M. Hamdani; M. Taki; M. Rahnama; A. Rohani; M. Rahmati-Joneidabad
Abstract
IntroductionControlling greenhouse microclimate not only influences the growth of plants, but is also critical in the spread of diseases inside the greenhouse. The microclimate parameters are inside air, roof, crop and soil temperature, relative humidity, light intensity, and carbon dioxide concentration. ...
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IntroductionControlling greenhouse microclimate not only influences the growth of plants, but is also critical in the spread of diseases inside the greenhouse. The microclimate parameters are inside air, roof, crop and soil temperature, relative humidity, light intensity, and carbon dioxide concentration. Predicting the microclimate conditions inside a greenhouse and enabling the use of automatic control systems are the two main objectives of greenhouse climate model. The microclimate inside a greenhouse can be predicted by conducting experiments or by using simulation. Static and dynamic models and also artificial neural networks (ANNs) are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Usually thermal simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. So the main objective of this paper is comparison between two types of artificial neural networks (MLP and RBF) for prediction 4 inside variables in an even-span glass greenhouse and help the development of simulation science in estimating the inside variables of intelligent greenhouses.Materials and MethodsIn this research, different sensors were used for collecting the temperature, solar, humidity and wind data. These sensors were used in different positions inside the greenhouse. After collecting the data, two types of ANNs were used with LM and Br training algorithms for prediction the inside variables in an even-span glass greenhouse in Mollasani, Ahvaz. MLP is a feed-forward layered network with one input layer, one output layer, and some hidden layers. Every node computes a weighted sum of its inputs and passes the sum through a soft nonlinearity. The soft nonlinearity or activity function of neurons should be non-decreasing and differentiable. One type of ANN is the radial basis function (RBF) neural network which uses radial basis functions as activation functions. An RBF has a single hidden layer. Each node of the hidden layer has a parameter vector called center. This center is used to compare with the network input vector to produce a radially symmetrical response. Responses of the hidden layer are scaled by the connection weights of the output layer and then combined to produce the network output. There are many types of cross-validation, such as repeated random sub-sampling validation, K-fold cross-validation, K×2 cross-validation, leave-one-out cross-validation and so on. In this study, we pick up K-fold cross- validation for selecting parameters of model. The K-fold cross-validation is a technique of dividing the original sample randomly into K sub-samples. Different performance criteria have been used in literature to assess model’s predictive ability. The mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2) are selected to evaluate the forecast accuracy of the models in this study.Results and Discussion The results of neural networks optimization models with different networks, dependent on the initial random values of the synaptic weights. So, the results in general will not be the same in two different trials even if the same training data have been used. So in this research K-fold cross validation was used and different data samples were made for train and test of ANN models. The results showed that trainlm for both of MLP and RBF models has the lower error than trainbr. Also MLP and RBF were trained with 40 and 80% of total data and results indicated that RBF has the lowest sensitivity to the size data. Comparison between RBF and MLP model showed that, RBF has the lowest error for prediction all the inside variables in greenhouse (Ta, Tp, Tri, Rha). In this paper, we tried to show the fact that innovative methods are simple and more accurate than physical heat and mass transfer method to predict the environment changes. Furthermore, this method can use to predict other changes in greenhouse such as final yield, evapotranspiration, humidity, cracking on the fruit, CO2 emission and so on. So the future research will focus on the other soft computing models such as ANFIS, GPR, Time Series and … to select the best one for modeling and finally online control of greenhouse in all climate and different environment.ConclusionThis research presents a comparison between two models of Artificial Neural Network (RBF-MLP) to predict 4 inside variables (Ta, Tp, Tri, Rha) in an even-span glass greenhouse. Comparison of the models indicated that RBF has lower error. The range of RMSE and MAPE factors for RBF model to predict all inside variables were between 0.25-0.55 and 0.60-1.10, respectively. Besides the results showed that RBF model can estimate all the inside variables with small size of data for training. Such forecasts can be used by farmers as an appropriate advanced notice for changes in temperatures. Thus, they can apply preventative measures to avoid damage caused by extreme temperatures. More specifically, predicting a greenhouse temperature can not only provide a basis for greenhouse environmental management decisions that can reduce the planting risks, but also could be as a basic research for the feedback-feed-forward type of climate control strategy.
M. Asafi; R. Meamar Dastjerdi; M. Noshad
Abstract
Introduction In recent years, with increasing population growth and improving livelihoods, the consumption of vegetable oils has been increasing and has led to an increase in the level of oilseed cultivation. Sesame (Sesamum indicum L.) is an economically important crop which is widely cultivated all ...
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Introduction In recent years, with increasing population growth and improving livelihoods, the consumption of vegetable oils has been increasing and has led to an increase in the level of oilseed cultivation. Sesame (Sesamum indicum L.) is an economically important crop which is widely cultivated all over the world. Sesame has been considered as an oil plant for cultivation in Iran's climatic conditions recently. Sesame contains about 58-44% oil, 18-25% protein and 13.5% carbohydrate. Sesame is grown mainly in the developing tropical and subtropical areas of Asia, Africa. The three countries of China, India and Myanmar are accounted as the largest producers of this product in the world. Screw pressing is the most reliable method for extracting oil from oilseed grains. This method is simpler than others and is more efficient in terms of cost and food security. The general objective of this research was to investigate the effects of rotational speed, temperature, type of screwing and die diameter on the amount of oil extraction from sesame oil and prediction of oil extraction using artificial neural network and compare to regression models. Materials and Methods In this research, a sesame oil extractor machine was designed and manufactured. Various experiments were carried out to determine the amount of oil extracted based on variable parameters such as the shape of the press screw, the rotational speed, the temperature and the diameter of the die. The experiment was performed at three levels of press screw type (constant pitch, variable pitch and conical), temperature (30, 60, 90), three levels of rotational speed (20, 50, 80 rpm) and three level of die diameter (6, 8, 10mm). The experimental design was factorial based on completely randomized design with three replications. The mathematical software (Matlab, 2012b) was used to determine the optimal neural network. The type of network was Multi-Layer Perceptron (MLP). In order to design this network, there were 3 neurons in the first layer (input), which was equal to the number of studied variable parameters (type of screw, rotational speed and temperature), the second layer was hidden layer, and the last layer (the output) had a neuron for the extracted oil) was equal to the number of outputs examined in this network. The Levenberg-Marquardt algorithm (LM) was used to train it, which is one of the fastest neural network training methods. The Second-order polynomial regressions were performed based on the step-by-step method and non-meaningful sentences were eliminated from the model. The accuracy of the models was determined by calculating the correlation coefficient and root mean square error (RMSE) indices. Results and Discussion The results of the experiments showed that the effect of type of press screw, rotational speed, extraction temperature and die diameter on the amount of oil extraction was significant (p≤0.01). The highest amount of extracted oil was obtained at conical press screw , rotational speed of 50 rpm, temperature of 60 °C and die diamter of 6 mm. An artificial neural network of three-layer perceptron and regression models were used to predict the amount of sesame oil extracted. The results showed that the artificial neural network model (1-8-3) with a correlation coefficient of 97.47% and a RMSE of 0.65 compared to linear regression and quadratic regression models had the higher efficiency in predicting the amount of extracted oil. Conclusion In this study, the effect of temperature, rotational speed, press screw type and die diameter on the amount of extracted oil were investigated. The results of this study showed that the change in the type of screw, rotational speed, diameter of die and temperature on the amount of extracted oil was significant at 1% level. Results also showed that the artificial neural network method was more efficient than linear and second order regression methods.
S. Rahnama; M. Maharlooei; M. A. Rostami; H. Maghsoudi
Abstract
Introduction Date palm is one of the most valuable horticultural products in Iran, which includes 16% of non-oil exports to the world. Kerman province has the second rank for the cultivation area of date palm in Iran. Having information about the exact cultivated area has gained importance for further ...
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Introduction Date palm is one of the most valuable horticultural products in Iran, which includes 16% of non-oil exports to the world. Kerman province has the second rank for the cultivation area of date palm in Iran. Having information about the exact cultivated area has gained importance for further decision makings. To determine the cultivated area, organizations usually use census which has the disadvantages of high cost, wasting time and labor intensive. The aim of this research was to study the feasibility of using Landsat 8 OLI images to identify and classify the area under date palm cultivation. To accomplish this purpose, four supervised classification methods were evaluated. Materials and Methods The study area was in Bam region located at 200 km southeast of Kerman province. In this research, a total of 14 images of Landsat8 OLI satellite from the study area during fall and winter were downloaded from Landsat official web page. After preliminary inspections for interested classes (Date palm gardens, Lands covered with bare soil and forage crop fields), one of the images that was taken on Jan 14, 2017, was selected for further analysis. After initial corrections and processing, 32 images of alfalfa farms, 32 images of date palm gardens and 32 images of lands covered with bare soil, were selected using GPS data points collected in study area scouting. Shape files of all selected fields were created and utilized for supervised classification training set. The same process was also done for the unsupervised classification method. To evaluate the classification methods confusion matrix and Kappa coefficient were used to determine the true and miss-classified area under date palm cultivation. It is worth mentioning that these factors alone cannot identify the most powerful method for classification and they just give us a general overview to choose acceptable methods among all available methods. To identify the most powerful method among selected methods, confusion matrix and investigating the pixel transfers between classes is the crucial method. Results and Discussion Results of classifications revealed that the overall classification accuracy by using NN, MLC, SVM, MDC, and K-Means were 99.10% (kappa 0.973), 98.77% (kappa 0.975), 98.66% (kappa 0.973), 98.52% (kappa 0.97), and 52.66% (kappa 0.31) respectively. Concerning the confusion matrix in the NN method, the percentage of producer accuracy error in date palm class was 0% and in user, accuracy error was 1.44%. In the review of other methods, the lowest producer accuracy error value in date palm class obtained by NN and SVM methods was 0% and the highest producer accuracy error belonged to MLC method which was 1.35%. Checking the recognition power of other classes showed that in the soil class, the highest producer accuracy error was 2.32% by MDC method and the least one was 0.64% by MLC. For forage class, the highest producer accuracy error was calculated 37.07% by SVM and the least accurate one was 4.92% by MDC. Although the K-Means method with Kappa Coefficient of 0.31 did not have a good classification quality, concerning classes and samples, it successfully could identify date palm according to selective samples with 100% accuracy. Results of calculated date palm area using supervised classification methods versus actual area measurements showed that NN and SVM methods with the coefficient of determination (R2) of 0.9995% and 0.9986% had the highest coefficients. K-Means method with R-square of 0.9228% had a good correlation. In general, all supervised classification methods obtained acceptable results for distinguishing between date palm classes and two other classes. NN and SVM methods could successfully recognize date palm class. K-Means method also could recognize date palm class but the recognition included some errors such as dark clay soil textures which were classified as the date palm. Conclusion In general, overall accuracy and kappa Coefficient alone cannot identify the most powerful method for classifying and these methods just give us a general overview to choose an acceptable percentage of accuracy coefficients among available methods. After the initial selection, to identify the most powerful method of classification the pixel transfer calculations in a confusion matrix would be an acceptable technique.
S. Haroni; M. J. Sheikhdavoodi; M. Kiani Deh Kiani
Abstract
Introduction One of the most important sources of the sugar production is sugarcane.Sugar is one of the eight human food sources (wheat, rice, corn, sugar, cattle, sorghum, millet and cassava). Also sugarcane is mainly used for livestock feed, electricity generation, fiber and fertilizer and in many ...
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Introduction One of the most important sources of the sugar production is sugarcane.Sugar is one of the eight human food sources (wheat, rice, corn, sugar, cattle, sorghum, millet and cassava). Also sugarcane is mainly used for livestock feed, electricity generation, fiber and fertilizer and in many countries sugarcane is a renewable source for the biofuel. The efficient use of inputs in agriculture lead to the sustainable production and help to reduce the fossil fuel consumption and greenhouse gases emission and save financial resources. Furthermore, detecting relationship between the energy consumption and the yield is necessary to approach the sustainable agriculture. It is generally accepted that many countries try to reduce their dependence to agricultural crop productions of other countries. The being Independent on agricultural productions lead to take more attention to modern methods and the objective of all these methods is increasing the performance with the efficient use of inputs or optimizing energy consumptions in agricultural systems. Energy modeling is a modern method for farm management that this model can predict yield with using the different amount of inputs. The objective of this study was to predict sugarcane production yield and (greenhouse gas) GHG emissions on the basis of energy inputs. Materials and Methods This study was carried out in Khouzestan province of Iran. Data were collected from 55 plant farms in Debel khazai Agro-Industry using face to face questionnaire method. In this study, the energy used in the sugarcane production has considered for the energy analysis without taking into account the environmental sources of the energy such as radiation, wind, rain, etc. Energy consumption in sugarcane production was calculated based on direct and indirect energy sources including human, diesel fuel, chemical fertilizers, pesticides, machinery, irrigation water, electricity and sugarcane stalk. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. Input energy in agricultural systems includes both direct and indirect energy and renewable and non-renewable forms. Direct energies include human labor, diesel fuel, water for irrigation and electricity and indirect energies consisted of machinery, seed (cultivation of sugarcane has been done with cutting of sugarcane instead of seed), chemical fertilizer. Renewable energies include machinery, sugarcane stalk, chemical fertilizer while non-renewable energy consisted of machinery, chemical fertilizer, electricity and diesel fuel. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. The amounts of GHG emissions from inputs in sugarcane production per hectare were calculated by CO2 emissions coefficient of agricultural inputs. Energy modeling is an attractive subject for engineers and scientists who are concerned about the energy management. In the energy area, many different of models have been applied for modeling future energy. An artificial neural network (ANN) is an artificial intelligence that it can applied as a predictive tool for nonlinear multi parametric. Artificial neural network has been applied successfully in structural engineering modeling ANNs are inspired by biological neural networks. Results and Discussion The total energy used in the farm operations during the sugarcane production and the energy output was 1742883.769 and 111000 MJha_1, respectively. Electricity (52%) and chemical fertilizers (16%) were the most influential factors in the energy consumption. The electricity contribution was the highest due to the low efficiency of energy conversion in electric motors which were used for irrigation in the study area. In some areas, inefficient surface irrigation wastes a lot of water and energy (in forms of electricity). Another reason is that electricity energy equivalent for Iranian electricity production is higher than developed countries because Iran’s electricity grid is highly dependent on fossil fuels, so that 95% of the electrical energy in Iran is generated in thermal power plants using fossil fuels sources. In addition, the electricity transmission system is too old. GHG emissions data analysis indicated that the total GHG emissions was 415337.62 kg ha-1 (CO2eq) kgCO2eq ha-1 in which burning trash with the share of 62% had the highest GHG emission and followed by electricity (32%), respectively. The ANN model with 7-5-15-1 and 5-5-1 structure were the best model for predicting the sugarcane yield and GHG emissions, respectively. The coefficients of determination (R2) of the best topology were 0.98 and 0.99 for the sugarcane yield and GHG emissions, respectively. The values of RMSE for sugarcane production and GHG emission were found to be 0.0037 and 4.52×10-6, respectively. Conclusion The statistical parameters of R2 and RMSE demonstrated that the proposed artificial neural networks results have best accuracy and can predict the yield and GHG emission. It is generally showed that artificial neural networks have good potential to predict the yield of the sugarcane production.
Image Processing
P. Ataieyan; P. Ahmadi Moghaddam; E. Sepehr
Abstract
Introduction The color of soil depends on its composition and this feature is easily available and rather stable. Fast and accurate determination of soil organic matter distribution in the agricultural fields is required, especially in precision farming. General laboratory methods for determining the ...
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Introduction The color of soil depends on its composition and this feature is easily available and rather stable. Fast and accurate determination of soil organic matter distribution in the agricultural fields is required, especially in precision farming. General laboratory methods for determining the soil organic carbon are expensive, time-consuming with many repetitions, and high consumption of chemicals. Soil scientists use the Munsell soil color diagrams to define the soil color. Due to the nature of Munsell color diagrams; this system is less suitable for recognizing exact color of the soil because of weak relationship and limited number of chips. Fast methods like image processing, colorimetric and spectroscopy provide a description of most physical characteristics of the soil color. Some of the advantages of using digital cameras was used in this study, are simple sampling of screened soil, being free from chemicals and toxic materials and being fast, inexpensive and precise. Materials and Methods In this research, 80 A-horizon (0-10 cm) soil samples were collected from various agricultural soils in west Azerbaijan, in the North West of Iran. Soil texture of these fields was loam clay and clay. The amount of organic carbon in samples was determined. The camera was installed at the distance of 0.5 m from the Petri dish on the lighting compartment. Captured images with the digital camera were saved in RGB color space. Processing operations were done by MATLAB 2012 software. The features extracted from the color images are used to model the soil organic carbon including the color features in different spaces. Four-color spaces including RGB, HSI, LAB and LUV were studied to find the relation between the color and the soil organic carbon. Results and Discussion The correlation of R component in the RGB model shows a strong single-parameter relation with the organic carbon as R2=0.83. This good relationship can be due to the compound information of the red color component on both brightness and chromaticity dimension. The results also show that the organic carbon has a relatively strong correlation with the light parameters, intensity and lightness in the HSI, Lab and LUV color spaces respectively. It also has a weak correlation with other parameters, since they cannot have a proper linear correlation with organic carbon due to their structural nature. Results show that the highest correlation is obtained when the R and G components participate in modeling and the component B is omitted. One explanation of this high correlation could be the high sensitivity of component B to the intensity and the angle of light. Even a small change in light changes this component. Thus, in order to reduce the effect of this component, it is better to omit it from the models and make models independent of it. In next section, 51 data were used to train neural network, 14 data were used to test the network and 12 data for network validating. The amount of soil organic carbon was output of the neural networks that was estimated after training using the color component values of each segment. To assess the accuracy of the network, estimated values and actual values of each sample were plotted in a graph. The minimum MSE values were 7.28e-6 with 16 neurons, 3.77e-6 with 14 neurons, 4.8e-3 with 10 neurons and 3.77e-6 with 12 neurons for RGB, HSI, Lab and LUV color spaces respectively. Conclusion The availability of digital cameras, possibility to use it in different situations, being inexpensive and providing many samples are the advantages of this method to estimate the soil organic carbon amount. Therefore, digital photography can be used as an analytical method to evaluate the soil fertility. It also requires a low cost of sample testing, and can provide a good possibility of time and place classification for studying a vast area. However to develop more reliable models, more effort is needed, such as collecting more soil samples of different areas that include a wide range of soil features.
S. I. Saedi; R. Alimardani; H. Mousazadeh
Abstract
Introduction Global solar radiation is the sum of direct, diffuse, and reflected solar radiation. Weather forecasts, agricultural practices, and solar equipment development are three major fields that need proper information about solar radiation. Furthermore, sun in regarded as a huge source of renewable ...
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Introduction Global solar radiation is the sum of direct, diffuse, and reflected solar radiation. Weather forecasts, agricultural practices, and solar equipment development are three major fields that need proper information about solar radiation. Furthermore, sun in regarded as a huge source of renewable and clean energy which can be used in numerous applications to get rid of environmental impacts of non-renewable fossil fuels. Therefore, easy and fast estimation of daily global solar radiation would play an effective role is these affairs. Materials and Methods This study aimed at predicting the daily global solar radiation by means of artificial neural network (ANN) method, based on easy-to-gain weather data i.e. daily mean, minimum and maximum temperatures. Having a variety of climates with long-term valid weather data, Washington State, located at the northwestern part of USA was chosen for this purpose. It has a total number of 19 weather stations to cover all the State climates. First, a station with the largest number of valid historical weather data (Lind) was chosen to develop, validate, and test different ANN models. Three training algorithms i.e. Levenberg – Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian regularization (BR) were tested in one and two hidden layer networks each with up to 20 neurons to derive six best architectures. R, RMSE, MAPE, and scatter plots were considered to evaluate each network in all steps. In order to investigate the generalizability of the best six models, they were tested in other Washington State weather stations. The most accurate and general models was evaluated in an Iran sample weather station which was chosen to be Mashhad. Results and Discussion The variation of MSE for the three training functions in one hidden layer models for Lind station indicated that SCG converged weights and biases in shorter time than LM, and LM did that faster than BR. It means that SCG provided the fastest performance. However, the story for accuracies was different i.e. the BR, LM, and SCG algorithms provided the most accurate performances, respectively, both among one or two hidden layers. According to the evaluation criteria, six most accurate derived models out of 1260 tested ones for Lind station was 3-14-1 and 3-11-19-1 with LM, 3-20-1 and 3-20-19-1 with BR, and 3-9-1 and 3-20-17-1 with SCG training algorithm, and 3-20-19-1 topology with BR showed the best performance out of all architectures. Results of the evaluation of the six accurate models in the remaining 18 stations of Washington State proved that regardless of the climate, in each weather station, BR with its inherent automatic regularization, provided the most accurate models (0.87 67.41 %), and then SCG (0.90>R>0.83, 3.91>RMSEMAPE > 77.28 %). Therefore, the Bayesian neural networks, which showed the best performance among all Washington State weather stations, were evaluated for Mashhad station, as an Iran sample climate. The results proved the ability of the said networks for this climate (R=0.82, RMSE=3.92 MJm-2, MAPE=79.92%). Conclusion The results indicated that the Bayesian neural networks are capable of predicting global solar radiation with minimum inputs in different climates. This was concluded both in Washington State weather stations, which has a variety of climates, and also in Mashhad as an Iran sample weather station. These models would eliminate the need for complex climate-dependent mathematical relations or other models which are mostly dependent on many inputs. So, this algorithm would be a good means first in weather forecast practices, also in the design and development of solar assisted equipment, as well as in managerial practices in agriculture when monitoring crop solar-dependent processes like photosynthesis and evapotranspiration.
S. F. Mousavi; M. H. Abbaspour-Fard; M. H. Aghkhani; E. Ebrahimi; A. Soheili Mehdizadeh
Abstract
Introduction
The diagnosis of agricultural machinery faults must be performed at an opportune time, in order to fulfill the agricultural operations in a timely manner and to optimize the accuracy and the integrity of a system, proper monitoring and fault diagnosis of the rotating parts is required. ...
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Introduction
The diagnosis of agricultural machinery faults must be performed at an opportune time, in order to fulfill the agricultural operations in a timely manner and to optimize the accuracy and the integrity of a system, proper monitoring and fault diagnosis of the rotating parts is required. With development of fault diagnosis methods of rotating equipment, especially bearing failure, the security, performance and availability of machines has been increasing. In general, fault detection is conducted through a specific procedure which starts with data acquisition and continues with features extraction, and subsequently failure of the machine would be detected. Several practical methods have been introduced for fault detection in rotating parts of machineries. The review of the literature shows that both Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been used for this purpose. However, the results show that SVM is more effective than Artificial Neural Networks in fault detection of such machineries. In some smart detection systems, incorporating an optimized method such as Genetic Algorithm in the Neural Network model, could improve the fault detection procedure. Consequently, the fault detection performance of neural networks may also be improved by combining with the Genetic Algorithm and hence will be comparable with the performance of the Support Vector Machine. In this study, the so called Genetic Algorithm (GA) method was used to optimize the structure of the Artificial Neural Networks (ANN) for fault detection of the clutch retainer mechanism of Massey Ferguson 285 tractor.
Materials and Methods
The test rig consists of some electro mechanical parts including the clutch retainer mechanism of Massey Ferguson 285 tractor, a supporting shaft, a single-phase electric motor, a loading mechanism to model the load of the tractor clutch and the corresponding power train gears. The data acquisition section consists of a data analyzer (PCA-40), a personal computer, a piezoelectric accelerometer (VMI-102, DT-2234B), a tachometer and two rubber vibration absorbing elements are located between the rig’s components and the plate holder. An evaluation function was employed in order to achieve the optimal structure of neural network models by selecting the number of layers, number of cells in the layers, transfer function, training function, learning functions, performance function, and number of epochs, in such a way that the MSE of the calculated output error was minimal. The data were collected by means of the accelerometer sensor attached on the clutch mechanism, with three different working conditions (normal condition, with worn bearing, and with worn shaft), and three rotational speeds including: 1000 rpm, 1500 rpm and 2000 rpm. The Wavelet Packet Transform (WPT) was applied on the data-set for features vector extraction and the principle component analyses (PCA) was applied for dimension reduction of the features vector. The signal processing and the features extraction are the most important characteristics of the monitoring methodology, by which the working condition of the machine can be determined. These characteristics may be acquired by transforming the signals from the time domain to the frequency domain and MATLAB software is used for this purpose. This software receives the vibration data (time series of output voltage) which are in Excel files format. To remove the noise a suitable filtering procedure was used and finally the statistical parameters of time - frequency were calculated.
Results and Discussion
To verify the accuracy of the Genetic Algorithm model, the required data were collected from the training and testing steps of the Neural Network. For this purpose, the statistical parameters such as mean squared error (MSE), mean absolute error (MAE) and correlation coefficient (r) were used. The optimal parameters of the neural network obtained for the family of Db4. A trial and error procedure was used to minimize the mean square error of the network output and the desired amount of training step. During the training step, four neural networks including Db4, Db30, Db35 and Db40 achieved a gradient descent weight in the learning bias and four neural networks including Db9, Db15, Db20 and Db25 achieved a gradient descent with momentum weight in the learning bias. The two of the achieved neural networks including Db4, Db20 have circular logarithm function and the remaining networks have annular hyperbolic tangent transfer function. The most appropriate networks configuration was acquired when the network exhibited the minimal error with the training and testing data sets. The results show that the highest accuracy of the GA-ANN Artificial neural networks for all rotational speeds (1000, 1500 and 2000 rpm), and working conditions (intact gear and shaft, damaged bearing and worn shaft) observed for the network family of Db4. The highest error observed for the family of Db20 with MSE of 0.011.
Conclusions
Artificial neural networks can somewhat think and make decisions similar to an expert person. In this project in order to predict the occurrence of a failure of the clutch mechanism of MF 285 tractor, the experimental data were obtained using some sensors, and the data were transferred to a computer by means of a data analytical. By training of the neural networks, the errors were identified separately. The output data from the combined Neural Network and Genetic Algorithm shows that the performance of the prediction model is enhanced. Based on the experiments and calculations, the best data set belongs to the family of Db4 network with the least MSE equal to 4.09E-07 and r equal to 0.99999, indicating that the model could precisely detect the faulty bearings or shafts.
Design and Construction
S. Khalili; B. Mohammadi Alasti; M. Abbasgholipour
Abstract
Introduction: Grading agricultural products always has a particular important position for submission to domestic and overseas markets. The grading causes more profitable product ranges and customer satisfaction. Grading treatment is carried out based on various parameters such as color, ripeness level, ...
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Introduction: Grading agricultural products always has a particular important position for submission to domestic and overseas markets. The grading causes more profitable product ranges and customer satisfaction. Grading treatment is carried out based on various parameters such as color, ripeness level, dimensions and weight. Product weight is one of the most effective parameters in grading operation. Egg weight is directly related to the smallness and coarseness of eggs. In egg grading, the largeness value is very important in marketing. This research aimed to design, fabricate and evaluate the egg weighing system based on its dielectric properties.Materials and Methods: To perform this research, the stages of work are divided into several sections including, design and construction of the hardware section, writing code for the software section to collect data, conducting nondestructive tests and data collection, analysis of obtained data using artificial intelligence, and giving the results of analysis for device calibration of the system as the software code. The large eggs as dielectric substances cause more increase in the capacity of the capacitive sensor. Furthermore, by derivation of a relation between capacity of capacitive sensor and egg weight, one can predict the weight of the sample. A prototype unit of weighing system was designed and fabricated. The designed unit was composed of a chassis, a voltage source, a sinusoidal signal generator, a voltage measurement unit, an AVR micro controller, a COM port, a capacitive sensor, and an LCD and a keyboard. Neural network technique was used for egg weight prediction. The designed net receives 16 voltage values at different frequencies as inputs and its output is the egg weight. In order to calibrate and evaluate the weighing unit, 150 fresh egg samples were provided on egg laying day from a local poultry farm. Experiments were divided into three groups. The experiments were carried out on egg-laying day, and the second and fourth day after laying.Results and Discussion: In this study, two networks were built and evaluated. In the first series, two-layer networks and in the second series, three-layer networks were developed. In the two-layer neural networks, the number of neurons in the hidden layer was changed from 2 to 10.According to the given results for two-layer networks, two layer networks with 10 neurons offer the best results (the highest R-value and minimum RMSE) and it can be chosen as the most effective two-layer network. Three-layer neural networks have been composed of two hidden layers. The number of neurons in the first hidden layer was 10 and in the second layer it was changed from 1 to 20. Between three-layer networks, the network with 7 neurons with the highest R-value and the lowest error is the most appropriate network. It is even more efficient than the two-layer network with 10 neurons. So, the most appropriate structure is 1-7-10-16 and it has been selected for calibration of the weighing device. To evaluate and assess the accuracy of the weighing machine, weights of 24 samples of fresh eggs were predicted and compared with the actual values obtained using a digital scale with the accuracy of 0.01 gr. The paired t-test has been used to compare the measured and predicted values and the Bland-Altman method has been used for charting the accordance between the measured and predicted values. Based on the findings, the difference between the measured and predicted values was observed up to 5.4 gr that is related to a very large sample. The mean absolute error is equal to 2.21 gr and the mean absolute percentage error is equal to 3.75 %. According to the findings, 95% of the actual and approximate matching range to compare the two weighing methods is between -5.3 gr and 3.36 gr. Thus, the dielectric technique may underestimate the egg weight up to 5.3 gr or it may overestimate it up to 3.36 gr more than the actual prediction.Conclusions: The best results were obtained with a 3 layers net having 10 and 7 neurons, respectively in the first and the second hidden layers with the highest R-value, 0.983 and the lowest error, 0.502. Therefore, this net was applied for egg weight prediction. To evaluate the device, the weights of 24 fresh eggs were estimated using the device and were compared with actual values and the maximum error was observed to be equal to 5.4 gr.
R. Mohammadigol; M. H. Khoshtaghaza; R. Malekfar; M. Mirabolfathi; A. M. Nikbakht
Abstract
Pistachio contamination to aflatoxin has been known as a serious problem for pistachio exportation. With regards to the increasing demand for Raman spectroscopy to detect and classify different materials and also the current experimental and technical problems for measuring toxin (such as being expensive ...
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Pistachio contamination to aflatoxin has been known as a serious problem for pistachio exportation. With regards to the increasing demand for Raman spectroscopy to detect and classify different materials and also the current experimental and technical problems for measuring toxin (such as being expensive and time-consuming), the main objective of this study was to detect aflatoxin contamination in pistachio by using Raman spectroscopy technique and artificial neural networks. Three sets of samples were prepared: non-contaminated (healthy) and contaminated samples with 20 and 100 ppb of the total aflatoxins (B1+B2+G1+G2). After spectral acquisition, considering to the results, spectral data were normalized and then principal components (PCs) were extracted to reduce the data dimensions. For classification of the samples spectra, an artificial neural network was used with a feed forward back propagation algorithm for 4 inputs and 3 neurons in hidden layer. Mean overall accuracy was achieved to be 98 percent; therefore, non-liner Raman spectra data modeling by ANN for samples classification was successful.
A. Rohani; H. Makarian
Abstract
With the rise of new powerful statistical techniques and neural networks models, the development of predictive species distribution models has rapidly increased in ecology. In this research, a learning vector quantization (LVQ) and multi layer perceptron (MLP) neural network models have been employed ...
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With the rise of new powerful statistical techniques and neural networks models, the development of predictive species distribution models has rapidly increased in ecology. In this research, a learning vector quantization (LVQ) and multi layer perceptron (MLP) neural network models have been employed to predict, classify and map the spatial distribution of A. repens L. density. This method was evaluated based on data of weed density counted at 550 points of a fallow field located in Faculty of Agriculture, Shahrood University of Technology, Semnan, Iran, in 2010. Some statistical tests, such as comparisions of the means, variance, statistical distribution as well as coefficient of determination in linear regression were used between the observed point sample data and the estimated weed seedling density surfaces by two neural networks to evaluate the performance of the pattern recognition method. Results showed that in the training and test phases non significant different was observed between average, variance, statistical distribution in the observed and the estimated weed density by using LVQ neural network. While this comparisions was significant except statistical distribution by using MLP neural network. In addition, results indicated that trained LVQ neural network has a high capability in predicting weed density with recognition erorr less than 0.64 percent at unsampled points. While, MLP neural network recognition erorr was less than 14.6 percent at unsampled points. The maps showed that, patchy weed distribution offers large potential for using site-specific weed control on this field.
