Post-harvest technology
Hassan Masoudi; Seyed Mahmood Asadi; Gholamreza Akbarizadeh
Abstract
Introduction: In sugar factories, control of sugar crystals growth in the granulation stages is very important to produce sugar grains with a special and required size. Machine vision systems can determine the size of sugar crystals. The main challenge of image processing systems is the lack of capable ...
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Introduction: In sugar factories, control of sugar crystals growth in the granulation stages is very important to produce sugar grains with a special and required size. Machine vision systems can determine the size of sugar crystals. The main challenge of image processing systems is the lack of capable algorithms to separate contact and overlap crystals accurately. So far, various algorithms have been developed to detect crystals and remove their overlapping. However, these methods have not been able to fully detect and separate the overlap of crystals. The purpose of this study was to provide an appropriate image processing algorithm for determining the size of crystals in sugar baking solution (massecuite), which gives us the characteristics of size and shape for the particles in the baking pot instantly to evaluate and improve the quality of the final product.Materials and Methods: The massecuite samples were provided from Debal Khozaei Sugarcane Agro-industrial Company, Ahvaz, Iran. After preparation of the sugar crystals samples under lam and lamer (microscope slides), a digital camera with two Megapixel resolution, attached to a ZS9 Olympus microscope, was used for image capturing. Then, using MATLAB image processing toolbox, the color image (RGB) transferred to grey scale. A mixture of structural operations such as erosion and expansion with spatial filters including median filter were used to remove the image noises. The function of histogram local adjustment was used to improve image contrast. Three methods of segmentation including convexity, fuzzy clustering, and multiplicative intrinsic component optimization (MICO), along with their combination were used to segment the image of massecuite crystals. A reference image was used to determine accuracy of the image processing algorithms. To do this, the massecuite crystals image was manually segmented by Image J software. All segmentation algorithms were applied on the reference image, and seven geometrical parameters, including the mean aperture (MA), coefficient of variation (CV), and standard deviation (SD) were calculated for all the sugar particles in the image. Finally, the percent of MA measurement error was calculated for each sugar crystal to find the best algorithm.Results and Discussion: In manual segmentation, the number of sugar crystals in the selected image was 26. In the manually segmented image, the average of MA, SD and CV for sugar grains in the image were 0.422 mm, 0.157 mm and 37.18% respectively. The relatively large CV of the calculated geometrical parameters indicated the non-uniformity of the sugar particles size inside the massecuite. The convexity method was able to perform well in some areas of the image, and in some other areas, it could not detect the contact between the crystals. The value of the SD and CV of all the geometric parameters determined by the convexity method were greater than the reference values determined by the manual segmentation. This indicates the weaker performance of this method in determining the sugar crystals size compared to the manual method. The values of SD and CV of all geometric parameters determined by the combined fuzzy-convexity method were greater than the reference values, but lower than the values of the convexity method alone. So, the combination of the fuzzy clustering method with the convexity method improved the segmentation performance of crystal images. The SD and CV values of all geometric parameters determined by the combined MICO-convexity method were greater than the reference values, but lower than the values of convexity and fuzzy-convexity methods. This point shows the better performance of the combined MICO-convexity method in segmenting the images of sugar crystals compared to the other two methods. The average of MA, SD and CV for sugar crystals in the image were 0.382 mm, 0.150 mm and 39.23% respectively and had no significant difference with the reference method values in 5% probability level. The mean error of MA determined by the combined MICO-convexity algorithm was 13.24% and Pearson correlation factor was 0.88. As a result, the combined MICO-convexity method was proposed to determine the size of sugar crystals in massecuite.Conclusion: After applying different algorithms on the selected image of sugar crystals in massecuite, it was found that the combined MICO-convexity method can separate sugar crystals well. Also, the CV obtained for this image segmentation algorithm was not much different from the CV of the manual reference method, so this algorithm can be used in the image processing system of the massecuite crystals.
shayan hajinajaf; shaban ghavami jolandan; Hassan Masoudi
Abstract
Investigation of effective factors on water production system using land coolingAbstractIntroduction Water scarcity has been a worrying issue and one of the obstacles to economic growth of countries, despite various water supply sources such as groundwater, seas, rivers and rainfall. Today, in many parts ...
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Investigation of effective factors on water production system using land coolingAbstractIntroduction Water scarcity has been a worrying issue and one of the obstacles to economic growth of countries, despite various water supply sources such as groundwater, seas, rivers and rainfall. Today, in many parts of the world, due to the scarcity of water resources, disputes over access to water resources have crossed national borders and access to these resources has become a strategic goal in the interaction between countries. According to statistics released by the World Resources Institute in recent years, about 35 countries will face water stress in 2040, of which Iran ranks 13th. Considering the average rainfall in Iran and also considering the amount of water resources and per capita consumption in the country, Iran is considered among the countries that are at risk of lack of physical water resources. The purpose of this study was to provide safe water for domestic use and drinking water without using fresh water sources and only with the benefit of the air humidity. In fact, the goal is to provide fresh water from the humidity, especially for remote areas and villages with small populations that do not have access to water. In this method, there is no need to use fossil and electrical energy and only wind energy, air humidity and depth of the earth are the factors of its production, and so it is also economically viable. The system considered in this research reduces the air temperature and cools it until the saturation phase by blowing the outside hot air into a buried pipe underground. In this way, some part of the air moisture is separated and appeared in the form of water droplets on the pipe wall; then the obtained water is stored in a tank and used. Materials and Methods In this research, a system was used that was partly underground and partly out of the soil. Buried sections include the copper pipes, the circuit breakers and connections, and a water tank and the sections on the ground include a cubic chamber with dimensions of 2×2 m, temperature and humidity sensors, fans, inlet air supply section, valves control levers, air conditioners, heaters and humidifiers. During the tests, the temperature and humidity inside the chamber were controlled by a microcontroller board and the effect of changes in air humidity (30, 50, 70 and 90%), air temperature (20, 30, 40 and 50 °C), inlet air flow (2.5, 5 and 7.5 m3/h , equal to the speeds of 1, 2 and 3 m/s , respectively) and the pipe effective length (2 and 4 m with a fixed diameter of 30 mm and a thickness of 1 mm) on the amount of extracted water was evaluated. The burial depth of the pipe was about 1 m and the soil temperature was measured by a sensor next to the buried pipes. The used statistical design was the split plots design in the form of completely randomized blocks and the results were analyzed and compared using SPSS software. In order to create and control different atmospheric conditions inside the chamber, it was necessary to consume electrical energy, while in the open space water can be produced from this system without the need for electrical energy.Results and Discussion the studied factors, including the pipe length, air humidity, air temperature and air flow rate (inside the pipe), affected on the amount of produced water significantly. By increasing of the air humidity, the air flow rate, the chamber air temperature and the pipe length, the amount of produced water was increased. The air temperature of 50 °C, the air velocity in 3 m/s, the humidity of 90% and 4 m length of the copper pipe had the maximum water production in a certain period of time.Conclusion The results of the present study show that water production from air humidity can be used as a method to produce fresh water, especially in remote and low populated areas with high air humidity that do not have access to the fresh water. Although the volume of water production by this method is not comparable with methods such as the multi-stage distillation, but it is economical and does not require any energy.
Precision Agriculture
Seyedeh Arefeh Hosseini; Hassan Masoudi; Seyed Majid Sajjadiyeh; Saman Abdanan Mehdizadeh
Abstract
Introduction Nitrogen is one of the essential elements for plants and is consumed more than other elements in plant nutrition. Nitrogen is an important component of the chlorophyll molecule and is present in the chlorophyll structure as a protein. Without nitrogen, plant growth decreases significantly. ...
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Introduction Nitrogen is one of the essential elements for plants and is consumed more than other elements in plant nutrition. Nitrogen is an important component of the chlorophyll molecule and is present in the chlorophyll structure as a protein. Without nitrogen, plant growth decreases significantly. This research was carried out to estimate the amount of nitrogen and chlorophyll of sugarcane leaves from color indices extracted from digital aerial images taken by a quad-copter at two 5 and 10 m altitudes in the fields of Debal Khozaie sugarcane agro-industry company, Khuzestan, Iran. The images used for this research are from three farms with different growth stages. Materials and Methods The imaging was carried out using a quad-copter, the Phantom 3 professional model, at two heights (5 and 10 meters) from the specified points in the fields. After taking the photos from all marked points by the quad-copter camera, four healthy cane branch - with 45 cm distance from each other - were picked at each point and placed in plastic bags. Then, samples were immediately transferred to the laboratory to measure the leaf chlorophyll value, moisture content and the amount of nitrogen. Using a hand-held chlorophyll meter (SPAD-502 model), the leaf chlorophyll index was measured and recorded at each point. After drying the samples, the nitrogen levels were measured using the manual Kjeldahl method. The designed image processing algorithm, to extract color indices from sugarcane fields' images, had these steps: image transfer, preprocessing, image smoothing, noise, and background removal, extracting and selecting of image attributes. After using the image processing algorithm, the color indices of the fields' images were obtained; then the relationship between color indices and nitrogen and chlorophyll content of sugarcane leaves were determined using multivariate regression. The preparation of the data was done in Excel 2013 software and the development of multiple regression equations in SPSS v.21 software. The student t-test was used to compare the performance of regression models in the prediction of nitrogen and chlorophyll content with real values. Results and Discussion Based on the results of the measurements, the dispersion of nitrogen was not uniform throughout and between the fields. The least nitrogen dispersion was in the first growth period and the greatest one in the second growth period. None of the fields had uniform dispersion in the chlorophyll content. The least dispersion was observed in the second growth period and the highest dispersion in the third growth period. Based on the Pearson correlation statistical analysis - from 48 features extracted by image processing including mean, variance, skewness, and peak value of each image color indices in RGB, HSV, HIS, and Lab color spaces - only 24 features were selected to determine the regressions equations. These indices had a correlation with the amount of nitrogen in sugarcane leaves. In the images of 5 meters height, the obtained regression equation for nitrogen estimation was significant at 1% probability level and had a 74.3% determination factor. The determination factor of the five regression equations presented for the images taken from 10 m height were 71, 74, 77, 79, and 82 percent. Also, all the regression equations were significant at 1% probability level, so these relationships are valid and can be used to estimate the amount of nitrogen in sugarcane from 10 m height. By increasing the number of color indices, the accuracy of the regression model in the estimation of nitrogen levels was increased. Accuracy of the 10 m regression model for estimating the amount of nitrogen in sugarcane was higher than the 5 m regression model. All four regression models presented for estimating the chlorophyll of leaf based on color indices of images taken from 5m height were significant at 1% probability level. The obtained determination coefficients for these models were 26, 45, 55, and 62%. By increasing the number of color indices, the accuracy of the regression model was increased for the estimation of chlorophyll content of the leaf. Also, the presented regression model for the estimation of leaf chlorophyll based on color indices obtained from 10 m height images was significant at 1% probability level. The determination factor for this model was 69%, which is more than the determination factor of the most accurate regression model presented for 5m height images. The regression model presented for estimating the sugarcane nitrogen content from leaf chlorophyll was significant at 1% probability level. The amount of determination factor for this model was 68%, which is very close to the amount reported by the Debal Khozaie sugarcane agro-industry company, Khuzestan, Iran. Conclusion Thecomparison of the results of regression equations with real values showed that nitrogen prediction with regression model for 5 m height images and two regression models for 10 m height images had no significant difference with each other. Also, the results of sugarcane nitrogen estimation using the leaves chlorophyll was not significantly different from the actual nitrogen content of leaves. On the other hand, chlorophyll prediction was performed by two regression models for 5 m height images and the regression model for 10 m height images were not significantly different from the actual amount of leaves chlorophyll. Therefore, the presented regression equations are valid and reliable and using these relationships can help know the state of nitrogen and chlorophyll in sugarcane fields.
H Masoudi; A Rohani
Abbas Rohani; Hasan Masoudi
Volume 36, Issue 2 , March 2014, , Pages 59-68
Abstract
Replacement of tractors is a very difficult task. Farm managers often need to make such economic decisions about their machines. Repair and maintenance expenditures can have significant impacts on these economic decisions and forecasts. The purpose of this research was to identify a regression model ...
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Replacement of tractors is a very difficult task. Farm managers often need to make such economic decisions about their machines. Repair and maintenance expenditures can have significant impacts on these economic decisions and forecasts. The purpose of this research was to identify a regression model that can determine economic life of two-wheel drive tractors. The study was conducted using empirical data on 60 two-wheel drive tractors from Astan Ghodse Razavi agro-industry during the years 1988 to 2005. A power model was selected as the best model for prediction of repair and maintenance costs. Based on the power model, the cumulative cost model (CCM) was used to predict the tractors economic life. 29025 hours was predicted as the economic life of the tractor by the CCM model, while according to cost minimization model (CMM) this parameter was equal to 27773 hours.
