فهرست مطالب مجید دولتی

The present study was carried out in order to compare the effect of four types of conventional pistachio drying systems including diesel- and gas-fueled wagon and carriage dryers on the pistachio shell opening rate, shell damage, drying uniformity, storage shelf-life, pistachio quality, fuel consumption, energy efficiency, and economic efficiency. Two well-known pistachios types including elongated and round types were chosen for this study. The obtained results showed that: A) The rate of pistachios shell opening using a diesel-fueled wagon dryer was profoundly higher than those of the other systems due to its high drying air temperature (90 oc) and the thin layer of the moist product (20 cm) within the dryer. B) The wagon dryers led to less damage (around 39 percent) to pistachio nuts because of the absence of agitation. C) Generally, the energy consumption of wagon drying systems was lower compared with the carriage systems (around 50 percent). D) Higher drying uniformity was obtained using the gas-fueled carriage dryer due to an appropriate agitation. E) Dryer type and storage time up to 6 months, had no significant effect on the oil percentage, peroxide number, and pistachios quality. F) The gas-fueled wagon dryer had the highest economic profitability among the systems studied. G) The gas-fueled wagon dryer had the highest energy efficiency compared (around 15 percent) with the other systems. H) The pistachio types did not have a significant effect on the investigated parameters.

Nowadays, citrus fruits, especially oranges, is very important in the human nutrition regime, and its quality characteristics assessment is very important. This study aimed to predict some biochemical characteristics of blood orange, using machine vision and artificial neural networks. In this experiment, the amount of vitamin C content, sugar content, and acidity (pH) were obtained using destructive laboratory methods. Images of blood orange samples were captured and 108 texture features and 57 color features were extracted on CIElab, RGB, HSV, and HIS color spaces and finally, the artificial neural networks method has been used to estimate the desired properties. To evaluate and select the most optimal artificial neural network, a feed-forward neural networks with Levenberg-Marquardt learning algorithm, the different number of neurons, and different transfer functions in the hidden and output layers was used. Finally, using the best neural network and 165 textural-color features, the amount of vitamin C content, sugar content, and pH were estimated with a correlation coefficient of 0.950, 0.968, and 0.884, respectively. Therefore, considering the appropriate correlation coefficient, machine vision and image processing technology can estimate some biochemical characteristics of blood oranges accurately.

Camel meat can be a suitable alternative for other red meat types in human nutrition, due to its low cholesterol and low-fat content and the appropriate protein content. This research aims to investigate and evaluate the fat content and freshness of camel meat using machine vision technology as a non-destructive method. Therefore, using image processing as a non-destructive method and Soxhlet device as a destructive method, the amount of fat content was predicted, and also the freshness was classified for camel meat. In the image processing section, 108 textual features and 39 color features were extracted in the RGB, HSV, HIS, and CIElab color spaces. Moreover, to predict the freshness and quality of meat, feed-forward back propagation artificial neural networks with one and two hidden layers, a various number of neurons, and threshold functions were used. Also, according to the regression diagram of fat content obtained from the destructive method (fat content obtained from Soxhlet device) with fat content obtained from non-destructive method (machine vision), the coefficient of determination and accuracy between them achieved 0.841. The results of the evaluation of the neural networks showed that the best desirable network for classification based on freshness is a one-hidden layer network with topology 147-3-1, tangent-sigmoid transfer function at hidden layer and purelin transfer function at output layer (R2= 0.996), and also to prete of fat content the best network is two-hidden layer network with linear, log-sigmoid, log-sigmoid transfer function at first hidden layer, second hidden layer and output layer respectively (R2= 0.99). Therefore, the results of this study show that the proposed system with the help of machine vision technology can predict the freshness and fat content of camel meat with acceptable accuracy.

Spices are the most valuable medicinal plants used in food and medical science industries and due to quality and price diversity between various species, distinction, classification and separation of them based on purity and quality degree have great importance. Spices are produced in different countries, including India, Pakistan, China, and East and South Asian countries. The difference in the percentage of aromatic compounds in various types of spices from different regions has led to a distinction between spices. Also, profitable individuals for economic purposes and more profit without regard to the general health of the community will lead to the creation of adulteration in different types of spices. The most important of these adulterations is the addition of volatile ingredients such as cubeb pepper and palm kernel powder in black pepper.

In this study, an olfactory machine system based on eight metal oxide semiconductor sensors in combination with pattern recognition methods were used to classify and separate of black pepper samples based on geographic origin and also to detect cubeb pepper adulteration and palm kernel powder. The adulterated black pepper samples were tested with different adulteration levels (10, 20 and 30%).The fractional method was used to improve and optimize the electronic nose output signals before entering diagnostic methods. In order to analyze the extracted data from the sensor response signal, the principal component analysis method (PCA) was used. Based on the results, PCA with two main components of 96% for black pepper and 95% of cubeb pepper and palm kernel adulteration can be described from the variance of data. Also, three methods of linear separation analysis (LDA), Support vector machine (SVM) and decision tree (DT) were used to classify the samples. The use of the LDA method for black pepper showed a classification precision of 100%, and for adulterations, accuracy was 97.14%.

The results showed that SVM with Gaussian function has the highest accuracy in classifying black pepper samples, cubeb pepper, and palm kernel adulteration Also, the success rate of the DT method in separating and categorizing black pepper, cubeb pepper, and palm kernel was 96.66% and 88.5%  respectively.According to the results obtained, the machine olfaction system in combination with pattern recognition methods has the ability to detect and classify different black pepper samples from different geographical origin and the lowest level of adulteration.

Non-chemical treatments are an approach for improving seed germination. In order to evaluate the effects of the magnetic field application on onion seed germination and seedling growth indices, a quadrupole magnetic field system was designed and fabricated. It was also compared with a dipole magnetic field system. In the quadrupole system, each coil consisted of three layers and the cores were moved inside the coils. These arrangements make it possible to change the magnetic field intensity in addition to input current setting. The experiments were conducted based on factors including the type of system (bipolar and quadrupole), magnetic flux density (75, 150, 300 and 600 μT) and duration of the field application (15, 30, 60 and 120 min). Germination percentage, germination rate, mean germination time, seedling vigor index, shoot length, root length, fresh weight of shoot and root, fresh weight of seedling, dry weight of shoot and root were measured. The results showed significant effects on seed germination and seedling growth of onion. In most germination characteristics, the quadrupole system had a better impact than the bipolar system. For many traits (except for weights), the increase in field intensity degraded the traits. Quadrupole system that applied the magnetic field of 600 μT for 15 minutes, yielded 63% increase in the total seedling weight. Most of the germination traits were not affected by exposure time. Further investigations are required for shorter exposure times compared to used durations in this study.

In present study, image processing technology and ANNs were used to identify the pistachio leaf pests include Ocneria terebinthina stgr and agonoscena pistaciae. The color images were captured from leaves of Ohadi pistachio variety and, color, texture, morphological and texture-color features were extracted from images in order to detection and classification of pests. To achieve the best models of ANNs, different types of ANNs and extracted features were evaluated and the following choices were selected according to the performance of different developed ANNs as the bests; A:The two-layer back propagation ANNs with two hidden layers with SigmoidAxon transfer function and TanhAxon as transfer function in output layer, by using the six color features (variance, mean, standard deviation, skewness, kurtosis and smoothness) with an accuracy of 93.3%, B: The two-layer back propagation ANNs with two hidden layers with SigmoidAxon transfer function and Linear transfer function in output layer, by using the five texture features (entropy, contrast, correlation, energy, homogeneity) with an accuracy of 95%, C: The two-layer back propagation ANNs with two hidden layers, with TanhAxon transfer function and Linear transfer function in output layer, by using the five morphological features (Area, perimeter, convex hull area, extent and solidity) and also, 11 texture-color features with accuracy of 86.7% and 98.3% respectively. The results showed the image processing technology and ANNs, had excellent ability to identify and classification of pistachio leaf pests.

Caraway as one of the most valuable herbs are widely used in pharmaceutical and food industries and due to the high cost and quality difference between different varieties of caraway, adulteration maybe carried out in this product in market that leads to the low satisfactory sense in consumers. In this study, an olfactory machine system based on eight metal oxide semiconductor sensors combined with the pattern recognition method was used to identify the different levels of adulteration in the caraway and its authenticity assessment. The principal component analysis method was used to analyze the extracted data from the sensor response signal. Based on the results, the principal component analysis with the two main components of PC1 and PC2 described %94 of the variance of the data set for the used samples. In the sensor array, MQ4 and FIS sensors revealed the highest loading coefficient values and MQ135, MQ3 and TGS813 sensors devoted the lowest ones. Then, the classification of samples was done using support vector machine (SVM) and decision tree (DT) techniques. SVM with linear kernel function showed the training and validation accuracy values as 100% and 97.5%, respectively. Also, the success rate of the DT method in the distinction and classification samples of adulterated caraway was estimated as 90%.

In recent years due to the benefits of conservation tillage and also disadvantages of crop residue burning, extension and education of conservation tillage, has been highlighted on the agenda of agriculture policymakers. In this regard, for farmers who use conservation tillage or crop residue burning on their farms considered subsidies or crimes respectively. Lack of information, cost, and time consuming of information gathering from the farm using conventional methods, led to the poor performance of law enforcement. Therefore, present research was carried out to find an accurate and fast method for monitoring the residue management. In this research, the ability of Landsat-8 satellite imagery for monitoring of burned fields was evaluated using spectral indices and linear spectral unmixing analysis.
The present research was carried out in the Orzooiyeh region of Kerman province. For conducting the experiment, an area with approximate size of 25 square kilometers is considered and 10 farms (include burnt residue of wheat or corn) were selected randomly in this area as 10 replications of experimental plots. Images were downloaded from the landsat-8 website and all features were extracted from images using ENVI software. On the other hand, the data of real burned areas on the farm were collected using handheld GPS device and also the exact date of residue burning was recorded directly on the field. The maps of experimental farms were prepared using ArcGis software. The correlation between data of real burned area on the farms and ENVI extracted data of burned areas were studied and real burned areas were expressed as a function of burned area that extracted from satellite images by a linear regression curve. Finally, the accuracy of regression functions and correlation between real data and satellite data were calculated. For this purpose, spectral indices include; Normalized Difference of Vegetation Index (NDVI), Burned Area Index (BAI), Normalized Burn Ratio (NBR) and Normalized Burn Ratio Thermal (NBRT) was created for experimental lands and four soil surface condition as experimental plots were considered include; no planted field, residue covered field, green vegetation field and burned residue field.
In the present study, because of extracting the pure spectral data of soil and residue, directly from Landsat-8 images, spectral unmixing analysis was not sensitive to the spectral changes that caused by conditions such as moisture content of soil and plant residue (Pacheco and McNairn, 2011). The average value of BAI index obtained 88.39, 9.29, 4.20 and 6.87 for burned residue field, no planted field, residue covered field and green vegetation field respectively. As can be seen, the average value of BAI index for burned residue field is significantly higher than values for other soil surface conditions. This difference is because of the very low percentage of spectral reflectance of ash in the red and near-infrared bands (Alonso, et al., 2007). Therefore, BAI index was selected as an indicator to distinguish between burnt residue and other three surface conditions in the farm. The result showed, there is a significant difference between means in four soil surface conditions of studied indices. Also, the results showed that the BAI index can be used as a good indicator for separation of burned fields. By the BAI index, location and area of trial burned farms were determined with higher accuracy than other indices. The area average of burned fields that had been separated from other fields using BAI index had high correlation (R2=0.95) with ground-truth data. Also, the area of burned fields that had been estimated by linear spectral unmixing analysis had a good correlation (R2=0.89) with obtained data from the ground-based method.
According to the results, BAI index had most accuracy for estimating burned area of farms and BAI index proposed for separation and determining the area of burnt fields. However, there is a slight error in estimating burned area using spectral indicators and linear spectral unmixing analysis, due to pixel nature of satellite images, basically. Since there is only one spectral data for each pixel of satellite images, spectral data of pixels that are more than the threshold value, are considered as the burned pixels while it is possible, only the part of pixels has been burned, it would be overestimating the actual amount of burned area. And for spectral data of pixels that are less than the threshold value, are considered as unburned pixels while that may be part of pixels is burned, it would be estimating the burned area less than the actual amount.

Annually, Chaff scale (Aonidiella orientalis) caused to damage of citrus orchards and reducing the marketability of products. Farmers have been attempted to eliminate this pest from citrus peel without using water. Therefore, this research conducted to design, construction and evaluation of a prototype model of the sandblasting system for cleaning of chaff scale from orange peel surface. In this system, the mix of sand and high-pressure air are throwing on the surface of pestiferous fruit and cause to remove the pests from fruit peel. In order to evaluate the device performance, different levels of air pressure (2, 3.5 and 5 bar) and different average diameters of sand (0.5, 0.75 and 1 mm) was used for cleaning of orange samples. To evaluate pest cleaning quality, images of samples was captured before and after the sandblasting using a machine vision setup. Finally, the percent of cleaning of pests from the orange surface were analyzed in different treatments, using image processing technology. According to the statistical results, the system had the best cleaning performance in 0.75 mm sand average diameter and 3.5 bar spraying pressure, with an accuracy of 94.9 percent.

Persian cumin (Bunium persicum) is one of the medicinal plants with high economic and expert value which its identification and classification is very important. Manual or visual inspection and classification of Persian cumin is very time-consuming and associated with errors. Therefore machine vision technology as a novel and non-destructive method can be a very good technique for identification and classification of the product. The aim of this study was to identify and to classify Persian cumin landraces based on color and texture features using image processing and artificial neural network. In this research, seven samples of Persian cumin landraces were collected from natural habitats of Kerman province and after image acquisition of samples, 36 color features and 108 textural features were extracted from the images. Identification of landraces was carried out using back propagation ANNs. Based on the results of the study, the mean classification accuracy using a one layer ANN, for the color, texture and color-texture features was equal to 93.55%, 93.50% and 96.40%, respectively. Also, the minimum value of mean square error for color, texture and color-texture features were obtained 0.172, 0.182 and 0.148, respectively.

In modern engines with higher compression ratios, severe pressures and non-uniform heating up is occurred for the engine parts. The piston as the most critical part among all automotive components has been the subject of numerous studies on calculation of temperature distribution, but thermal stress analyses are limited. In this study, the piston of gasoline engine XU7 which is widespread in Iran transport section is considered for thermo-mechanical investigation. A 3D linear static stress analysis is constructed in COSMOS Works to determine the stress distribution during the combustion stroke. The maximum normal stress takes place at the middle surface of the piston crown. The von Mises stress is found to be critical on upper parts of the pin-hole area. Effect of ceramic coating layer on stress distributions are evaluated and compared with results obtained for a traditional uncoated piston. According to the results, the maximum stress can be readily controlled by increasing the thickness of ceramic layer.