Continuous Wavelet Transform Analysis Research Articles

Identification of damage in timber I-beams using continuous wavelet transform of deflection measured with digital image correlation

The article presents a method for detecting damage in timber I-beams based on the continuous wavelet transform (CWT) of their static deflection, measured using the ARAMIS digital image correlation (DIC) system. So far, no detailed uncertainty analysis for this type of identification has been carried out. Filling this gap was the main motivation for the authors to conduct this research. For this purpose, analytical and experimental static deflection CWT analyses were performed for the timber I-beams with softwood flanges and oriented strand board webs, in which local cross-sectional weakenings were artificially introduced for verification purposes. A detailed analysis of the CWT uncertainty resulting from measurement errors and numerical data processing allowed its expression as a function of the beam axis position and the scale parameter. Based on the research, a new methodology was formulated for the diagnostic defect identification in timber beams related to bending stiffness reduction.

Continuous Wavelet Transform Analysis of Climate Variability, Resiliency, and Restoration Strategies in Mesohaline Tidal Creeks

This research article employs the continuous wavelet transform analysis to identify the climatological effects among various water quality parameters to identify the successfulness of upland stream restoration on the receiving mesohaline tidal creeks. Estuaries and their corresponding tidal creeks have been impacted by human anthropogenic influences for decades, allowing a variety of restoration practices to be implemented in upland streams. In the face of climate variability and continuous human development pressures, this research performs statistical analysis and a wavelet coherence on, before, and after stream restoration for water quality changes in Chesapeake Bay’s tidal tributaries in the Lower Western Shore to identify if the restoration strategies have been effective in the mesohaline tidal creeks. Statistical analysis showed that currently, the receiving tidal basins are not seeing the required positive improvements in water quality after years of upland stream restoration. Compounding this is the fact climate variability cannot be ignored. Results indicate that the North Atlantic Oscillation (NAO) has significant wavelet coherence with bottom dissolved oxygen, precipitation, and nutrients. This suggests that current restoration efforts may not be able to keep up with climate variability, and other techniques (restoration or policies) may need to be implemented.

The Impact of Climate Change on Road Traffic Crashes in Ghana.

Despite the substantial injuries and fatalities from Road Traffic Crashes (RTCs), evidence of climate change's impact on RTCs in Ghana is lacking. This study assessed the impact of climate change on RTCs in Ghana by combining quantitative (Mann-Kendall trend tests, Continuous Wavelet Transform analysis, causal inference analysis) and qualitative (15 key stakeholder interviews) methods. The quantitative analysis employed monthly rainfall and temperature data (1991-2021) alongside RTC data (1998-2021) across 10 regions. While rainfall trends varied regionally, the wet season (April through mid-October) showed a strong link to crash severity for all regions across Ghana. Wavelet analysis showed higher crash severity in the wet season within every 2-8 months period in a particular annual year during the study period. Causal inference analysis revealed rainfall's stronger influence (3.59%) on fatal crashes during the wet season compared to temperature (0.04%). Key stakeholder interviews highlighted perceived changes in temperature and intense rainfall patterns affecting RTCs, especially during rainy seasons suggesting an association between increased rainfall and crash severity. These findings emphasize the multifaceted role of climate change on road safety and the need to address weather-specific risks.

Mechanism-driven improved SVMD: an indirect approach for rail corrugation detection using axle box acceleration

Abstract Addressing the challenge of the current inability to qualitatively identify rail corrugation damage accurately from axle box acceleration data, this study proposes a novel approach. To indirectly identify rail corrugation from axle box acceleration, we introduce an improved successive variational mode decomposition (SVMD) algorithm, coupled with a deep learning model for corrugation recognition. First, a numerical model of the vehicle-rail-track slab system is established, considering rail corrugation. Indicator analysis is integrated into the SVMD. The improved SVMD is employed to decompose and reconstruct axle box acceleration, achieving noise reduction and extraction of useful components. Next, we apply the fast Continuous Wavelet Transform analysis method to effectively transform one-dimensional data into two-dimensional images. Finally, the You Only Look Once (YOLO) model serves as a classifier for the classification and recognition of corrugation with different wavelengths. The results demonstrate that the mechanism-driven improved SVMD effectively extracts corrugation components from axle box acceleration, while the YOLO model achieves rapid and efficient identification and classification of corrugation with different wavelengths. The results show that compared with other traditional models, the training time of the YOLO model is 60%–90% of the training time of the traditional algorithm, and the Recall rate is 1.15–1.45 times that of the traditional algorithm. In terms of wavelength identification, the YOLO model has a recognition rate of 98% for different wavelengths. The proposed approach offers an innovative and efficient solution for identifying rail corrugation damage in axle box acceleration data.

Drone-Borne Magnetic Gradiometry in Archaeological Applications.

The use of magnetometers arranged in a gradiometer configuration offers a practical and widely used solution, particularly in archaeological applications where the sources of interest are generally shallow. Since magnetic anomalies due to archaeological remains often have low amplitudes, highly sensitive magnetic sensors are kept very close to the ground to reveal buried structures. However, the deployment of Unmanned Aerial Vehicles (UAVs) is increasingly becoming a reliable and valuable tool for the acquisition of magnetic data, providing uniform coverage of large areas and access to even very steep terrain, saving time and reducing risks. However, the application of a vertical gradiometer for drone-borne measurements is still challenging due to the instability of the system drone magnetometer in flight and noise issues due to the magnetic interference of the mobile platform or related to the oscillation of the suspended sensors. We present the implementation of a magnetic vertical gradiometer UAV system and its use in an archaeological area of Southern Italy. To reduce the magnetic and electromagnetic noise caused by the aircraft, the magnetometer was suspended 3m below the drone using ropes. A Continuous Wavelet Transform analysis of data collected in controlled tests confirmed that several characteristic power spectrum peaks occur at frequencies compatible with the magnetometer oscillations. This noise was then eliminated with a properly designed low-pass filter. The resulting drone-borne vertical gradient data compare very well with ground-based magnetic measurements collected in the same area and taken as a control dataset.

Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets

Climate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent variability of ecosystem frequencies. We applied our method to centred and standardized MODIS NDVI time series for the period 2000–2021, using an experimental field station in northern Patagonia as a case study. By performing a continuous wavelet transform on the data for each pixel, we obtained instantaneous power spectra, capturing variability across different dates and pixels. These spectrums were then consolidated into a comprehensive database, and subsequently classified using archetypal analysis. We identified a convex combination of archetypal spectrums that best represented the entire power spectrum database. Mapping the resulting archetypes and their weights in both space and time allowed us to explore pixels' variations in archetype weights in relation to factors such as time, topography, and climate. In addition, to examine the potential relationship between the NDVI time series and climate drivers, we computed the Average Cross-Wavelet Power Spectrum (ACWPS) to different climatic indices. Three archetypes were sufficient to explain the majority of power spectrum variability in the study area. These archetypes exhibited distinctive characteristics: 1) medium-frequency variability (2–4 years), 2) low-frequency variability (>4 years), and 3) an annual (i.e. seasonal) cycle with low-frequency variability. Spatially, the first two archetypes were predominantly observed in highland steppes, while the third archetype prevailed in lowland areas associated with meadows. At the beginning of the studied period, Archetypes 1 and 3 dominated, but after the Puyehue-Cordón Caulle Volcanic Complex eruption in 2011 their prominence diminished, and Archetype 2 became more prevalent in the whole study area. Finally, all three NDVI series representative of archetypes showed a relative peak at approximately four years, which could be linked to the Indian Ocean Dipole variability. These results highlight an abrupt shift in the system's behaviour, primarily related to changes in variability distribution rather than mean values. This disturbance-induced transition aligns with the theory of state and transitions in ecological system dynamics. We propose that the states in this model are not fixed but represent alternative dynamic behaviours, akin to different types of limit cycles. Consequently, employing a wavelet analysis-based classification method provides a robust means of studying and understanding such variability and transitions, thereby offering clarity and comprehension of ecosystem states. Notably, this methodology proves particularly effective for large databases of detailed time series.

Demonstrating the Potential of Low-Cost GNSS Receiver for tidal monitoring, storms, and flood detecting: example of 2022 Noru Storm in Thua Thien Hue province, Vietnam

Extreme hydrological events such as tsunamis, high tides, or storm surges seriously threaten coastal communities. These events result in flooding, property damage, loss of life, and long-term economic and social impacts. Therefore, monitoring and detecting extreme hydrological events significantly affect coastal areas in disaster response efforts. However, the cost of installing and maintaining these stations can be a significant challenge for developing countries. The objective of this study is to use a low-cost GNSS receiver to monitor tides and detect extreme coastal hydrological phenomena by analyzing changes in water level, using analysis of the signal-to-noise ratio (SNR) data. Data used in this study were collected from a GNSS station located in the Tam Giang Lagoon area, Thua Thien Hue, Vietnam, from September to October 2022. The water level based on GNSS-R is compared with the sensor's measured water level, with the Pearson correlation coefficient reaching 0.96 and RMSE of 0.08m. Continuous Wavelet Transform analysis demonstrated the relationship between water levels and extreme hydrological events. The results show that distinct signatures in the data correspond to the Noru typhoon from September 27-29, 2022, and the inundation from October 14-19, 2022, in Thua Thien Hue. This information is the basis for forecasting and early warning of extreme events and informing disaster response and management efforts.

Analysis of Cosmic Ray Fluxes at Different Stations during Geomagnetic Storms using Wavelet Based Approaches: Continuous Wavelet Transform and Multi-Resolution Analysis

Analysis of Cosmic Ray Fluxes at Different Stations during Geomagnetic Storms using Wavelet Based Approaches: Continuous Wavelet Transform and Multi-Resolution Analysis

Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends

Since December 2019 we have been living with a virus called SARS-CoV-2 which has led to health policies being given prevalence over economic ones, causing serious consequences with regard to China's economic growth. For this purpose, we have built a Real Time Leading Economic Indicator based on Google Trends that improves the performance of Composite Leading Indicators (CLIs) to anticipate GDP trends and turning points for the Chinese economy. First, we assess the effectiveness of this new leading indicator relative to China's GDP by analyzing its statistical properties. We use fractional integration techniques to show the high degree of persistence of the new Real Time Leading Economic Indicator (RT-LEI) for China. Second, we observe the same relationship between GDP and RT-LEI in the long term using a Fractional Cointegration VAR (FCVAR) model. Third, we use a multivariate Continuous Wavelet Transform analysis to show which leading indicator best fits GDP and to identify when a structural change occurs. Finally, we forecast, using Artificial Neural Networks and a KNN model based on Machine Learning, our RT-LEI predicting the conclusion of a bearish scenario, after which the recovery begins in mid-2022.

Long short‐term memory model for predicting groundwater level in Alabama

AbstractGroundwater serves as a primary source of public‐water and agricultural supply in many areas of Alabama, in particular during drought periods. Long‐term climatic models for the southeastern United States indicate that the region will be subjected to more intense and more frequent precipitation events, with no overall change in the amount of precipitation, resulting in increased runoff and reduced aquifer recharge. Reliable prediction of groundwater levels would be beneficial to water resources decision makers and stakeholders especially for time‐sensitive decisions. This paper uses a compound application of continuous wavelet transform (CWT) analysis and long short‐term memory (LSTM) framework to address the major question with regards to groundwater level: “how long does it take for groundwater to respond to major precipitation events and what is the magnitude of the response?” CWT analysis is used to answer the “how long” part in this question, while the LSTM is used to answer the “what is the magnitude” part of the question. The insights from CWT analysis related to the short‐term and long‐term response in groundwater level were used to set the parameters of the LSTM model. The LSTM model uses daily groundwater levels, precipitation, and maximum/minimum temperatures as input data. The model was able to provide predictions within a 95% confidence interval of actual groundwater levels. The findings of this study suggest a workflow for groundwater level forecasting in the wells of Alabama given a minimum amount of easy‐to‐measure and widely available data.

Side scan sonar data quantification for seabed classification in Yos Sudarso Bay, Jayapura

Side Scan Sonar (SSS) is an instrument that is usually used in surveys to perform seabed imaging. SSS was able to show two-dimensional images of the seabed with contour, topographic, and target conditions simultaneously. This technology is capable of distinguishing seabed types such as rocks, sand, or others. In addition, the usefulness of this technology can also detect the existence of marine pipes and cables, shallow structures of the seabed, dredges, environmental studies, military, archaeology, fisheries, and mining. The aims of this research were to identify and interpret the seabed, measure the acoustic backscatter (dB) and amplitude (mV) of the seabed substrate, and compute SSS data using Continuous Wavelet Transform (CWT) in the Yos Sudarso Bay, Jayapura. Processing data was performed by radiometric Beam Angle Correction (BAC), Automatic Gain Control (AGC), and geometric (bottom tracking) corrections using SonarWiz 7 software. Pre-processed SSS data were then converted to XtfToSegy and the amplitude values were exported by SeiSee software, and CWT was calculated using Matlab. Amplitude data was visualized using Microsoft Excel to display a relation between amplitude, time, and CWT graph. The results of seabed detection were three areas of sand and one coral target. The amplitude value of sand 1 ranged from 4319 to 30976 mV with the = backscatter value was -17.10 dB. The amplitude value of sand 2 ranged from 1411 to 12288 mV with the backscatter value was -18.79 dB. The amplitude value of sand 3 ranged from 1391.625 to 12032 mV with the backscatter value was -18.70 dB. The amplitude of the coral ranged from 24960 to 32640 mV with the backscatter value was -2.30 dB. The CWT analysis results show that the yellow-coloured coral’s energy represents a rougher and harder object. The energy generated from the blue-coloured sand represents the object has smooth and non-hardening characteristics.

Impact of Transverse Acoustic Excitation and Air Swirl on a Confined Hollow Cone Sheet

Abstract This study investigates the effect of imposing symmetric transverse acoustic excitation and counter-rotating air swirl flow on a confined hollow cone spray sheet. We study the impact of these forcing conditions on two unstable modes—sheet flapping and sinuous wave growth modes—and their phasic relationship. We used the shadowgraphy technique, and only the sheet edges were analyzed using the method of snapshot proper orthogonal decomposition (POD). In this study, we used postprocessing techniques to estimate the nondimensional breakup length, phase averaged cone angle, and continuous wavelet transform (CWT) frequency. Results show that imposing acoustic excitation alone causes the sheet edges to flap, axisymmetrically, at the pressure antinode, while at the pressure node and base flow condition, the asymmetric sinuous wave mode is dominant. At the pressure intermediary, we find the possible dominance of both modes. While the dominant effect of air swirl alone was difficult to ascertain, results from CWT analysis show that POD mode 1 and mode 3 have very similar spectral content, with mode 3 showing a flapping mode. Finally, air swirl flow has a more dominant effect than acoustics in causing sheet breakup.

Simplified Muscle-Recruitment Strategy During Walking in Parkinson's Disease People: A Time-Frequency Analysis of EMG Signal

ObjectiveAlthough gait analysis has been widely adopted to describe Parkinson's disease (PD) dysfunctions during walking, few efforts have been made to understand muscle activity role. The current study aims to characterize lower-limb-muscle recruitment during walking in time-frequency domain, based on Continuous Wavelet Transform (CWT) analysis of surface-electromyography (sEMG) signal from lower-limb muscles. Materials and methodssEMG signals from Tibialis Anterior (TA), Gastrocnemius Lateralis (GL), Rectus Femoris (RF), and Biceps Femoris (BF) of 20 people with PD and 10 age-matched healthy controls (HC) were acquired during gait. sEMG signals were processed applying a CWT-based approach to assess the occurrence frequency (OF, i.e., the percentage of strides of each muscle activation occurrence) and the frequency content of each muscle activation (in Hz). These parameters are rarely quantified in PD. ResultsCompared to HC, people with PD displayed a significant decrease (p<0.05) in median OF on RF, BF, and TA, indicating a tendency to reduce the global involvement of lower-limb muscles. No significant differences (p>0.05) in OF were detected among muscle within the same population. No significant changes (p>0.05) in frequency content were revealed in PD. ConclusionThis analysis suggests that people with PD are characterized by a reduced recruitment of those muscles typically adopted to finely control body-segment motion and a concomitant increased recruitment of those muscles mainly involved in locomotion. No substantial alteration in recruiting muscle fibers is associated with PD. These findings suggest that people with PD are inclined to adopt simpler muscular-recruitment strategies during walking, compared to HC.

Time-Resolved Electrochemical Impedance Spectroscopy of Stochastic Nanoparticle Collision: Short Time Fourier Transform versus Continuous Wavelet Transform.

This work demonstrates the utilization of short-time Fourier transform (STFT), and continuous wavelet transform (CWT) electrochemical impedance spectroscopy (EIS) for time-resolved analysis of stochastic collision events of platinum nanoparticles (NPs) onto gold ultramicroelectrode (UME). The enhanced electrocatalytic activity is observed in both chronoamperometry (CA) and EIS. CA provides the impact moment and rough estimation of the size of NPs. The quantitative information such as charge transfer resistance (Rct ) relevant to the exchange current density of a single Pt NP is estimated from EIS. The CWT analysis of the phase angle parameter is better for NP collision detection in terms of time resolution compared to the STFT method.

Broadband Achromatic Terahertz Metalens Constituted by Si–SiO2–Si Hybrid Meta‐Atoms

AbstractMetasurfaces have proved to possess extraordinary properties and propelled the development of versatile functional devices in the terahertz domain, but the intrinsic dispersion severely impedes their broadband applications. Sporadic studies on achromatic terahertz metasurfaces are either subject to specific polarization operation or challenged in the fabrication process for extended bandwidth and numerical aperture. Herein a novel meta‐atom scheme made up of a silicon–silica–silicon sandwich‐shaped structure is presented that enlarges the propagation phase as well as improves the dispersion engineering capability at low losses. As a classic case, an achromatic metalens is constituted by these meta‐atoms and experimentally demonstrated which shows a remarkable achromatic focusing performance from 0.5 to 1.1 THz with a numerical aperture of 0.47 and an average efficiency as high as 43.1%. Continuous wavelet transformation analysis further proves the metalens has negligible group delay dispersion. This study not only demonstrates an outstanding terahertz achromatic metalens but also provides a novel meta‐atom paradigm for constructing achromatic metasurfaces such as holographic plate, beam deflector, and Bessel axicon, further not limited to THz bands.

Fault Diagnosis of PMSM Stator Winding Based on Continuous Wavelet Transform Analysis of Stator Phase Current Signal and Selected Artificial Intelligence Techniques

High efficiency, high reliability and excellent dynamic performance have been key aspects considered in recent years when selecting motors for modern drive systems. These features characterize permanent magnet synchronous motors (PMSMs). This paper presents the application of continuous wavelet transform (CWT) and artificial intelligence (AI) techniques to the detection and classification of PMSM stator winding faults. The complex generalized Morse wavelet used for CWT analysis of three different diagnostic signals—the stator phase current, its envelope and the space vector module—is used to extract the symptoms most sensitive to the interturn short circuits (ITSCs) at the incipient stage of the damage. The effectiveness of automatic stator winding fault classification is compared for three selected ML algorithms: multilayer perceptron, support vector machine and k-nearest neighbors. The effect of the ML models’ hyperparameters on their accuracy is also verified. The high effectiveness of the proposed methodology is confirmed by the results of the experimental verification carried out for different load torque levels and supply voltage frequency values.

Rapid adjustments to autonomic control of cardiac rhythm at the onset of isometric exercise in healthy young adults.

Sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) influences on cardiac rhythm at the onset of exercise, a time of rapid autonomic adjustments, are clinically important areas of investigation. Continuous wavelet transform (CWT) involves time-frequency-based heart rate variability (HRV) analysis allowing investigation of autonomic influences on cardiac rhythm during short durations of exercise. Therefore, the purpose of this study was to characterize SNS and PNS influences on cardiac rhythm at the onset of isometric exercise in healthy young adults. CWT analysis was retrospectively applied to R-R interval data (electrocardiogram) previously collected from 14 healthy young adults (26 ± 2 years) who performed 30-s, one-legged, isometric, calf exercise at 70% maximal voluntary contraction (MVC; 70% MVC trial) or rested (0% MVC trial). Absolute and normalized low-frequency (aLF, nLF; 0.04-0.15 Hz) and high-frequency (aHF, nHF; 0.15-0.4Hz) bands and LF/HF were used to analyze one 30-s baseline period and six 5-s time windows during the 30-s exercise (70% MVC) or rest (0% MVC). Statistical analysis involved two-way analysis of variance with post-hoc analysis. aHF, aLF, LF/HF, nHF, and nLF displayed a trial-time interaction (all p ≤ 0.027). In the 70% compared to the 0% MVC trial, aHF and nHF were lower after 5-30 s (all p ≤ 0.040), aLF was lower after 20-30 s (all p ≤ 0.011) and LF/HF and nLF were higher after 5-20 s (all p ≤ 0.045). These results indicate the reduction of the PNS influence on cardiac rhythm begins sooner than the augmentation of the SNS influence at the onset of isometric exercise in healthy young adults.

The utility of combining continuous wavelet transform analysis and high-density voltage map in predicting the long-term outcomes after ablation of persistent atrial fibrillation.

Continuous wavelet transform (CWT) analysis is a frequency analysis to detect areas of stable high-frequent activity (stable pseudo frequency [sPF]) during atrial fibrillation (AF). As previously reported, patients with the highest sPF area in pulmonary veins (PV) showed better short-term outcomes after PV isolation (PVI). This study sought to evaluate the efficacy of CWT analysis in predicting the long-term (2years) outcomes after PVI. We also combined the left atrial (LA) voltage map with CWT analysis to further predict the outcome. Persistent AF patients (n = 109, age 65 ± 10) underwent a CWT analysis at PVs and 8 LA sites during AF for pre-PVI analysis. After PVI during AF, CWT analysis was performed again in the LA as post-PVI analysis and was compared with pre-PVI analysis. A sinus voltage map of LA was created after cardioversion. Seventy patients had the highest sPF within PVs (PV-dominant group), while 39 patients had the highest sPF outside PVs (LA-dominant group). The global frequency in the LA showed a significant decrease after PVI only in PV-dominant group (6.55 ± 0.27 to 6.43 ± 0.37, P < 0.01). AF-free survival was better in PV-dominant group than LA-dominant group at 2-year follow-up (87.1% vs. 64.3%, P < 0.002). This trend was recognized throughout all degrees of low voltage area in the LA (LA-LVA), and AF-free survival was well predicted by combining CWT analysis and LA-LVA. By combining CWT analysis and sinus LA-LVA, the long-term AF-free survival after PVI was well stratified and predicted.

Analysis of direct current resistivity data using continuous wavelet transform

We have developed a new method for the direct current resistivity interpretation, based on the continuous wavelet transform (CWT) of electric potential-difference data. It exploits the main properties of the CWT, such as stability versus noise, and does not require a starting model or other a priori information such as a model weighting function or constraints. Because the approximate integral equation of the resistivity problem has the same form as the forward problem for potential fields, the authors analyze geoelectric data (with dipole-dipole configuration) using the wavelets belonging to the Poisson kernel semigroup. They find that the CWT analysis of the measured electric potential difference is able to identify buried bodies, defining their depth, position, and extent. Such parameters are estimated with no prior knowledge of the resistivity contrast between the bodies and the background. We consider several synthetic models, such as dikes, compact bodies, and contacts. In general, the depth and the lateral thickness of the buried bodies are estimated with good accuracy, using a diagram relating the singular point estimations to the different values of the dipole separation factor n. Thanks to the good results obtained from synthetic data, we test the method with data generated during laboratory experiments. In two laboratory-scale models, our method displays a better precision compared with smoothness-constrained least-squares inversion in identifying the exact position of the edges of a buried body. Finally, we find that combining CWT and inversion is advantageous: after constraining the inverse problem with a priori information from the CWT analysis, we obtain an improved inverse model.

Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning

Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI1 and WFCI2). Considering the average classification accuracy (ACA) in both years of experiments, WFCI1 manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI1 at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI1 and WFCI2 with independent data produced R2 and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI1 and WFCI2) manifested the maximum accuracy for disease estimation with RMSE of 11.61 and R2 = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.