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Introduction Adopting new technologies for crop growth has the characteristics of improving disaster resistance and stress tolerance, ensuring stable yields, and improving product quality. Currently, the cultivation of seed trays relies on huge labor power, and further mechanization is needed to increase production. However, there are some problems in this operation, such as the difficulty of improving the speed of a single machine, seedling deficiency detection, automatic planting, and controlling the quality, which need to be solved urgently. To solve these problems, there are already some meaningful attempts. Si et al. (2012) applied a photoelectric sensor to a vegetable transplanter, which can measure the distance between seedlings and the movement speed of seedlings in a seedling guide tube, to prevent omission transplantation. Yang et al. (2018) designed a seedling separation device with reciprocating movement of the seedling cup for rice transplanting. Tests show that the structure of the mechanical parts of the seedling separation device meets the requirements of seed movement. The optimization of the control system can improve the positioning accuracy according to requirements and achieve the purpose of automatic seedling division. Chen et al. (2020) designed and tested of soft-pot-tray automatic embedding system for a light-economical pot seedling nursery machine. The experimental results showed that the embedded-hard-tray automatic lowering mechanism was reliable and stable as the tray placement success rate was greater than 99%. The successful tray embedding rate was 100% and the seed exposure rate was less than 1% with a linear velocity of the conveyor belt of 0.92 m s-1. The experiment findings agreed well with the analytical results. Despite the sharp decline in Iran's water resources and growing population, the need to produce food and agricultural products is greater than ever. In the past, most seeds were planted directly into the soil, and many water resources, especially groundwater, were used for direct seed sowing and plant germination. One way to reduce the consumption of water, fertilizers, and pesticides is to plant seedlings instead of direct seed sowing. Therefore, the purpose of this study was dynamic modeling and fabrication of seed planting systems in seedling trays. Material and Methods In this experiment, Flores sugar beet seeds (Maribo company, Denmark) were used. The seedling trays had dimensions of 29.5*60 cm with openings and holes of 5.5 and 4 cm, respectively. To plant seeds in seedling trays, first, a planter arm was modeled and its position was obtained at any time. Then, based on dynamic modeling, the arm was constructed and a capacitive proximity sensor (CR30-15AC, China) and IR infrared proximity sensor (E18-D80NK, China) were used to find the location of seedling trays on the input conveyor and position of discharging arm, respectively. To achieve a stable and effective control system, a micro-controller-based circuit was developed to signal the planting system. The seed planting operation was performed in the seedling tray according to the coordinates which were provided through the image processing method. The planting system was evaluated at two levels of forward speed (5 and 10 cm s-1). Moreover, a smartphone program was implemented to monitor the operation of the planting system. Results and Discussion The planting system was assessed for sugar beet seeds using two levels of forward speed (5 and 10 cm s-1). The nominal capacity of this planter ranged from 3579 to 4613 cells per hour, with a miss and multiple implantation indices of 0.03% and 8.17%, respectively, in 3000 cells. Due to its planting accuracy, speed, and low energy consumption (25.56 watt-hours), this system has the potential to replace manual seeding in seedling trays. Conclusion In the present study, a seed-sowing system for planting seedling trays was designed, constructed, and evaluated based on dynamic modeling. In the developed system, unlike previous research, planting location detection was conducted through image processing. Additionally, a smartphone program was established to monitor the operation of the planting system without interfering with its performance. This study demonstrates that image processing can successfully detect planting locations and can effectively improve efficiency over time for major producers. [ABSTRACT FROM AUTHOR]

Introduction: Maize is one of the most important cereal crops worldwide, providing staple food for people globally. Counting maize tassels provides essential information about yield prediction, growth status, and plant phenotyping, but traditional manual approaches are expensive and time-consuming. Recent developments in technology, including high-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs) and advanced machine-learning techniques such as deep learning (DL), have been used to analyze genotypes, phenotypes, and crops. In this study, we modified the YOLOv5s single-stage object detection technique based on a deep convolutional neural network and named it MYOLOv5s. We incorporated BottleneckCSP structures, Hardswish activation function, and two-dimensional spatial dropout layers to increase tassel detection accuracy and reduce overfitting. Our method's performance was compared with three state-of-the-art algorithms: Tasselnetv2+, RetinaNet, and Faster R-CNN. The results obtained from our proposed method demonstrate the effectiveness of MYOLOv5s in detecting and counting maize tassels. Materials and Methods: The High-Intensity Phenotyping Site (HIPS) dataset was collected from the large field at the Agronomy Center for Research and Education (ACRE) of Purdue University, located in West Lafayette, Indiana, USA during the 2020 growing season. A Sony Alpha 7R-III RGB camera mounted on a UAV at a 20m altitude captured high-resolution orthophotos with a pixel resolution of 0.25 cm. The dataset consisted of two replications of 22 entries each for hybrids and inbreds, planted on May 12 using a two-row segment plot layout with a plant population of 30,000 per acre. The hybrids and inbreds in this dataset had varying flowering dates, ranging from 20 days between the first and last variety. This article uses orthophotos taken on July 20th and 24th to train and test the proposed deep network "MYOLOv5s." These orthophotos were divided into 15 images (3670×2150) and then cropped to obtain 150 images (608 × 2048) for each date. Three modifications were applied to the original YOLOv5s to form MYOLOv5s: BottleneckCSP structures were added to the neck part of the YOLOv5s, replacing some C3 modules; two-dimensional spatial dropout layers were used in the defect layer; and the Hardswish activation function was utilized in the convolution structures. These modifications improved tassel detection accuracy. MYOLOv5s was implemented in the Pytorch framework, and the Adam algorithm was applied to optimize it. Hyper-parameters such as the number of epochs, batch size, and learning rates were also optimized to increase tassel detection accuracy. Results and Discussion: In this study, we first compared the original and modified YOLOv5s techniques, and our results show that MYOLOv5s improved tassel detection accuracy by approximately 2.80%. We then compared MYOLOv5s performance to the counting-based approach TasselNetv2+ and two detection-based techniques: Faster R-CNN and RetinaNet. Our results demonstrated the superiority of MYOLOv5s in terms of both accuracy and inference time. The proposed method achieved an AP value of 95.30% and an RMSE of 1.9% at 84 FPS, making it about 1.4 times faster than the other techniques. Additionally, MYOLOv5s correctly detected the highest number of maize tassels and showed at least a 17.64% improvement in AP value compared to Faster R-CNN and RetinaNet, respectively. Furthermore, our technique had the lowest false positive and false negative values. The regression plots show that MYOLOv5s provided slightly higher fidelity counts than other methods. Finally, we investigated the effect of score values on the performance of detection-based models and calculated the optimal values of hyperparameters. Conclusion: 1. The MYOLOv5s technique outperformed other state-of-the-art models in detecting maize tassels, achieving the highest precision, recall, and average precision (AP) values. 2. The MYOLOv5s method had the lowest root mean square error (RMSE) value in the error counting metric, demonstrating its accuracy in detecting and counting maize tassels. 3. We evaluated the correlation between predicted and ground-truth values of maize tassels using the R2 score, and for the MYOLOv5s method, the R2 score was approximately 99.28%, indicating a strong correlation between predicted and actual values. 4. The MYOLOv5s method performed exceptionally well in detecting tassels, even in highly overlapping areas. It accurately distinguished and detected tassels, regardless of their proximity or overlap with other objects. When compared to the counting-based approach TasselNetv2+, our proposed MYOLOv5s method showed faster inference times. This suggests that the MYOLOv5s method is computationally efficient while maintaining accurate tassel detection capabilities. [ABSTRACT FROM AUTHOR]

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]

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]

Introduction The automatic detection of plant diseases in early stages in large farms, in addition to increasing the quality of the final product, could prevent the occurrence of irreparable damage. To this end, accurate and timely diagnosis of farm conditions is of great importance. In order to facilitate production potential and prevent a significant decline in yield, disease diagnosis is necessary periodically throughout the whole life of the. On the other hand, early detection of the disease in its early stages of growth can also prevent the spread of diseases. One of the most common methods for diagnosing plant diseases is the use of visual methods, but this method is difficult to evaluate the performance of a number of parameters such as the effects of the environment, nutrients, and organisms and so on. Furthermore, the accuracy of repetitions is very much related to individual fatigue of inspector. Research on activities that have the ability to identify diseases at an early stage and prevent the spread of contagious diseases are of great importance. Therefore, the use of new applications and new detection technologies to protect can significantly reduce the risk of product loss. Therefore, the purpose of this research is to design and construct an intelligent control system that automatically detects the health of the lilium plant and to improve the plant's condition. Materials and Methods Sample collection In this study, 80 pots of four kilograms (including healthy and disease plants) were considered for plant growth in vegetative stage. The spring onions were grown in pots with 20 cm diameter and 30 cm height. Experiments were carried out in a greenhouse with a temperatoe of 27.15٥c day/night and a relative humidity of 70-75%. Image processing In this research, the camera was placed at a constant distance of 50 cm from the flower to evaluate the stem and the leaves attached to it. The images were captured under the constant light conditions in the greenhouse during a specific hour of the day (10 to 12) every other day. The image was taken in RGB color space with a resolution of 1024 X §40 pixels, and after image transfer to the computer, image processing was performed using Matlab 2O16a. After examining the plant image, 9 color channels (R, G, B, L, a, b, H, s, and V) were examined from three color spaces (RGB, Lab and HSV) and stem length to diagnosis of Botrytis elliptica disease. Feature selection and classification In this research, after improving the image and extracting the feature, the linguistic hedges method was used to select the features and the K-means clustering was applied in the !-division of the:-clustering specified by the user. In this method, each attribute was assigned to a cluster closer to the mean vector. This method continues until there was no significant change in the mean vectors between successive repetitions of the algorithm. Results and Discussion According to the results of feature selection L leaf, L stem, a leaf, b leaf, H leaf, b stem, H stem, V leaf and stem length, were the best features. Moreover, the accuracy of diagnosis for the diseased and healthy plants were 96.42 and 100 percent, respectively, and the overall classification accuracy was 97.63 percent. Therefore, in general, it can be said that the proposed image processing method is desirable and acceptable in order to diagnose the disease. According to this, zhuang et al. (2017) used sparse representation (SR) classification and K-means clustering to identify leaf-based cucumber disease. In the proposed method, it has been shown that system could detect cucumber diseases with accuracy rate of 85.7%. Therefore, the proposed image processing technique seems to be able to diagnose the disease quickly and easily. Conclusions Today, in the modern agricultural systems, numerous computational metliods have been designed to help farmers to control the proper growth of their products. However, there are still major problems with the rapid, accurate and classification of diseases in the early days of the disease. Therefore, the purpose of this study ١vas to design, construct and evaluate a smart system based on image processing in order to identify and classify the leaf disease of the leaves of the lilium plant and remove it by spraying the contaminated parts. For this purpose, the linguistic hedges method was used to select the characteristics and k-means method to classify the infected plant from healthy. The results of the classification for the diseased and healthy plants were 96.42 and 100 percent, respectively, and the overall classification accuracy was 97.63 percent, which indicates the acceptable accuracy of the machine vision system in detecting the disease. [ABSTRACT FROM AUTHOR]

Introduction Control of walnut diseases and pests requires the mapping of the extent of contamination within possible shortest time. Therefore, it is necessary to develop systems to detect and determine the prevalence and location of contamination for researchers and gardeners. Image processing has been proposed as an approach to determine the extent and type of damage to different products in farms and gardens. The aim of this study was to design an algorithm based on the processing of walnut leaf images under natural light conditions in order to provide a rapid and nondestructive detection of diseases for the protection of trees using imaging methods. In this research, the possibility of detecting Anthracnose disease was investigated by processing walnut leaf images. The disease was detected using in situ images taken from the leaves to provide the basis for designing application software on smart mechatronic systems. Materials and Methods Images of leaves on walnut trees were taken under outdoor light conditions. Color and morphological properties extracted from the images were used to detect the pest on the leaves. Gnomonia leptostyla disease diagnostic algorithm was based on process of color and morphological characteristics, leaves background and disease-stained spots. The range of changes in R, G, and B indices was obtained in histograms and then twodimensional spaces were analyzed statistically on GR, GB, and BR planes. All points from these regions were used as statistical samples, for which bivariate regressions of GR, GB, and BR were obtained as y = bo ا bx Segments containing anthracnose spots from the leaves were segregated by extracting the coordinates of the points in each side on the RGB color space cube. Finally, anthracnose content was detected based on the number of spots detected by the algorithms. The percentage of contamination was used to determine the amount of contamination in each imaged area. Results and Discussion Examination of the colored spaces indicated that the domain of the anthracnose color components on the GR side has nothing in common with the color components of the leaves. The analysis of color space data revealed that the leaves and anthracnose were more distinguishable on the GB and RB sides, respectively. According to the histogram of the HSV color space, anthracnose spots were isolated from the leaves by determining the H range. In the evaluation of the proposed method for diagnosis of anthracnose, the infection severity calculated by the algorithm with the true infection intensity. T-test results for comparing the mean of the two infection intensity samples showed no significant differences between the two groups at 1% probability level. Conclusions The evaluation of the proposed method showed a 98% segregation accuracy for G. leptostyla detection method. Based on the results, the proposed method for detecting anthracnose spots is suitable for determining the contamination severity in the imaged areas. [ABSTRACT FROM AUTHOR]

One of the most important parts of infrared search and tracking systems is image processing, which detects the targets in infrared images. In this paper, a new infrared small target detection algorithm is proposed. The proposed method uses heuristic optimization algorithms to find small targets in infrared images. In this way, the particle swarm optimization algorithm is used as one of the best heuristic optimization algorithms. The performance of the proposed algorithm is evaluated using real and simulated infrared images. These images include a variety of false response sources like high-intensity edges, cloudy background, complex sea-sky background, and a target close to high-intensity background clutter. The experimental results are compared with the four common methods of infrared small target detection. The experimental results show the effectiveness and performance of the proposed algorithm. Also, the runtime of the proposed method is comparable to other commonly used methods, and this makes it usable in real-time programs. [ABSTRACT FROM AUTHOR]

Increasing information about color kinetic changes has made the improvement of color parameters possible. Analysis of the color kinetics data allows for undesirable changes to be minimized and optimizes the color retention in food products. Imaging and image processing operation were performed to extract color parameters in the color space L*a*b. Image J software was applied to analysis the images, and changing the color space was done using a two-stage method. The temperature sensitivity of rate constants was evaluated using Arrhenius equation. Results showed that the increase of ΔE during time at the beginning of frying was fast and then has been slower. The activation energy was determined 44.80 kJ/mol. Statistical analyses showed that frying time and frying temperature and also interaction of them had significant effects on total color changes of the meat cubes. Color measurement was carried out successfully using image processing as a non-destructive and simple method in evaluation of ostrich meat crust color and the results of machine vision system correlated with the visual observations. Kinetic of formation or deterioration of color during deep fat frying was well modeled using a first-order kinetic equation. [ABSTRACT FROM AUTHOR]

Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent classification method based on artificial intelligence methods to accurately detect the amount of liver fat is essential. This paper aims to develop an advanced machine learning model based on texture features to assess liver fat levels based on liver ultrasound images. Methods: In this analytic study, which is done from April to November 2020 in Tehran, ultrasound images of 55 obese people who have undergone laparoscopic surgery have been used and the histological result of a liver biopsy has been employed as a reference for liver fat. First, 88 texture-based features were extracted from the images using the Gray-Level Co-Occurrence Matrix (GLCM) method. In the next step, using the method of minimum redundancy and maximum correlation, the top features were selected from among 88 features and applied to the classifier input. Finally, using the three classifiers of linear discriminant analysis, support vector machine and AdaBoost, the images were classified into 4 groups based on the amount of liver fat. Results: The accuracy of the automatic liver fat prediction model from ultrasound images for AdaBoost classification was 92.72%. However, the accuracies obtained for support vector machine and linear discriminant analysis classification were 87.88% and 75.76%, respectively. Conclusion: The proposed approach based on texture features using the GLCM and the AdaBoost classification from ultrasound images automatically detects the amount of liver fat with high accuracy and can help physicians and radiologists in the final diagnosis. [ABSTRACT FROM AUTHOR]

Background: Surgery and accurate removal of the brain tumor in the operating room and after opening the scalp is one of the major challenges for neurosurgeons due to the removal of skull pressure and displacement and deformation of the brain tissue. This displacement of the brain changes the location of the tumor relative to the MR image taken preoperatively. Methods: This study, which is done from March to December 2019 in Tehran, is evaluated on the available database of RetroSpective Evaluation of Cerebral Tumors (RESECT) including pre-operative MR images, and intra-operative ultrasound from 22 patients with low-grade gliomas who underwent surgeries at St. Olavs University Hospital. This study is used for image registration of preoperative MR imaging and ultrasound imaging after resection of the skull to compensate for brain changes. By this method, we obtained a third image that resembles preoperative MR imaging but has the geometry of the brain shape changes. We used a combination of the two transformations named Affine and non-rigid Free Form Deformation (FFD) for hierarchically moving the pixels to compensate for global variations, and also nonlinear local and small variations. Also, by applying the mutual information function, we consider the entropy value as the criterion of similarity due to the non-similarity of the nature of the images. Also, Limited Broyden-Fletcher-Goldfarb-Shannon method is used for optimization. Results: The results of the proposed method were presented on the available database of RetroSpective Evaluation of Cerebral Tumors (RESECT) including images of 22 patients with glioma type 2 tumors and evaluated based on 15 landmarks per patient and also mutual information criteria. The mean target registration error for affine, FFD and the proposed method are 46.19, 42.85 and 38.01, respectively. It was shown that the proposed method achieved high accuracy by combining the two transformations of affine and FFD compared to the separate use of each of the two models. Conclusion: In image registration of preoperative MR and ultrasound images for compensation of brain shift, the combination of affine and FFD transformations had better results than the individual use of each of the transformations. [ABSTRACT FROM AUTHOR]

Background and Objective: Early diagnosis of internal root resorption is a challenge in endodontics. Image-processing filters are applied to facilitate image interpretation for diagnostic or treatment purposes. The aim of this study was to investigate the effect of contrast alterations in detecting internal root resorption in CBCT images. Materials and Methods: Fifty single root teeth with were splited mesiodistally in this in vitro case-control study. Artificial internal resorption lesions were created in cervical, middle or apical areas. CBCT images were taken before and after cavity preparation. CBCT images was evaluated by two radiologists in low, intermediate and high contrasts. The data were analyzed using Kappa and MacNemar tests on SPSS software. Overall accuracy was evaluated with sub curve analysis of ROC. Results: Low contrast images had the highest sensitivity, specificity and overall accuracy (all 100% for first observer and 92, 100, 96% for second observer, respectively). In apical area in low, intermediate and high contrasts, sensitivity, specificity and overall accuracy were 100%. There was a significant difference with reality in high contrast-cervical resorption group (P=0.016). Conclusion: The low contrast may be an acceptable processing filter in detection of internal root resorption. [ABSTRACT FROM AUTHOR]

It is important to control the ripening stages of agricultural products during storage and their quality grading based on their ripening stage. Edible coatings can prolong the storage life of agricultural products and protect them through the handling, storage, processing and marketing. The purpose of the current study was to develop an artificial vision system for quality control and segregation of cherry tomatoes in two different conditions including with and without Aloe vera gel coating. For this purpose, physicochemical properties including titrable acidity, total soluble solids and firmness of cherry tomatoes were measured in both conditions. Based on these properties, the ripening index (RPI) was determined and the samples were classified to MS1 and MS2 according to the ripening stage. Subsequently, the samples were classified using color features,color texture features separately and their combination through principal component analysis (PCA) and back propagation neural network (BPNN). Classification results showed that the use of color and color texture features combination made the classification more accurate; PCA and BPNN methods were able to segregate the samples with high accuracy (85.72 and 98.21, respectively) using the 21 color and color texture features. The higher accuracy of the BPNN method is due to its nonlinear performance. The results of this study indicate that Aloevera gel is promising in delaying the ripening process of cherry tomatoes and artificial vision system can be used as a non-destructive method for evaluation of cherry tomato ripening level based on the color and color texture features. [ABSTRACT FROM AUTHOR]

The present study was aimed at investigating the effect of enrichment of sponge cake with pumpkin powder (0, 4, 8 & 12%) on the physicochemical, textural, nutritional and sensory properties of the final product during storage. The results showed that while increase in the level of pumpkin powder promoted the moisture content of the sponge cake, storage time affected it negatively. The changes in peroxide value revealed that pumpkin powder improved the oxidative stability of the product so that the control sample had higher peroxide value than the treatments throughout the storage period. The results of image processing showed that addition of pumpkin powder into the formulation of sponge cake was concomitant with a decrease in lightness and an increase in redness, yellowness and browning index. It was also observed that the highest porosity was belonged to the sample containing 8% pumpkin powder. The instrumental texture analysis indicated that increase in the level of pumpkin powder increased the hardness, the force required to punch and the force required to cut the samples and decreased the cohesiveness and elasticity. The storage time almost had the same effects on the textural parameters as the pumpkin powder. The pumpkin powder improved the nutritional quality of sponge cake in terms of fiber, beta-carotene and potassium contents, which remained unaffected during storage. The sponge cakes enriched with pumpkin powder were more appreciated by consumers compared to the control sample; however, the sample containing 8% pumpkin powder received the highest sensory scores. In conclusion, pumpkin powder could be used for the development of a functional sponge cake with desirable sensory properties and long shelf life. [ABSTRACT FROM AUTHOR]

Drying is one of the ways of storing of persimmon. In this study, to increasing shelf life of persimmon and producing high-quality products, infrared dryer was used and mass transfer kinetics, density, rehydration and color of samples were measured. The results showed that radiation lamp power and distance of lamp from sample had significant effect on the moisture loss kinetics and drying time (P<0.05). With increasing in radiation power, as well as reducing the distance of samples from the source of radiation, drying time decreased. The average density and rehydration for the dried samples in infrared were 639 kg /m3 and 270 %, respectively. The average calculated color changes (ΔE) for the power of 200, 300 and 400 w were 14.43, 10.09 and 20.04, respectively. The results of modeling by genetic algorithm-artificial neural network showed that artificial neural network combined with genetic algorithm provides better results and with combine them the speed of analysis and accuracy of modeling process increases. Using a network with 15 neurons in the hidden layer and using the hyperbolic tangent activation function and percentage data used to training/validation/testing equal 20/20/60 may be predicted drying kinetics of persimmon. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the image processing technique in rice blast disease detection in field and controlled conditions. Using MATLAB software, images taken from field and controlled conditions were processed in three RGB, HSI and LAB color spaces. Then it was extracted by the gray area intensity profile, color properties, and threshold value for background image removal. After removing background in RGB, HSI and LAB color spaces, disease spots on rice leaf were determined. In RGB color space, by subtracting arrays by test and error, the blast patches on the leaf were separated from the rest of the image pixels. Hue was used in the HSI color space because this component was independent of light intensity variations, so blast blot identification was performed more accurately than the S and I components. In the LAB color space, the Kmeans clustering algorithm was used to segment the images into three clusters and was displayed in an independent cluster after labeling the image of blast disease spots. Finally, in order to determine the performance of the algorithms designed in three color spaces, the sensitivity factor, specificity and total accuracy were tested on the basis of the perturbation matrix for 500 image samples. In field and controlled conditions, the highest accuracy in detecting blast blots in the LAB color space was 94% and 98%, respectively. Overall, the results showed that the image processing method can be used to detect rice blast disease. [ABSTRACT FROM AUTHOR]

Researchers are always attempt to produce bread with longer shelf life and high quality. Aware of the harmful effects of chemicals additives increased consumer tend to use natural additives. The aim of this project was the production of cake (wheat-millet) containing different levels of Persian gum or Zedu (0, 0.3, 0.6 and 1%). Viscosity of dough and moisture, specific volume, firmness, crust color and sensory properties were evaluated. Viscosity and moisture were increased by increasing of Persian gum in cake formulation. The sample containing 0.6% Persian gum had the lowest firmness and the highest specific volume, L* and score of sensory properties. A* and b* values of samples containing gum did not have significant difference (P<0.05). Micro-structure of sample containing 0.6% Persian gum was compared by Micro-structure of blank (sample without Persian gum). The sample containing gum had uniformed structure compared to blank. [ABSTRACT FROM AUTHOR]

In this project, low-fat composite (wheat-quinoa) cup cake was produced. Plantago major L. gum and water (1.5-7.5, 3-15, 4.5-22.5 and 6 -30%) was used as a fat replacer in levels of 0, 25, 50, 75 and 100 % in cup- cake. PH, specific gravity, viscosity, moisture, firmness, crust color and overall acceptability were evaluated. PH and b* value were decrease and moisture and a* value were increased by increasing of Barhang gum. The samples containing 1.5 and 3 % gum had the lowest specific gravity and the highest specific volume and L* value. Also, the highest viscosity was indicated in the sample containing 3 % gum. On the other hand, the sample containing 3 % gum had the lowest firmness in 2 hours and 1 week after baking. Finally, the samples containing 1.5 and 3 % gum had the best overall acceptability. Therefore, it can be said that the removal of 50% of oil in cake formulation was performed successfully. [ABSTRACT FROM AUTHOR]

Due to the impact of climate change on the plant water demand and the availability of water, especially in drylands, it is vital to estimate the evapotranspiration rates accurately. In this study, the vegetation status in the marginal desert areas of Varamin Plain was studied, and the actual evapotranspiration and water demand of intercropped farms were assessed. This study also evaluated the potential relationship between the evapotranspiration of different agricultural lands and their vegetation index using remote sensing techniques. A collection of satellite images from Landsat 7 in consecutive seasons was used to determine the greenness rate of marginal desert areas during 2013 and 2014. ENVI software was used for the image processing, which included geometric corrections and atmospheric corrections, to develop NDVI maps. Also, weather data and crop properties of Varamin Plain were collected, and the actual evapotranspiration rate of plant cover was estimated using CropWat. The correlation between NDVI extracted from satellite images and the evaluated evapotranspiration rate was assessed. The results showed a strong relationship between evapotranspiration of heterogeneous agricultural lands and NDVI. This confirmed that the NDVI derived by remote sensing approach could be a useful index to evaluate vegetation status and water demand of farmlands in the desert borders. [ABSTRACT FROM AUTHOR]

In this project, low-fat composite (wheat-millet) cup cake was produced. Fenugreek seed gum (0.25, 0.50, 0.75 and 1%) was used as a fat replacer in levels of 0, 25, 50, 75 and 100 %. specific gravity and consistency of batter and moisture, specific volume, porosity, firmness (2 hours and 1 week after baking), crust color and sensory properties of chiffon cake were measured. The sample containing 0.25% Fenugreek seed gum had the lowest specific gravity and firmness and the highest specific volume, porosity, L* value and overall acceptability score. Also, the result showed the technological and sensory properties of sample containing 0.25% Fenugreek seed gum was similar to control. Moisture and a* value were increased and b* value was decreased by increasing Fenugreek seed gum. Finally, the removal of 50% of oil in chiffon cake formulation was performed successfully. [ABSTRACT FROM AUTHOR]

The mechanical properties of fruit are one of the most important determinants of standards for designing, transforming, processing, and packaging systems. One of the methods for changes demonstraction in the internal structure of fruits during storage is to perform stress relaxation tests in different predetermined strains. Therefore, the purpose of this study was to investigate the orange compression behavior in quasi-static mechanical loadings by performing stress-relaxation tests of samples at predetermined levels of strain and modeling the Maxwell and Peleg method and comparing it with image processing method. In addition, in this study, cross-sectional area changes were measured during loading by image processing. The modulus of elasticity in the image processing method, the Maxwell model and the Peleg model on days 0 to 9 increased from 3.91 to 4.5, 3.6 to 4.53 and 2.7 to 3.45, respectively. which in spite of increasing trend there was no significant difference between them. Since, no significant difference in the output of these two models (Maxwell and Peleg) was observed Peleg with the less number of elements (only two constants) was preferred compared to Maxwell method. [ABSTRACT FROM AUTHOR]

Noughl (sugar coated almond or walnut) is a traditional confectionary sugar-based product which is also produced by addition of spices and dried damask rose in recent years.In spite of its popularity, noughl suffers from dehydration in a short time after being exited from the package. Thereupon, the aim of the present study was to investigate the additives' impact (cinnamon, damask rose and saffron) on physico-mechanical properties (moisture content, texture, surface area, color and aw) of noughls and also the trend of moisture loss during storage. The results indicated that the most moisture loss took place at the first 3 days after opening the package. Based on the rheological tests, the highest and the lowest breakage energy belonged to damask rose (35.48 N.mm) and cinnamon (9.1 N.mm) noughls, respectively. Besides, it was found that storing for 14 days led to increase of vanilla noughl firmness for more than three times. Moreover, mean value of water activity of noughls was lower than 0.56. Comparison of mean value of the color total difference (ΔE*) at 0 and 21st days declared that cinnamon and vanilla noughls had the highest and the lowest color alteration, respectively. According to image processing analyses, suitable indicators to show color changes during timewere a* and L* channels for cinnamon and damask rose noughls and b* and L* channels for saffron and vanilla noughls. [ABSTRACT FROM AUTHOR]

Background and Aim: Intra-bone lesions are common in lesions, among them periapical lesions have a higher incidence. Todays, all direct digital imaging systems provide various types of image processing techniques. The purpose of this study is to investigate the effects of different digital image processing techniques on the diagnosis of bony defects. Materials and Methods: In this experimental study, 50 sheep inferior half-jawbones were used. Lesions were created by round bars with numbers 1/4, 1/2, 1, 2 and 4. Digital images were obtained by edge enhance, sharpening, invert gray scale processing method and unprocessed investigated by two radiologists and in case of disagreement them observers by third observer. The method of recording the results was definitive diagnosis of lesion number 1, misdiagnosis number 2 and possible diagnosis number 3 .The results were analyzed by chi-square and Fisher's exact tests. Results: Percent of diagnostic sensitivity in unprocessed Images (71.8%) and images processed with Sharpen filter (71.4%) were significantly higher than images processed with invert grayscale (68.5%) and edge Enhancer (53%).(p<0.001) Conclusion: To detect bone lesions, unprocessed and processed digital images with Sharpen filters have higher diagnostic accuracy. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the quality characteristics of bread produced by yeasts and lactobacillus isolated from native Iranian sourdough. Initially for the preparation sourdough, fresh microbial cells with certain number of colonies, by centrifugation were isolated of initial culturing bacteria and yeasts. Then equivalent 10% weight flour, microbial cells mixed with equal amounts of water and flour. After processing identical samples to assess the quality characteristics of bread used of sensory evaluation test, image processing and Texture analysis. The experiments in a completely randomized design and completely randomized block with three replications were performed. The results showed that the samples significant effect on the quality characteristics. The results indicate that sourdough can be increasing the porosity of the sample So that e treat with 42 percent had the highest degree of porosity also sourdough delaying staling. According to the results from this study, treatment of Lactobacillus reuteri and Saccharomyces cerevisiae had best Sensory evaluation, porosity and texture. [ABSTRACT FROM AUTHOR]

Quality and shelf life of bread as a main diet has the great importance on health and the national economy. So the aim of this study was to investigate the effect of water, melon water, oil, cow milk and soy milk as a glazing component of baguettes on the moisture content, specific volume, texture, crust color and overall acceptance of final product. In this study, Image J software was used for crust color measurement. Based on results, the samples have melon water, cow's milk and soy milk glaze had the highest moisture content. However, the samples containing water and water melon glaze had the highest specific volume. The results clearly showed that the samples were coating by water and melon water had the lowest firmness respectively in 2 and 72hr after baking. This indicates that melon water as a coating agent is better for preserving moisture and to retard the staling. In addition the highest amount of L* value and a* value and overall acceptance score in organoleptic evaluation were observed in the two samples coating by melon water and cow's milk. In general we can say that glazing is one of the techniques that are simple, low-cost addition to being a great role in maintaining and even improving the quantity and quality of bread. Also according to the results of this study, melon water, with its unique function as a new glaze in bakery industry is introduced. [ABSTRACT FROM AUTHOR]

In this study the effect of different amounts of gelatin (1, 2, and 3 g) and sodium caseinate (1, 2, and 3 g) on sensory characteristics, image processing and texture of chocolate mousse was investigated and the relationship between sensory parameters - device was determined using principal components analysis method. Results showed that the gelatin had a great impact on the sensory characteristics. Results of texture analyzer showed that hardness, adhesiveness, gumminess and chewiness was being greatly affected by gelatin and these parameters increased with increasing gelatin but springiness was more dependent on the sodium caseinate. Entropy, fractal dimension, bubble size and porosity decreased with increasing gelatin. The amount of spherical bubbles also showed no significant difference. Results of principal components analysis showed that some textural characteristics (hardness, gumminess and chewiness) had close relationship. [ABSTRACT FROM AUTHOR]

Osmotic dehydration is one of the drying methods that have been considered in recent years. Water and solute transfer through process can be determined by the percentage of water loss (WL) and solid gain (SG). In this study, image processing technique has been used to analyse osmotic dehydration of potato and determining the amount of water loss that is the main parameter in this process. Experiments performed for two different solutions (15 wt% salt and mixture of 35 wt% sucrose and 10 wt% salt) and three temperature levels at 35, 45 and 55 ° C. Water loss estimated from changes in samples volume; Sample’s volumes determined using image processing of pictures from different sides of samples at the start of process and at the end of it. Experimental results obtained from the osmotic dehydration of potatoes can be summarized as: the amount of water loss increased with increasing temperature; using a mixture of salt and sucrose as osmotic medium gets more water loss comparing use of salt as an osmotic agent in solution. The overall results of the image processing indicate that the approach used in this study can be reached acceptable data; While the pace of work is much more than the conventional methods. [ABSTRACT FROM AUTHOR]

Background and Objectives: periapical lesions in a clinical problem is that there are often difficulties in detecting it. Since the diagnosis of periapical lesions in the periapical radiolucency on the observation area، so the diagnosis of periapical radiographic evaluation is important .The Target of this study is to assess the diagnostic exactness of indirect digital radiography (PSP) and the conventional processing in diagnosis of periapical lesions at invitro conditions. Subjects and Methods: In this experimental - laboratory research، 60 synthetic lesions were formed under the apex of teeth using of single-root premolar teeth placed in the dried Mandible bone of sheep. These lesions were taken radiographs by two methods of digital and conventional. Two oral and maxillofacial radiologists examined the presence or absent of lesions in PSP pictures by conventional and reverse processing in each tooth. Results: The average sensitiveness of observers in PSP radiography with conventional processing in mm1، 0mm/5، mm%25 are 46%، 63.5%، and 93.3% respectively which were reduced to 45%، 60% and 86.6% with PSP radiography of reverse processing. Digital radiography with conventional processing has the characteristics of (66%) in comparison with the (63.3%) digital radiography of reverse processing. Conclusion: There is no significant difference between the sensitiveness of digital radiography with conventional and reverse processing. [ABSTRACT FROM AUTHOR]

Background and Objectives: Accurate identification of landmarks of lateral cephalometric radiographic is important for analysis. The objective of this study was to investigate the effects of digital image processing filters on detection of lateral cephalometric landmarks. Subjects and Methods: Lateral cephalometric radiographs were acquired from 30 patients. The unprocessed images and those processed using Shadow، Sharpen، Negative and 3D Emboss filters were assessed by two observers and scored for clarity of points A، B، N، Orbitale، Pronasal، Subnasal، Pogonion' and Menton'. The results were statistically analyzed using Friedman and Wilcoxon tests. Results: For detection of points N، Orbitale and Pronasal، 3d emboss filter had the lowest rating and the other methods were not significantly different. For detection of points A، Subnasal، Pogonion' and Menton'، 3d emboss and negative filters had the lowest rating and these two filters were not significantly different and the other methods were not significantly different. For point B all methods were not significantly different. Conclusions: The results of this study clearly showed that processing of lateral cephalometric radiographs with different filters does not necessarily lead to improvement of the image but a wrong selection algorithms and filters may reduce the diagnostic quality of the images. [ABSTRACT FROM AUTHOR]

Background and Objective: Recently، most digital imaging systems provide the option of image post-processing with different techniques. This clinical study aims to compare these techniques on perception of anatomical structures and overall image quality in direct digital panoramic radiographs. Subjects and Methods: Panoramic radiographs were acquired from 45 patients. The original and the processed images using shadow، sharpen، sigmoidal or exponential، Negative and 3D embossed filters were assessed by 4 observers and scored for perception of the floor of maxillary sinus، dentino enamel junction، root canal space، periodontal ligament space، inferior mandibular cortex، bone trabecular pattern، inferior alveolar canal and overall image quality. The results were statistically analyzed using Friedman and Wilcoxon tests. Results: The best diagnostic quality in all anatomical structures and overall image quality was obtained using sharpen processing method. The negative filter earned the second place in floor of maxillary sinus، dentine -enamel junction and root canal space. The original images provided the second best diagnostic quality in periodontal ligament space، inferior mandibular cortex، bone trabecular pattern، inferior alveolar canal and overall image quality. Conclusions: To perceive the anatomical structures and overall image quality the sharpened image processing method is recommended. It should be noted that the processing techniques should be chosen carefully considering the anatomical structure that is being studied. [ABSTRACT FROM AUTHOR]

Background and Aim: Considering the importance of quality and bacterial contamination in food products, a rapid, accurate and able method to deliver consistent results for counting bacteria is an essential. Application of image processing in the measurement of quality is one of important research topics. This method is very accurate and non-destructive with constant results. The aim of this study was the use of image processing in bacterial count (Quail flora, sourdough and drinks kefir) and comparison with Colony Counter method. Materials and Methods: First, of intestinal microbial flora Quail, sourdough and drink kefir, Dilutions were prepared from 10-1 to 10-9. Then respectively in plate count agar, YGC agar and MRS agar were cultured surface (Quail flora and lactic acid bacteria in drink kefir at 37 ° C and yeast in sourdough at 25 °C). After 24 hours, colonies grown by Colony Counter and Image J software were counted in triplicate. Results: The results showed that in dilutions of 10-5 to 10-9, don't difference between the numbers reported by the two methods but in dilutions of 10-1 to 10-4, there were significant difference between the numbers reported by the two methods. Conclusions: According to the results of this research can be introduced image processing an accurate and rapid method for bacterial count. [ABSTRACT FROM AUTHOR]

Reduction of bread loss and wheat consumption have particular importance in the national economy, has attracted many researcher's mind. Thus in this study sorghum flour was used as a substitution for the part of wheat flour in bread making. On the other hand, the purposes of this study were investigation on the effects of steaming time (three levels of 0, 90 and 180 second) and baking time (three levels of 10, 15 and 20 minute), on moisture content, water activity, crust color, firmness and overall acceptability of composite bread (wheat- sorghum). Based on the result, the steaming and baking time were effective on staling rate and bread shelf life at P<0.05 and cured sample at steaming time 90 and 180 second and baked at 15 minute, had the lowest rate of staling during 72 hours after baking. Also, the sample was treated with 90 second steaming and baked at 15 minute had the best crust color and overall acceptability. [ABSTRACT FROM AUTHOR]