Showing papers in "Measurement in 2014"

Abstract: Planetary gearboxes significantly differ from fixed-axis gearboxes and exhibit unique behaviors, which invalidate fault diagnosis methods working well for fixed-axis gearboxes. Much work has been done for condition monitoring and fault diagnosis of fixed-axis gearboxes, while studies on planetary gearboxes are not that many. However, we still notice that a number of publications on condition monitoring and fault diagnosis of planetary gearboxes have appeared in academic journals, conference proceedings and technical reports. This paper aims to review and summarize these publications and provide comprehensive references for researchers interested in this topic. The structures of a planetary gearbox as well as a fixed-axis one are briefly introduced and contrasted. The unique behaviors and fault characteristics of planetary gearboxes are identified and analyzed. Investigations on condition monitoring and fault diagnosis of planetary gearboxes are summarized based on the adopted methodologies. Finally, open problems are discussed and potential research topics are pointed out.

Abstract: In this paper, the Taguchi method and regression analysis have been applied to evaluate the machinability of Hadfield steel with PVD TiAlN- and CVD TiCN/Al 2 O 3 -coated carbide inserts under dry milling conditions. Several experiments were conducted using the L 18 (2 × 3 × 3) full-factorial design with a mixed orthogonal array on a CNC vertical machining center. Analysis of variance (ANOVA) was used to determine the effects of the machining parameters on surface roughness and flank wear. The cutting tool, cutting speed and feed rate were selected as machining parameters. The analysis results revealed that the feed rate was the dominant factor affecting surface roughness and cutting speed was the dominant factor affecting flank wear. Linear and quadratic regression analyses were applied to predict the outcomes of the experiment. The predicted values and measured values were very close to each other. Confirmation test results showed that the Taguchi method was very successful in the optimization of machining parameters for minimum surface roughness and flank wear in the milling the Hadfield steel.

Abstract: Soil moisture content has paramount importance in dictating engineering, agronomic, geological, ecological, biological and hydrological characteristics of the soil mass. Though earlier researchers have employed various techniques of moisture content determination of soils, both in laboratory and in situ conditions, ascertaining the applicability of these techniques to soils of entirely different characteristics and the ‘types of moisture content’, which they can measure, is still a point of debate. As such, a critical review of all the established and emerging soil moisture measurement techniques with respect to their merits and demerits becomes necessary. With this in view, efforts have been made in this paper to critically evaluate all the soil moisture measurement techniques, limitations associated with them and the influence of various soil-specific parameters (viz., mineralogy, salinity, porosity, ambient temperature, presence of the organic matter and matrix structure of the soil) on the measured soil moisture content. This paper also highlights the importance of various innovations based on Micro Electro Mechanical Systems (MEMS) and nano-sensors that are emerging in this context.

Abstract: The application of artificial neural network (ANN) in predicting pile bearing capacity is underlined in several studies. However, ANN deficiencies in finding global minima as well as its slow rate of convergence are the major drawbacks of implementing this technique. The current study aimed at developing an ANN-based predictive model enhanced with genetic algorithm (GA) optimization technique to predict the bearing capacity of piles. To provide necessary dataset required for establishing the model, 50 dynamic load tests were conducted on precast concrete piles in Pekanbaru, Indonesia. The pile geometrical properties, pile set, hammer weight and drop height were set to be the network inputs and the pile ultimate bearing capacity was set to be the output of the GA-based ANN model. The best predictive model was selected after conducting a sensitivity analysis for determining the optimum GA parameters coupled with a trial-and-error method for finding the optimum network architecture i.e. number of hidden nodes. Results indicate that the pile bearing capacities predicted by GA-based ANN are in close agreement with measured bearing capacities. Coefficient of determination as well as mean square error equal to 0.990 and 0.002 for testing datasets respectively, suggest that implementation of GA-based ANN models as a highly-reliable, efficient and practical tool in predicting the pile bearing capacity is of advantage.

Abstract: In machining processes several phenomena occur during material cutting. These phenomena can affect the production through the reduction of quality or accuracy, or by increasing costs (tools, materials, time). Thus, an understanding of machining phenomena is needed not only to define the cutting parameters for maximizing production, but also to ensure worker safety. An easy way to identify these phenomena is by monitoring machining processes, such as the measurement of cutting force, temperature and vibration. The acquired signal can have information about tool life, quality of cutting and defects in the workpiece. This review paper discusses the first steps involved in choosing and defining various techniques that may be used to monitor machining processes. Furthermore, this paper also outlines the techniques to acquire and process the signals of the monitoring processes. Hence, the objective of this paper is to help the reader understand the procedures for monitoring machining processes, and define methods, parameters, targets and other factors involved in doing so.

Abstract: In this work, the dry turning parameters of two different grades of nitrogen alloyed duplex stainless steel are optimized by using Taguchi method. The turning operations were carried out with TiC and TiCN coated carbide cutting tool inserts. The experiments were conducted at three different cutting speeds (80, 100 and 120 m/min) with three different feed rates (0.04, 0.08 and 0.12 mm/rev) and a constant depth of cut (0.5 mm). The cutting parameters are optimized using signal to noise ratio and the analysis of variance. The effects of cutting speed and feed rate on surface roughness, cutting force and tool wear were analyzed. The results revealed that the feed rate is the more significant parameter influencing the surface roughness and cutting force. The cutting speed was identified as the more significant parameter influencing the tool wear. Tool wear was analyzed using scanning electron microscope image. The confirmation tests are carried out at optimum cutting conditions. The results at optimum cutting condition are predicted using estimated signal to noise ratio equation. The predicted results are found to be closer to experimental results within 8% deviations.

Abstract: The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification.

Abstract: Targeting the non-linear dynamic characteristics of roller bearing faulty signals, a fault feature extraction method based on hierarchical entropy (HE) is proposed in this paper. SampEns of 8 hierarchical decomposition nodes (e.g. HE at scale 4) are calculated to serve as fault feature vectors, which takes into account not only the low frequency components but also high frequency components of the bearing vibration signals. HE can extract more faulty information than multi-scale entropy (MSE) which considers only the low frequency components. After extracting HE as feature vectors, a multi-class support vector machine (SVM) is trained to achieve a prediction model by using particle swarm optimization (PSO) to seek the optimal parameters of SVM, and then ten different bearing conditions are identified through the obtained SVM model. The experimental results indicate that HE can depict the characteristics of the bearing vibration signal more accurately and more completely than MSE, and the proposed approach based on HE can identify various bearing conditions effectively and accurately and is superior to that based on MSE.

Abstract: In rotary complex machines, collapse of a component may inexplicably occur usually accompanied by a noise or a disturbance emanating from other sources. Rolling bearings constitute a vital part in many rotational machines and the vibration generated by a faulty bearing easily affects the neighboring components. Continuous monitoring, fault diagnosis and predictive maintenance, is a crucial task to reduce the degree of damage and stopping time for a rotating machine. Analysis of fault-related vibration signal is a usual method for accurate diagnosis. Among the resonant demodulation techniques, a well-known resolution often used for fault diagnosis is envelope analysis. But, usually this method may not be adequate enough to indicate satisfactory results. It may require some auxiliary additional techniques. This study suggests some methods to extract features using envelope analysis accompanied by Hilbert Transform and Fast Fourier Transform. The proposed artificial neural network (ANN) based fault estimation algorithm was verified with experimental tests and promising results. Every test was initiated with a reference ANN architecture to avoid inappropriate classification during the evaluation of fitness value. Later, ANN model was modified using a genetic algorithm providing, an optimal skillful fast-reacting network architecture with improved classification results.

Abstract: This paper describes a novel functional body-to-sensor calibration procedure for inertial sensor-based gait analysis. The procedure is designed to be easily and autonomously performable by the subject, without the need for precise sensor positioning, or the performance of specific movements. The procedure consists in measuring the vertical axis during two static positions, and is not affected by magnetic field distortion. The procedure has been validated on ten healthy subjects using an optoelectronic system to measure the actual body-to-sensor rotation matrices. The effects of different sensor positions on each body segment, or different levels of subject inclination during the second static position of the procedure, resulted unnoticeable. The procedure showed accuracy and repeatability values less than 4° for each angle except for the ankle int–external rotation (9.7°, 7.2°). The results demonstrate the validity of the procedure, since they are comparable with those reported for the most-adopted protocols in gait analysis.

Abstract: The counts, the so-called differential counts, and sizes of different types of white blood cells provide invaluable information to evaluate a wide range of important hematic pathologies from infections to leukemia. Today, the diagnosis of diseases can still be achieved mainly by manual techniques. However, this traditional method is very tedious and time-consuming. The accuracy of it depends on the operator’s expertise. There are laser based cytometers used in laboratories. These advanced devices are costly and requires accurate hardware calibration. They also use actual blood samples. Thus there is always a need for a cost effective and robust automated system. The proposed system in this paper automatically counts the white blood cells, determine their sizes accurately and classifies them into five types such as basophil, lymphocyte, neutrophil, monocyte and eosinophil. The aim of the system is to help for diagnosing diseases. In our work, a new and completely automatic counting, segmentation and classification process is developed. The outputs of the system are the number of white blood cells, their sizes and types.

Abstract: Machining of stainless steel is difficult due to their hardening tendency. In boring of stainless steels, tool wear and surface roughness are affected by vibration of boring bar. In this paper, tool wear, surface roughness and vibration of work piece were studied in boring of AISI 316 steel with cemented carbide tool inserts. A Laser Doppler Vibrometer was used for online data acquisition of work piece vibration and a high-speed Fast Fourier Transform analyzer was used to process the acousto optic emission signals for the work piece vibration. Experimental data was collected and imported to artificial neural network techniques. A multilayer perceptron model was used with back-propagation algorithm using the input parameters of nose radius, cutting speed, feed and volume of material removed. The artificial neural network was used to predict surface roughness, tool wear and amplitude of work piece vibration. The predicted values were compared with the collected experimental data and percentage error was computed.

Abstract: In this paper, we propose a car-following model with consideration of the reliability of inter-vehicle communication (IVC) to study each vehicle’s speed, headway, fuel consumption and exhaust emissions under an incident. The numerical results show that considering IVC will reduce each vehicle’s velocity, fuel consumption and exhaust emissions during the braking process while enhance each vehicle’s speed, fuel consumption and exhaust emissions during the starting process, but the effects will become more prominent with the increase of the reliability of IVC.

Abstract: Medical image analysis is one of the major research areas in the last four decades Many researchers have contributed quite good algorithms and reported results In this paper, real coded genetic algorithm with Simulated Binary Crossover (SBX) based multilevel thresholding is used for the segmentation of medical brain images The T2 weighted Magnetic Resonance Imaging (MRI) brain images are considered for image segmentation The optimum multilevel thresholding is found by maximizing the entropy The results are compared with the results of the existing algorithms like Nelder–Mead simplex, PSO, BF and ABF The statistical performances of the 100 independent runs are reported The results reveal that the performance of real coded genetic algorithm with SBX crossover based optimal multilevel thresholding for medical image is better and has consistent performance than already reported methods

Abstract: Image thresholding is an important field in image processing. It has been employed to segment the images and extract objects. A variety of algorithms have been proposed in this field. However, these methods perform well on the images without noise, and their results on the noisy images are not good. Neutrosophic set (NS) is a new general formal framework to study the neutralities’ origin, nature, and scope. It has an inherent ability to handle the indeterminant information. Noise is one kind of indeterminant information on images. Therefore, NS has been successfully applied into image processing and computer vision research fields. This paper proposed a novel algorithm based on neutrosophic similarity score to perform thresholding on image. We utilize the neutrosophic set in image processing field and define a new concept for image thresholding. At first, an image is represented in the neutrosophic set domain via three membership subsets T, I and F. Then, a neutrosophic similarity score (NSS) is defined and employed to measure the degree to the ideal object. Finally, an optimized value is selected on the NSS to complete the image thresholding task. Experiments have been conducted on a variety of artificial and real images. Several measurements are used to evaluate the proposed method’s performance. The experimental results demonstrate that the proposed method selects the threshold values effectively and properly. It can process both images without noise and noisy images having different levels of noises well. It will be helpful to applications in image processing and computer vision.

Abstract: The growing demand for miniaturization of systems necessitates the production of smaller components with high dimensional accuracy. In this experimental investigation, through holes were machined in a Ti–6Al–4V plate of 0.4 mm thickness using twisted carbide drill bits of 0.4 mm diameter by conventional dry drilling. The Taguchi’s experimental design and Analysis of Variance (ANOVA) techniques have been implemented to understand the effects, contribution, significance and optimal machine settings of process parameters, namely, spindle speed, feed rate and air pressure. The performance characteristics of the small hole drilling were evaluated through thrust force, overcut, circularity and taper. Multi-performance optimization of the process parameters was realized by using grey relational analysis and mathematical modeling was done by regression analysis. The outcome of this research revealed that spindle speed and air pressure have the most significant impact on the dimensional accuracy of the hole; spindle speed and feed rate controls the thrust force.

Abstract: Owing to the scattered nature of Denial-of-Service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a hybrid clustering method is introduced, namely a density-based fuzzy imperialist competitive clustering algorithm (D-FICCA). Hereby, the imperialist competitive algorithm (ICA) is modified with a density-based algorithm and fuzzy logic for optimum clustering in WSNs. A density-based clustering algorithm helps improve the imperialist competitive algorithm for the formation of arbitrary cluster shapes as well as handling noise. The fuzzy logic controller (FLC) assimilates to imperialistic competition by adjusting the fuzzy rules to avoid possible errors of the worst imperialist action selection strategy. The proposed method aims to enhance the accuracy of malicious detection. D-FICCA is evaluated on a publicly available dataset consisting of real measurements collected from sensors deployed at the Intel Berkeley Research Lab. Its performance is compared against existing empirical methods, such as K-MICA, K-mean, and DBSCAN. The results demonstrate that the proposed framework achieves higher detection accuracy 87% and clustering quality 0.99 compared to existing approaches.

Abstract: Shear strength is one of the most important features in engineering design of geotechnical structures such as embankments, earth dams, tunnels and foundations. Shear strength parameters describe how rock material resists deformation induced by shear stress. Rock shear strength parameters are usually measured through laboratory tests, and these methods are destructive, time consuming and expensive. In addition, providing good-quality core samples is difficult especially in highly fractured and weathered rocks. This paper presents an indirect measure of shear strength parameters of shale by means of rock index tests. In this regard, 230 shale samples were collected from an excavation site in Malaysia and shear strength parameters of samples were obtained using triaxial compression test. Furthermore, rock index tests including dry density, point load index, Brazilian tensile strength, ultrasonic velocity, and Schmidt hammer test were conducted for each sample. A particle swarm optimization-artificial neural network (PSO-ANN) integrated model was developed by setting the results of rock index tests as inputs and shear strength parameters as outputs of the model. The obtained correlation of determination of 0.966 and 0.944 for training and testing datasets show the applicability of the proposed model to predict shale shear strength parameters with high accuracy.

Abstract: In this paper, we use empirical data to calibrate the speed-headway function and propose a car-following model to investigate the effects of real-time road condition on each vehicle’s speed, acceleration, headway, fuel consumption, CO, HC and NOX under uniform flow. Numerical results illustrate that real-time road condition produces oscillating phenomena and enhance each vehicle’s fuel consumption and exhaust emissions. These results can help researchers understand the effects of real-time road condition on the driving behavior and help traffic engineers construct the theory of homogeneous roads in order to reduce the vehicle’s fuel consumption and exhaust emissions.

Abstract: As it is difficult to identify the scale and aperture of small leaks occurring in a natural gas pipeline, this paper proposes a small leak feature extraction and recognition method based on local mean decomposition (LMD) envelope spectrum entropy and support vector machine (SVM). First, LMD is used to decompose the leakage signals into several FM–AM signals, i.e. into product function (PF) components. Then, based on their kurtosis features, the principal PF components that contain most of the leakage information are selected. Wavelet packet decomposition and energy methods are used to analyze and then reconstruct the principal PF components. The Hilbert transform is applied to these reconstructed principal PF components in order to acquire the envelope spectrum, from which the envelope spectrum entropy is obtained. Finally the normalized envelope spectrum entropy features are input into the SVM as leakage feature vectors in order to enable leak aperture category identification. By analyzing the acquired pipeline leakage signals in field experiments, it shows that this method can effectively identify different leak categories.

Abstract: A novel intelligent fault diagnosis model based on multi-kernel support vector machine (MSVM) with chaotic particle swarm optimization (CPSO) for roller bearing fault diagnosis is proposed. Multi-kernel support vector machine is a powerful new tool for roller bearing fault diagnosis with small sampling, nonlinearity and high dimension. Chaotic particle swarm optimization is developed in this study to determine the optimal parameters for MSVM with high accuracy and great generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by time-domain, frequency-domain and empirical mode decomposition (EMD) and the typical manifold learning method LTSA is used to select salient features. The experimental results indicate that this proposed approach is an effective method for roller bearing fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the single kernel SVM or the MSVM which parameters are randomly extracted.

Abstract: Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

Abstract: Artificial neural network (ANN) is an appropriate method used to handle the modeling, prediction and classification problems. In this study, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source, a proposed ANN architecture is used to predict the oil, water and air percentage, precisely. A multi-layer perceptron (MLP) neural network is used to develop the ANN model in MATLAB 7.0.4 software. In this work, number of detectors and ANN input features were reduced to one and two, respectively. The input parameters of ANN are first and second full energy peaks of the detector output signal, and the outputs are oil and water percentage. The obtained results show that the proposed ANN model has achieved good agreement with the simulation data with a negligible error between the estimated and simulated values. Defined MAE% error was obtained less than 1%.

Abstract: The non-parametric Data Envelopment Analysis (DEA) literature on network-structured performance analysis normally considers desirable intermediate measures These measures are the outputs from the first stage and are used as inputs to the second stage In many real situations, the intermediate measures consist of desirable and undesirable outputs This subject has recently attracted considerable attention among DEA researchers The motivation of this study is the application of the weak disposability to modeling network DEA with undesirable intermediate measures Undesirable products in this paper are studied in two different cases: either as final outputs or as intermediate measures In both cases, cooperative and non-cooperative game theories are proposed to assess the relative performance of the operational units A real case on 39 Spanish Airports in 2008 has been illustrated to verify the applicability of the proposed approaches

Abstract: In the present work, experimental investigations carried out to assess the applicability of HiPIMS (High Power Impulse Magnetron Sputtering)-coated carbide tools to hard turning (55 HRC) and to address the widely debated topic about the use of coolants in hard turning are presented. Tool wear progressions and hence, tool life, different tool wear forms and wear mechanisms observed for tools coated with HiPIMS coating technique, namely, nanocomposite AlTiN, nanocomposite multi-layer TiAlN/TiSiN and nanocrystalline AlTiCrN are presented along with the images captured by digital and electron microscope. Characterization results of all the coated tools in terms of their average coating thickness (measured using Calotest and Fractographs), adhesion strength of the coating(s) (determined using Scratch test), composition and microhardness (using EDAX and Vickers microhardness test, respectively) are presented. Experimental observations indicate higher tool life with nanocrystalline AlTiCrN coated carbide tools which shows encouraging potential of these tools to hard turning. Improvement in tool life of almost 20–25% has been observed under minimum quantity lubrication (MQL) due to better cooling and lubricating effects. However, this effect was more prominent at higher cutting speed of 150 m/min.

Abstract: Ball burnishing is a popular post-machining metal finishing operation. An attempt has been made in this paper to optimize the process parameters during burnishing of titanium alloy (Ti–6Al–4V). Ball burnishing process parameters such as burnishing speed, burnishing feed, burnishing force and number of passes were considered to minimize the surface roughness and maximize the hardness. The lubricated ball burnishing experiments were planned as per L25 orthogonal array and signal to noise (S/N) ratio was applied to measure the proposed performance characteristics. The validation tests with the optimal levels of parameters were performed to illustrate the effectiveness of Taguchi optimization. The optimization results revealed that burnishing feed and burnishing speed are the significant parameters for minimizing the surface roughness, whereas burnishing force and number of passes play important roles in maximizing the hardness. The optimization results showed greater improvements in surface finish (77%) and hardness (17%) when compared to premachined surfaces.

Abstract: In this article a modified algorithm (grey based fuzzy algorithm) is used to optimize multiple performance characteristics in drilling of bone. Experiments have been performed with different cutting conditions using full factorial design. The quality parameters considered are temperature, force and surface roughness. Grey relational analysis (GRA) coupled with fuzzy logic is employed to obtain a grey fuzzy reasoning grade (GFRG) combining all the quality characteristics. The highest GFRG is obtained for the feed rate of 40 mm/min and the speed of 500 rpm and is the optimal level. Analysis of variance (ANOVA) carried out to find the significance of parameters on multiple performance characteristics revealed that the feed rate has the highest contribution on GFRG followed by the spindle speed. The optimum level of the process parameters obtained is validated by the confirmation experiment.

Abstract: This paper reports the advantages and limitations of the digital image correlation (DIC)-based non-contact measurement technique through full-scale testing of prestressed concrete (PC) structures. Specifically, two ultimate load tests were conducted on a full-scale pre-stressed I-shaped beam and measurements were collected from both conventional instruments (displacement transducers) and a pair of high definition cameras. Stereographic images were processed using the DIC technique to obtain full field 3-D displacement (and strain) fields. The results were compared with those from conventional instruments. It was observed that the DIC technique could provide very accurate and detailed information, which is not possible to obtain using conventional techniques, including the in-plane and out-of-plane strains and their spatial variations, and the locations of high tensile and compressive strains which at later stages of loading result in cracking or crushing of concrete. Nevertheless, certain limitations of the DIC technique exist when used in testing of concrete structures such as the sensitivity to external light sources and preparation of the measurement surface, loss of data points after spalling of cover concrete, and the inability of the used algorithm to identify cracks and report crack widths. Currently, research is being conducted to address these limitations. Although providing useful information on the pros and cons of the DIC-based non-contact measurements for PC structures, it is noted that the results presented in this paper are based on a limited number of tests. Future research is needed to draw general conclusions.

Abstract: The changing economic conditions have challenged many financial institutions to search for more efficient and effective ways to assess their operations. Data Envelopment Analysis (DEA) is a widely used mathematical programming approach for comparing the inputs and outputs of a set of homogenous Decision Making Units (DMUs) by evaluating their relative efficiency. The traditional DEA treats DMUs as black boxes and calculates their efficiencies by considering their initial inputs and their final outputs. As a result, some intermediate measures are lost in the process of changing the inputs to outputs. In this study we propose a three-stage DEA model with two independent parallel stages linking to a third final stage. We calculate the efficiency of this model by considering a series of intermediate measures and constraints. We present a case study in the banking industry to exhibit the efficacy of the procedures and demonstrate the applicability of the proposed model.

Abstract: Plant pathologists detect diseases directly with the naked eye. However, such detection usually requires continuous monitoring, which is time consuming and very expensive on large farms. Therefore, seeking rapid, automated, economical, and accurate methods of plant disease detection is very important. In this study, three different apple diseases appearing on leaves, namely Alternaria, apple black spot, and apple leaf miner pest were selected for detection via image processing technique. This paper presents three soft-computing approaches for disease classification, of artificial neural networks (ANNs), and support vector machines (SVMs). Following sampling, the infected leaves were transferred to the laboratory and then leaf images were captured under controlled light. Next, K-means clustering was employed to detect infected regions. The images were then processed and features were extracted. The SVM approach provided better results than the ANNs for disease classification.