Mixture Model Research Articles

INS/UWB Fusion Localization Algorithm in Indoor Environment Based on Variational Bayesian and Error Compensation

Localization technology is crucial for indoor robot navigation. However, because of the intricacies inherent in the indoor setting, the signal transmission is vulnerable to the interference of obstacles, which leads to the decline of positioning accuracy. Ultra-Wideband (UWB) has the characteristics of channel insensitivity and high localization accuracy. Inertial navigation system (INS) functions independently as a navigation system, and its positioning results will not be affected due to non-line-of-sight (NLOS) interference. When using UWB t locate the mobile node, the Variational Bayesian Gaussian Mixture Model (VBGMM) clustering algorithm based on Gaussian Mixture Model (GMM) is applied to lessen the influence of NLOS propagation. This paper proposed a loose coupling of the INS and UWB, which combines the advantages of the two subsystems and improves the performance of the positioning system. On the basis of INS autonomous positioning, the maximum entropy fuzzy generalized probability data association filter (MEF-GPDAF) is used to modify the INS positioning results, and then the virtual inertia points are further built to compensate the error of the corrected coordinates. Finally, Unscented Kalman Filter is applied to the compensated coordinates for enhanced positioning. Simulation indicates that the proposed approach in this paper exhibits superior location accuracy. The real experimental results show that the proposed algorithm achieves an average improvement of 61.12% in positioning accuracy.

Trajectories of depressive symptoms in Korean adults with diabetes: Individual differences and associations with life satisfaction and mortality.

We examined trajectories of depressive symptoms and their predictors in adults with diabetes. We assessed whether these trajectories were related to life satisfaction and mortality. Longitudinal, prospective observational study. We analysed data from 1217 adults with diabetes (aged ≥45 years) in the Korean Longitudinal Study of Aging (2006-2018). Three trajectories of depressive symptomology were identified in growth mixture models: low/stable (i.e., low and stable levels of symptoms; 85.56%), high/decreasing (i.e., high levels of symptoms with a decreasing trajectory; 7.47%), and moderate/increasing (i.e., moderate levels of symptoms with an increasing trajectory; 6.98%). Participants with poor perceived health status at baseline were more likely to be in the moderate/increasing or high/decreasing classes than in the low/stable class. The moderate/increasing class had the lowest satisfaction with quality of life, followed by the high/decreasing and low/stable classes. The moderate/increasing and the high/decreasing classes had lower satisfaction with relationships with spouse and children than the low/stable class. The high/decreasing class had a higher mortality risk than the low/stable class. Long-term monitoring of depressive symptoms in adults with diabetes is warranted given their potential adverse impact on life satisfaction and mortality.

Improved automated parallel implementation of GMM background subtraction on a multicore digital signal processor

<p><span>Scene segmentation is an essential step in a wide range of video processing applications, for instance, object recognition and tracking. The Gaussian mixture model (GMM) for background subtraction (BS) has gained widespread usage in scene segmentation, despite its known computational intensity. To tackle this challenge, we propose a practical solution to accelerate processing through a parallel implementation on an embedded multicore platform. In this paper, we present an improved automated parallel implementation of the GMM algorithm using the Orphan directive provided by open multiprocessing (OpenMP). Experimental assessments conducted on the eight cores of the C6678 digital signal processor (DSP) demonstrate significant advancements in parallel efficiency, particularly when handling high-resolution frames, including high-definition (HD) and full-HD resolutions. The achieved parallel efficiency surpasses the results obtained with classical OpenMP scheduling modes, encompassing dynamic, static, and guided approaches. Specifically, the parallel efficiency reaches approximately 82% for full-HD resolution frames and, 99.3% for low-resolution frames, respectively.</span></p>

On the equivalence of two mixture models for rating data

On the equivalence of two mixture models for rating data

BMI Trajectories and the Influence of Missing Data

Abstract Introduction Body Mass Index (BMI) trajectories have been estimated in various ways. These estimates are important to understand how BMI develops over time and for use in cost-effectiveness analysis. However, missing data is often stated as a limitation in studies that analyse BMI over time and there is little research into how missing data can influence these BMI trajectories. The aim of this study is to determine how much influence missing data can have when estimating BMI trajectories and to explore the effects this has on subsequent analysis. Methods This study uses data from the English Longitudinal Study of Aging. First, a growth mixture model is used to estimate distinct BMI trajectories in adults over the age of 50. Next, methods that assume data is missing at random (MAR) are used: complete case analysis and multiple imputation. Finally, Diggle Kenward and Roy methods that assume data is missing not at random (MNAR) are implemented. Estimated trajectories from each method are then used to predict the risk of developing type 2 diabetes (T2DM) using discrete-time survival analysis. Results Four distinct trajectories are identified using each of the methods to account for missing data: stable overweight, elevated BMI, increasing BMI, and decreasing BMI. However, the likelihoods of individuals following the different trajectories differs between the different methods. Results show that the influence of BMI trajectory on T2DM is reduced after accounting for missing data. More work is needed to understand which methods for missing data are most appropriate and give the most reliable results. Conclusions Missing data can significantly influence estimations of BMI trajectories. When using BMI trajectories to inform cost-effectiveness analysis or policymaking, missing data should be considered. More research is needed to examine the extent to which accounting for missing data might influence the cost-effectiveness of policies, e.g. weight management interventions. Key messages • Missing data is important when modelling BMI trajectories. • More research is needed to examine the extent to which accounting for missing data might influence the cost-effectiveness of policies, e.g. weight management interventions.

Body mass index trajectories from birth to early adulthood and lung function development.

Limited studies have investigated the influence of body mass index (BMI) trajectories on lung function covering the entire growth period. We conducted a prospective study utilizing data from the Swedish BAMSE birth cohort. Latent class mixture modelling was employed to examine the diversity in BMI z-scores from birth to 24 years of age. Participants with four or more BMI z-scores were included (n=3204, 78·4%). Pre-bronchodilator (BD) spirometry was tested at 8, 16, and 24 years, while post-BD spirometry, multiple-breath nitrogen washout (for lung clearance index, LCI), and urinary metabolomics data were assessed at 24 years. Six distinct BMI development groups were identified. Compared to the stable normal BMI group, the accelerated increasing BMI group exhibited reduced pre- and post-BD FEV1/FVC ratio z scores (β=-0·26, 95% CI=[-0·44, -0·08], and -0·22, [ -0·39, -0·05], respectively), along with elevated LCI (0·30, [0·22, 0·42]) at 24 years. The persistent high BMI group demonstrated lower FEV1, and FVC z scores growth between 16 and 24 years (-0.24, [-0.42, -0.05], and -0.27, [-0.45, -0.01], respectively), and elevated LCI (0·20, [0·03, 0·39]) at 24 years. However, those impairments were not observed in the accelerated resolving BMI group. Conversely, the persistent low BMI group displayed persistently decreased FEV1, and FVC from 8 to 24 years, as well as decreased lung function growth. Additionally, histidine-related metabolites were associated with pre- and post-BD FEV1 (hypergeometric FDR=0.008 and <0.001, respectively). Early interventions aiming for normal BMI during childhood may contribute to improved lung health later in life.

A Calculating Method for the Height of Multi-Type Buildings Based on 3D Point Cloud

Building height is a critical variable in urban studies, and the automated acquisition of the precise building height is essential for intelligent construction, safety, and the sustainable development of cities. The building height is often approximated by the building’s highest point. However, the calculation method of the building height of the various roof types differs according to building codes, making it challenging to accurately calculate the height of buildings with complex roof structures or multiple upper appendages. Consequently, this paper utilizes point clouds to propose an automated method for calculating building heights conforming to design codes. The model considers roof types and allows for fast, automated, and highly accurate building height estimation. First, roofs are extracted from the point cloud by combining normal vector density clustering with a region-growing algorithm. Second, combined with variational Bayes, a Gaussian mixture model is employed to segment the roof surfaces. Finally, roofs are classified based on slope characteristics, achieving the automatic acquisition of building heights for various roof types over large areas. Experiments were conducted on Vaihingen and STPLS3D datasets. In the Vaihingen area, the maximum error, root-mean-square-error (RMSE), and mean absolute error (MAE) of the measured heights are 1.92 cm, 1.18 cm, and 1.13 cm, respectively. In the STPLS3D area, these values are 1.79 cm, 0.82 cm, and 0.68 cm, respectively. The results demonstrate that the proposed method is reliable and effective, which offers valuable data for the development, construction, and planning of three-dimensional (3D) cities.

Genetic determinism of sensitivity to environmental challenges using daily feed intake records in three lines of pigs.

In pig breeding, environmental challenges can affect the welfare and productivity of animals. Resilient animals have the capacity to be minimally affected by these environmental challenges. Understanding the genetic basis of sensitivity to these environmental challenges is crucial for selecting more resilient animals, thereby enhancing welfare and productivity. The aims of this study were to: (1) estimate the probability of the occurrence of an unrecorded environmental challenge at a given day using daily feed intake (DFI) data, and (2) evaluate the genetic determinism of environmental sensitivity in three pig lines bred in real selection conditions. Data comprised of 100,799, 186,247, and 304,826 DFI records from 1,618, 2,517, and 3,788 Landrace (LA), Large White (LW) and Piétrain (PI) male pigs, respectively. The pedigree included 3,730, 5,649, and 9,293 animals for LA, LW, and PI, respectively. The probabilities of the occurrence of an unrecorded environmental challenge at a given day were estimated via a mixture model. The probabilities (p) of being "high CV days" were then taken as reference and used in genetic analysis as an environmental descriptor to describe the environment. DFI records were analysed using two linear models: a linear reaction norm animal model (RNAM) and the animal model. (Co)variance components were estimated using average-information restricted maximum likelihood (AI-REML). The means of the probabilities of the occurrence of an environmental challenge for LA, LW, and PI were 0.24, 0.10, and 0.22, respectively, indicating that the probability of an environmental challenge was low for most of the days. The genetic correlations between the intercept and the slope obtained from the RNAM for LA, LW, PI were -0.52, 0.06, and -0.36, respectively. These findings suggest that selecting hypothetically for decreased DFI in non-stressful conditions would result in pigs with increased DFI in stressful conditions in the LA and PI lines, whereas it would have a minor impact on the environmental sensitivity of LW. The proportion of resilient animals for LA, LW, and PI was 75.0, 74.2, and 72.2%, respectively, implying that most of the animals were resilient. The study demonstrated that the slope of DFI is heritable and can effectively be used as an indicator of sensitivity to environmental challenges. These results are valuable in improving the resilience of livestock species to environmental challenges through genetic selection.

The Power of High-precision Broadband Photometry: Tracing the Milky Way Density Profile with Blue Horizontal Branch Stars in the Dark Energy Survey

Blue horizontal branch stars (BHBs), excellent distant tracers for probing the Milky Way’s halo density profile, are distinguished in the g−r0 versus (i − z)0 color space from another class of stars, blue straggler stars. We develop a Bayesian mixture model to classify BHBs using high-precision photometry data from the Dark Energy Survey Data Release 2 (DES DR2). We select ∼2100 highly probable BHBs based on their griz photometry and the associated uncertainties, and we use these stars to map the stellar halo over the Galactocentric radial range 20 kpc ≲ R ≲ 70 kpc. After excluding known stellar overdensities, we find that the number density n ⋆ of BHBs can be represented by a power-law density profile n ⋆ ∝ R −α with an index of α=4.34−0.12+0.13±0.52 , consistent with existing literature values. In addition, we examine the impact of systematic errors and the spatial inhomogeneity on the fitted density profile. Our work demonstrates the effectiveness of high-precision griz photometry in selecting BHBs. The upcoming photometric survey from the Rubin Observatory, expected to reach depths 2–3 mag greater than DES during its 10 yr mission, will enable us to investigate the density profile of the Milky Way’s halo out to the virial radius, unraveling the complex processes of formation and evolution in our Galaxy.

Real-world analysis of levetiracetam-associated rhabdomyolysis: insights from the FDA adverse event reporting system

ABSTRACT Background Levetiracetam, a widely prescribed antiseizure medication, is recognized for its broad-spectrum efficacy, good tolerability, and minimal drug interactions. This study examines the association between levetiracetam and rhabdomyolysis, utilizing real-world data from the FDA Adverse Event Reporting System (FAERS) database to further elucidate its safety profile. Methods This study extracted adverse events related to levetiracetam from the FAERS database (Q1 2013 to Q1 2024). Four types of disproportionality analysis identified rhabdomyolysis as a significant adverse even. Logistic regression assessed risk factors, including gender, age, and severity. A Gaussian Mixture Model analyzed the time-to-onset distribution of rhabdomyolysis, while the impact of concomitant medications on its risk was evaluated using Reporting Odds Ratio (ROR). Results Levetiracetam significantly increased rhabdomyolysis risk (ROR = 13.5). Males showed a higher incidence (OR = 2.60). Most adverse events occurred within the first 30 days, with a bimodal onset distribution. Co-administration of antibiotics, antipsychotics, and PPIs elevated the risk while other antiseizure medications did not. Conclusion This study found a significant association between levetiracetam and the risk of rhabdomyolysis, highlighting the need for increased clinical vigilance in this patient population. Future research should focus on elucidating the underlying mechanisms and optimizing clinical guidelines.

Bayesian mixture model approach to quantifying the empirical nuclear saturation point

Bayesian mixture model approach to quantifying the empirical nuclear saturation point

Model uncertainty quantification of a degradation model of miter gates using normalizing flow-based likelihood-free inference

Degradation modeling is essential for failure prognostics of miter gates and subsequent risk-informed maintenance or operational decision-making. Typically, degradation models are formulated based on certain assumptions and simplifications and may not fully capture the complexity of underlying degradation mechanisms. Even minor errors in such models may lead to significant discrepancies in failure prognostics due to error accumulation over time. Aiming to improve the accuracy of the degradation model and ensure reliable failure prognostics, this article proposes a degradation model updating framework for miter gates using normalizing flow-based likelihood-free inference, accounting for both model parameter uncertainty and model form uncertainty (i.e., model bias). The proposed work consists of two phases: the offline training phase and the online updating phase. During the offline training phase, the unknown model bias term is modeled as a random noise with uncertain standard deviation. Synthetic data are then generated using a physical model based on prior knowledge of the uncertain model parameters and model bias. The synthetic data are utilized to train an inference model, which has a summary network for data compression and a conditional invertible neural network for parameter estimation. In the online updating phase, the trained inference model uses strain observations to obtain posterior samples. The Gaussian mixture model and dual particle filter techniques are utilized to recursively update posterior samples, thereby improving the estimation accuracy. Subsequently, model bias is inversely estimated using the degradation model, which uses the updated model parameters and the gap length from the inference model. Following that, a regression model is constructed to correct the biases inherent in the simplified degradation model. This process is implemented iteratively over time to perform continuous model updating and failure prognostics. Results of a case study demonstrate the efficacy of the proposed framework in reducing both model parameter uncertainty and recovering model biases and thereby improving the accuracy of failure prognostics of miter gates.

Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models

Volatility in financial markets refers to the variation in asset prices over time. High volatility indicates increased risk, making its evaluation essential for effective risk management. Various methods are used to assess volatility, with the standard deviation of log-returns being a common approach. However, this implicitly assumes that log-returns follow a Gaussian distribution, which is not always valid. In this paper, we explore the use of (differential) entropy to evaluate the volatility of financial log-returns. Estimation of entropy is obtained using a Gaussian mixture model to approximate the underlying density of log-returns. Following this modeling approach, popular risk measures such as Value at Risk and Expected Shortfall can also be computed. By integrating Gaussian mixture modeling and entropy into the analysis of log-returns, we aim to provide a more accurate and robust framework for assessing financial volatility and risk measures.

Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy)

The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure’s behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure’s behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and GMMs (Gaussian Mixture Models), are used in order to reliably estimate the system poles, and hence the corresponding vibration characteristics. The performance of the considered methods is compared on the Valgadena bridge case study, showing that the use of GMM clustering reduces, with respect to DBSCAN, the impact of the choice of certain parameter values in the considered case.

The effects of the changes in the depression on suicidal ideation among older adults aged 75 and above before and after the COVID-19

BackgroundThe unprecedented pandemic situation of COVID-19 has had a negative impact on the mental health of many people, especially among the "old-old" older adults who are aged 75 or older. Therefore, the aim of this study is to investigate the changes in depression among "old-old" older adults before and after the onset of COVID-19, and the extent to which depression affects suicidal ideations.MethodThe 12th to 16th Korea Welfare Panel Study(KoWePS) conducted from 2017 to 2021 was used for analysis. For this study, 771 older individuals with complete data to estimate the degree of change of depression were selected as the final analysis subjects.ResultA Growth Mixture Modeling(GMM) analysis was conducted, resulting in the classification of two groups: an increasing group and a decreasing group. The study findings showed that "old-old" older individuals with high levels of depression, specifically those in the decreasing group, may be more susceptible to suicidal ideation. Despite this steep change slope, the decreasing group still exhibited a higher level of depression in 2021 compared to the increasing group. As per characteristics, the decreasing group, which showed a higher prevalence of suicidal ideation, had a higher proportion of women and individuals with lower levels of education, those living alone, and a lower household income compared to the increasing group.ConclusionIt is important to note that although the study emphasized the need to prioritize intervention for the decreasing group with consistently high levels of depression, the majority of individuals belong to the increasing group, which exhibited a gradual increase in depression levels over time. Therefore, intervention plans should be developed concurrently for both groups. Also, it is crucial to implement proactive efforts targeting groups with understandings of these characteristics when establishing preventative measures for depression and suicidal ideation among "old-old" older adults.

A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping.

Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well-known clustering methods: Agglomerative Hierarchical Clustering; K-means; Fuzzy C-means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C-means and K-means had the strongest (κ = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (κ = 0.51) between each other. The K-means showed the best clustering performance, followed by the Fuzzy C-means in most evaluation criteria. Moreover, Fuzzy C-means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C-means, in the application of obstructive sleep apnea phenotyping.

STAD: Ship trajectory anomaly detection in ocean with dynamic pattern clustering

With the continuous increase of maritime traffic, developing an efficient and accurate model for ship trajectory anomaly detection has become crucial for ensuring maritime transportation safety. The high complexity and variability of the marine environment lead to diverse ship trajectory patterns, making it challenging to learn effective trajectory representations for accurately identifying anomalies. We thus proposed an unsupervised deep learning model called STAD for ship trajectory anomaly detection in ocean to address this challenge. Concretely, STAD leverages offset reconstruction-based representation learning and a deep Gaussian Mixture Model (GMM) estimation network to learn the underlying complex clustering patterns of ship trajectories and utilize the learned patterns to enhance trajectory anomaly detection. Extensive experiments on multiple AIS datasets indicate that our model significantly outperforms existing methods in detecting multiple representative types of ship trajectory anomalies, including shift deviation, abnormal heading, and abnormal speeding. This study could help closely monitor the status of ship movement and detect abnormal behaviors in advance, thus benefiting maritime safety.

Interactions between sexual signaling and wing size drive ecology and evolution of wing colors in Odonata

Insect coloration has evolved in response to multiple pressures, and in Odonata (dragonflies and damselflies) a body of work supports a role of wing color in a variety of visual signals and potentially in thermoregulation. Previous efforts have focused primarily on melanistic coloration even though wings are often multicolored, and there has yet to be comprehensive comparative analyses of wing color across broad geographic regions and phylogenetic groups. Percher vs. flier flight-style, a trait with thermoregulatory and signaling consequences, has not yet been studied with regard to color. We used a new color clustering approach to quantify color across a dataset of over 8,000 odonate wing images representing 343 Nearctic species. We then utilized phylogenetically informed Bayesian zero-inflated mixture models to test how color varies with mean ambient temperature, body size, sex and flight-style. We found that wing coloration clustered into two groups across all specimens - light brown-yellow and black-dark brown - with black-dark brown being a much more cohesive grouping. Male perchers have a greater proportion of black-dark brown color on their wings as do species with longer wings. In colder climates, odonates were more likely to have black-dark brown color present, but we found no relationship between the proportion of black and temperature. Light brown-yellow showed similar scaling with wing length, but no relationship with temperature. Our results suggest that black-dark brown coloration may have a limited role in thermoregulation, while light brown-yellow does not have such a role. We also find that the odonate sexes are divergent in wing color in percher species only, suggesting a strong role for color in signaling in more territorial males. Our research contributes to an understanding of complex interactions driving ecological and evolutionary dynamics of color in animals.

Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting

This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to 27.7%, and the reduction in Mean Absolute Percentage Error (MAPE) was up to 45.7% compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to 10.0% compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods.

Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99–100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.