Design and Construction
S. M. Nassiri; S. Samsami; M. Loghavi
Abstract
Introduction Iran is a frontier of pomegranate fruit production in the world (with almost 40 % of the world`s production). However due to traditional processing operations is not ranked as the largest pomegranate exporter. Saveh, Neyriz and Ferdows are the top pomegranate producing cities in Iran. Pomegranate ...
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Introduction Iran is a frontier of pomegranate fruit production in the world (with almost 40 % of the world`s production). However due to traditional processing operations is not ranked as the largest pomegranate exporter. Saveh, Neyriz and Ferdows are the top pomegranate producing cities in Iran. Pomegranate is consumed as a fresh fruit as well as processed product as food additive, paste, syrup, jelly, pectin, jam, beverage, essence, vinegar and concentrate. Aril extraction is the first and essential postharvest operation for pomegranate processing. Arils are mostly extracted manually even in large scales for fresh and processed consumption. This labor intensive operation is rational when aril quality is an important index for consumer. But whenever pomegranate juice is desired, the aril quality has no priority for consumer, and therefore arils can be extracted with less care. Sarig (1985) was the first inventor of a pomegranate aril extractor who employed air jet force to extract the arils. Later, other researchers employed the same method as well as water jet to extract fruit juice and sac. In the present study, fabrication and evaluation of vibratory aril extractor augmented with air system was conducted. Materials and Methods The study was conducted using Rabab cultivar samples which were manually harvested from an orchard in Neyriz town, Fars province. Samples were kept in refrigerator at 5 0C till experimental trials. Initial moisture content of fruit skin, arils and internal fleshes were measured by gravimetric method as 31.7±2.6 %, 61.5±1.8 % and 42.8±1.4 %, respectively and for a whole fruit was measured 45.3±11.5 % (w.b.). For conducting laboratory tests, an aril extraction unit was designed and fabricated. It comprised a steel main frame, a 746 W electric motor, drive mechanism (eccentric and shaft), sample retentive unit, air jet unit, aril tank, and an air compressor-tank assembly. Sample retentive unit was designed in such a manner to hold a halved fruit. This unit was made from four elements, a hemisphere bowel, four pressure (spring) arms to apply force on skin of the sample, and four tension (spring) arms for fixing the sample in the bowel by applying pressure on the edges of the halved sample. Such configuration helped sample to open more and more while extracting the arils to expose trapped aril for easier extraction. Sample retentive assembly was vibrated by the electric motor and drive mechanism. Electric motor was equipped with an electric convertor to create different levels of vibration frequency. Also, the drive mechanism was designed in such a manner to create different levels of vibration amplitudes. According to the previous studies, 2 nozzles with 3.5 mm diameter were selected for air jet unit. Nozzles were spaced at 8 cm apart according to the measured mean diameter of samples. Outlet air jet from nozzles covered the cross sectional area of the halved fruit. Nozzles assembly was rotated 180 degrees clockwise and counterclockwise with an electronically controlled stepper motor. Pressurized air (from air tank) was transferred to nozzles assembly by flexible pipes. Air pressure was controlled at 500 kPa level by air regulator. To conduct experimental trials, samples halved at three different cutting directions (horizontal (equatorial), vertical and oblique) by a sharp cutter and halved samples were used for tests. Halved sample was fixed in bowel and then the unit was excited by the electric motor. The assembly was vibrated for 60 seconds before blowing the air jet for extra 30 seconds. Tests for air jet alone were conducted for 90 seconds and percentage of detached and damaged arils were calculated. Damaged aril during cutting process was subtracted from total damaged arils for each trial. Collected data were analyzed according to factorial experiments based on completely randomized design, and means were compared by Duncan post-hoc test. Data of combined and air jet alone systems were analyzed by two independent sample t tests. Results and Discussion ANOVA results revealed that cutting type, frequency and amplitude, significantly influenced the percentage of aril extraction at 5 % level of significance. The highest amount of extraction was obtained at 30 Hz frequency and 4 mm amplitude for diagonal cutting by 87 %. At this condition, 13.9 % of arils were damaged by air jet pressure. A significant difference in percentage of extracted and damaged arils was observed between vibratory-air and air systems at 5 % level of significance. The highest amount of aril extraction as well as damage was observed for vibratory-air system with the means of 80.1 % and 9.9 %, respectively. Conclusion Maximum percentages of extraction and aril damage were achieved by applying the combined system with as compared to air jet system alone, so that combined system increased aril extraction by 7.1 % with 2.2 % extra damages.
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.
M. Maharlooei; M. Loghavi; S. M. Nassiri
Abstract
Precision Agriculture is continuously trying to address the sources and factors affecting the in-field variability and taking appropriate managerial decisions. One of the popular research focuses in the recent three decades has been on the development of new approaches to making yield variability maps. ...
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Precision Agriculture is continuously trying to address the sources and factors affecting the in-field variability and taking appropriate managerial decisions. One of the popular research focuses in the recent three decades has been on the development of new approaches to making yield variability maps. Advancement in development of sensors and the importance of quality factor in high value crops has motivated scientists to investigate real-time and nondestructive testing methods. This study tried to introduce and evaluate a new approach to concurrent yield mapping and to estimate some nutritional qualitative factors of alfalfa production. In this study, yield quantity was determined by measurement of added hay slice in every hay compression cycle by employing a new star wheel and integrating its output with positioning data from Global Positioning System. To predict some nutritional quality properties, measurement of specific shear energy applied on the cutting blade and compressive energy on plunger head of a hay baler in field conditions were also evaluated. The results of statistical analysis of yield quantity measurement data showed a very good correlation between the suggested approach and yield mass (r=0.96 and R2=0.92). The results of using specific shear energy for estimation of crude fiber and cumulative index RFV with regard to field conditions were rated as acceptable. Using specific compression energy was suitable only for estimating the dry matter. None of the suggested methods was able to estimate the hay crude protein. Further investigations at more extensive variations of quality indices and alfalfa varieties are suggested.
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
