A. Nourmohamadi-Moghadami; D. Zare; Sh. Kamfiroozi; A. A. Jafari; M. A. Nematollahi; R. Kamali
Abstract
Introduction During filling a silo, granular material containing a range of particle sizes, the fine material accumulates under the filling point. The inclined surface of stationary bed particle which is formed in silos during filling process acts similar to a sieve through which the smaller particle ...
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Introduction During filling a silo, granular material containing a range of particle sizes, the fine material accumulates under the filling point. The inclined surface of stationary bed particle which is formed in silos during filling process acts similar to a sieve through which the smaller particle fall. This effect is called sifting. As a result of the mentioned effect, much finer particles form a vertical cylindrical zone of high concentration at the silo center. For optimal design in industrial process such as aeration of stored products in silos, filling silos, and wherever granular materials are handled, it is necessary to survey the distribution of the fine materials depending on product and process properties. The objectives of present study were: (a) To study fine change as affected by substantial parameters, (b) To model fine changes at different conditions in silos. Materials and Methods In the present study, an experimental setup consist of a main container, elevator, trapezoidal container and experimental silo was designed and built. Fine content was defined by BCFM (broken corn and foreign material). By applying a new approach, sampling was performed in a radial and vertical direction. The position of each sampling point was determined with a scaled distance from center (R) and from bottom (Z). Local BCFM (BCL) was defined as the value of BCFM in each sampling point. Influential parameters namely, initial BCFM (BCI), volume flow rate (Q) and fill pipe diameter (DF) were considered as treatments. Non-linear regression technique was applied on the experimental data to predict the distribution pattern of fines into the pilot-scale silo. The most appropriate model in a try and error procedure was selected based on highest value of R2 and least value of χ2, RMSE and MRDM. Results and Discussion According to the results of ANOVA, it was found that the effects of all parameters were significant at 5% probability. BCL decreased nonlinearly with a concave down decreasing trend along radial direction due to sifting effect. As a result, most amount of fines remained in the sections closer to the center of the silo. Fine distribution became more uniform with decreasing Z and increasing BCI and DF. Also, the distribution of fine became more uniform with increasing Q. BCL was a nonlinear function of R and a linear function of Z, BCI, Q and DF. Although including more and complex terms increased the model complexity but in the present study considering BCL as an exponential function of R and as an implicit function of Z and R (ZR) improved the quality of the model significantly. The values of 0.94, 1.14, 1.06, 11.39 for R2, χ2, RMSE and MRDM, respectively, gave the best model. The results showed, considerable accumulation of fines occurred at the center of the silo which increased with increase of level of Z. Also, low concentration of fine occurred at the periphery of the silo especially at higher levels of Z. It means that maximum non-uniformity of fine distribution occurred at higher levels of Z. Conclusion The present study investigated distribution of fines during filling affected by main parameters namely, initial BCFM, volume flow rate and fill pipe diameter in a pilot scale silo. A new procedure was developed for measuring the fine material along radial and vertical directions. Distribution of fine was modeled using a developed equation considering the effects of main parameters. The results showed that distribution of fine becomes more uniform with decreasing height and increasing initial BCFM, volume flow rate and fill pipe diameter.
A. A. Jafari; E. Tatar
Abstract
Introduction Science of rheology has numerous applications in various fields of the food industry such as process assessment, acceptance of products and sales. Fluid behavior changes during processing due to an adverse change in the consistency and due to the combined operations such as mixing, heating, ...
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Introduction Science of rheology has numerous applications in various fields of the food industry such as process assessment, acceptance of products and sales. Fluid behavior changes during processing due to an adverse change in the consistency and due to the combined operations such as mixing, heating, cooling, etc. In this regard, viscosity is an important factor for quality assessment in most of the materials. To measure the viscosity, Viscometer devices are used which are directly in contact with the material. Working with these devices is time consuming, costly, under the influence of human factors and in some cases periodic calibration is required. Materials and Methods Date syrup was used as a viscous material in this study because it industrially is produced. An apparatus including a reservoir with an outlet orifice at the bottom was made to provide free flow of the liquid. Two sets of circular and rectangular orifices with different dimensions were used to investigate the effect of the orifice characteristics on the shape of the flow. Firstly, date syrup viscosity was measured by a conventional viscometer at 5 temperature levels and 6 concentration levels and behavior of the syrup were studied. Free flow of date syrup was photographed in the aforementioned temperatures and concentrations. On the other hand extracted features from the images were used as inputs to the neural network to give outputs as a fluid flow behavior index and consistency index. Measurement data were divided to three sets including training, validation and test sets whereas 70% of the data were used for training the neural networks, 15% as the validation set and 15% for testing the networks. Results and Discussion Results showed that similar to most of the liquids, viscosity of date syrup decreases when temperature increases. The experiments also revealed that the date syrup behavior is expressible with power law and can be determined using power equation. Date syrup has different behavior at different concentration levels. It changes from a pseudoplastic liquid to a Newtonian and then a dilatant liquid when concentration increases. Flow behavior index and consistency index corresponding to all three behavior of the fluid were determined. Results showed that the neural networks were able to accurately estimate the behavior and consistency indices with coefficient of correlations up to 0.99. Networks with three hidden layers were completely suitable for the estimation of the indices. These results revealed that in spite of different behavior of the liquid ranged from pseudoplastic to dilatant, the method was still able to determine the apparent viscosity of the fluid. Although the circular orifices were more efficient in determination of the indices than the rectangular orifices, there was not a significant difference between the uses of circular or rectangular orifices as well as no significant different between the orifices with different dimensions. The correlation between the actual and estimated values for fluid flow behavior index and consistency index was 0.98 whereas the mean square error of the validation sets was about 0.0138 which showed the accuracy of the method. Conclusion In this study a new method of viscosity determination was proposed. Machine vision was employed to estimate the viscosity based on the visual characteristics of the fluid free flow. Date syrup as a liquid with different rheological behaviors was used to assess the performance of the method. The strong correlation between the extracted features and fluid flow behavior index as well as a consistency index proved the reliability and accuracy of the method for viscosity estimation.
S. M. Hosseini; A. A. Jafari
Abstract
Introduction Great areas of the orchards in the world are dedicated to cultivation of the grapevine. Normally grape vineyards are pruned twice a year. Among the operations of grape production, winter pruning of the bushes is the only operation that still has not been fully mechanized while it is known ...
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Introduction Great areas of the orchards in the world are dedicated to cultivation of the grapevine. Normally grape vineyards are pruned twice a year. Among the operations of grape production, winter pruning of the bushes is the only operation that still has not been fully mechanized while it is known as the most laborious jobs in the farm. Some of the grape producing countries use various mechanical machines to prune the grapevines, but in most cases, these machines do not have a good performance. Therefore intelligent pruning machine seems to be necessary in this regard and this intelligent pruning machines can reduce the labor required to prune the vineyards. It this study in was attempted to develop an algorithm that uses image processing techniques to identify which parts of the grapevine should be cut. Stereo vision technique was used to obtain three dimensional images from the bare bushes whose leaves were fallen in autumn. Stereo vision systems are used to determine the depth from two images taken at the same time but from slightly different viewpoints using two cameras. Each pair of images of a common scene is related by a popular geometry, and corresponding points in the images pairs are constrained to lie on pairs of conjugate popular lines. Materials and Methods Photos were taken from gardens of the Research Center for Agriculture and Natural Resources of Fars province, Iran. At first, the distance between the plants and the cameras should be determined. The distance between the plants and cameras can be obtained by using the stereo vision techniques. Therefore, this method was used in this paper by two pictures taken from each plant with the left and right cameras. The algorithm was written in MATLAB. To facilitate the segmentation of the branches from the rows at the back, a blue plate with dimensions of 2×2 m2 were used at the background. After invoking the images, branches were segmented from the background to produce the binary image. Then, the plant distance from the cameras was calculated by using the stereo vision. In next stage, the main trunk and one year old branches were identified and branches with thicknesses less than 7 mm were removed from the image. To omit these branches consecutive dilation and erosion operations were applied with circular structures having radii of 2 and 4 pixels. Then, based on the branch diameter, one-year-old branches were detected and pruned through considering the pruning parameters. The branches were pruned so that only three buds were left on them. For this aim, the branches should be pruned to have a length of 15 cm. To truncate the branches to 15 cm, the length of the main stem was measured for each of the branches, and branches with length less than 15 cm were omitted from the images. Then the main skeleton of grapevine was determined. Using this skeleton, the attaching points of the branches as well as attachment points to the trunk were identified. Distance between the branches was maintained. At the last step, the cutting points on the branches were determined by labeling the removed branches at each step. Results and Discussion The results indicated that the color components in the texture of the branches could not be used to identify one year old branches and evaluation results of algorithm showed that the proposed algorithm had acceptable performance and in all photos, one year old branches were correctly identified and pruning point of the grapevines were correctly marked. Also among 254 cut off-points extracted from 20 images, just 7 pruning points were misdiagnosed. These results revealed that the accuracy of the algorithm was about 96.8 percent. Conclusion Based on the reasonable achievement of the algorithm it can be concluded that it is possible to use machine vision routines to determine the most suitable cut off points for pruning robots. By an intelligent pruning robot, the one year old branches are diagnosed properly and the cut off points of the plants are determined. This can reduce the required labor to perform winter pruning in vineyards which subsequently reduces the time required and the costs needed for pruning the vineyards.
A. Bakhshipour Ziaratgahi; A. A. Jafari; Y. Emam; S. M. Nassiri; S. Kamgar; D. Zare
Abstract
Introduction Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important ...
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Introduction Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques. Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT) is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points) to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT. Materials and Methods Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing. Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images. A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four-leaf stage was a stretched S-shaped curve at the junction of the leaf and petiole. This unique shape characteristic was used as the pattern for sugar beet detection using GHT. To implement the Hough transform in the images, a 50-member group of samples was prepared from S-shaped curve to build appropriate patterns. Desired features for the Hough transformation were extracted from the patterns. In the next step, the attempts were made to find the images for the shapes similar to each of the patterns. Results and Discussion Plants were thoroughly separated from soil and residues. The accuracy of segmentation algorithm was achieved by almost 100%. The accuracy of the generalized Hough algorithm was evaluated in two stages. In the first stage, the algorithm accuracy was assessed in detecting patterns in the images. Results showed that the accuracy of the algorithm was 96.21%. In the second stage, the algorithm was evaluated for some other test images, whereas the algorithm achieved an overall accuracy of 91.65%. In some cases, the presence of a large overlap between objects in the image reduced the detection accuracy. This was because of two main reasons; 1) high interference and ambiguity in the object edges, so that Hough transform was not able to detect the predefined patterns in the objects and, 2) weeds highly overlapped with sugar beet plants and thereby they were wrongly detected as sugar beet. However, since there is no or little interference between plants at the four-leaf stage, this interference can be eliminated by morphological operations. Due to this fact, it can be said that the results of GHT algorithm are acceptable for the detection of sugar beet in the plants close to four-leaf stage. Conclusion A special feature in the shape of sugar beet leaves was used as a criterion to distinguish between sugar beet and weeds. The results showed that by quantifying this special feature, which is an S-shaped curve near the petioles connection of beet leaves, sugar beet can be discriminated from weeds with an accuracy of 91.65 %. Recalled that this feature is a shape characteristic, therefore, the generalized Hough algorithm must be applied prior to plant canopy development, which is consistent with the critical period of weed control in sugar beet fields.
Image Processing
H. Asaei; A. A. Jafari; M. Loghavi
Abstract
IntroductionIn conventional methods of spraying in orchards, the amount of pesticide sprayed, is not targeted. The pesticide consumption data indicates that the application rate of pesticide in greenhouses and orchards is more than required. Less than 30% of pesticide sprayed actually reaches nursery ...
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IntroductionIn conventional methods of spraying in orchards, the amount of pesticide sprayed, is not targeted. The pesticide consumption data indicates that the application rate of pesticide in greenhouses and orchards is more than required. Less than 30% of pesticide sprayed actually reaches nursery canopies while the rest are lost and wasted. Nowadays, variable rate spray applicators using intelligent control systems can greatly reduce pesticide use and off-target contamination of environment in nurseries and orchards. In this research a prototype orchard sprayer based on machine vision technology was developed and evaluated. This sprayer performs real-time spraying based on the tree canopy structure and its greenness extent which improves the efficiency of spraying operation in orchards. Materials and MethodsThe equipment used in this study comprised of three main parts generally: 1- Mechanical Equipment 2- Data collection and image processing system 3- Electronic control systemTwo booms were designed to support the spray nozzles and to provide flexibility in directing the spray nozzles to the target. The boom comprised two parts, the vertical part and inclined part. The vertical part of the boom was used to spray one side of the trees during forward movement of the tractor and inclined part of the boom was designed to spray the upper half of the tree canopy. Three nozzles were considered on each boom. On the vertical part of the boom, two nozzles were placed, whereas one other nozzle was mounted on the inclined part of the boom. To achieve different tree heights, the vertical part of the boom was able to slide up and down. Labview (version 2011) was used for real time image processing. Images were captured through RGB cameras mounted on a horizontal bar attached on top of the tractor to take images separately for each side of the sprayer. Images were captured from the top of the canopies looking downward. The triggering signal for actuating the solenoid valves was initially sent to the electronic control unit as the result of image processing. Electronic control unit was used to adjust the right time of spraying based on the signals received from the encoder to precisely spray the targeted tree. The distance between the camera and spraying nozzles was considered in the microcontroller program. The solenoid would be turned off and stop the spraying when the vision system realized that there was a gap between the trees.Water sensitive papers (WSP) were used to evaluate the sprayer performance in prompt spraying of the trees and cutting off at hollow spaces between the trees. Water sensitive papers were attached to three ropes extended along the movement direction of the tractor at top, middle, and bottom of the trees so that each tree comprised 9 WSPs whereas other 9 WSPs were placed at each gap between two successive trees. Three levels of forward speed of 2 km h-1, 3.5 km h-1and 5 km h-1 was tried in these experiments to evaluate the effect of forward speed on spraying performance. Experiments were conducted in three replications. Liquid consumption of the sprayer designed in this research was compared with the conventional overall spraying.Results and DiscussionAnalysis of variances of data gained from water sensitive paper corresponding to the sprayed areas showed a significant effect of forward speed on prompt spraying.Comparison of means of spraying coverage on WSPs at different forward speeds with four replications showed that the maximum amount of targeted sprayed pesticide has been achieved at the lowest speed (2 km h-1) while the lowest amount of sprayed was seen at the maximum speed. Although higher forward speed is preferred because it increases the operation capacity of the sprayer, increasing the forward speed of the sprayer reduces the coverage density of the liquids on WSPs because the output rates of the nozzles are constant. Therefore, in cases that higher forward speed is demanded, more nozzles should be added to the sprayer booms Comparison between the liquid consumptions of the proposed system and conventional overall spraying showed that in this study, up to 54% less material has been used for the experiment in olive orchard. ConclusionsThe sprayer designed in this study was able to detect the gap between the trees in orchards using a machine vision system to stop the spraying on places where no tree exists. Results showed that employing the new sprayer decreased a significant amount of spray liquids which can be important both economically and environmentally. Considering to lack of pesticide spraying in the hollow spaces between the trees, certainly, more significant reduction is expected to achieve in young orchards where trees are small and there are larger gaps between the trees
Image Processing
H. Payman; A. Bakhshipour Ziaratgahi; A. A. Jafari
Abstract
Introduction: Rice is a very important staple food crop provides more than half of the world caloric supply. Rice diseases lead to significant annual crop losses, have negative impacts on quality of the final product and destroy plant variety. Rice Blast is one of the most widespread and most destructive ...
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Introduction: Rice is a very important staple food crop provides more than half of the world caloric supply. Rice diseases lead to significant annual crop losses, have negative impacts on quality of the final product and destroy plant variety. Rice Blast is one of the most widespread and most destructive fungal diseases in tropical and subtropical humid areas, which causes significant decrease in the amount of paddy yield and quality of milled rice.Brown spot disease is another important fungal disease in rice which infects the plant during the rice growing season from the nursery period up to farm growth stage and productivity phase. The later the disease is diagnosed the higher the amount of chemicals is needed for treatment. Due to high costs and harmful environmental impacts of chemical toxins, the accurate early detection and treatment of plant disease is seemed to be necessary.In general, observation with the naked eye is used for disease detection. However, the results are indeed depend on the intelligence of the person performing the operation. So usually the accurate determination of the severity and progression of the disease can’t be achieved. On the other side, the use of experts for continuous monitoring of large farms might be prohibitively expensive and time consuming. Thus, investigating the new approaches for rapid, automated, inexpensive and accurate plant disease diagnosis is very important.Machine vision and image processing is a new technique which can capture images from a scene of interest, analyze the images and accurately extract the desired information. Studies show that image processing techniques have been successfully used for plant disease detection.The aim of this study was to investigate the ability of image processing techniques for diagnosing the rice blast and rice brown spot.Materials and Methods: The samples of rice leaf infected by brown spot and rice blast diseases were collected from rice fields and the required images were obtained from each sample.The images of infected leaves were then introduced to image processing toolbox of MATLAB software. The RGB images were converted to gray-scale. Using a suitable threshold, the leaf surface was segmented from image background and the first binary image was achieved. Leaf image with zero background pixels was obtained after multiplying the black-and-white image to original color image. The resulting image was transformed to HSV color space and the Hue color component was extracted. The final binary image was created by applying an appropriate threshold on the image that obtained from Hue color component.As there was a high color similarity between the symptoms of two diseases, it was not possible to use Hue color component to distinguish between them. Therefore the shape processing was applied.Four dimensionless morphological features such as Roundness, Aspect Ratio, Compactness and Area Ratio were extracted from stain areas and based on these features, disease type diagnosis was performed.Results and Discussion: Results showed that the proposed algorithm successfully diagnosed the diseases stains on the rice leaves. A detection accuracy of 97.4±1.4 % was achieved.Regarding the results of t-test, among the extracted shape characteristics, only in the case of Area Ratio, there was no significant difference between two disease symptoms. While in the case of Roundness, Aspect Ratio and Compactness, a highly significant difference (P<0.01) was discovered and revealed between rice blast and rice brown spot stains. The developed algorithm was capable of distinguishing between disease symptoms with an exactness of over 96.6%. This means that of the 60 samples (30 samples rice blast and 30 samples of rice brown spot); only two were placed in the wrong category.Conclusions: It was concluded from this study that image processing technique can not only accurately determine whether the rice is healthy or infected but also can determine the type of plant disease with reliable precision. Considering the fact that plant diseases spread area by area through the field, early and accurate diagnosis of plant diseases in a part of a farm - which is provided by image processing techniques and machine vision systems – is very useful for timely and effective disease treatment which in turn leads to lower crop losses. Also, by using the site-specific chemical application technologies, the need for chemicals can be minimized, an important factor that can considerably reduce the costs.
M. A. Rostami; M. H. Raoufat; A. A. Jafari; M. Loghavi; M. Kasraei; S. M. J. Nazemsadat
Abstract
Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect ...
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Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. The present study was devoted to investigate accurate methods for monitoring residue management and tillage practices. The satellite imagery technique was used as a rapid and spatially explicit method for delineating crop residue coverage and as an estimator of conservation tillage adoption and intensity. The potential of multispectral high-spatial resolution WorldView-2 local data was evaluated using the total of eleven satellite spectral indices and Linear Spectral Unmixing Analysis (LSUA). The total of ninety locations was selected for this study and for each location the residue coverage was measured by the image processing method and recorded as ground control. The output of indices and LSUA method were individually correlated to the control and the relevant R2 was calculated. Results indicated that crop residue cover was related to IPVI, RVI1, RVI2 and GNDVI spectral indices and satisfactory correlations were established (0.74 - 0.81). The crop residue coverage estimated from the LSUA approach was found to be correlated with the ground residue data (0.75). Two effective indices named as Infrared Percentage Vegetation Index (IPVI) and Ratio Vegetation Index (RVI) with maximum R2 were considered for classification of tillage intensity. Results indicated that the classification accuracy with IPVI and RVI indices in different conditions varied from 78-100 percent and therefore in good agreement with ground measurement, observations and field records.
Image Processing
S. Latifaltojar; A. A. Jafari; S. M. Nassiri; H. Sharirfi
Abstract
Crop yield estimation is one of the most important parameters for information and resources management in precision agriculture. This information is employed for optimizing the field inputs for successive cultivations. In the present study, the feasibility of sugar beet yield estimation by means of machine ...
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Crop yield estimation is one of the most important parameters for information and resources management in precision agriculture. This information is employed for optimizing the field inputs for successive cultivations. In the present study, the feasibility of sugar beet yield estimation by means of machine vision was studied. For the field experiments stripped images were taken during the growth season with one month intervals. The image of horizontal view of plants canopy was prepared at the end of each month. At the end of growth season, beet roots were harvested and the correlation between the sugar beet canopy in each month of growth period and corresponding weight of the roots were investigated. Results showed that there was a strong correlation between the beet yield and green surface area of autumn cultivated sugar beets. The highest coefficient of determination was 0.85 at three months before harvest. In order to assess the accuracy of the final model, the second year of study was performed with the same methodology. The results depicted a strong relationship between the actual and estimated beet weights with R2=0.94. The model estimated beet yield with about 9 percent relative error. It is concluded that this method has appropriate potential for estimation of sugar beet yield based on band imaging prior to harvest