Modeling
J. Baradaran Motie; M. H. Aghkhani; A. Rohani; A. Lakziyan
Abstract
Introduction Presently, the loss of ground water levels and the increase in dissolved salts have given importance to the determination of salinity and the management of their variations in irrigated farms. Soil electrical conductivity is an indirect method to measure soil salts. The direct electrode ...
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Introduction Presently, the loss of ground water levels and the increase in dissolved salts have given importance to the determination of salinity and the management of their variations in irrigated farms. Soil electrical conductivity is an indirect method to measure soil salts. The direct electrode contact method (Wenner method) is one of the widely used methods to rapidly measure soil ECa in farms. However, soil scientists prefer soil actual electrical conductivity (saturated extract electrical conductivity) (ECe) as an indicator of soil salinity, though its measurement is only possible in the laboratory. The aim of this study was to find a relationship between the prediction of soil actual electrical conductivity (ECe) in terms of temperature, moisture, bulk density and apparent electrical conductivity of soil (ECa). Thereby, the estimation of ECe would allow the partial calculation of ECa that is dependent upon soil salinity and dissolved salts. Materials and Methods This study used RBF neural network in Box-Behnken statistical design to explore the impacts of effective parameters on direct contact method in the measurement of soil ECa and provided a model to estimate ECe from ECa, temperature, moisture content and bulk density. In this study soil apparent electrical conductivity (ECa) was measured by direct contact (Wenner) method. The present study considered four most effective factors: ECa (saturated paste extract EC), moisture, bulk density, and temperature (Baradaran Motie et al., 2010). Given the characteristics of farming soils in Khorasan Razavi Province (Iran), the maximum and minimum of each independent variable were assumed as 0.5-6 mS.cm-1 for ECe, 5-25% for moisture content, 1-1.8 g.cm-3 for bulk density, and 2-37°C for soil temperature. Considering the experimental design, three moisture levels (5, 15 and 25%), three salinity levels (0.5, 3.25 and 6 mS.cm-1), three temperature levels (2, 19 and 37°C) and three compaction levels with bulk densities of 1, 1.4 and 1.8 g.cm-3 were assumed in 27 trials with predetermined arrangement on the basis of Box-Behnken technique. 13 common algorithms were explored in MATLAB software package for the training of the artificial neural network in order to find the optimum algorithm (Table 4). The input layer of the network designed by integrating a Randomized Complete Block Design (RCBD) with k-fold cross-validation. Using k-fold cross-validation, 20 different datasets were generated for training and validation of RBF neural network. Results and Discussion A combination of an RCBD and k-fold cross-validation was used. The results of both training and validation phases should be considered in the selection of training algorithm. In addition, R2 of T1 training algorithm had a much lower standard deviation than other training algorithms. The lower standard deviation is, the more capable the algorithm would be in learning from different datasets. Considering all aspects, trainbr (T2) training algorithm was found to have the best performance among all 13 training algorithms of the neural network. Table 7 tabulates the results of means comparison for R2 of RBF model for both training and validation phases resulted from the application of some combinations of S and L2 factors as interaction. As can be observed, R2 = 0.99 for all of them with no significant difference. However, the magnitude of order differed between training and validation phases. Given the importance of the training phase, L2=9 and S=0.1 were regarded as the optimum values. The sensitivity analysis of the network revealed that soil ECa, moisture, bulk density, and temperature had the highest to lowest impact on the estimation of soil ECe, respectively. This model can improve the precision of soil ECa measurement systems in the estimation and preparation of soil salinity maps. Furthermore, this model can save in time of data analysing and soil EC mapping because it does not need data recollection for the calibration of systems. A validation prose was done with a 60 field collected data set. The results of validation show R2=0.986 between predicted and measured ECa. Conclusion The present research focused on improving the precision of soil ECe measurement on the basis of easily accessible parameters (ECa, temperature, moisture, and bulk density). In conventional methods of soil EC mapping, the systems only measure soil ECa and then calibrate it to ECe by collecting some samples and using statistical methods. In this study, Soil ECe was estimated with R2 = 0.99 by a multivariate artificial neural network model with the inputs, including ECa, temperature, moisture, and bulk density of soil without any need to collect further soil samples and calibration process. The Bayesian training algorithm was introduced as the best training algorithm for this neural network. Thereby, soil EC variation maps can be prepared with higher precision to estimate the spatial spread of salinity in farms. Also, the results imply that soil ECa, moisture, bulk density and temperature have the highest to lowest effectiveness on the estimation of soil ECe, respectively.
M. H. Aghkhani; J. Baradaran Motie
Abstract
Introduction: Separation and grading of agricultural products from the production to supply, has notable importance. The separation can be done based on physical, electrical, magnetic, optical properties and etc. It is necessary for any development of new systems to study enough on the properties and ...
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Introduction: Separation and grading of agricultural products from the production to supply, has notable importance. The separation can be done based on physical, electrical, magnetic, optical properties and etc. It is necessary for any development of new systems to study enough on the properties and behavior of agricultural products.
Some characteristics for separation are size (length, width and thickness), hardness, shape, density, surface roughness, color, speed limit, aerodynamic properties, electrical conductivity, elasticity and coefficient of static friction point.
So far, the friction properties of agricultural products used in the separating process, but the effect of electrostatic charging on static and dynamic coefficients of friction for separation had little attention. The aim of this study was to find out the interactions between electrostatic and friction properties to find a way to separate products that separation is not possible with conventional methods or not sufficiently accurate. In this paper, the separation of close and smiley pistachios by electrostatic charging was investigated.
Materials and Methods: Kallehghoochi pistachio cultivar has the top rank in production in Iran. Therefore, it was used as a sample.
The experimental design that used in this study, had moisture content at three levels (24.2, 14.5 and 8.1 percent), electric field intensity at three levels (zero, 4000 and 7000 V), speed of movement on the surface at three levels (1300, 2500 and 3300 mm per minute), friction surface (galvanized sheet iron, aluminum and flat rubber) and pistachio type at two levels (filled splits and closed) that was measured and analyzed in completely randomized factorial design.
A friction measuring device (built in Ferdowsi University of Mashhad) used to measure the friction force. It has a removable table that can move in two directions with adjustable speed. The test sample put into the vessel with internal dimensions of 300 × 150 × 25 mm and with wall thickness of 5 mm placed on trolleys. In the bottom of the container a separate aluminum plate was installed as the negative pole of the electric field. The friction plates as a positive pole placed on top of the sample. There were no contact between friction plates and walls of vessel (samples were about 2 to 3 mm higher from the edges of wall).
Frictional force changes due to movement of table, measured and recorded by an accurate load cell. From force-displacement curves, the coefficient of dynamic friction and static coefficient of friction calculated. In general, according to the experimental design, 486 tests were performed.
Results and Discussion: According to the results of statistical analysis, there is significant interaction affect between pistachios type and electrical field, as well as, the interaction between electrical field and speed, on dynamic coefficient of friction. It means two pistachio types can be separated by electrical charging.
Different physical properties of surface of filled non-splits pistachio nuts (such as corners and edges) and filled splits ones, caused differences in the distribution of electric charge and as a result, its interaction with the electric field were significant.
Changes in dynamic coefficient of friction according to the electric field intensity at different levels of moisture content and speed on the friction surfaces of iron, aluminum and rubber, was drawn in Fig.4, 5 and 6, respectively. These figures reflected the reduction of dynamic coefficient of friction by increasing the movement speed of table.
According to Fig.7, increasing the intensity of the electric field increases the dynamic coefficient of friction. Because this leads to build the opposition charge on samples and galvanized iron sheets, and with increase of electrical field, these charges will rise.
Fig.9 shows different trends of variation of dynamic coefficient of friction against moisture on rubber surface. This chart shows the higher coefficient of friction of filled non-splits samples than filled splits in all cases and shows an increasing trend with increasing humidity.
Conclusions: Table 2 presents the dynamic coefficients of friction in different states on different levels of moisture content. According to this table, the maximum difference was achieved in moisture content of 8% (which is close to the product storage moisture) in rubber surface with field strength of 7000 V and 1300 mm per minute speed. On 14 percent moisture content, the maximum difference was achieved on aluminum surface by 2500 millimeter per minute speed and 7000 V field strength. By the results, on 24 percent moisture content (the moisture close to peeling process) the maximum difference between filled non-splits and filled splits pistachios friction was achieved on aluminum surface, 7000 V electric field strength and 2500 millimeter per minute table speed.
Thus, to have a separation system, the aluminum surface, 7000 V electric field strength and adjustable speed between 1300 to 2500 mm per minute is recommended.
Design and Construction
A. Damirchi; M. H. Aghkhani; M. Khojastehpour; J. Baradaran Motie
Abstract
Introduction: In conventional farming, the soil and crop are considered uniform in different locations of the farm and the fertilizers are applied according to the average of soil needs with an additional percentage for safety (Loghavi, 2003). Non-essential chemical fertilizers in the field have harmful ...
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Introduction: In conventional farming, the soil and crop are considered uniform in different locations of the farm and the fertilizers are applied according to the average of soil needs with an additional percentage for safety (Loghavi, 2003). Non-essential chemical fertilizers in the field have harmful effects and social, economic and environmental concerns will increase. Many fertilizers go into the surface waters and ground waters and cause poisoning and environmental pollution without being absorbed by the plants. In variable rate technology, the soil fertilizer needs a map of all parts of the farm which is prepared with the GIS system. This map is uploaded on the computer before variable rate fertilizer machine starts. The computer continually controls the fertilizing rate for each part of the farm using a fertilizing map and global positioning system. The purpose of this study is to construct and evaluate a map-based variable rate fertilizer system that can be installed on a common fertilizer in Iran to be used as a variable rate system.Materials and methods: In common variable rate fertilizers, the rotational speed change of the distributor shaft is used to apply fertilizers. In this way, a DC motor is assembled on the main shaft of all distributors, which reduces the fertilizing accuracy. The reason for this is that there is no separation for units along the width of the fertilizer. Therefore, we used one DC motor for each distributor and another motor to rotate the agitator in the tank.System Set up: To design and select a suitable engine, the required torque for the rotation distributor shaft was measured by a torque meter and the amount of 2.1 Nm was acquired for that. With regard to the maximum rate of nitrogen fertilizer for land and tractor speed at the time of fertilizing, the order of 350 kg per hectare and 8 km per hour, the maximum distributor shaft speed and power required to rotate distributor shaft were calculated to be 55 rpm and 6.9 watts, respectively. The selected motor was rated 27.5 watts, 24 volts and 7.5 amperes (Since there were no 6.9 watts motors in the market, a more powerful motor was selected). According to the gear ratio and motor speed, the speed of the distributor shaft was adjustable in the range of 0 to 65 rpm. To determine the speed and position with respect to the direction, a central encoder (E50S8-600-6-L-5 model manufactured by Autonix Korea) was used on the ground wheel. The encoder had 600 pulses per revolution of the axis.Performance evaluation of the system: Performance evaluation of the system consists of two parts; static and moving tests. In static tests, the purpose was the determination of the fertilizer loss (in grams), due to changes in distributor speed as well as the accuracy of the electromechanical control system according to the command values sent to the device. Results of this part were used for the calibration of the device.In motion tests: In motion tests, the assessment of fertilizer loss was due to values set in a given situation and the accuracy of planted fertilizer in place (delay and acceleration) is reviewed. The delay is found by the determination of the distance that the fertilizer was placed after the desired location on the ground and the acceleration is found by the determination of the distance that the fertilizer is placed before the desired location on the ground.Results and discussion: The distributor flow rate on F0 valve position was measured for different rotation speeds. The correlation (linear regression) between the planted fertilizer and rotation speed of distributor shaft (rpm) were 0.99 for y=71.636x+75.182. So, it can be deduced that these two parameters have a good linear correlation. The results achieved from diagrams and regression model were used in the programming of the system control unit. Thus, by reading the distributors motor speed, the amount of fertilizer can be calculated and the amount of used fertilizer according to the need of the farm in each part is controlled. The effect of plot length on the amount of fertilizing on 25% need level was not significant, but it was significant on 50% need level. This is due to stopping and starting fertilizer flow during the test, changes in motors speed and error of these on fertilizer output at a certain amount of fertilizing so that at the 25% need level, the error resulting from these factors had less share on the amount of plant fertilizer and the effect of plot length was not significant according to the system default. On the other hand, the effect of forward speed was significant on the 50% need level and insignificant on the 25% need level.Conclusions: In order to calculate the accuracy of the system, the error from the application amount of fertilizer was measured at different fertilizing rates. The correlation between the adjusted fertilizing rate and the measured fertilizing rate was 0.98 with regression model of y=1.0475x which shows the good accuracy of the system.
Design and Construction
J. Baradaran Motie; M. H. Aghkhani; M. H. Abbaspour-Fard; A. Lakziyan
Abstract
The issue of soil salinity is one of the snags for increasing agricultural productivity, which must be inhibited by appropriate devise and scientific management. One way to identify salty areas of farm lands is to prepare salinity maps. In this study, a prototype soil apparent electrical conductivity ...
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The issue of soil salinity is one of the snags for increasing agricultural productivity, which must be inhibited by appropriate devise and scientific management. One way to identify salty areas of farm lands is to prepare salinity maps. In this study, a prototype soil apparent electrical conductivity measuring and mapping device, was designed and built. This device employs direct contact method of electrodes with soil (Also called Wenner method). The system inputs include power supply voltage, location signal from a GPS receiver and signal of voltage between the electrodes. The outputs include the apparent electrical conductivity with respective to geographical coordinate that created in a TEXT file, and then transmitted through a RS-232 serial port to a PC. Electrical conductivity data calibrated and mapped using ESAP-95 software package. To evaluate the device, electrical conductivity map of a land with area of 0.8 Ha surveyed in two ways: using the on the go EC mapper and capturing soil samples manually. The results of these two methods were then compared. Assessment of the device in a clay-loamy soil with low salt level, showed a good correlation with the laboratory EC, having mean error (ME) of -15.27μS.cm-1. Point to point comparison between surveyed data and laboratory EC’s shown that in 67 percent of measurements the errors were under 10 percent. These errors are acceptable mainly due to unknown soil variables and in comparison with other research findings.
