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.
Precised Equipment
Hojat Hejazipoor; Jafar Massah; Keyvan Asefpour Vakilian; Mohsen Soryani; Gholamreza Chegini
Abstract
One of the most important issues in spraying fields and greenhouses is reducing the use of pesticides, reducing the dangerous effects of spraying, protecting the environment, improving the quality of spraying and increasing people's health. Children have weaker immune systems and are unable to detoxify ...
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One of the most important issues in spraying fields and greenhouses is reducing the use of pesticides, reducing the dangerous effects of spraying, protecting the environment, improving the quality of spraying and increasing people's health. Children have weaker immune systems and are unable to detoxify toxic and harmful compounds. For this reason, the adverse effects of poisons on children's health are more important than adults, and the need to reduce the use of poisons and follow the principles of spraying to prevent children from developing cancer is twofold. In this study, the robot sprays by measuring the volume of plant mass and in order to reduce the consumption of poisons. The robot is mechanically designed to be able to move between rows of products and open its manipulator step by step and take deep pictures of each plant in front of it, then analyze the image of each section and observe the plant volume. Detect and spray the same section based on the calculated volume. The process of imaging, volume detection and spraying of the solution based on the estimated volume is repeated at each stage of manipulator opening until the height of the plant is completed and at the end the whole manipulator is retracted.Robot acts intelligently in detecting plant height and closes in the last section after imaging and spraying the solution. The manipulator is able to assess and spray plants up to 270 cm in height. The above robot consists of different parts including camera chamber and nozzle, nozzle and Kinect American camera version 1, manipulator and manipulator actuator mechanism, pump and solution tank, processor, Arduino and relay boards, cart and robot actuator system. To design the above robot, first the static forces applied to the manipulators were examined and then the kinematic calculations of the manipulator were performed. The result of the calculations showed the accuracy of the kinematic equations. After performing calculations to design the robot, examining the environmental conditions and considering the construction cost, the three-dimensional model of the robot was designed in Solidworks 2016 software and based on the above model, the construction work was done step by step. The robot is controlled by Matlab 2010 software. The entire robot working algorithm is coded in Matlab software. For this reason, the main part of controlling the robot is the laptop processor. The laptop controlled by the robot is located in the built-in place behind the robot and transmits all the robot commands to the set of operators through the Arduino board and the relay board. The input information is transmitted to the processor by the Kinect camera, and the processor makes the necessary decisions according to the coded program. Finally, the output commands from the processor are transferred to Arduino board and the relay board to start the actuators. ADM A10-4655M APU processor was used. Developer Toolkit Browser v1.8.0, KinectExplorer-D2D, and Kinect for Windows Software Development Kit (SDK) were used to connect the Kinect camera to a Windows laptop. Two coefficients α and β are needed to determine the plant volume in each section. α is the average plant volume of several plants that has been calculated manually and β is the correction factor multiplied by the amount of plant volume estimated by the robot so that the actual volume of sprayed solution is more in line with the plant needs and the opinion of relevant experts. The volume estimated by the robot in each section is the product of the volume factor multiplied by the average plant volume of the plant (α). The volume factor is the average observed plant width (M) divided by the distance between two consecutive plants in pixels (D). Multiply the volume of the plant observed in the section by multiplying the volume factor by the calculated volume (α) using the Scale Invariant method (independent of the distance from the camera to the object).To calculate the average plant volume manually, several plants should be selected randomly and the plant volume should be calculated by computational methods or flooding method. Then introduced the average volume of these few plants as α to the program. Therefore, the more accurately the manual volume is calculated, and the greater the number of selected plants, Finally, the value of α and the final volume of the plant will be calculated more accurately. The robot should be able to spray the right amount of solution depending on the type of plant and its conditions. Spraying the solution to the plant may not be scientifically justified by experts and specialists according to the type of plant, time of spraying, poison concentration and plant needs. Therefore, the correction factor β should be multiplied by the volume estimated by the robot to the actual volume. Spray the solution to the plant according to the needs of the plant and the opinion of experts. The results of the evaluation show that the robot is able to spray different amounts of solution in the detection of plants with different volumes and the amount of solution sprayed by the robot was proportional to the volume of plants. The average volume of solution sprayed by the robot is 27.1 cc and the average volume of solution sprayed by the worker is 33.1 cc. Also, the standard deviation of the average volume of solution sprayed by the robot and the worker is 2.94 and 3.11, respectively. In other words, the robot is able to spray more accurately and the amount of poison consumption in the robot is estimated less than the worker. It was mentioned that the evaluation of the robot is reported in order to reduce the consumption of acceptable poisons. The feature of being online includes collecting plant information and spraying the solution moments after data processing is one of the important features of the above research. Also, the ability of the robot in online and scale invariant (independent of the distance from the camera to the object) evaluation of the robot was considered acceptable and useful.
Post Harvesting Technology
Mohammad Rasool Afifi; Yaghoob Mansoori; hassan zaki dizaji; Gholamreza Akbarizadeh
Abstract
Introduction Date fruit is a strategic horticultural product in the Middle East that plays an important role as an economic product to develop exports. Iran is the second world producer that contains 14% production of date fruit in the world and has a high potential in order to ideally exploit this valuable ...
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Introduction Date fruit is a strategic horticultural product in the Middle East that plays an important role as an economic product to develop exports. Iran is the second world producer that contains 14% production of date fruit in the world and has a high potential in order to ideally exploit this valuable product. Condidering the low price of Iran's exported dates due to poor preparation and packaging process it is necessary to use new technologies for classification and grading of them. The application of machine vision in agriculture has increased considerably in recent years.There are many fields in which computer vision is involved in order to develop precision agriculture. Machine vision systems by elimination of manual inspection in the field of postharvest technologies improve accurate and uniform quality control of agricultural products. In most of these applications, the method of image analysis for product categories, with the determination of some external features such as color, size, shape, and surface texture has been used (Blasco et al., 2012). Alohali used RGB images taken from Date fruits and defined a set of qualitative external features of dates and categorized them into three categories in terms of quality. One of the characteristics of the soft tissue was detected using color intensity distribution. The final precision of carefully designed system using a propagation neural network was 80% (Alohali, 2011). The size is a particular aspect of external appearance of fruits and vegetables; the price of agricultural products is usually related with their size; therefore, grading of fruits and vegetables into different size groups of size is always necessary in the postharvest handling and processing stages (Zhang et al., 2014). Texture is the other significant sensory quality attribute that has been frequently used in the external quality inspecting and grading systems for the agricultural product quality evaluation. Texture is closely related to some internal quality of fruits and vegetables, such as maturity and sugar content. Therefore, texture is one of the widely used indicators the consumer uses for quality assessment of fruits and vegetables. Texture analysis can also play an important role in defect recognition and segmentation in grading systems due to its powerful discriminating ability (Lee et al. 2008). Materials and Methods The current study examined image processing technology for grading Zahedi cultivar dates in Khuzestan province. Each date fruit was placed under the camera and imaged. At the same time, the samples were classified by an experienced grader. Imaging was conducted in a lighting box to avoid the effects of ambient light. Capturing images was done by a digital camera using CCD sensor. External features of dates such as color, size, shape and surface texture were extracted by image processing methods using MATLAB software (Version R2013a, The Mathworks Inc., Natick, MA, USA). Eleven size and shape features, nine color features, and six external texture features were extracted. The features which led to better separablity for classification were selected using stepwise discriminant analysis (SDA). Selecting the best features is effective to increase accuracy and speed of the algorithm. Two methods of learning machine were used for final classification: discriminant analysis that is a statistical technique and neural networks (ANN). Discriminant analysis method and Neural networks were implemented in SPSS 22.0 and neurosolution 7.0 software respectively. Results and Discussion The best channel to separate dates from background was identified by comparison of the histogram of 9 channels from RGB, HSI and Lab color spaces. The histogram graph, which has more breakdown in the range of intensities, is more suitable to apply thresholding operation because it has a good contrast with the background. Channel B from RGB color space was chosen to segment dates from background. Channel of B has a better contrast between the color channels and also its corresponding histogram intensity values led to the best separability. Five features of size and shape, three features of color and three features of external texture were selected by SDA method to reduce dimension of features space. Degrees marked from 1 to 3 for qualitative grading and sorting by size define levels of quality and determine size from big to small dates respectively. According to table 4, accuracy of classifications for grading, sorting by size and inspection of wrinkled date fruits from healthy ones were 93.6%, 94.4% and 90% respectively. Classifications by MLP neural networks were done. The most important factor for evaluation of neural networks is Correct classification rate (CCR%). The results based on CCR from Confusion matrix is reported in table 5. Accuracy of classifications for grading, sorting by size and inspection of wrinkled date fruits from healthy ones using ANN were 95.7%, 92.3% and 93.8% respectively. Conclusion Final accuracy of classification using discriminant analysis and neural network was achieved 92.7% and 93.9% respectively. Results show relative superiority of neural networks over statistical methods due to its accuracy. According to high accuracy of classification using learning machine methods, it can be concluded that using image processing algorithm was successful in extracting external features for sorting and grading of dates.
Mohammad Ebrahimi; Seyed Saeid Mohtasebi; Shahin Rafiee; Amin Nasiri; Soleiman Hosseinpour
Volume 36, Issue 2 , March 2014, , Pages 81-92
Abstract
This study was investigated the effective parameters on the banana slices shrinkage during drying, using the response surface technique. In this study, the banana slices were dried using a thin-layer dryer made based on a computer vision system. Therefore, the shrinkage of the slices was determined using ...
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This study was investigated the effective parameters on the banana slices shrinkage during drying, using the response surface technique. In this study, the banana slices were dried using a thin-layer dryer made based on a computer vision system. Therefore, the shrinkage of the slices was determined using an image processing technique in the MATLAB environment. The response surface technique, central composite diagram (CCD) with four parameters, was used to investigate the effect of drying time, drying temperature, slice thickness and air velocity during the drying process (as the process parameters) on the shrinkage (as the process response). The second-order model was selected to describe the shrinkage as a function of the independent parameters (time, temperature, slice thickness and air velocity) due to RMSE=0.033 and R2=0.951. The results showed that the drying time, drying temperature, slice thickness and air velocity had the most effect on the banana slices shrinkage, respectively.
