Digital Image Processing Techniques Research Articles

Non-destructive Machine Vision System based Rice Classification using Ensemble Machine Learning Algorithms

Aims and Background: Agriculture plays a major role in the global economy, providing food, raw materials, and jobs to billions of people and driving economic growth and poverty reduction. Rice is the most widely consumed crop domestically, making it a particularly important crop for rural populations. The exact number of rice varieties worldwide is difficult to determine as new varieties are constantly being developed and marketed. Objectives: The most common method of rice variety identification is a comparison of its physical and chemical properties to a reference collection of known types. Methodology: This is a relatively quick and cost-effective approach that can be used to accurately differentiate between distinct varieties. In some cases, genetic testing may be used to confirm the identity of a variety, although this technique is more expensive and time-consuming. However, we can also utilize efficient, precise, and cost-effective digital image processing and machine vision techniques. Results: This study describes different types of ensemble methods, such as bagging (Decision Tree, Random Forest, Extra Tree), boosting (AdaBoost, Gradient Boost, and XGBoost), and voting classifiers to classify five different varieties of rice. Extreme Gradient Boosting (XGBoost) has achieved the highest average classification accuracy of 99.60% among all the algorithms. Conclusion: The findings of the performance measurement indicated that the proposed model was successful in classifying the various varieties of rice.

Meso-structural evolution characteristics of porous asphalt mixtures with stress-induced and their effects on environmental functions

This study aims to investigate the meso-structural evolution characteristics of porous asphalt mixtures (PAM) under stress and their effects on drainage and noise reduction environmental functions. Digital image processing technique was used to accurately identify the internal voids during uniaxial compression testing. The focus was on analyzing and simulating meso-scale differences in internal void structures before and after deformation and quantifying the evolution of these voids during the strain process. Drainage and noise reduction tests were conducted, and correlation analysis and factor screening were performed. The results indicate that in stress-induced meso-structural evolution, contact between aggregates generates force chains, and with increasing strain, the length of these chains and the number of aggregate contacts increase. Significant forces occur at the interface between aggregates and asphalt mortar. Aggregates undergo relative translational motion due to resistance to loads, resulting in compressed voids, cracks between asphalt mortar and aggregates, and gradual deterioration of the mixture structure. Additionally, the increase of strain significantly reduces the drainage and noise reduction performance of porous asphalt mixtures. The scale relationship diagram between macro- and meso-scale indicators identifies strain, void area and void sphericity as the primary influencing factors. The research findings contribute to understanding the stress transfer characteristics of PAM at the meso-structural scale, thereby enabling adjustments to grading, optimization of void structures, and enhancement of their long-term environmental functions.

Controlling of Pest using Image Processing and Machine Learning Methods

In addition to giving humans food, agriculture is a major source of resources for every nation's economy. Pests and insects cause damage to crops, making them extremely risky for the crop's overall growth. In the world of agriculture, early tormentor identification may provide significant challenges. Using insecticides is the greatest way to control the tormentor infection. On the other hand, overuse of pesticides is detrimental to animals, plants, and even the general population's automatic method of early insect detection. Digital image processing techniques are widely used in agricultural research and offer great perspectives, especially in the field of plant protection, which leads to crop management. This study discusses a novel approach to pest monitoring through early detection. Using a photography camera, one may create nonheritable images of pest-ridden leaves. An automated system that can identify the type of pests on crops in addition to examining the crops to detect infestations of tormentors is required. Support Vector Machine (SVM) is utilized for the categorization of images with and without pests, while the YOLO algorithmic method is used for tormentor detection. Key Words: Image Processing, Pesticides, Machine Learning, Agriculture, Plants, YOLO.

Surface Bubbles Emergence as an Indicator for Optimal Concrete Compaction.

Compaction quality significantly influences the strength and durability of concrete in structures. Under-compacting can retain entrapped air, reducing strength, while over-compacting can lead to segregation, creating local variances in strength distribution and modulus of elasticity in the concrete structure. This study examines the widely adopted concept that compaction is optimal when bubbles cease to emerge on the concrete surface. We recorded the surface activity of six comparable concrete specimens during the compaction process using a 4K video camera. Four specimens were compacted using a table vibrator and two with a poker vibrator. From the video frames, we isolated the bubbles for analysis, employing digital image processing techniques to distinguish newly risen bubbles per frame. It was found that the bubbles continuously rose to the surface in all specimens throughout the compaction process, suggesting a need for extended compaction, with some specimens showing a slow in the rate of the bubbles' emergence. However, upon examining the segregation levels, it was discovered that all the specimens were segregated, some severely, despite the continued bubble emergence. These findings undermine the reliability of using bubble emergence as a principle to stop compaction and support the need for developing online measurement tools for evaluating compaction quality.

Automated system for the detection of risk in agricultural sugarcane harvesting using digital image processing and deep learning

In recent years, significant changes have been presented in the climatological trends due to climatic change, originating negative impacts on the agricultural production, diminishing mainly the harvest efficiency. The following research proposes the optimization of the agricultural risk identification method for the prediction of the variables: temperature and precipitation; the risk identification method was developed through the Digital Image Processing technique (DIP) and Deep Learning (DL); Subsequently, with the processed images, Convolutional Neural Networks (CNN's) were developed for the detection of areas where there is a potential risk in the sugar cane crop harvest in the southeast of Veracruz in Mexico. The efficiency of CNN detects temperatures over 38ºC and the levels of precipitation under 70 millimeters. The efficiency of network detection is 0.9716 and 0.9948 for predicting the temperatures and precipitation variables, which represent a solid basis for detecting zones that depict a risk for the sugarcane harvest.

Investigation of corrosion-induced cracks using corrosion products quantified by an X-ray technique and FE analysis of single- and multiple-rebar beams

The effects of corrosion methods, galvanostatic (GS) vs. artificial climate environment (ACE), on the relationships between corrosion products and corrosion cracks of corroded reinforced concrete beams are investigated. The experimental results show that the GS method induces smaller corrosion crack width. A novel quantitative detection method of corrosion product thickness using X-ray and digital image processing techniques is proposed for investigating the continuous development of corrosion products. As the corrosion level increases, a smaller steel-to-rust volume expansion ratio caused by continuous leakage and a lower oxidation degree of corrosion products for the GS specimens are the major reasons for the smaller corrosion cracks. Regarding the effect of rebar arrangement on the development of corrosion-induced cracks, the expansion stress from corner-located rebars restrained the growth of corrosion products and further limited the propagation of corrosion-induced cracks of the center rebar. Estimated corrosion crack width via FE analysis using measured corrosion products as input show good agreement with experimental results.

Measures of angularity in digital images.

In light of the growing interest in studying the affective and aesthetic attributes of curvature, the present paper describes four digital image processing techniques that can be used to objectively discriminate between angular and curvilinear stimuli. MATLAB scripts for each of the techniques accompany the paper. Three studies are then reported that evaluate the efficacy of five metrics, derived from the four techniques, at quantifying the degree of angularity depicted in an image. Images of simple polygons (Study 1), artistic drawings of everyday objects (Study 2), and real-world objects, typefaces, and abstract patterns (Study 3) were analyzed. Logistic regression models were used to determine the relative importance of the metrics at distinguishing between angular and curvilinear items. With one exception, all of the metrics were capable of distinguishing between angular and curvilinear items at a level above chance, but some metrics were better at doing so than others, and their discriminative capacity was influenced by the characteristics of the image. The strengths and limitations of the metrics are discussed, as well as some practical recommendations.

Respiratory System Analysis System for Patient Care Against a Possible Risk of Tuberculosis

According to the studies developed in advance, there is a crucial problem of doctors analysing computerised images of the chest visually, making a generalised diagnosis for their patients based on their experience, and making mistakes due to the different characteristics of each patient affected by bacteria in their respiratory tract. An infectious disease that has been increasing over the years is pulmonary tuberculosis, which has had around 12.7 million patients infected in 2020, with low-income countries being the main ones affected by this lung disease that is transmitted from person to person, so it cannot be based on the visual experience of the doctor, as this disease causes an increase of bacteria in the bloodstream and damages the alveoli, although there are various methods of detection, they do not provide a complete result on the patient's condition. The aim of this research is to develop a respiratory tract analysis system that will help doctors to detect tuberculosis earlier and more accurately and avoid prolonged infections that could be fatal for patients. The methodology used for this research is based on carrying out a computer analysis of the patient's chest and then carrying out image processing using MATLAB, using its various digital image processing techniques to detect these conditions. According to the system tests, it was observed that the system performs the detection of tuberculosis with an efficiency of 97.40% in its handling, standing out notoriously for its high value of efficiency, in addition to having the precise time for the determination of tuberculosis in the analysis of computerised images. In conclusion, this system can be used in different circumstances of the patient's condition, from the initial symptoms to an advanced stage of the patient's condition.

Computer Vision-Based Estimation of The Effects of Vibration in Slipform Paving

Slipform paving is a complex multistep process encompassing concrete pouring, vibration, consolidation, correction, and curing. One of the most critical aspects in the process is that the frequency and duration of vibration can influence the distribution of coarse aggregate particles in the cured concrete. However, there is still a fundamental lack of methodologies to formally characterize the interrelationships between vibration characteristics and the quality of the cured pavement. This study aims to address this issue by proposing a novel toolset for the assessment of the spatial features of coarse aggregate distribution in doweled pavements. This system is based on paver consolidation simulations and employs computer vision as the primary tool for analysis. The experimental protocol, recently developed by the authors, employs coarse aggregate particles color-coded by gradation to aid in recognizing the distribution of different granulometric sizes within the specimens. High-resolution, two-dimensional digital images obtained from cured specimens were used to analyze aggregate characteristics, orientation, and dispersion, with particular emphasis on quantifying the effect of the distance from the vibrator head on such parameters. The results showed that the proposed digital imaging processing techniques were capable of quantitatively characterizing the distribution of coarse aggregate particles in the specimens. Moreover, novel metrics were proposed to characterize the quality of the consolidation based on the digital image data.

Investigation of interfacial degradation mechanism and debonding behavior for degraded rubber asphalt-aggregate molecular system using a molecular dynamics method

This study aims to investigate the debonding damage behaviors in rubber asphalt-aggregate interfaces under different rubber degradation degrees from a molecular-scale perspective. The pull-off test was simulated using molecular dynamics (MD) to determine the damage patterns at the asphalt-aggregate interface. The digital image processing (DIP) technique was employed to quantify the percentages of cohesive debonding, and the radial distribution function (RDF) and mean square displacement (MSD) was performed to explain the adhesion and cohesion damage mechanisms. In addition, the pull-out test results were compared with the simulated values to verify the reliability of the asphalt-aggregate molecular model. The findings of this study reveal that the presence of rubber powder molecules enhances the cohesion among asphalt molecules and mitigates damage to the cohesion at the asphalt-aggregate molecular interface. As rubber degradation progresses, the damage mode undergoes a gradual transition from adhesive damage to cohesive damage at the asphalt-aggregate interface. At lower temperatures, there is an increase in the internal aggregation of asphalt molecules, and the debonding rate of asphalt molecules at the mineral-aggregate interface rises from 7.67% to 35.67% following complete rubber degradation. Conversely, at higher temperatures, the asphalt-aggregate molecular system is primarily governed by the bonding of “double bond” and “single bond” groups, with cohesive damage dominating the asphalt matrix. The pull-out test results showed similar trends with the simulated values, indicating that the asphalt-aggregate molecular model in this study was relatively reasonable. The present study provides some guidance on the damage behavior of degraded rubberized asphalt-aggregate interface.

CNN-Based Ocular Disease Identification in Retinal Images

Abstract: Approximately 15 million people across India suffer from blindness, and unfortunately, 75% of these instances were treatable at one point in time. There are 10,000 patients for every doctor in India. Vision can be affected by a variety of eye diseases, such as cataracts, corneal ulcers, and trachoma, among others. Research indicates that untreated early-stage diseases are the primary causes of blindness in India. Only by receiving a proper diagnosis early on in the progression of these eye conditions can they be prevented. There are many different symptoms that can be seen with these eye conditions. Analyzing a wide variety of symptoms is necessary in order to make an appropriate diagnosis of eye conditions. With the use of deep learning techniques like convolution neural networks and digital image processing techniques like segmentation and morphology, we offer a novel approach to automatically identify eye diseases based on visually perceptible symptoms. The suggested approach is used to analyze and classify seven eye conditions: Diabetic retinopathy (DR), glaucoma, cataract, and age-related macular degeneration(AMD) are the most common ocular illnesses. Early detection of eye problems is facilitated by the proposed deep neural network model. In case that screening is necessary, the model advises people to contact an ophthalmologist.

Contrastive Analysis of Interferometric Techniques for Small Channel Temperature Measurements

This research employs a state-of-the-art digital interferometric technique to investigate the temperature distribution within a confined rectangular channel, with a hydraulic diameter of 3 mm. The experimental setup incorporates an optical glass channel, with nanofluids (Aluminium oxide) as the test fluid at 0.001% volume concentration. To induce controlled heating, a heater filament is strategically placed along the bottom wall of the channel. Simultaneously, a T-type thermocouple is carefully placed to measure the temperature along the upper wall. Two distinct interferometric methods, namely the Michelson interferometer and the Mach-Zehnder interferometer, are employed to capture the complex details of fringes resulting from the evolving temperature field within the test section. A high-resolution CCD camera is employed to capture these fringes, and sophisticated digital image processing techniques are subsequently applied for in-depth fringe analysis. The culmination of these efforts results in the origin of a comprehensive localized temperature distribution map within the small-sized channel. The obtained temperature profiles are meticulously compared, providing valuable insights into the effectiveness and reliability of the Michelson and Mach-Zehnder interferometric techniques in this specific experimental context. This detailed comparative analysis contributes to the broader understanding of interferometric methods for temperature measurements in confined fluidic systems.

Motion and interaction of in‐line bubbles in quiescent non‐aqueous solutions of hydrocarbon resin

AbstractHydrocarbon resin (HR) is an essential fine chemical. The preparation and application process of HR involves lots of gas–liquid heterogeneous reactions, and the bubbly flow behavior influences them significantly. Using high‐speed photography and digital image processing techniques, the motion and interaction of in‐line bubbles in non‐aqueous solutions of HR are examined in this article. The results show a critical gas flow rate that can change the bubbling regime. It can be observed that viscosity features prominently in changing the shape of bubbles and their motion. As the viscosity increases, the bubbles are more prone to coalescence, and the bubble coalescence process gradually changes from connected slip‐rising coalescence to connected‐rising coalescence. The viscosity transition region between non‐coalescent and coalescent systems in non‐aqueous solutions of HR occurs at 3.6–9.2 mPa·s. Further, a force analysis shows that in paired bubbles, the leading bubble can be viewed as an individual bubble unaffected by trailing bubble before the two bubbles collide, but in the wake of the leading bubble, the drag force on the trailing bubble decreases and the added mass force increases.

Material Point Method-Based Simulation of Run-Out Characteristics for a Soil–Rock Mixed Landslide Induced by Surcharge

Surcharge loading on slopes is a prevalent engineering practice that can precipitate landslides, posing significant risks to construction integrity and safety. This study elucidates the impact of surcharge loading on mixed soil–rock slopes and benchmarks their response against that of pure soil slopes under analogous loading conditions. Investigating damage manifestations, this research quantifies the distribution of plastic zones, the morphological alterations of slopes at characteristic stages, the dynamics of slip velocity at monitoring points, and the extent of landslide run-out. The material point method is adopted for its proficiency in simulating large deformation scenarios. Two-dimensional models of a representative soil–rock mixed slope and a pure soil slope are meticulously crafted using digital image processing techniques. The distinct damage profiles exhibited by the mixed and pure soil slopes are compared. The comparative assessment elucidates the distinct damage patterns of different slopes, enhancing the understanding of their behavior under variable surcharge intensities and contributing to the discourse on slope stability assessments.

The Potential of Landsat 8 OLI Images in Coastline Identification: The Case Study of Basra, Iraq

Coastline extraction plays important functions in coastal resource management, natural resource preservation, and sustainable coastal development. Long-term records of Landsat data series are available for free downloading, being highly potential for coastline extraction. Furthermore, remote sensing imagery systems along with the application of digital image processing techniques can be utilized in coastline extraction. However, it is challenging to accurately extract coastlines with coarse spatial resolution due to the dynamic properties of various types of coastlines produced by sea-level changes from tides and storms. Moreover, the use of conventional surveys and the need for high-resolution images involve substantial costs and efforts, especially when coastlines of long distances are involved. The current study compared the accuracy of extracting coastlines from Landsat 8 OLI with the observed coastlines from GPS data. In particular, this study focused on distinguishing water areas from non-water areas on the coastline of a selected concrete port. The analysis involved the use of both MNDWI and NDWI indexes. The statistical results demonstrated the accuracy of MNDWI (RMSE of 2.363) and NDWI (RMSE of 2.491 m), which suggested the potential of using Landsat 8 OLI in coastline identification.

Parametric image-based concrete defect assessment method

Structural health monitoring aims to ensure the integrity of infrastructure. Assessing structural integrity through image classification techniques based on human perception is often challenging. Incorporating a morphological image-based crack detection algorithm can mitigate these limitations. This study proposes a parametric approach for concrete crack analysis using digital image processing techniques. The objective is to detect damage to the concrete surface of infrastructures, potentially exacerbating structural deterioration. The research justifies the utilization of digital parametric image processing techniques, including a weighted median filter, grayscale, Otsu filter, and other methods. These techniques were applied to evaluate different concrete images for crack detection. The findings were validated through Image Quality Assessment (IQA), determining the condition of the studied images and statistical properties using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE), and Perceptual Image Quality Evaluator (PIQE). Additionally, uncontrollable factors such as variations in lighting effects under different conditions, testing setups, and different textures of the concrete were considered in this study. The comprehensive experimental results demonstrated that the Otsu filter outperforms other filtering techniques. Overall, the study achieved remarkable accuracy, approximately 95%, in detecting cracks on building infrastructure. The proposed method holds potential for integration with advanced techniques in practical applications for the maintenance and safety of infrastructure. This research not only contributes to technological advancements in concrete defect assessment but also carries significant implications for the future of automated and reliable structural health monitoring.

Analysis of the pore characteristics of backfill with an air-entraining agent based on SEM images by using fractal theory

ABSTRACT Air-entraining agents can introduce tiny bubbles into a filling slurry, thereby improving its fluidity. However, the introduction of bubbles can change the pore characteristics of backfill materials. Pore characteristics can be visually observed using scanning electron microscopy (SEM). SEM images contain a large amount of microstructural characteristic information. However, the analysis of the image information is superficial. We used SEM images to qualitatively analyse the microstructural characteristics of backfill materials, and morphological operations and digital image processing techniques were used to obtain a binarization image for the semi-quantitative analys is of pore distribution. Two fractal theories: box-counting and slit island methods, were used to quantitatively analyse the binarization image to comprehensively analyse the pore characteristics of the backfill materials. The results show that: (1) the pore shape characteristics are obtained quantitatively (Rr = 4.73–5.48, R = 1.46–1.60), and most pores are regular. (2) The pores of backfill materials have good fractal characteristics (R2 > 0.97), which can be quantitatively analysed and studied using the fractal theory (DI = 1.086–1.160 and DB = 1.50–1.62). (3) The research result suggest that the more regular the pore, the smaller its fractal dimension, which is given quantitatively as (D = a*R r +b*R+c).

Evaluation of the effects of oxygen enrichment on combustion stability of biodiesel through a PSO-EMD-RBF model: An experimental study

<abstract> <p>In this study, we processed the flame images of biodiesel combustion in industrial furnaces, classified and evaluated flame states using digital image processing techniques, and proposed a combustion stability index (CSI) using the particle swarm optimization (PSO) algorithm. In order to more accurately predict the combustion stability under different oxygen concentrations, we proposed a method that combines the Multi-Input Radial basis function neural network (RBF-NN) with empirical mode decomposition (EMD). Initially, the EMD method was employed to decompose the original time series of CSI. Subsequently, a decomposition model incorporating initial parameters and CSI was established using the radial basis function. The results of the computations indicate that the EMD-RBF-NN model significantly outperforms existing models in enhancing the accuracy of CSI.</p> </abstract>

Environmental Changes and Food Security in Ado-Ekiti, Nigeria

This study aims at evaluating the role of Geo-spatial techniques in vegetation cover analysis and mapping, environmental changes and food security in Nigeria. The challenges of food security in Nigeria especially in urban centres are enormous. The unmitigated rural-urban drift as a result of socio-economic development has made it imperative to address the problem of food security in the study area. Also, the issue of rapid urban development without appropriate attention given to the important issue of food security with persistent deteriorating infrastructure for urban food processing has led to rapid price changes, in food and other agricultural products. This study, therefore, attempts to demonstrate that the recent advancements in remote sensing integration with Geographic Information System technologies provide powerful tools for mapping and detecting changes in land use and land cover using Ado-Ekiti as a case study. The digital image processing techniques of remote sensing (RS) was employed to enable speedy, accurate and objective interpretation of the multispectral data used for the study, in order to understand the extent, rate and the magnitude of land use and land cover dynamics in the study area. The study reveals that the agricultural land and natural vegetation have the highest change. Much of their land areas were converted to built-up areas for the three study epochs (1992,2007,2022). The study concludes that there is a progressive encroachment of built-up areas to other land use in the study area, thereby resulting in significant environmental changes and affecting food security.

Linear Diophantine Fuzzy Sets: Image Edge Detection Techniques based on Similarity Measures

In the digital imaging process, fuzzy logic provides many advantages, including uncertainty management, adaptability to variations, noise tolerance, and adaptive classification. One of the techniques of digital image processing is the edge detection. The edge detection process is an essential tool to segment the foreground objects from the image background. So, it facilitates subsequent analysis and comprehension of the image’s underlying structural properties. This process can be moved on with the notion of fuzzy sets and their generalizations. The concept of Linear Diophantine fuzzy sets is a generalization of fuzzy sets where reference parameters correspond to membership and non-membership grades. This study aims to apply linear Diophantine fuzzy sets (LDFSs) to edge detection of images. The novelty of this paper is twofold. The first one is that we conduct a comprehensive evaluation to ascertain the similarity values using the linear Diophantine fuzzy similarity measure by leveraging the gray normalized membership values associated with fundamental edge detection techniques. The other is to modify the image pixels into the LDFSs and then filter the images by using the presented similarity measure operators given in the LDFS environment.