. We developed predictive models via seven machine learning approaches, with partial least squares regression (PLSR) and random forest (RF) emerging as the most robust algorithms for pigment estimation. Chlorophyll a and b are highly and positively correlated (r > 0.9), which is consistent with their hyperspectral reflectance imaging results. The hyperspectral data consistently outperformed the multispectral data in terms of predictive accuracy (e.g., R² = 0.91 and 0.76 for anthocyanins and flavonoids via RF) and phenolic compounds with R² = 0.79, capturing subtle spectral features linked to biochemical variation. Spatial maps revealed strong genotype-dependent heterogeneity in pigment and phenolic distributions, supporting the potential of this approach for cultivar discrimination and pigment phenotyping. These findings demonstrate that hyperspectral imaging integrated with data-driven modelling offers a powerful, nondestructive framework for the biochemical monitoring of leafy vegetables, supporting breeding, precision agriculture, and food quality assessment. Full article

Fuel Moisture Content (FMC) is a critical indicator for assessing forest fire risk and formulating early warning strategies, as its spatiotemporal dynamics directly influence the accuracy of fire danger rating. To improve the accuracy of forest FMC estimation, this study proposes an innovative deep learning method integrating multi-source remote sensing data. By combining the global feature extraction capability of the Transformer architecture with the local temporal modeling advantages of Gated Recurrent Units (GRU) (referred to as the Transformer-GRU model), a high-precision FMC estimation framework is established. The study focuses on forested areas in California, USA, utilizing ground-measured FMC data alongside multi-source remote sensing datasets from MODIS, Sentinel-1, and Sentinel-2. A systematic comparison was conducted among Transformer-GRU model, standalone Transformer models, single GRU models, and two classical machine learning models (Random Forest, RF, and Support Vector Regression, SVR). Additionally, forward feature selection was employed to evaluate the performance of different models and feature combinations. The results demonstrate that (1) All models effectively utilize the derived features from multi-source remote sensing data, confirming the significant enhancement of multi-source data fusion for forest FMC estimation; (2) The Transformer-GRU model outperforms other models in capturing the nonlinear relationship between FMC and remote sensing data, achieving superior estimation accuracy (R² = 0.79, MAE = 8.70%, RMSE = 11.44%, rRMSE = 12.60%); (3) The spatiotemporal distribution patterns of forest FMC in California generated by the Transformer-GRU model align well with regional geographic characteristics and climatic variability, while exhibiting a strong relationship with historical wildfire occurrences. The proposed Transformer-GRU model provides a novel approach for high-precision FMC estimation, offering reliable technical support for dynamic forest fire risk early warning and resource management. Full article

(1) Background: Monilinia and Botrytis blight are significant diseases affecting wild blueberry fields, leading to substantial yield losses. Traditional methods for disease assessment rely on destructive sampling, which is labor-intensive and subjective. This study explored the use of multispectral and hyperspectral sensors through simple and machine learning approaches to detect and assess Monilinia and Botrytis blight diseases. (2) Methods: In this study, we adopted two experimental approaches: plot and patch assessment trials. These were conducted using a randomized complete block design at three locations in Nova Scotia. Disease detection was performed using vegetative indices (VIs) and spectral reflectance analysis, with destructive samples also assessed. Analysis of variance, correlations and classification approaches were used in the analysis. (3) Results: Significant spectral differences were observed between healthy and diseased plants, particularly in the near-infrared region (715–1050 nm). Nine significant wavelength bands were identified for blight disease detection. Classifier analysis revealed that support vector machines (SVM) and random forests (RF) outperformed k-nearest neighbors (KNN), achieving an overall accuracy of 96.6% and 76.8% in the broad and severity disease level classifications. (4) Conclusions: Despite some limitations, these findings underscore the potential of remote sensing tools for efficient, non-destructive disease management in wild blueberry fields. Full article

This study presents a novel, fully passive radiofrequency (RF)-based localization system designed to detect the position of a robotic hand on a flat surface within its tactile range, particularly in scenarios where other sensing systems may face limitations. The system employs U-shaped, chipless resonator tags printed on the surface using a customized conductive ink, together with a coplanar RF probe integrated into the robotic hand, to determine position through impedance variations. Unlike conventional approaches, the proposed method provides a compact, low-cost, and robust solution that is resilient to variations in lighting, dust, and other environmental conditions. The resonator tags are arranged in a structured grid inspired by a Sudoku pattern, enabling both position and orientation detection in the near-field region. The system is fabricated on 3D-printed flexible substrates using a flexible and stretchable conductive ink, and its performance is validated through both electromagnetic simulations and experimental measurements. The results confirm that the proposed approach enables accurate and repeatable two-dimensional localization of the robotic hand under various configurations. This work introduces a scalable, high-precision, and vision-independent sensing platform with strong potential for robotic manipulation in challenging environments. Full article

The distribution of iron oxide (FeO) across the lunar surface is a key parameter for reconstructing the Moon’s geological evolution and evaluating its in situ resource potential for future exploration. This study applies a spectral-based approach to estimate FeO concentrations using remote sensing reflectance data combined with a Random Forest (RF) regression model. The model was trained on a dataset comprising 89 lunar samples from the Reflectance Experiment Laboratory (RELAB) database, supplemented with compositional data from Apollo samples available via the Lunar Sample Compendium and reflectance spectra from the Clementine mission. Spectral data spanning the visible to shortwave infrared range (415–2780 nm) were analysed, with diagnostic absorption features centred around 950 nm, typically associated with Fe²⁺. Model validation was conducted against FeO estimates from independent nearside locations not included in the training set, as reported by an external remote sensing study. The trained model was also applied to produce a new global FeO abundance map, demonstrating strong spatial consistency with recent high-resolution reference datasets. These results confirm the model’s predictive accuracy and support the use of legacy multispectral data for large-scale lunar geochemical mapping. This work highlights the potential of combining machine learning techniques, such as Random Forest, with remote sensing data to enhance lunar surface composition analysis, supporting the planning of future exploration and resource utilisation missions. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

Maize (Zea mays L.) is a key global cereal crop with significant relevance to food security. Maize yield prediction is challenged by cultivar diversity and varying management practices. This preliminary study was conducted at Youyi Farm, Heilongjiang Province, China. Three maize cultivars (Songyu 438, Dika 1220, Dika 2188), two fertilization rates (700 and 800 kg·ha⁻¹), and three planting densities (70,000, 75,000, and 80,000 plants·ha⁻¹) were evaluated across 18 distinct cropping treatments. During the V6 (Vegetative 6-leaf stage), VT (Tasseling stage), R3 (Milk stage), and R6 (Physiological maturity) growth stages of maize, multi-temporal canopy spectral images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor. In situ measurements of key agronomic traits, including plant height (PH), stem diameter (SD), leaf area index (LAI), and relative chlorophyll content (SPAD), were conducted. The optimal vegetation indices (VIs) and agronomic traits were selected for developing a maize yield prediction model using the random forest (RF) algorithm. Results showed the following: (1) Vegetation indices derived from the red-edge band, particularly the normalized difference red-edge index (NDRE), exhibited a strong correlation with maize yield (R = 0.664), especially during the tasseling to milk ripening stage; (2) The integration of LAI and SPAD with NDRE improved model performance, achieving an R² of 0.69—an increase of 23.2% compared to models based solely on VIs; (3) Incorporating SPAD values from middle-canopy leaves during the milk ripening stage further enhanced prediction accuracy (R² = 0.74, RMSE = 0.88 t·ha⁻¹), highlighting the value of vertical-scale physiological parameters in yield modeling. This study not only furnishes critical technical support for the application of UAV-based remote sensing in precision agriculture at the field-plot scale, but also charts a clear direction for the synergistic optimization of multi-dimensional agronomic traits and spectral features. Full article

Accurate monitoring of canopy nitrogen content in wolfberry (Lycium barbarum L.) is essential for optimizing fertilization management, improving crop yield, and promoting sustainable agriculture. However, the sparse, architecturally complex canopy of this perennial shrub—featuring coexisting branches, leaves, flowers, and fruits across maturity stages—poses significant challenges for canopy spectral-based nitrogen assessment. This study integrates methods across canopy spectral acquisition, transformation, feature spectral selection, and model construction, and specifically explores the potential of hyperspectral remote sensing, integrated with spectral mathematical transformations and machine learning algorithms, for predicting canopy nitrogen content in wolfberry. The overarching goal is to establish a feasible technical framework and predictive model for monitoring canopy nitrogen in wolfberry. In this study, canopy spectral measurements are systematically collected from densely overlapping leaf regions within the east, south, west, and north orientations of the wolfberry canopy. Spectral data undergo mathematical transformation using first-derivative (FD) and continuum-removal (CR) techniques. Optimal spectral variables are identified through correlation analysis combined with Recursive Feature Elimination (RFE). Subsequently, predictive models are constructed using five machine learning algorithms and three linear regression methods. Key results demonstrate that (1) FD and CR transformations enhance the correlation with nitrogen content (max correlation coefficient (r) = −0.577 and 0.522, respectively; p < 0.01), surpassing original spectra (OS, −0.411), while concurrently improving model predictive capability. Validation tests yield maximum R² values of 0.712 (FD) and 0.521 (CR) versus 0.407 for OS, confirming FD’s superior performance enhancement. (2) Nonlinear machine learning models, by capturing complex canopy-light interactions, outperform linear methods and exhibit superior predictive performance, achieving R² values ranging from 0.768 to 0.976 in the training set—significantly outperforming linear regression models (R² = 0.107–0.669). (3) The Random Forest (RF) model trained on FD-processed spectra achieves the highest accuracy, with R² values of 0.914 (training set) and 0.712 (validation set), along with an RPD of 1.772. This study demonstrates the efficacy of spectral transformations and nonlinear regression methods in enhancing nitrogen content estimation. It establishes the first effective field monitoring strategy and optimal predictive model for canopy nitrogen content in wolfberry. Full article

Accurately estimating aboveground carbon storage (AGC) of urban vegetation remains a major challenge, due to the heterogeneity and vertical complexity of urban environments, where traditional forest-based remote sensing models often perform poorly. This study integrates multimodal remote sensing data and incorporates two three-dimensional structural features—mean tree height ($H_{mean}$) and canopy cover ratio (CCR)—in addition to conventional spectral and textural variables. To minimize redundancy, the Boruta algorithm was applied for feature selection, and four machine learning models (SVR, RF, XGBoost, and CatBoost) were evaluated. Results demonstrate that under multimodal data fusion, three-dimensional features emerge as the dominant predictors, with XGBoost using Boruta-selected variables achieving the highest accuracy (R² = 0.701, RMSE = 0.894 tC/400 m²). Spatial mapping of AGC revealed a “high-aggregation, low-dispersion” pattern, with the model performing best in large, continuous green spaces, while accuracy declined in fragmented or small-scale vegetation patches. Overall, this study highlights the potential of machine learning with multi-source variable inputs for fine-scale urban AGC estimation, emphasizes the importance of three-dimensional vegetation indicators, and provides practical insights for urban carbon assessment and green infrastructure planning. Full article

Aboveground biomass (AGB) serves as a crucial indicator for assessing vegetation carbon sequestration capacity. While AGB levels vary significantly across different vegetation types and regions, the spatial distribution of AGB for specific wetland communities remains poorly characterized. To address this, we integrated field-collected data with Sentinel-2 spectral bands and remote sensing indices, employing random forest (RF) regression and Backpropagation Neural Network (BPNN) for AGB modeling. Through comparative evaluation of their inversion performance, the optimal model was selected to estimate vegetation AGB in the Nanji Wetland. By incorporating wetland classification data, we further generated spatial distribution maps of AGB for four dominant vegetation types during the dry season. The main findings are as follows. Important variables for the RF model included spectral bands B12, B11, B3, B2, B9, B1, B8, B6, and B4 and the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI), Kernel Normalized Difference Vegetation Index (KNDVI), and Simple Ratio Index (SR). RF demonstrated significantly higher predictive accuracy (R² = 0.945, RMSE = 109.205 g·m⁻²) compared to the BPNN (R² = 0.821, RMSE = 176.025 g·m⁻²). The total estimated AGB reached 4.03 × 10⁹ g; Carex spp. dominated AGB accumulation (1.49 × 10⁹ g), followed by P. australis spp. (6.69 × 10⁸ g), M. lutarioriparius spp. (4.60 × 10⁸ g), and Polygonum spp. (3.61 × 10⁸ g). The AGB exhibited a clear spatial gradient, decreasing from higher-elevation lakeshore areas towards the central lake. The results provide detailed spatial quantification of AGB stocks across dominant vegetation types, revealing distinct spatial characteristics and interspecies variations in AGB. This study offers a valuable baseline and methodological framework for monitoring wetland carbon dynamics. Full article

Crop health assessment is essential for the early detection of nutrient deficiencies, diseases, and pests, allowing for timely interventions that optimize yield, reduce losses, and support sustainable agricultural practices. While traditional methods and satellite-based remote sensing offer broad scale monitoring, they often suffer from coarse spatial resolution, and insufficient precision at the plant level. These limitations hinder accurate and dynamic assessment of crop health, particularly for high-resolution applications such as nutrient diagnosis during different crop growth stages. This study addresses these gaps by leveraging high-resolution UAV (Unmanned Aerial Vehicle) imagery to monitor the health of paddy crops across multiple temporal stages. A novel methodology was implemented to assess the crop health condition from the predicted Above-Ground Biomass (AGB) and essential macro-nutrients (N, P, K) using vegetation indices derived from UAV imagery. Four machine learning models were used to predict these parameters based on field observed data, with Random Forest (RF) and XGBoost outperforming other algorithms, achieving high regression scores (AGB > 0.92, N > 0.96, P > 0.92, K > 0.97) and low prediction errors (AGB < 80 gm/m², N < 0.11%, P < 0.007%, K < 0.08%). A significant contribution of this study lies in the development of decision-making rules based on threshold values of AGB and specific nutrient critical, optimum, and toxic levels for the paddy crop. These rules were used to derive crop health maps from the predicted AGB and NPK values. The resulting spatial health maps, generated using RF and XGBoost models with high classification accuracy (Kappa coefficient > 0.64), visualize intra-field variability, allowing for site-specific interventions. This research contributes significantly to precision agriculture by offering a robust, plant-level monitoring approach that supports timely, site-specific nutrient management and enhances sustainable crop production practices. Full article

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics. Full article

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas. Full article

Remote sensing technology provides an efficient and low-cost approach for acquiring large-scale soil information, offering notable advantages for soil organic matter (SOM) mapping. However, in recent years, the bare soil period of cultivated land in Northeast China has significantly shortened, posing serious challenges to traditional SOM prediction and mapping methods that rely on optical imagery. Meanwhile, current approaches that integrate optical imagery, radar imagery, and environmental covariates have yet to fully exploit the potential of remote sensing data in SOM mapping. To address this, this study focuses on the typical black soil region in Northeastern China, acquiring median synthetic images from different time periods (crop sowing, growing, and harvest stages) along with vegetation and radar indices. Six data groups were created by integrating environmental covariate data. Four machine learning models—XGBoost, BRT, ET, and RF—were used to analyze the SOM prediction accuracy of different groups. The group and model with the highest prediction accuracy were selected for SOM mapping in cultivated land. The results show that: (1) in the same model, incorporating radar images and their related indices significantly improves SOM prediction accuracy; (2) when using four machine learning models for SOM prediction, the RF model, which integrates optical images, radar images, vegetation indices, and radar indices from the crop sowing and growing periods, achieves the highest accuracy (R² = 0.530, RMSE = 6.130, MAE = 4.822); (3) in the optimal SOM prediction model, temperature, precipitation, and elevation are relatively more important, with radar indices showing greater importance than vegetation indices; (4) uncertainty analysis and accuracy verification at the raster scale confirm that the SOM mapping results obtained in this study are highly reliable. This study made significant progress in SOM prediction and mapping by employing a radar–optical image fusion strategy combined with crop growth information. It helped address existing research gaps and provided new approaches and technical solutions for remote sensing-based SOM monitoring in regions with short bare soil periods. Full article