Image Processing
D. Mohammad Zamani; S. M. Javidan; M. Zand; M. Rasouli
Abstract
The main purpose of this study was to provide a method for accurately identifying the position of cucumber fruit in digital images of the greenhouse cucumber plant. After balancing the brightness histogram of the desired image, it multiplies the image with a window containing the image of a cucumber ...
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The main purpose of this study was to provide a method for accurately identifying the position of cucumber fruit in digital images of the greenhouse cucumber plant. After balancing the brightness histogram of the desired image, it multiplies the image with a window containing the image of a cucumber fruit, which causes larger coefficients to be obtained in areas with suspected cucumber. By extracting these local maximums, clusters of initial points are obtained as possible windows of cucumber existence. Then, in order to accurately detect the location of the cucumbers, these points and areas around them are referred to a neural network that has been trained using a number of images including cucumber images, non-cucumber images and their optimal responses. The proposed method was implemented in the Simulink toolbox of MATLAB software. The proposed method was then simulated using this network structure and tested on 120 images obtained from a greenhouse by a digital camera. The areas obtained from this network led to the accurate detection of the location of the cucumbers in the image. The proposed method was then simulated and tested on 120 images. The proposed method had a low error and was able to detect high levels of cucumber fruit in the images. This detection took an average of 5.12 seconds for each image. The accuracy of the network in correctly identifying the position of the cucumber fruit in the images was 95.3%. This method had low error and was able to detect a high rate at a good time of cucumber fruits in discover images.
Image Processing
Z. Azizpour; H. Vahedi; A. N. Lorestani
Abstract
IntroductionPistachio or Green Gold is one of the most important agricultural crops and is especially important for Iranian exports. A group of pistachio's pests mainly feed on pistachio, among which Idiocerus stali is very important. Conventional methods for identifying insects using identification ...
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IntroductionPistachio or Green Gold is one of the most important agricultural crops and is especially important for Iranian exports. A group of pistachio's pests mainly feed on pistachio, among which Idiocerus stali is very important. Conventional methods for identifying insects using identification keys are time-consuming and costly. Due to the rapid development of the Pistachio industry, the use of artificial intelligence techniques such as image processing, for identification and population monitoring is highly recommended. On the other hand, little research was carried out on I. stali. Therefore, in this research, I. stali was selected as a target insect for the identification and counting on sticky yellow cards using image processing techniques and artificial neural networks. The purpose of this study was to determine the feasibility of I. stali identification algorithm by image processing, to determine the possibility of separation and counting of I. stali from other non-target insects by artificial neural network and to determine its accuracy in identification of I. Stali.Materials and MethodsIdiocerus stali was selected as the target insect for identification. Sticky yellow cards were used for collecting samples. Taking the photos with the help of a SONY Handycam Camera, which had a 12-megapixel resolution and G lens, was carried out (SONY, HDR-XR500, CMOS, SONY Lens G, Made in Japan). Then insects were counted on each card manually and the data was recorded. The data, which were digital images of yellow sticky cards, were imported into the MatLab R2017b software environment. A total of 357 color properties and 20 shape's features for the identification of I. stali were extracted by an image processing algorithm. Color properties were divided into two categories of mean and standard deviation and characteristics related to vegetation indices. An ANN-PSO (Artificial Neural Network hybrid method-Particle Swarm Optimization) algorithm was used to select the effective features. The selected effective characteristics for insect classification were: Color index for extra collective vegetation related to HSL color space, normalized difference index for LCH color space, gray channel for color space YCbCr, second component index minus third component for color space YCbCr, area and mean of the first, second and third components of color space Luv.Results and DiscussionComparing the results with the results of Qiao et al. (2008), we found that in his study, which divided the data into three categories, for medium and high-density groups, the detection rate was 95.2% and 94.6%, respectively. On the other hand, in low densities (less than 10 trapped insects); its detection rate was 72.9%, while the detection rate of the classifier system designed in this study for different densities of trapped insects, was identical and equal to 99.59%. Also, comparing the results of this study with Espinoza et al. (2016), we found that their algorithm in whiteflies detection had a high accuracy of about 0.96 on a sticky yellow card, while the Thrips identification algorithm accuracy was 0.92 on a sticky blue card. As stated above, the correct detection rate of I. stali by the algorithm designed in this study was 99.72%.ConclusionThe results showed the feasibility of the new method for identifying the pest insects without destroying them on the farm and in natural light conditions and in a short time and with very high accuracy. This suggests that this algorithm can be applied to the machine vision system and can be used in future in the construction of agricultural robots.
M. Zandi; A. Ganjloo; M. Bimakr
Abstract
IntroductionIn recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. Neural networks have the capability of identifying complex nonlinear systems with their own high learning ability. Artificial Neural Networks ...
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IntroductionIn recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. Neural networks have the capability of identifying complex nonlinear systems with their own high learning ability. Artificial Neural Networks as a modern approach has successfully been used to solve an extensive variety of problems in the science and engineering, exclusively for some space where the conventional modeling procedure fail. A well-trained Artificial Neural Networks can be used as a predictive model for a special use, which is a data processing system inspired by biological neural system. The short storage life of hawthorn fruit and its high susceptibility to water loss and browning are the main factors limiting its marketability. So, it is important to evaluate parameters that affected the hawthorn quality. An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi-Sugeno fuzzy inference system. To estimate changes in fruit quality as a function of storage conditions, the evolution of certain quality-indicative properties such as color, firmness or weight can be used to provide related information on the quality grade of the product stored. Measurement of these parameters is an expensive and time-consuming process. Therefore, parameter prediction due to affecting factors will be more useful. In this study, the physicochemical properties of hawthorn fruit during various storage was predicted using artificial neural networks method. Hawthorn (Crataegus pinnatifida), belonging to the Rosaceae family, consists of small trees and shrubs. The color of the ripe fruit ranges from yellow, through green to red, and on to dark purple. Hawthorn is one of the most widely consumed horticultural products, either in fresh or processed form. It is also an important component of many processed food products because of its excellent flavor, attractive color and high content of many macro- and micro-nutrients.Materials and MethodsThe purpose of this study was a prediction of color, physical and mechanical properties of hawthorn fruit (Crataegus pinnatifida) during storage condition using artificial neural networks (ANNs) and adaptive network-based fuzzy inference system (ANFIS). Experimental data obtained from fruit storage, were used for training and testing the network. In the present research, artificial neural networks were used for modeling the relationship between physicochemical properties and color attributes with different storage time. Several criteria such as training algorithm, learning function, number of hidden layers, number of neurons in each hidden layer and activation function were given to improve the performance of the artificial neural networks. The total number of hidden layers and the number of neurons in each hidden layer were chosen by trial and error. The network’s inputs include storage time, hawthorn moisture content and storage temperature and the network’s output were the values of the physicochemical and color properties. The training rules were Momentum and Levenberg-Marquardt. The transfer functions were TanhAxon and SigmoidAxon.Results and DiscussionTo predict the weight loss and firmness multilayer perceptron network with the momentum learning algorithm, topologies of 3-15-5-1 and 3-8-5-1 with R2=0.9938 and 0.9953 were optimal arrangement, respectively. The optimal topologies for color change, hue, Chroma were 3-9-7-1 (R2=0.9421), 3-9-3-1 (R2=0.9947) and 3-7-1 (R2=0.9535) respectively, with momentum learning algorithm and TanhAxon activation function. The best network for ripening index prediction was Multilayer perceptron network with the TanhAxon activation function, Levenberg-Marquardt Levenberg-Marquardt learning algorithm, topology of 3-5-1-1 and R2=0.9956.Conclusion Three factors including firmness, total soluble solids and titratable acidity were considered for ripening index calculation during fruits storage condition. Momentum and Levenberg-Marquardt learning algorithms with SigmoidAxon and TanhAxon activation functions were used for training the patterns. Results indicated artificial neural networks to be accurate and versatile and they predicted the quality changes in hawthorn fruits. The outcomes of this study provide additional and useful information for hawthorn fruits storage conditions.
Modeling
M. Rahmatian; R. Yeganeh; M. A. Nematollahi
Abstract
IntroductionTillage is a very important operation that influences the growth and productivity of agricultural products. It is necessary to introduce some conditions to improve soil physical properties, aeration, permeability and root development in tillage operations. However, in primary tillage, especially ...
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IntroductionTillage is a very important operation that influences the growth and productivity of agricultural products. It is necessary to introduce some conditions to improve soil physical properties, aeration, permeability and root development in tillage operations. However, in primary tillage, especially when moldboard ploughs are used, this may be time consuming and costly for researchers to use it in their research. Some researchers use physical experiments to perform the work, which the accuracy of the results is dependent on the measuring instruments precision. However, some other researchers use simulation and mathematical modeling to reduce the time and costs and increase the relative accuracy of the research results. Many studies have also shown that modeling the forces involved in tillage is a good way to estimate the performance of different tillage tools and improve their geometry. However, the key to success in numerical simulation of tillage operations is to simulate the exact instrumentation, based on the correct assumptions as well as the proper methods. The prediction of the forces involved in tillage tools has an important role in their design. Collecting data on the forces involved in tillage tool under different farm conditions is a time consuming and costly task. Therefore, the prediction of a tillage tool forces is very important for the designer and the user in order to achieve better performance of the tool. Materials and MethodsIn this study, a cylindrical moldboard made by Alpler Company in Turkey was used to simulate the moldboard. A measuring device was designed and constructed to measure the various points of the desired moldboard. Then, the spatial points obtained by the measuring device were presented to the SolidWorks 2016 software and the desired moldboard was modeled. The finite element method by Abacus 2016 was then used to simulate the interaction between soil and moldboard. Treatments used in simulated tillage operations included tillage depths (5, 10, 15, 20 and 25 cm) and forward speed (1, 1.5, 2, 2.5 and 3 millimeters per second). The independent variables were considered as tensile, vertical and lateral forces (Kilo newton). After simulating the tillage operations, tensile, vertical and lateral forces were obtained. These forces were modeled using response surface and artificial neural networks techniques. Then, the obtained models were compared using R2, RMSE and MRDM statistical indices and the best model was selected. Results and DiscussionWhen using the response surface method, the quadratic model was selected by using the maximum value of the statistical indices R2, R2a and R2p, among the linear, two-factor and quadratic models. Then, the significance of model variables was evaluated by using variance analysis. The forces were also modeled by using the neural network method. According to the fitting curves and statistical indices of R2, RMSE and MRDM for the tensile, vertical and lateral forces, it is revealed that both methods could well predict the forces but artificial neural network was more suitable than the response surface method. Moreover, by investigating the interactions of tillage treatments and forward speed on the forces in this research, it was observed that by increasing the depth of tillage and velocity, tensile, vertical and lateral forces were increased nonlinearly by 66.55%, 68.47%, and 64.76%, respectively. ConclusionRegarding all the results obtained from this study, it can be concluded that the developed models using the artificial neural network in this research was a good and powerful tool for predicting the forces involved in moldboard ploughs both in the field operations and in related studies. It is also recommended that the developed models in this study can be used to manage the tillage operations, such as selecting the proper tractor. However, it is also suggested that other affecting factors, such as moldboard angles, should be included in future models to increase the ability of the model to predict the forces involved in moldboard plows.
M. Kaveh; Y. Abbaspour Gilandeh; R. Amiri Chayjan; R. Mohammadigol
Abstract
Introduction Garlic (Allium sativum L.) is an important Allium crop in the world. Due to its therapeutic properties, it was cultivated in many countries. Furthermore, garlic is usually used as a flavoring agent; it may be used in the shape of powder or granule as a valuable condiment for foods. In addition ...
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Introduction Garlic (Allium sativum L.) is an important Allium crop in the world. Due to its therapeutic properties, it was cultivated in many countries. Furthermore, garlic is usually used as a flavoring agent; it may be used in the shape of powder or granule as a valuable condiment for foods. In addition to its use in food products, it was also widely used as an anticancer agent. Shallot (Allium hiertifolium Boiss. L) is a perennial and bulbous plant. It is from Alliaceae family and is an important medicinal plant. The shallot is native of Iran, and grows in the high pastures. Shallot is consumed in dry areas in most parts of the country. Also shallots have been well known in Iranian folk medicine and its bulbs have been widely used for treating rheumatic and inflammatory disorders. In addition, this plant is used in the preparation of significant amounts of potassium, phosphorus, calcium, magnesium, sodium, pickles and as an additive to yogurt and pickles. ANN as a modern approach has successfully been used to solve an extensive variety of problems in the science and engineering, exclusively for some space where the conventional modeling procedure fail. A well-trained ANN can be used as a predictive model for a special use, which is a data processing system inspired by biological neural system. When mathematical equations are difficult to extrapolate, and fuzzy logic is better when decisions must be made with the estimated values below the incomplete information. The fuzzy logic theory effectively addresses the uncertainty problems that solve the ambiguity. Materials and Methods The aim of this study was to predict moisture ratio of garlic and shallot during the drying process with fluidized bed dryer using mathematical model, artificial neural networks and fuzzy logic methods. Tests were carried out on three levels of inlet air temperature (40, 55 and 70 °C) and three inlet air velocities (0.5, 1.5 and 2.5 m s-1). To estimate the drying kinetic of garlic and shallot, five mathematical models were used to fit the experimental data of thin layer drying. Three factors (air temperature, air velocity and drying time) to forecast moisture ratio in fluidized bed dryer as independent variables for artificial neural networks and fuzzy logic was considered. Cascade forward back propagation (CFBP) and feed forward back propagation (FFBP) with Levenberg-Marquardt (LM), Bayesian learning (BR) algorithms for ANN and the Mamdani Fuzzy Inference System using triangular membership function were used for training patterns. Results and Discussion Consequently, the Page and Midilli et al. model was selected as the best mathematical model to describe the drying kinetics of the garlic and shallot slices, respectively. The results of artificial neural networks model for predicting MR showed that the R2 of 0.9994 and 0.9996; and and RMSE of 0.0036 and 0.0014 were obtained for garlic and shallot, respectively. Also, The fuzzy inference system presented the R2 of 0.9997 and 0.9998; and and RMSE of 0.0027 and 0.0011 for garlic and shallot, respectively. Comparing the results obtained from mathematical models, artificial neural networks and fuzzy logic, showed that the RMSE in the fuzzy logic was lower than artificial neural network and mathematical models. Conclusion Three factors (air temperature, air velocity and drying time) were considered for forecasting moisture ratio in fluidized bed dryer as independent variables using mathematical model, artificial neural networks and fuzzy logic. Cascade forward back propagation (CFBP) and feed forward back propagation (FFBP) with Levenberg-Marquardt (LM), Bayesian learning (BR) algorithms and the Mamdani Fuzzy Inference System using triangular membership function were used for training the patterns. Comparing the results obtained from mathematical models, artificial neural networks and fuzzy logic, showed that the root mean square error in fuzzy logic was lower than others.
Modeling
Gh. Shahgholi; H. Ghafouri Chiyaneh; T. Mesri Gundoshmian
Abstract
Introduction Soil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The ...
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Introduction Soil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The soil destruction may be as surface deformation or as subsurface compaction. Any way machine traffic destructs soil structure and as result has unfavorable effect on the yield. Hence, soil compaction recognition and its management are very important. In general, soil compaction is the most destructive effect of machine traffic. Modeling of ecological systems by conventional modeling methods due to the multitude effective parameters has always been challenging. Artificial intelligence and soft computing methods due to their simplicity, high precision in simulation of complex and nonlinear processes are highly regarded. The purpose of this research was the modeling of soil compaction system affected by soil moisture content, the tractor forward velocity and soil depth by multilayer perceptron neural network. Materials and Methods In order to carry out the field experiments, a tractor MF285 which was equipped with a three-tilt moldboard plough was used. Experiments were conducted at the Agricultural research field of University of Mohaghegh Ardabili in five levels of moisture content of 11, 14, 16, 19 and 22%, forward velocity of 1, 2, 3, 4 and 5 km.h-1, and soil depths of 20, 25, 30, 35 and 40 cm as a randomized complete block design with three replications. In this study, perceptron neural network with five neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was designed and trained. Results and Discussion Field experiments showed three main factors were significant on the bulk density (P<0.01). The mutual effect of moisture on depth and mutual binary effect of moisture on velocity and depth on velocity were significant (P<0.01). Mutual triplet effect of moisture on velocity on depth was significant (P<0.05). Maximum bulk density of 1362 kg/m3 was obtained at the highest moisture of 22% and the lowest forward velocity of 1 km/h at the depth of 20 cm. Whilst the minimum value of 1234.5 kg/m3 was related to the moisture, forward velocity and depth of 11%, 5 km/h and depth of 40 cm, respectively. Compaction increased as soil moisture content increased up to 22% which was critical moisture. In contrast, soil compaction decreased as the tractor velocity and soil depth increased. A comparison of neural network output and experimental results indicated a high determination coefficient of R2 = 0.99 between them. Also, the mean square error of the model was 0.174, in addition, mean absolute percentage error of the system (MAPE) was equal to %0.29 which showed high accuracy of neural network to model soil compaction.ConclusionIt was concluded that soil compaction increased as soil moisture content increased up to a critical value. Increasing soil moisture act as lubricant and soil layers compacted together. Hence knowledge of soil moisture can help us to manage soil compaction during agricultural operations. Increasing the tractor forward velocity reduced soil compaction. However, agricultural operations should be conducted at certain speeds to carry out the duty properly and increasing speed more that value decreases the efficiency of work.Neural network of MLP with 5 neurons in hidden layer and sigmoid function in middle layer and one neuron with linear transfer function was found the most accurate and precise in prediction of the soil bulk density. A high determination coefficient of R2 = 0.99 was found between measured and predicted values.
N. Gholamrezaei; K. Qaderi; K. Jafari Naeimi
Abstract
Introduction Energy consumption management is one of the most important issues in poultry halls management. Considering the situation of poultry as one of the largest and most developed industries, it is needed to control growing condition based on world standards. The neural networks as one of the intelligent ...
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Introduction Energy consumption management is one of the most important issues in poultry halls management. Considering the situation of poultry as one of the largest and most developed industries, it is needed to control growing condition based on world standards. The neural networks as one of the intelligent methods are applied in a lot of fields such as classification, pattern recognition, prediction and modeling of processes. To detect and classify several agricultural crops, a research was conducted based on texture and color feature. The highest classification accuracy for vegetables, grains and fruits with using artificial neural network were 80%, 86% and 70%. In this research, the ability to Multilayer Perceptron (MLP) Neural Network in predicting energy consumption, temperature and humidity in different coordinate placement of electronic control unit sensors in the poultry house environment was examined. Materials and Methods The experiments were conducted in a poultry unit (3000 pieces) that is located in Fars province, Marvdasht city, Ramjerd town, with dimensions of 32 meters long, 7 meters wide and 2.2 meters height. To determine the appropriate placement of the sensor, 60 different points in terms of length, width and height in poultry were selected. Initially, the data was divided into two datasets. 80 percent of total data as a training set and 20 percent of total data as a test set. From180 observations, 144 data were used to train network and 36 data were used to test the process. There are several criteria for evaluating predictive models that they are mainly based according to the difference between the predicted outputs and actual outputs. To evaluate the performance of the model, two statistical indexes, mean squared error (MSE) and the coefficient of determination (R²) were used. Results and Discussions In this study, to train artificial neural network for predicting the temperature, humidity and energy consumption, the trainlm algorithm (Levenberg-Marquardt) was used. To simulate temperature, humidity and energy consumption, networks were trained with two and three layers, respectively. Network with two layers with10 neurons in the hidden layer and one neuron in the output layer with (R²) equal to 0.96 and (MSE) equal to 0.00912, was given the best result for predicting temperature. For humidity electronic sensors, results showed that network with three layers with the 10 neurons in the first hidden layer, 20 neurons in the second hidden layer and one neuron in the output layer with (R²) equal to 0.8 and (MSE) equal to 0.00783 was the best for predicting humidity. Finally, network with two layers with 10 neurons in the first hidden layer, 10 neurons in the second hidden layer and one neuron in the output layer was selected as the optimal structure for predicting energy consumption. For this topology, (R²) and MSE were determined to 0.98 and 0.00114, respectively. Linear and multivariate regression for the parameters affecting temperature, humidity and energy consumption of electronic sensors was determined by the STATGR software. Correlation coefficients indicated that parameters such as length, height and width of the electronic control sensors placed in the poultry hall justified 82% of the temperature changes, 61% of the humidity changes and 92% of the energy consumption changes. Therefore, comparing with correlation coefficients obtained from the neural network models, the highest correlation coefficient was related to energy parameter and the lowest correlation was linked to humidity parameter. Conclusion The results of the study indicated the high performance for predicting temperature, humidity and energy consumption. The networks hadthree inputs including length, width and height of electronic sensor positions and an output for temperature, humidity and energy consumption. For training networks the multiple layer perceptron (MLP) with error back propagation learning algorithm (BP) was used. Functions activity for all networks in hidden layers were tangentsigmoid and in the output layer, linear (purelin). Comparing the results of artificial neural network and logistic regression model showed that artificial neural network model with correlation coefficients of 0.98 (energy), 0.96 (temperature) and 0.8 (humidity) provided closer data to the actual data compared with regression models with correlation coefficients of 0.92, 0.82 and 0.61 for the energy, temperature and humidity respectively.
F. Nadi; S. Abdanan Mehdizadeh; O. Nourani Zonouz
Abstract
Introduction The significant of solar energy as a renewable energy source, clean and without damage to the environment, for the production of electricity and heat is of great importance. Furthermore, due to the oil crisis as well as reducing the cost of home heating by 70%, solar energy in the past two ...
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Introduction The significant of solar energy as a renewable energy source, clean and without damage to the environment, for the production of electricity and heat is of great importance. Furthermore, due to the oil crisis as well as reducing the cost of home heating by 70%, solar energy in the past two decades has been a favorite of many researchers. Solar collectors are devices for collecting solar radiant energy through which this energy is converted into heat and then heat is transferred to a fluid (usually air or water). Therefore, a key component in performance improvement of solar heating system is a solar collector optimization under different testing conditions. However, estimation of output parameters under different testing conditions is costly, time consuming and mostly impossible. As a result, smart use of neural networks as well as CFD (computational fluid dynamics) to predict the properties with which desired output would have been acquired is valuable. To the best of our knowledge, there are no any studies that compare experimental results with CFD and ANN. Materials and Methods A corrugated galvanized iron sheet of 2 m length, 1 m wide and 0.5 mm in thickness was used as an absorber plate for absorbing the incident solar radiation (Fig. 1 and 2). Corrugations in absorber were caused turbulent air and improved heat transfer coefficient. Computational fluid dynamics K-ε turbulence model was used for simulation. The following assumptions are made in the analysis. (1) Air is a continuous medium and incompressible. (2) The flow is steady and possesses have turbulent flow characteristics, due to the high velocity of flow. (3) The thermal-physical properties of the absorber sheet and the absorber tube are constant with respect to the operating temperature. (4) The bottom side of the absorber tube and the absorber plate are assumed to be adiabatic. Artificial neural network In this research a one-hidden-layer feed-forward network based on the back propagation learning rule was used to simulate the output temperature of a solar collector. The number of neurons within the hidden layer varied from 1 to 20. The hyperbolic tan- sigmoid and pure-line were used as the transfer function in the hidden layer and output layer, respectively. Minimization of error was achieved using the Levenberg-Marquardt algorithm. To carry out the aforementioned steps, the dataset (105 observations) was split into training (70 observations), and test (35 observations) data. Training sets used to develop models included air velocity, solar radiation, time of the day, ambient moisture and temperature values as inputs with an associated temperature of the collector as outputs. The aim of every training algorithm is to reduce this global error by adjusting the weights and biases. Results and Discussion Compare experimental results with ANN The performance of the three-layer ANN for the prediction of output temperature of flat-plate solar collector by the Levenberg–Marquardt training algorithm was illustrated in Fig. 4. ANN predicted output temperatures with R2 and RMSE of 0.92 and 1.23, respectively. Furthermore, the maximum error in prediction of output temperature of solar collector was 3.3 K. These results are in agreement with Tripathy and Kumar, (2009) those who have predicted the output temperatures of food product in the solar drier using ANN with and RMSE of 0.95 and 0.77, respectively. Compare experimental results with CFD simulation Fig. 6 shows that over the starting length of the absorber plate, there is a variation of the velocity profile which is caused by sharp geometry and it leads to some recirculation of the air in this part of absorber plate. After this part of boundary layers, flow is fully developed and velocity profile becomes smoother and constant. Fig. 8 shows that the predicted temperature was within the experimentally measured temperature. The highest differences between simulated and experimental temperatures were around -2.4K to 4.6K for different time periods. The temperature differences of 4K were reported by Selmi et al. (2008). This disagreement is due possibly to the fact that there are unknown experimental inputs such as turbulence intensity, radiative heat loss from the absorber sheet to the surroundings, Leakage, and measurement tool errors which were not accounted in the model simulations. These losses by radiation are significant at high irradiation levels. This result agrees with studies done in Badache et al. (2012). Thickness of absorber plate and radiation loss, in CFD model, does not take into consideration. For this reason maximum output temperature is seen in maximum radiation which is 12 p.m. While in real condition, it takes some time for absorber plate to get to its maximum temperature. Moreover, the numerical temperature is smaller than the real temperature after 12 p.m. This may occur because of the thickness of metal which keeping the absorbed heat and losing it after awhile. Generally there is a time step hysteresis for the numerical temperature. Conclusion According to this study it can be concluded that the ANN operates better than CFD to predict the output temperature operation. However, ANN method does not give any information about the prediction of temperature distribution and velocity profiles in the solar collector. Although prediction accuracy of the CFD method is less than ANN method, but the provided information on the velocity and temperature profile of the solar collector is still valuable.
Modeling
A. R. Tahavvor; Sh. Heidari; P. Zarrinchang
Abstract
IntroductionAutomation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized ...
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IntroductionAutomation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN) was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system.Materials and MethodsDesigning and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of action. The mechanical parts were computer-generated by engineering software in assembled, exploded and standard two-dimensional drawing required for the manufacturing process. Carrier and framework of control unit and actuator mainly designed to have the capability to support and hold the hardware and sensor assembly in an easy mountable fashion. This arrangement performed feasibility of the movement and allocating of control unit along the travel length of belt above the conveyor unit.In this work a multilayer perceptron network with different training algorithm was used and it is found that the backpropagation algorithm with Levenberge-Marquardt learning rule was the best choice for this analysis because of the accurate and faster training procedure. The Levenberg-Marquardt algorithm was an iterative technique that locates the minimum of a multivariate function that was expressed as the sum of squares of nonlinear real-valued functions. It has become a standard technique for non-linear least-squares problems, widely adopted in a broad spectrum of disciplines. LM can be thought of as a combination of steepest descent and the Gauss-Newton method. When the current solution was far from the correct one, the algorithm behaves like a steepest descent method: slow, but guaranteed to converge. When the current solution is close to the correct solution, it becomes a Gauss-Newton method. The Levenberg algorithm is:1. Do an update as directed by the rule above.2. Evaluate the error at the new parameter vector.3. If the error has increased as a result the update, then retract the step (i.e. reset the weights to their previous values) and increase l by a factor of 10 or some such significant factor, then goes to (1) and try an update again.4. If the error has decreased as a result of the update, then accept the step (i.e. keep the weights at their new values) and decrease l by a factor of 10 or so.Results and DiscussionThe study of multi artificial neural network learning algorithm by using base Levenberg–Marquardt was the best choice to estimate function experimental data convergence. Artificial neural networks databases were generated by experimental measurement data condition scales.It has been observed that the artificial neural networks could be used in height control. The function estimation problem with parameters in Levenberg–Marquardt algorithm showed a high performance and has a high speed, the error in the most cases were decrease and show a high convergence. Sum square error between ANN predictions and experimental measurements was less than 0.001 and correlation coefficient is above 0.99.ConclusionsANN method was capable to predict and capture the behavior of experimental measurements.ANN method can easily be used to determine new results with considerably less computational cost and time. Results show that the back-propagation method with Levenberg-Marquardt learning rule was suitable for training the networks.The Sum square error between ANN predictions and experimental measurements was less than 0.001 and the correlation coefficient is above 0.99.Replacement of the identity matrix with the diagonal of the Hessian in Levenberge-Marquardt update equation has great advantages in convergence and computation time.
F. Gheshlaghi; A. Mardani
Abstract
Introduction: Rolling resistance is one of the most substantial energy losses when the wheel moves on soft soil. Rolling resistance value optimization will help to improve energy efficiency. Accurate modeling of the interaction soil-tire is an important key to this optimization and has eliminated the ...
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Introduction: Rolling resistance is one of the most substantial energy losses when the wheel moves on soft soil. Rolling resistance value optimization will help to improve energy efficiency. Accurate modeling of the interaction soil-tire is an important key to this optimization and has eliminated the need for costly field tests and has reduced the time required to test.
Rolling resistance will change because of the tire and wheel motion parameters and characteristics of the ground surface. Some tire design parameters are more important such as the tire diameter, width, tire aspect ratio, lugs form, inflation pressure and mechanical properties of tire structure. On the other hand, the soil or ground surface characteristics include soil type; moisture content and bulk density have an important role in this phenomenon. In addition, the vertical load and the wheel motion parameters such as velocity and tire slip are the other factors which impact on tire rolling resistance. According to same studies about the rolling resistance of the wheel, the wheel is significantly affected by the dynamic load.
Tire inflation pressure impacted on rolling resistance of tires that were moving on hard surfaces. Studies showed that the rolling resistance of tires with low inflation pressure (less than 100 kPa) was too high.
According to Zoz and Griss researches, increasing the tire pressure increases rolling resistance on soft soil but reduces the rolling resistance of on-road tires and tire-hard surface interaction. Based on these reports, the effect of velocity on tire rolling resistance for tractors and vehicles with low velocity (less than 5 meters per second) is usually insignificant.
According to Self and Summers studies, rolling resistance of the wheel is dramatically affected by dynamic load on the wheel.
Artificial Neural Network is one of the best computational methods capable of complex regression estimation which is an advantage of this method compared with the analytical and statistical methods.
It is expected that the neural network can more accurately predict the rolling resistance. In this study, the neural network for experimental data was trained and the relationship among some parameters of velocity, dynamic load and tire pressure and rolling resistance were evaluated.
Materials and Methods: The soil bin and single wheel tester of Biosystem Engineering Mechanics Department of Urmia University was used in this study. This soil bin has 24 m length, 2 m width and 1 m depth including a
single-wheel tester and the carrier.
Tester consists of four horizontal arms and a vertical arm to vertical load. The S-shaped load cells were employed in horizontal arms with a load capacity of 200 kg and another 500 kg in the vertical arm was embedded. The tire used in this study was a general pneumatic tire (Good year 9.5L-14, 6 ply)
In this study, artificial neural networks were used for optimizing the rolling resistance by 35 neurons, 6 inputs and 1 output choices. Comparison of neural network models according to the mean square error and correlation coefficient was used. In addition, 60% of the data on training, 20% on test and finally 20% of the credits was allocated to the validation and Output parameter of the neural network model has determined the tire rolling resistance. Finally, this study predicts the effects of changing parameters of tire pressure, vertical load and velocity on rolling resistance using a trained neural network.
Results and Discussion: Based on obtained error of Levenberg- Marquardt algorithm, neural network with 35 neurons in the hidden layer with sigmoid tangent function and one neuron in the output layer with linear actuator function were selected. The regression coefficient of tested network is 0.92 which seems acceptable, considering the complexity of the studied process. Some of the input parameters to the network are speed, pressure and vertical load which their relationship with the rolling resistance is discussed.
The results indicated that in general trend of changes, the velocity is not affected by rolling resistance. Rolling resistance increases when tire pressure decreases. This is due to energy consumption for creating deflection on the body of the tire at the lower levels of tire inflation pressure. Another variable parameter is the vertical load on the wheel and its logical relation with rolling resistance using neural network. The results showed that increasing the vertical load increases the rolling resistance.
Conclusions: The major purpose of this study was the feasibility of using learning algorithms for interaction between wheel and soil. The parameters of the wheel when clashes with soil are not stochastic and in spite of their complexity follow a specific model, certainly. Artificial neural network trained with a correlation coefficient of 0.92 relatively had a good performance in education, testing and validation parts. To validate the network results, the impact of some factors on the extraction process such as velocity, load and inflation pressure was simulated. The main objective of this article is comparing the network performance with basic principles and other scientific reports.
In this regard, the predictions by trained neural network indicated that rolling resistance is independent of the velocity of the wheel. On the other hand, rolling resistance decreases by increasing tire inflation pressure which is a general trend similar to other studies and reports in the same mechanical condition of the soil tested. Rolling resistance changes are directly proportional to load vertical variations on the wheel in terms of quantity and quality, similar to experimental models such as Wismer and Luth.
M. Eskandari; A. Hosainpour
Abstract
Many research projects have been conducted about using ultrasonic sensors to estimate canopy volume. This study investigates using software applications such as artificial neural network (ANN) to improve the estimation of canopy volume by using ultrasonic sensors. A special experimental system was built. ...
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Many research projects have been conducted about using ultrasonic sensors to estimate canopy volume. This study investigates using software applications such as artificial neural network (ANN) to improve the estimation of canopy volume by using ultrasonic sensors. A special experimental system was built. The system had three ultrasonic sensors mounted vertically on a wooden pole with an equal distance of 0.6 m. As the wooden pole moves with a constant speed, the ultrasonic sensors measure the thickness of tree canopy with sampling rate of 4 Hz. Experiments were conducted on 5 samples of Benjamin tree at three speed levels of 35,45 and 55 cm s-1 in three replications. The real volume of trees was measured manually with rectangular elements method. After a full passing of ultrasonic sensors, potential features such as canopy diameter, average width of tree canopy and height of the tree canopy were considered as the inputs to the ANN model and the manually volume as the output of the model. Optimal ANN model was selected based on mean square error and correlation coefficient. The results showed that 13-16-7-1 was the optimal neuron numbers in ANN topology for estimating canopy volume.
R. Sedghi; Y. Abbaspour Gilandeh
Abstract
Suitable soil structure is important for crop growth. One of the main characteristics of soil structure is the size of soil aggregates. There are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most common ...
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Suitable soil structure is important for crop growth. One of the main characteristics of soil structure is the size of soil aggregates. There are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most common method. In this paper, a method based on adaptive neuro fuzzy inference system (ANFIS) was used to describe the soil fragmentation for seedbed preparation with combination of primary and secondary tillage implements including subsoiler, moldboard plow and disk harrow. Adaptive neuro fuzzy inference system (ANFIS) is a suitable approach to solving non-linear problems. ANFIS is a combination of fuzzy inference system (FIS) and an artificial neural network (ANN) method and it uses the ability of both models. In this study, the model inputs included “soil moisture content”, “tractor forward speed”and “working depth”. The performance of the model was evaluated using the statistical parameters of root mean square error (RMSE), percentage of relative error (ε), mean absolute error (MAE) and the coefficient of determination (R2). These parameters were determined as 0.135, 3.6%, 0.122 and 0.981, respectively. For the evaluation of the ANFIS model, the predicted data using this model were compared to the data of artificial neural network model. The simulation results by ANFIS model showed to be closer to the actual data compared with those made by the artificial neural network model.
