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been successfully used for quantitative and qualitative evaluation of the characteristics of food products. In this study, the effect of sugarcane bagasse as a source of fiber enrichment (0-25%), soybean water-soluble polysaccharide (0-2%) and frying time (2-5 minutes) on the image characteristics of donuts such as color, number, density and size of bubbles, porosity, energy, homogeneity, contrast and entropy were investigated. The findings of this research showed that the increase of sugarcane bagasse fiber increases the a* index and decreases the L* and b* components of the crust and crumb and porosity. On the other hand, increasing the frying temperature increases the redness of the crust and decreases the lightness of the crust and the porosity of the texture. Meanwhile, the polysaccharide soluble in soybean water had no significant effect on the characteristics of the treatments. The optimization results also showed that if 8.58% sugarcane bagasse and 0.36% soybean soluble polysaccharide are added to the formula and the sample is fried within 5 minutes, the best quality will be obtained. [ABSTRACT FROM AUTHOR]

The Charpy impact test is an experimental method for determining a material's dynamic properties at different temperatures to investigate tested materials' ductile to brittle transition behavior. The percentages of ductile and brittle fractures can be evaluated based on the fracture area of Charpy specimen (according to API E23 standard) by visual techniques, which do not provide exact percentages of these fractures. In this study, a method is proposed to calculate the exact percentage of ductile fractures using image processing, which makes it possible to quantitatively examine different parts of the fracture surface with high accuracy. All steps of image processing are described for eleven Charpy standard specimens of API X70 steel, tested at temperatures between +20 to -80 °C with a temperature increment of 10 °C. In this research, converting a qualitative image of a fracture surface to a quantitative matrix is described for the first time. Prediction of the shape of ductile and brittle parts of the fracture surface at temperatures between +20 and - 80 °C is one of the results of this study. The percentages of ductile fractures using image processing for temperatures of +20, 0, -20, -40, -40, -60 and -80 °C were obtained as 100, 100, 86, 53, 36 and 0, respectively. The transition temperature was -45 °C for this steel, corresponding to 50% ductile fracture. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the current study, a simple image processing method based on a RGB images using machine vision system has been designed to evaluate the color and texture features of low -calorie doughnut crumb. The effect of maltodextrin substitution with margarine at the levels of 25, 50, 75 and 100% (w/w) was evaluated on vision properties. These features were crumb color (L*, a* and b*), pore area fraction, number of cells/cm2, pore shape, Euler number, fractal dimension of pore boundaries and crumb texture (contrast, entropy, correlation, energy and homogeneity). Results revealed that the addition of maltodextrin increased L* value and decrease a* value of crumb color significantly. The contrast, entropy and Euler number of doughnut crumb containing 75 and 100% maltodextrin were considerably higher than control, 25% and 50% samples. Correlation, energy, homogeneity, mean pore area and diameter, number of cells per square centimeter and pore area fraction decreased aftermaltodextrinaddition (75 or 100%) but no significant difference observed between control, 25% and 50% samples. The fractal dimension value of pore boundaries in doughnut containing 100% maltodextrin were the highest which indicates that the addition of maltodextrin caused more irregular and tortuosity porous boundaries. [ABSTRACT FROM AUTHOR]

Many foods have a structures base on foam; hence their stability has a key role in their textural, structural and acceptance properties. In food industrials, polysaccharides such as gums and gelatin use as stabilizer in foam-based food system. In this research effect of gelatin (1, 2, 3%) on foaming properties of albumin and sodium caseinatate (1 and 2%) has been investigated. Overrun, foam value fraction and drainage have been measured and also image processing used for evaluate porosity, fractal count, entropy and sphericity. Result showed that adding gelatin decreased porosity, entropy, fractal count, drainage, foam value fraction on and overrun. Adding protein caused porosity, overrun, fractal count and circularity increased while bubble size decreased. Therefore, we can use various amount of gelatin for stabilizing foam-base food. [ABSTRACT FROM AUTHOR]

In the present study, in the first step, the effect of Aloe vera gel (75% v/v) coating containing different concentrations of hemp seed oil (1-5% v/v) on some physicochemical properties of cherry tomatoes during storage at room temperature was investigated. The results revealed the ability of hemp seed oil to improve the physicochemical properties of cherry tomatoes during storage, although no significant difference was observed between 3 and 5% levels of hemp seed oil (p> 0.05). Slope change in the ripening index trend occurred for A. vera gel (75% v/v) coated sample on day 12 and for A. vera gel containing 3% hemp seed oil coated sample on day 16. Using an image processing system, the changes of the coated samples were evaluated based on the color statistical and color texture features extracted from the images and were graded through different procedures. The results showed that the principal component analysis (PCA) and artificial neural network (ANN) methods were able to divide the cherry tomatoes into intact and blemished grades which the ANN method was graded samples using color texture features with higher accuracy (97.41%). The adaptive neuro-fuzzy inference system (ANFIS) method had higher diagnostic power than the other two methods and was able to grade the samples into three grades including intact, grade 2 and unusable with accuracy of 98.96%. [ABSTRACT FROM AUTHOR]

The control pests and diseases is considered one of the most important operations of Citrus in the protection stage. Today, a lot of research has been done in various fields of agriculture, including the diagnosis of plant pests and diseases by using machine vision methods. One of the problems that reduce the accuracy of the machine for detecting pests in farm conditions is the presence of adverse factors such as shade and changes in light intensity at different times of the day. In this study, in order to find the appropriate light intensity at different times of the day and increase the brightness of the shady parts of the trees, lighting by a lamp at the imaging site has been used. For detect pest-infected trees (in this snail study) has been used to Deep learning method which has been studied and evaluated by various optimization algorithms such as RMSProp, Adam and SGDm. To evaluate and test the algorithm used, 8000 images were examined in 9 farm conditions and one laboratory state In farm conditions, the lowest detection accuracy of algorithms with 64.32% related to imaging in cloudy days with light intensity of 350 to 700 lux was obtained using RMSProp algorithm, which Detection accuracy was improved up to 95.25% using SGDm algorithm by creating a light intensity controlled by a lamp (approximately 9000 lux). In laboratory conditions where the images were prepared in a controlled environment with constant light intensity, the detection accuracy was Obtained 98.73% with SGDm algorithm. [ABSTRACT FROM AUTHOR]