Precision Farming
J. Nasrollahi Azar; R. Farrokhi Teimourlou; V. Rostampour
Abstract
IntroductionPrecision agriculture is a modern approach to farming that ensures the crops and soil receive exactly what they need for optimum health and productivity. Precision agriculture offers the potential to automate and simplify the collection and analysis of information. It allows management decisions ...
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IntroductionPrecision agriculture is a modern approach to farming that ensures the crops and soil receive exactly what they need for optimum health and productivity. Precision agriculture offers the potential to automate and simplify the collection and analysis of information. It allows management decisions to be quickly made and implemented in small areas of larger fields. Measuring acoustic signals with a cone penetrometer is an advanced and inexpensive method that provides a lot of information about the soil within the shortest amount of time and with the lowest cost. The texture of the soil determines the percentage of the constituents of the mineral part of the soil such as sand, silt, and clay.In this study, an acoustic penetrometer is developed to provide an accurate method for determining the soil texture. This system uses a microphone to record the sound produced by the cone-soil contact and correlates this data with the soil texture.Materials and MethodsAn acoustic cone penetrometer (ACPT) was designed to determine if there is a relationship between the sound produced at the cone-soil contact and soil particle size. Three types of cones with angles of 30, 45, and 60 degrees, diameter of 20.27 mm, and rod length of 300 mm according to ASAE standard S313.3 FEB1999ED (R2013) were used to determine the relationship between sound and soil texture and to choose the best angle. A microphone (20-20,000 Hz) suitable for fast dynamic responses was used to record the audio signals produced from the soil. Audio signals were stored online through the oscilloscope section of Matlab software. To create the controlled vertical movement of the cones, a mechanical mechanism with electronic controllers was designed. This mechanism can be connected to the rails of the soilbin available in Urmia University, Iran, and is made of a 5 hp electric motor with a gearbox, an inverter for controlling the rotational speed of the electric motor, and a digital ruler for recording vertical movement. Soil samples were tested in 19-liter bins.Acoustic signals received from the microphone were processed in the time-frequency domain using wavelet transform. In this research, Daubechi function type 3 is used to analyze acoustic signals. It is not possible to use the processed acoustic signals directly for statistical analysis. Therefore, the relevant features should be extracted from them. From the 30 features of time domain signals, the most effective and main features include: SUM, Max, RMS, average, Var, kurtosis, and Moment4. They were ranked using the feature selection section of WEKA 3.9.2 software to avoid increasing the volume of calculations, increase processing speed, and reduce errors. The characteristic vector of the sub-signals of several different soil samples was analyzed to distinguish the soil type and constituents namely sand, silt, and clay.Results and DiscussionThe best type of cone was selected using WEKA software. The number of features in the d1 sub-signals was higher for the 45-degree cone, and it can be concluded that with this cone, the soil type can be better recognized.The average values of characteristics in clay, loam, and sand had an increasing trend, respectively, and were statistically significant with a probability of 1% and 5%.Acoustic signals for clay soil, which has a heavy texture and small particles, have minimum amplitude, and for loamy and sandy soils, they were observed as medium and maximum, respectively. This will cause the values of the selected features of clay soil to be low, and as a result, the average values, variance, and standard deviation are also low. They would be higher for loamy and sandy soil which have larger particles. It can be deduced that, as the size of the soil particles increases, the particles hitting the cone wall would become heavier and would affect the frequency and amplitude of the signal. This will result in the increase of signal amplitude values and, the sum, max, and mean values as well.ConclusionAmong the sub-signals, the maximum effect of soil texture type changes was related to d1 sub-signals for the 45̊ cone, and these signals had more potential to identify the soil texture type. Among the features, the sum, average, VAR, and RMS were significant at 1% probability levels. Therefore, these features have more potential to detect the type of soil texture in the mentioned sub-signal. Additionally, the effect of soil texture change on Moment and Kurtosis characteristics was significant at 5% probability levels.
Modeling
A. Zareei; R. Farrokhi Teimourlou; L. Naderloo; M. H. Komarizade
Abstract
Introduction Spiral conveyors effectively carry solid masses as free or partly free flow of materials. They create good throughput and they are the perfect solution to solve the problems of transport, due to their simple structure, high efficiency and low maintenance costs. This study aims to investigate ...
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Introduction Spiral conveyors effectively carry solid masses as free or partly free flow of materials. They create good throughput and they are the perfect solution to solve the problems of transport, due to their simple structure, high efficiency and low maintenance costs. This study aims to investigate the performance characteristics of conveyors as function of auger diameter, rotational speed and handling inclination angle. The performance characteristic was investigated according to volumetric efficiency. In another words, the purpose of this study was obtaining a suitable model for volumetric efficiency changes of steep auger to transfer agricultural products. Three different diameters of auger, five levels of rotational speed and three slope angles were used to investigate the effects of changes in these parameters on volumetric efficiency of auger. The used method is novel in this area and the results show that performance by ANFIS models is much better than common statistical models. Materials and Methods The experiments were conducted in Department of Mechanical Engineering of Agricultural Machinery in Urmia University. In this study, SAYOS cultivar of wheat was used. This cultivar of wheat had hard seeds and the humidity was 12% (based on wet). Before testing, all foreign material was separated from the wheat such as stone, dust, plant residues and green seeds. Bulk density of wheat was 790 kg m-3. The auger shaft of the spiral conveyor was received its rotational force through belt and electric motor and its rotation leading to transfer the product to the output. In this study, three conveyors at diameters of 13, 17.5, and 22.5 cm, five levels of rotational speed at 100, 200, 300, 400, and 500 rpm and three handling angles of 10, 20, and 30º were tested. Adaptive Nero-fuzzy inference system (ANFIS) is the combination of fuzzy systems and artificial neural network, so it has both benefits. This system is useful to solve the complex non-linear problems in agricultural engineering applications. ANFIS by linguistic concepts can establish and inference non-linear relationship between inputs and outputs. In this research, generally modeling was performed by using toolbox of ANFIS and coding in MATLAB software. Five important and effective factors in modeling were optimized until the best ANFIS model was obtained. The five factors were: type of fuzzy sets for inputs, number of fuzzy sets for inputs, type of fuzzy set for output, method of optimization and number of epochs. The statistical model was done by using SPSS and in the multivariate regression method. In multivariate linear regression in statistical model, the independent variables were auger blade diameter, rotational speed and the angle of slope of the auger and dependent variable was volumetric efficiency. The factorial test in randomized complete block design was conducted for variance analysis of volumetric efficiency. Mean Comparison of volumetric efficiency in different levels of factors was performed using Duncan' test in 5% level. Conclusion In this study, volumetric efficiency of spiral conveyors was investigated as a function of auger blade diameter, auger rotational speed and slope of transfer. The performance was measured in terms of volumetric efficiency using ANFIS and statistical models with SPSS. The results showed that: Volumetric efficiency almost decreased by increasing of rotational speed, for all three conveyors. Maximum volumetric efficiency in all three spiral conveyors was in the speed range of 100 to 200 rpm. Volumetric efficiency significantly reduced in all three spiral conveyors by increasing in rotational speed and slope of transferring in spiral conveyors. Effect of spiral conveyor diameter on the volumetric efficiency in product transferring was irregular and no specific process is appeared. The correlation coefficient between the actual and predicted values was obtained as 0.98 in ANFIS model and 0.94 in multivariate linear regression with SPSS which showed the ANFIS model was more accurate than statistical model. Comparison between performances of spiral conveyor to transfer the seeds of wheat, with results by other researchers that has been reported for spiral conveyors with the same slope to transfer of corn kernels, was found that the angle effect on volumetric efficiency is quite significant. Therefore, it proves that performances of spiral conveyor are impressed by characteristics of transition material considerably. The maximum volumetric efficiency was corresponded in rotational speed of 100 rpm, inclination angle of 10º, and blade diameter of 17.5 cm that it was approximately 29.11%.
Image Processing
M. Jafarlou; R. Farrokhi Teimourlou
Abstract
Physical properties of agricultural products such as volume are the most important parameters influencing grading and packaging systems. They should be measured accurately as they are considered for any good system design. Image processing and neural network techniques are both non-destructive and useful ...
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Physical properties of agricultural products such as volume are the most important parameters influencing grading and packaging systems. They should be measured accurately as they are considered for any good system design. Image processing and neural network techniques are both non-destructive and useful methods which are recently used for such purpose. In this study, the images of apples were captured from a constant distance and then were processed in MATLAB software and the edges of apple images were extracted. The interior area of apple image was divided into some thin trapezoidal elements perpendicular to longitudinal axis. Total volume of apple was estimated by the summation of incremental volumes of these elements revolved around the apple’s longitudinal axis. The picture of half cut apple was also captured in order to obtain the apple shape’s indentation volume, which was subtracted from the previously estimated total volume of apple. The real volume of apples was measured using water displacement method and the relation between the real volume and estimated volume was obtained. The t-test and Bland-Altman indicated that the difference between the real volume and the estimated volume was not significantly different (p>0.05) i.e. the mean difference was 1.52 cm3 and the accuracy of measurement was 92%. Utilizing neural network with input variables of dimension and mass has increased the accuracy up to 97% and the difference between the mean of volumes decreased to 0.7 cm3.
Image Processing
M. R. Larijani; R. Farrokhi Teimourlou
Abstract
In order to use new and low cost methods in precision agriculture, nitrogen should be supplied for plants on time and precisely. For determining the required nitrogen of paddy rice in the clustering stage, a series of experiments were conducted using three different methods of: image processing, kjeldahl ...
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In order to use new and low cost methods in precision agriculture, nitrogen should be supplied for plants on time and precisely. For determining the required nitrogen of paddy rice in the clustering stage, a series of experiments were conducted using three different methods of: image processing, kjeldahl and chlorophyll meter set (SPAD-502), in a randomized complete block design with three replications during 2010 at Rice Research Center of Tonekabon, Iran. Four experimental treatments were different level of fertilizer (Urea with 46% nitrogen). In the clustering stage, some images from rice plants were taken vertically by a digital camera and were analyzed using image processing technique. Simultaneously the chlorophyll index of plants was measured by SPAD-502 chlorophyll meter set and the percentage amount of nitrogen was measured using of the so called kjeldahl laboratory method. The results showed that the three methods of determining nitrogen of rice plant were highly correlated. Moreover, the correlation among the three methods and crop yield were almost the same. In general, the method of image processing could have a high potential for nitrogen management in the field, while this method was low-cost, faster and also nondestructive in comparison to the other methods.
