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The valuable saffron metabolites have encouraged researchers to study their laboratory production. In this study, two experiments have been designed to optimize saffron corm callogenesis in-vitro conditions and assessed the application of image analysis to address some characteristics of calli without cell destruction. Effect of physiological age of the corm, different types of media, and plant growth regulators (PGRs) with different concentrations as independent variables were investigated on cell growth index, the redness intensity (a*value) of callus and the percentage of black spot as dependent variables. Results showed that the mature corm-derived explants on Gamborg (B5) medium create the best callogenesis and production of red-colored cells with the least black spot. It was also revealed that naphthalene acetic acid (NAA), (2 mg/L) in combination with 6-Benzylaminopurine (BA) (1 mg/L) increased the growth index to 1.14. In contrast, the combination of indole-3-acetic acid (IAA), (2 mg/L) or indole-3-butyric acid (IBA), (10 mg/L) with kinetin (KIN), (1 mg/L) was the most effective PGRs for the production of red cells. The HPLC results confirmed that the red color of the calli was related to crocin presence. It is concluded that although the cultivation of saffron mature corms in the B5 medium is the most suitable option for biomass growth and crocin production, the choice of PGRs varies depending on the purpose of the culture (biomass or crocin production). Moreover, image analysis can be considered as a promising, alternative, un-destructive, and fast method for estimating the cell growth index and crocin content.

Counting the number of soil particles in grain size studies is of great importance, especially in geological, agricultural, environmental, and engineering sciences. It can be used for detailed analysis of soil properties, determining soil structure, and environmental analysis. The main goal of this research is to evaluate the grain size and shape recognition of soil particles using image processing techniques. In this study, initially, color images of soil grain size were acquired. Then, they were processed using Python programming language and the Scikit-Image library. Finally, for model validation, 17 rice grains and 8 coins were used. The results demonstrated that this method was able to accurately detect the number and shape of these particles. It also performed well in identifying the number and shape of soil particles. Furthermore, when compared to several other software tools in the same field, it provided better results. This approach can be utilized to plot the soil grain size curve, ultimately leading to reduced computational costs and time.

Accurately and promptly monitoring the nutritional conditions of fruit orchards is crucial for providing optimal fertilizer recommendations, which in turn improves yield and enhances the quality of agricultural products. The current laboratory methods used to evaluate nutritional condition in fruit trees are expensive, challenging, time-consuming, and require an expert. In this study, image processing methods and neural network models was utilized to determine the stages of iron deficiency in peach trees. Therefore, a database containing 800 images of peach leaf samples was acquired. These images were then classified into four categories using the KNN clustering method: no deficiency, low deficiency, moderate deficiency, and severe deficiency. The preprocessing, feature extraction, and modeling operations were performed in the MATLAB software, version 2017. Features such as mean and standard deviation were extracted from the RGB, HSV, and Lab color space components of each image. Subsequently, the principal component analysis (PCA) algorithm was applied to the feature vector. To determine the optimal structure of the network, criteria including precision, accuracy, recall, and the F1-score were evaluated. These criteria helped ascertain the number of optimal inputs and the corresponding number of neurons for each combination of input features (PCs). Results indicated that the neural network model, structured as 6-36-4, achieved an accuracy of 89.73 ± 0.54%, precision of 89.59 ± 0.57%, recall of 89.52 ± 0.51%, and an F1-score of 89.55 ± 0.54% in detecting levels of iron deficiency in peach tree leaves. The findings from the confusion matrix and the developed model reveal that this method can effectively and efficiently detect the severity of iron deficiency in peach tree leaves. [ABSTRACT FROM AUTHOR]

A large number of printed board circuits (PCBs) are analyzed by X-ray imaging in reverse engineering for the purpose of identifying their connections, their damage, and how they are linked together. Using this method, an X-ray is passed through single or multi-layer boards in order to determine how they are connected and how the parts are arranged. According to the findings, there is fogging in the radiographs due to X-ray scattering and the thinness of copper connections on the boards as well as the small sizes of electronic components. This research employs the Modified Total Variation method (MTV) with alternating gradient, which is an iterative method based on gradient changes in Total Variation, to increase contrast. Based on the results of the implementation of the MTV algorithm on radiographs of various ranges, the contrast has increased, and besides the copper connections, the internal components of the electronic components have also become clearer. Reconstructed images show a contrast improvement of approximately 20 to 40%, according to experts. Using this information, it is possible to repair or build boards in reverse engineering.

The analysis of heat transfer in the channel in many types of heat exchangers, such as electric cooling equipment, solar collectors, heat exchanger systems, high-performance boilers, gas turbine blade coolers, etc., is the basis of the design, construction, and optimization. Controlling heat transfer to increase the rate of heat transfer in such systems by improving the cooling method is an effective energy engineering from the point of saving energy. Increasing the heat transfer performance in the scales of macro and microchannels is crucial. The use of expanded surfaces in the channel is a practical method to increase the heat transfer coefficient. In the upcoming article, the smart design of a two-dimensional nanofluid heat exchanger has been studied numerically in order to achieve optimal performance conditions in terms of heat transfer rate, the amount of deposition of nanoparticles in the structure of the exchanger, as well as the fluid pressure drop while passing through it. It can be seen that the geometric structure optimized by the combination of genetic algorithm and computational fluid dynamics of this channel causes an increase of 1.14% in the enthalpy of the passing nanofluid, a decrease of 11.21% in the pressure drop of the passing nanofluid, and a reduction of 8.44% percentage in the deposition of nanoparticles inside the channel and a total increase of 24.82% in the fitting function defined in terms of these three variables, compared to the channel designed in previous studies. Therefore, this optimal channel has a higher heat transfer rate with a pressure drop and a lower amount of nanoparticle deposition compared to the previous channel, which proves the ability of the genetic algorithm with computational fluid dynamics in the optimal design of all types of heat exchangers.

Introduction Due to the importance of product appearance quality in product grading and the impact of factors such as area, uniformity, and various defects on the product quality, and also, the ability to recognize these features at a very low cost, image processing techniques, is one of the methods used to evaluate food quality. Therefore, in this study, a non-destructive image processing method was used to investigate the factors affecting the color and shrinkage of apple slices during drying. Materials and Methods Golden delicious apples were used in this research. The central part of the apple (including the rivet, seeds, and tail) was removed by a kernel separator and sliced into 3, 5, and 7mm thickness and approximately 7 mm diameter slices using a hand slicer without separating the skin. Three temperatures of 60, 70, and 80 °C were used to dry the samples. To determine the moisture content of a sliced apple, the samples were first weighed on a digital scale, then placed in a dryer, and the experiment was continued until the samples reached equilibrium mass. Due to the high importance of moisture ratio in controlling the drying process, moisture rate (MR) and moisture content (MC) were calculated, and samples were taken to investigate the amount of surface shrinkage, general color changes and browning index. After extracting L*, a*, and b* values, total color changes and browning index (to show the intensity of brown color in the product) for all samples before and after drying were calculated and evaluated to describe color changes after drying. Results and Discussion The drying kinetics results showed that the drying process significantly depends on the thickness of the samples. According to drying curves, at the early stages of drying, the decrease in humidity occurs more severely and the graph has a steeper slope, but as the process continues and the moisture content of the product decreases, the slope of the curve decreases. In the early stages of drying, due to the presence of water inside the fresh fruit cells, there is a pressure balance between the fruit and the surrounding environment, which causes the fruit to remain swollen. However, as the drying time progressed, contractile stresses are created, which cause superficial shrinkage. In this study, it was observed that increasing the thickness from 3mm to 7mm, reduced the final shrinkage on the surface of apple slices by 11% at 60 °C, 12% at 70 °C, and 13% at 80 °C. After moisture leaves the surface of the product and heat penetrates into the product, moisture begins to leave the product by conducting interstitial convection. When moisture moves to the surface, the mechanical balance and consequently the textural structure of the sample is disturbed due to the creation of different spaces in thickness. According to the results, increasing drying time and thus decreasing the moisture content, increases the percentage of apple shrinkage. On the other hand, at a certain thickness, with increasing temperature, the percentage of shrinkage changes in the thickness of the product decreases. Therefore, at thicknesses of 3, 5, and 7 mm, the increase in temperature from 60°C to 80°C, decreased the amount of shrinkage thickness by 16, 12, and 8%, respectively. It is in higher thicknesses that react with heat and change the color of the fruit due to the Maillard reaction. After complete drying of apple samples, the highest amount of color change was related to the thickness of 7 mm and a temperature of 80°C, which was equal to 1.254. Also, the lowest rate of discoloration of apple slices in a thickness of 3 mm and a temperature of 60 °C was 0.889. The browning index (Bi) in the high thickness of apple slices is less affected by the process temperature due to the increase in moisture level. For this reason, the rate of browning was very low among the experimental samples and the highest rate of browning was related to the thickness of 7 mm and the temperature of 80 °C was 585/2559. Also, the lowest rate of browning of apple slices was observed in the thickness of 3 mm and the temperature of 60 °C was 584.254. Conclusion Finally, it was found that the thickness and temperature factors can have an effect on the quality of product during drying process. The results of this study can provide a cheap and fast way to control the quality of fruits during drying and help producers of these products select the main process factors that affect the final quality.

IntroductionAdopting 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 MethodsIn 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 DiscussionThe 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.ConclusionIn 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.

The common methods that are usually used to identify the devoid rough rice from the healthy ones are often time-consuming and expensive. For this reason, in this research, a smart and fast method based on machine vision system coupled with artificial neural networks is presented in order to predict the percentage of devoid/healthy rough rice grains. Digital images of five varieties of paddy were prepared in three states: healthy, devoid, and mixed, in two states scattered and piled. After pre-processing and segmentation, 3 color features and 5 morphological features were extracted for each rice grain. Principal component analysis (PCA) method was then used in order to identify the most effective features in distinguishing devoid rough from healthy rice. In the next step, multilayer perceptron (MLP) algorithm based on the main components obtained by PCA method was used to create models for identifying and classifying the samples. Root Mean Square Error (RMSE), correlation coefficient (R2), specificity and sensitivity were used to evaluate the modeling capability and validation of each algorithm. The obtained results showed that the designed intelligent method can identify devoid rough rice seeds with acceptable accuracy in all cultivars (R2P>0.81, RMSEp0.8 & Specificity>0.98). Therefore, the machine vision system in combination with artificial neural networks can be used as an intelligent and fast method at the entrance of rice bleaching factories to evaluate the quality of harvested rough rice and predict the percentage of unhealthy rough rice.

IntroductionMaize 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 MethodsThe 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 DiscussionIn 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.ConclusionThe MYOLOv5s technique outperformed other state-of-the-art models in detecting maize tassels, achieving the highest precision, recall, and average precision (AP) values.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.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.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.

Introduction: COVID-19 has had a devastating impact on public health around the world. Since early diagnosis and timely treatment have an impact on reducing mortality due to infection with COVID-19 and existing diagnostic methods such as RT-PCR test are prone to error, the alternative solution is to use artificial intelligence and image processing techniques. The overall goal is to introduce an intelligent model based on deep learning and convolutional neural network to identify cases of COVID-19 and pneumonia for the purpose of subsequent treatment measures with the help of lung medical images. Method: The proposed model includes two datasets of radiography and CT-scan. These datasets are pre -processed and the data enhancement process is applied to the images. In the next step, three architectures EfficientNetB4, InceptionV3, and InceptionResNetV2 are used using transfer learning method. Results: The best result obtained for CT-scan images belongs to the InceptionResNetV2 architecture with an accuracy of 99.366% and for radiology images related to the InceptionV3 architecture with an accuracy of 96.943%. In addition, the results indicate that CT-scan images have more features than radiographic images, and disease diagnosis is performed more accurately on this type of data. Conclusion: The proposed model based on a convolutional neural network has higher accuracy than other similar models. Also, this method by generating instant results can help in the initial evaluation of patients in medical centers, especially during the peak of epidemics, when medical centers face various challenges, such as lacking specialists and medical staffs.

Neutron Computed Tomography (nCT) is one of the modern applications of research reactors. The combination of the Three-dimensional representation capability of tomographic imaging and the unique properties of neutron interaction with materials creates valuable information about the internal structure of materials and components. In this study, the effect of data acquisition geometry and method of image reconstruction on tomographic images of the Tehran Research Reactor Imaging Facility (TRRIF) have been investigated based on contrast and the ratio of contrast to noise parameters. In experimental data acquisition, two radiation geometries have been aligned with angular steps of 1 and 0.5 degrees, respectively. Two samples were turned within the neutron radiation field at a half-screen. Image reconstruction has been performed through FBP and ART, SART, and SIRT algorithms, using Python programming language. The stack of images has been rendered to gain sample volume. Initially, in order to reconstruct the image through a high-quality set of projections, experimental data acquisition parameters have been optimized. Then, pre-processing has been carried out on projections. The results showed that the sample components and some defects could be differentiated from each other. Contrast and CNR were improved in data acquisition geometry with smaller angular steps and through SIRT reconstruction method.

Oil condition monitoring is an effective method for detecting abnormal erosions or defects in mechanical equipment and systems. One of the issues in the field of condition monitoring with the help of oil analysis is the cost and time required to inspect all samples by an expert. However, not all oil analysis samples need to be reviewed by an expert, and less than 10% of this data indicates a critical situation that requires rapid planning and action. The goal of this article is to turn the oil status into an image so that you can quickly identify the oil status by looking at the image. Also, by processing these images by software, the state of failure can be extracted through artificial intelligence. In this research, the data are taken from the sample test of motor oils of road construction rollers. First, the data were converted to gray scale images using baseline for diesel engines and through MATLAB software. In the next step, these images are processed using the convolutional neural network method to determine the oil status. Comparison of the obtained results showed that the visualization of the oil analysis results helps to understand the general condition of the oil for the user and the critical samples and the need for action are identified more quickly among the mass of oil samples.

The application of machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fetal death. The application of innovative machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an open-source tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a Dull-Razor preprocessing method to remove extra details such as hair. Next, histogram adjustments and lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results show that ANN could achieve better accuracy (83.5%). In order to mitigate the biases in existing studies, the source code of this research is available at hadi-naghavipour.com/ml to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.

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 The construction date of the ‘Twelve ImamsMausoleum’ in Yazd goes back to the end of the Buyidsand Daylamites periods. The architectural ornaments of this building include murals, plasterworks, faience mosaics, and brickworks; also, an inscription with decorative Kufic script has decorated its parts. The research conducted on this building is limited to the architecture and restoration of its external walls, while the dome has valuable ornaments, including the inscription of ‘Ayatul Kursi’ in its Grivar. This inscriptionis implemented using the technique of ‘painting on plaster’. This historical monument has been seriously damaged due to the age of the building and the destructive environmental effects on it. These damages are either erosion destroying the appearance of the building or the structural destruction destroying the foundation of the building; therefore, knowing the damage so as to prevent further destruction and reconstruction is of particular importance to preserving this thousand-year-old monument. Research Method This research tries to study the damages inflicted on this inscription with the help of reconstruction and damage photography. It, also, tries to to know the structure and motifs used in it in comparison with similar inscriptions. The questions raised in this research were: 1- What were the damages caused to the Grivar inscription in the Twelve Imams Mausoleum? 2- What was the general structure and decorations used for this ornament? This research was descriptive-analytical, and data collection was based on library studies and field surveys. Its approach in the image processing section of the inscription to determine the amount of damage was quantitative, and in the structural analysis it was qualitative. The primary research sources were provided using field photography; the damage mapping of the sides of the inscription was reconstructed based on photographs and images; image processing was provided using a computer program and a color analysis method. The images processed based on color; the numerical results were separated and analyzed based on tables, and the possibility of reproducing the inscription provided the conditions for its structural investigation concerning the analysis of external components and visual features. Research Findings The mentioned inscription is in Kufic (Moashaq Moshajar) script, written in dark azure color on a plaster base. This inscription began with "Bismillah" from the third side of the top of the entrance door and ended with the phrase ‘Al-Ali al-Azeem’ at the end of the eighth side. Over time, damages were imposed on this inscription, leading to the destruction of many parts. Among these damages were humidity, mud,and water flow from the upper parts on the surface of the inscription, the effect of suspended particles in the air, pollution, and dust, the change in the writing's color, the loosening of the paint layer, and its separation from the plaster bed due to the passage of time, penetration termites and the porosity caused by their activity in the lower layers, the cracks caused by earthquakes or other shocks and the forces entering the building, and the traces of droppings of birds or other creatures on the surface of the inscription. Measuring the accurate amount of damage on each side was done in the form of three separate images for each side: 1- The reproduced image of each side of the inscription was in black and white without destruction (the processing of this image provides the amount of positive (text) and negative (background) space to determine the amount of damage in other destructions). 2- The recreated image of each side showed infrastructure destruction in red and paint loss in blue 3- A black-and-white image of each side of the inscription was used to estimate surface pollution. Based on this damage imaging method, by comparing the white and black colors on each side and the percentage of white and black in the ‘initial recreated design’, it was noted whether the destruction damaged the writing itself or the background of the inscription. Accordingly, the fifth side had the most serious destruction of the background with 8%, and the the most serious destruction of the text with 7%. The results of the processing of the blue color, which represents the ‘paint loss’ showed that the first side had the highest amount of paint loss (7% loss), which caused the color of the design and the background to be the same. The lowest amount of paintloss was assigned to the second and the third sides. According to the results, ‘surface pollution,’ had a 100% extent in the inscription. Conclusion Based on the reconstruction of the destructed parts, damage imaging, and image processing with computer programs, the damages have been divided into three general categories: 1) destruction of infrastructure materials, 2) paint loss, 3) surface pollution. The results indicate that the most serious damage was caused mainly by the fall of infrastructure materials, paint loss, and environmental pollution. Structural analysis showed that this inscription had all three structural components of inscriptions including the plant system in the upper part, the geometric system in the middle part, and the writing system in the lower part. The greatest similarity was in the plantmotif, and the greatest diversity was seen in the knots of the geometric part. Although these systems were not necessarily used in all architectural inscriptions, this inscription had all three parts in a structured way showing that the designer had designed each component in harmony to achieve a coherent whole. [ABSTRACT FROM AUTHOR]

Introduction: Farmers use the traditional method (unaided eye and dimensional examination of the seeds) to determine the percentage of healthy rice paddies after harvesting and drying. In recent years, however, machine vision has grown significantly and many researchers have used this system to classify and measure the quality of agricultural and food products, identify different varieties of products, determine the geographical origin of products, identify losses, estimate effective substances, and other related subjects. For example, in one study, the mixing percentage of wheat grains and the determination of grain hardness were performed with the help of machine vision (Fazaeli and Afkari-Sayah, 2009). In order to calculate the hardness of seeds, they used the weight percentage and to measure the mixing percentage, they used the color characteristics of the samples. The classification accuracy obtained in this research was 93%. In another research, artificial neural networks (ANN) and some statistical techniques were used to identify and classify 5 varieties of Iranian rice (Hatami et al., 2010). In their study, samples of 300 seeds were randomly selected from each cultivar, and images of the samples were captured using a digital camera. Then, using the thresholding process, the images were segmented and the features of the area, length of major and minor axis, perimeter, convexity level, strength, extent, and equivalent diameter were extracted. In the created regression model, the highest detection percentage was obtained in Garde and Ramadani cultivars equal to 93.6 and 91.48%, respectively. According to the previous studies, it was found that determining the percentage of crop loss and the percentage of paddy harvested from the field before the bleaching stage is very important so that the percentage of paddy harvested from the field shows the yield of the crop per hectare. Therefore, in the current research, an intelligent machine vision system was developed and evaluated to quickly detect and determine the percentage of healthy paddy seeds. Materials and methods: First, five varieties of rice paddy were prepared. The samples included devoid and healthy seeds. In order to determine the moisture content of the samples, the samples were placed in an oven with a temperature of 70ºC for 24 hours. Next, the images were captured online with the help of a 16-megapixel camera that was placed perpendicular to the surface of the samples in the chamber of the imaging system. The distance between the camera and the samples was fixed at 30 cm. In addition to the imaging camera, the system includes a cubic enclosure made of stainless steel and a lighting unit system. Two projector light sources were installed in the upper half of the chamber so that proper and completely uniform lighting (using optical filters) was guaranteed. To prepare pictures, 250 seeds were randomly selected from healthy samples and pictures were taken. Then the images containing the devoid seeds were taken according to the previous settings and under the same lighting conditions. Moreover, in order to evaluate the algorithm, samples were taken and stored as a combination of healthy and devoid rice with 5%, 10%, and 15% devoid percentages. Next, the machine vision system algorithm was designed and coded in MATLAB software. In the order that the images were read first in the software, pre-processing was applied to the images and finally, their color and shape characteristics were extracted. Finally, a multilayer perceptron (MLP) artificial neural network with the Levenberg-Marquardt algorithm was used for clustering and separating full and devoid seeds. The input layer of MLP had the number of neurons equal to the number of features considered in each stage of training. In the present study, a neural network with a hidden layer was used, the number of neurons of which was selected as 10 by trial and error. Results and discussion: First, the color characteristics of healthy and devoid rice were extracted. Due to the non-uniformity of the results obtained from the analysis of color channels, PCA analysis was used to check the discriminating power of color features. For example, in the Binam cultivar, R color channels related to healthy and devoid seeds overlapped and it was not suitable for separating and clustering the seeds. For this variety, color channel B was chosen as a suitable option for separating or distinguishing between healthy and devoid seeds. For the Neda variety, the R color channel was very clearly separated for healthy and infected samples. In the second stage, the PCA analysis method was used to determine the more effective features that play a more important role in identifying healthy seeds from devoid ones. The results of the PCA analysis showed that by using all the shape and color features, a high percentage of mixing between healthy and devoid samples was created and it is not possible to use all the features to separate healthy and devoid samples in this way. Due to the fact that it is not possible to recognize the shape characteristics of rice grains when they are moved by volume on the conveyor belt, PCA analysis was performed using 3 extracted color features, where it is possible to check the image data pixel-by-pixel and to evaluate the operational usability of the system in online conditions. PCA analysis graphs, considering only 3 color characteristics of healthy and devoid rice samples, show that in the PCA score diagrams, healthy and devoid data can be separated to an acceptable extent and are separated in a certain range. Also, in the PCA loading diagrams, it can be seen that in all the tested samples, the color features are located at the farthest position from the center. This shows the ability of high resolution by all color features. The results of the training, validation, and testing of the perceptron neural network model created with 8 input neurons, one hidden layer with 10 hidden neurons, and two output linear layers were used to distinguish between devoid and healthy samples for different varieties of rice. According to the obtained results, the R² coefficient in training and final evaluation for the Roshan variety was equal to 0.86. In the same way, the highest R² coefficient in the final evaluation of the models created based on color characteristics was obtained for the Neda cultivar equal to 0.96. Conclusion: In this research, an intelligent system based on image processing methods and artificial neural networks was implemented and evaluated to identify the devoid rice seeds from five newly improved rice cultivars. The system proposed in this research has high accuracy and speed compared to conventional experimental methods and scientific methods that have been used so far. PCA method was used for a more detailed examination of the effective components in distinguishing between healthy and devoid seeds. Due to the need for the system to be online and the quality measurement of rice grains in piles, three color features were selected from among the features and modeled using MLP artificial neural networks. The final results, based on the statistical parameters, showed that the implemented system can be used as an online, fast, and cheap system for measuring the quality of paddy in the stages of entering the lines of rice bleaching factories. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to provide a method for accurately identifying the position of cucumber fruit in digital images of the greenhouse cucumber plant. After balancing the brightness histogram of the desired image, it multiplies the image with a window containing the image of a cucumber fruit, which causes larger coefficients to be obtained in areas with suspected cucumber. By extracting these local maximums, clusters of initial points are obtained as possible windows of cucumber existence. Then, in order to accurately detect the location of the cucumbers, these points and areas around them are referred to a neural network that has been trained using a number of images including cucumber images, non-cucumber images and their optimal responses. The proposed method was implemented in the Simulink toolbox of MATLAB software. The proposed method was then simulated using this network structure and tested on 120 images obtained from a greenhouse by a digital camera. The areas obtained from this network led to the accurate detection of the location of the cucumbers in the image. The proposed method was then simulated and tested on 120 images. The proposed method had a low error and was able to detect high levels of cucumber fruit in the images. This detection took an average of 5.12 seconds for each image. The accuracy of the network in correctly identifying the position of the cucumber fruit in the images was 95.3%. This method had low error and was able to detect a high rate at a good time of cucumber fruits in discover images.

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]

Detection of salient objects is done with the aim of identifying and segmenting prominent objects or areas in an image. Fully Convolutional Networks (FCNs) have shown their advantages in salient object detection; however, many previous works have focused on the accuracy of the prominent area without paying attention to its edge. This paper focuses on the complementarity between edge information and salient object one and added an edge recognition module to explicitly model edge information to maintain salient object boundaries. Our proposed network is trying to improve these two tasks simultaneously. The presence of objects with different scales in related datasets is another problem in this area. It requires an appropriate cost function to deal with the imbalance problem between background and foreground in images. So, we have used the hybrid cost function in the training phase, which is not sensitive to the scale of objects and can better manage the problem of spatial coherence and uniformly highlight salient areas without additional parameters. A Comparison of the quantitative and qualitative results obtained by the proposed method with other advanced methods in six widely used protrusion detection datasets shows that the proposed method has a good performance and can quickly identify prominent areas. In particular, according to the quantitative results, our method gets the best result on three widely used test datasets in terms of F-measure and MAE criteria, demonstrating the proposed method's efficiency.

Dehydration of Noughl, as a sugar-based confectionary product, negatively affects its texture which can be hindered by increasing relative humidity (RH) of atmosphere of storage container. In this research, glucose syrup (GS) and its mixture with salt at different ratios (8:1, 4:1 and 2:1) was utilized as RH-modifier inside the storage container in order to prevent hardness of Noughl. The effect of RH-modifiers on moisture, hardness, area and color of samples was investigated during 30 days. Color and area changes of samples were measured using image processing. According to the findings, the lowest change in moisture content, color and area of saffron Noughls was observed when GS-salt mixture was used at the ratio of 8:1. It is found that the RH-modifier could re-increase the RH of container internal space after opening and reclosing lid of the container for 29 times. Furthermore, hardness of Noughls was hindered since the average hardness values were 32.40 and 23.38 N on zero and 30 days, respectively. However, hardness of control sample reached to 149.85 N on day 5. Based on sensory evaluation, there was no significant difference between the overall acceptability of fresh Noughls and those stored for 30 days (P

Sewage flow path is the main component of urban sewerage network infrastructure. Damage to sewers is less noticeable due to invisibility, and this failure to handle the damage leads to emergencies and unreasonable costs. These vital arteries need to be maintained and rebuilt during service for optimal performance in all dimensions. Nowadays, the methods of processing and classifying photos and videos taken by mobile videometer robots are widely used to inspect the sewer network. One of the successful algorithms in the field of image processing is the convolutional neural network algorithm, which is a subset of deep learning algorithm. In this paper, a convolutional neural network algorithm is used to classify images of sewer network damage and cases affecting the improvement, accuracy and performance of this algorithm. The images were obtained by a videometric robot from the sewer network. Results of using the proposed algorithm in the sewerage network, achieving 98% accuracy in classifying network faults and compared to other methods and also reducing the relatively low execution time of the proposed architecture (91 minutes) compared to other architectures valid ones are the same in deep learning on the same hardware platform. Also, in the future, the proposed algorithm will be used to analyze networks without the need for specialized personnel and also to control an automatic network videometry robot.

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 purpose of this study is to use ontologies in the information retrieval of digital collections with an emphasis on images. The present research is of applied type and has been done by library method and systematic review of resources with an analytical approach. Using this method, the sources were collected around the research axis and selected for a more detailed review, and finally, the sources were reviewed according to specific and systematic instructions. In this method, all research in the field of ontology and its effect on image retrieval has been reviewed. The research findings show that image retrieval techniques, considering the backgrounds, include: text-based image retrieval, content-based image retrieval, composite model image retrieval, and semantic (conceptual) retrieval that used related feedback schemes to improve performance as well as the accuracy of the image retrieval process.Most of the researched studies have emphasized the necessity of using ontologies in digital collections. Therefore, in the current situation, we are moving towards the Semantic Web in order to organize digital images that deal with various structural and linguistic problems such as ambiguities, doubts, and human metaphors for the machine, and the use of ontologies. It is very effective in improving accessibility and retrieval accuracy.

Introduction: Evaluation of treatment response is one of the most challenging tasks in the treatment planning of cancer cases. Regarding the significant effect of tumor recurrence in the treatment planning of patients with lung cancer, finding an approach to predict the recurrence of these tumors is of great importance. Nowadays, this process is done experimentally, and its accuracy depends on the experience and proficiency of the oncologist. This study aimed to provide an automated method to detect the recurrence of lung cancer based on imaging and clinical features. Method: Our proposed method was evaluated in 162 patients with non-small cell lung cancer (NSCLC) using the NSCLC radiogenomic database in the Cancer Imaging Archive (TCIA) portal. After pre-processing, segmentation was performed using the Otsu method. In the next step, the radiomic features were extracted using pre-trained AlexNet and GoogleNet models, and along with clinical features, they were used to detect lesion recurrence. Finally, all cases were classified into two classes using machine learning methods. Results: The proposed method used clinical and deep features. The classification was done using various models, and the accuracy of the support vector machine by AlexNet features resulted in the highest performance. The mean values of accuracy, sensitivity, and specificity for this model are 99.76, 99.77, and 99.76%, respectively. Conclusion: The main finding of this study was revealing the capability of deep learning methods in extracting features from medical images. For example, the AlexNet was able to extract features from CT images of NSCLC patients, which are very helpful in the recurrence prediction of these lesions.

IntroductionPistachio or Green Gold is one of the most important agricultural crops and is especially important for Iranian exports. A group of pistachio's pests mainly feed on pistachio, among which Idiocerus stali is very important. Conventional methods for identifying insects using identification keys are time-consuming and costly. Due to the rapid development of the Pistachio industry, the use of artificial intelligence techniques such as image processing, for identification and population monitoring is highly recommended. On the other hand, little research was carried out on I. stali. Therefore, in this research, I. stali was selected as a target insect for the identification and counting on sticky yellow cards using image processing techniques and artificial neural networks. The purpose of this study was to determine the feasibility of I. stali identification algorithm by image processing, to determine the possibility of separation and counting of I. stali from other non-target insects by artificial neural network and to determine its accuracy in identification of I. Stali.Materials and MethodsIdiocerus stali was selected as the target insect for identification. Sticky yellow cards were used for collecting samples. Taking the photos with the help of a SONY Handycam Camera, which had a 12-megapixel resolution and G lens, was carried out (SONY, HDR-XR500, CMOS, SONY Lens G, Made in Japan). Then insects were counted on each card manually and the data was recorded. The data, which were digital images of yellow sticky cards, were imported into the MatLab R2017b software environment. A total of 357 color properties and 20 shape's features for the identification of I. stali were extracted by an image processing algorithm. Color properties were divided into two categories of mean and standard deviation and characteristics related to vegetation indices. An ANN-PSO (Artificial Neural Network hybrid method-Particle Swarm Optimization) algorithm was used to select the effective features. The selected effective characteristics for insect classification were: Color index for extra collective vegetation related to HSL color space, normalized difference index for LCH color space, gray channel for color space YCbCr, second component index minus third component for color space YCbCr, area and mean of the first, second and third components of color space Luv.Results and DiscussionComparing the results with the results of Qiao et al. (2008), we found that in his study, which divided the data into three categories, for medium and high-density groups, the detection rate was 95.2% and 94.6%, respectively. On the other hand, in low densities (less than 10 trapped insects); its detection rate was 72.9%, while the detection rate of the classifier system designed in this study for different densities of trapped insects, was identical and equal to 99.59%. Also, comparing the results of this study with Espinoza et al. (2016), we found that their algorithm in whiteflies detection had a high accuracy of about 0.96 on a sticky yellow card, while the Thrips identification algorithm accuracy was 0.92 on a sticky blue card. As stated above, the correct detection rate of I. stali by the algorithm designed in this study was 99.72%.ConclusionThe results showed the feasibility of the new method for identifying the pest insects without destroying them on the farm and in natural light conditions and in a short time and with very high accuracy. This suggests that this algorithm can be applied to the machine vision system and can be used in future in the construction of agricultural robots.

One of the welding controls in health monitoring of structures is to visually control the appearance of welding defects (cracks, Spatter, Overlap, Lack of Fusion). Currently, according to regulations, the appearance quality of welding is controlled by an inspector visually. The accuracy of work in this method depends on the skill level of the inspector. Non using of equipment and technology leads to a high error in identifying visual defects. In this research, a method is proposed to be able to more accurately identify the appearance of welding defects with the help of imaging using machine vision based on deep learning. Convolutional network is used for deep learning to extract features from the image. The results show that the proposed method can identify welding defects with an acceptable accuracy (over 85%). Also, the results show that by using the proposed method, welding defects are evaluated more quickly compared to the traditional method. [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]

X-rays and neutron radiography images are one of the most effective defects and structure detection methods. The interactions between neutrons and X-rays are different in the material, and therefore, different information can be obtained from the radiographs. Due to neutron and X-rays photon scattering, focal spot size, electronic noises, etc., the images are blurred and their quality is reduced. In this study, while investigating the radiographs of X-rays, and neutrons, the defects and internal structure of objects are investigated. The results show that neutron radiography performs very well in detecting the internal structure of low atomic number materials. X-ray radiography is effective for high atomic numbers as metal. Gaussian convolution is used to enhance the radiography images and reduce blurriness components. The results show that by reducing the background, the blurriness components can be reduced and the defects areas and internal structure of the objects can be better investigated. Specialists evaluated the results in radiography; the results show that the expert’s evaluation approved the image enhancement.

Introduction: Grape is a non-climacteric fruit with a low rate of physiological activity but is subject to serious physiological and parasitic disorders after harvest and during long term storage (Ciccarese et al., 2013). Currently, Edible coatings have been studied as potential substitutes for conventional plastics in food packaging. Edible coating is a thin layer of edible material formed as a coating on a food product. Edible coating can offer several advantages to the fresh fruit and vegetable industry such as improvement in the retention of color, acids, sugar and flavor components, the maintenance of quality during shipping and storage, the reduction of storage disorders and improved consumer appeal (Antoniou et al., 2015; Cazon et al., 2017; Fakhouri et al., 2015; Galus & Kadzińska, 2015). Farsi gum as a novel source of polysaccharides has drawn much attention in a wide range of various fields such as pharmaceutics, food and cosmetics industries. Functional properties of Farsi gum are influenced by its structure and molecular weight (Hadian et al., 2016; Joukar et al., 2017). By inclusion of bioactive compounds in the Farsi gum network the aforementioned impairments could be overcome and moreover, new protective and functional valences could be added. The inclusion of lipid-based component in Farsi gum gives it excellent light and moisture barrier properties. The benefic impact on human health of hemp seed oil is worldwide recognized. A recent study demonstrated the antimicrobial properties of hemp seed oil. Due to their abundance in biologically active compounds, hemp seed oil is promising natural alternatives that may extend the shelf-life, microbiological safety and nutritional values of food (Cozmuta et al., 2015; Leizer et al., 2000; Salarnia et al., 2018). Growing awareness of the quality of fruit has necessitated increasing effort to develop rapid and non-destructive methods for evaluating fruit quality (Bhargava & Bansal, 2020; Rachmawati et al., 2017; Tao & Zhou, 2017; Wu & Sun, 2013). The aim of this study was the consideration of image processing application for grape sorting based on visual surface characterize. Materials and Methods: Coating emulsion was prepared using (Farsi gum (0%, 1.5% and 3%), hemp seed oil (0%, 0.075% and 0.15%) and glyceride (0.3%)). grape fruit were coated by immersion in coating dispersion for 5 min. Samples were then allowed to loss the excess coating dispersion. Coatings were developed at room temperature during an hour. Samples were refrigerated at 4± 1°C for 28 days and analyses were performed at days 0 and 28. Defect identification and maturity detection of grape fruits are challenging task for the computer vision to achieve near human levels of recognition. The image acquisition was performed in a homogenously controlled lighting condition. Considering the camera lens’s focal length, the samples were placed 25 cm under the camera’s lens to be under camera’s field of view. The images of grape were segmented from the background using thresholding of the high contrast images via MATLAB software (R2019a, image processing toolbox). The optimum threshold value was obtained to be 0.35, 0.45 and 0.30 for R, G and B channel, respectively. Results and Discussion: The proposed techniques can separate between the defected and the healthy grape fruits, and then detect and classify the actual defected area. Classification is performed in two manners which in the first one, an input grape is classified with two different algorithms (RGB and binary). The Result showed that the accuracies for detecting the surface defects on grape were 97.73% and 96.30% using RGB and binary algorithms, respectively. Proposed method can be used to detect the visible defects of coated grape, and to grade the grape in high speed and precision. Conclusions: The results of this research and similar ones can provide helpful recommendations in grading fruits for fresh consumption. The simplicity and the efficiency of the proposed techniques make them appropriate for designing a low-cost hardware kit that can be used for real applications.

The significant defects in the artistic paints are rupture, scratches, and twisting, which are caused by variations in temperature, exposure to moisture, and erosion. Industrial radiography can detect the defect locations, which is a non-destructive test, can be carried out. In radiography testing (RT), the produced radiographs may suffer from some degree of blurriness and low contrast due to the inherent scattering of X-ray, geometric factors such as the size of the beam source, the thickness of the part, and the source to film distance (SFD). The diagnosis of the shapes and sizes of the damages is difficult in some cases. Image processing methods can be used as additional tools for enhancing image quality and more accessible interpretation. In the present study, a Gabor filter technique, based on wavelet analysis with an automatic threshold level, was used to reduce the fogginess of radiographs. The reconstructed images of the proposed algorithm have better contrast, and the defects and signs are more precise than the original image. This algorithm can help the restoration experts for repairing the artistic paintings.

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]

IntroductionThe 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 MethodsSample collectionIn 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 temperature of 27.15°C 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 × 840 pixels, and after image transfer to the computer, image processing was performed using Matlab 2016a. 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 N-division of the k-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 DiscussionAccording 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.ConclusionsToday, in the modern agricultural systems, numerous computational methods 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 was 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.

IntroductionControl 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 non-destructive 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 MethodsImages 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 two-dimensional 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 = b0 + b1x. 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 DiscussionExamination 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. ConclusionsThe 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.

Introduction: In recent years, due to physical problems of patients or restrictions on social interactions in COVID-19 pandemic, telerehabilitation based on virtual reality (VR) attracted the attention of many researchers. Method: In this applied research, a Kinect sensor and an image processing software were used to design and implement a motion simulator for rehabilitation of patients with physical problems in upper or lower limb. To do this, the patient was supposed to touch or catch moving objects such as a bee. The movement of objects was based on physician’s decision and while comparing patients’ movements with reference ones in the designed system, the patient was encouraged to correct his/her movements to finally be able to do them in the desired manner. In the designed system, it is possible to monitor the patient’ movements outside the clinical environment and based on telerehabilitation. Results: The efficiency of the designed system in patients with physical disabilities was investigated based on quantitative analysis (via motion charts) and qualitative analysis (through questionnaire). Conclusion: The results showed the high efficiency of this system in analysis of patients’ movements (through comparing it with reference ones), and the possibility to practically use it in rehabilitation centers especially in recent situation of covid-19 pandemic

Introduction: Most skin cancers are treatable in the early stages; thus, an early and rapid diagnosis can be very important to save patients’ lives. Today, with artificial intelligence, early detection of cancer in the initial stages is possible. Method: In this descriptive-analytical study, a computerized diagnostic system based on image processing techniques was presented, which is much more helpful for the patient. In this method, dermoscopic images of actinic keratosis and squamous cell carcinoma were improved by preprocessing techniques and the potential noises were removed. Then, segmentation was performed using the thresholding method to separate the lesion from the underlying skin. Thereafter, from the segmented area, texture, shape, and color information and features were extracted. Finally, the feature reduction method and support vector machine (SVM) were used to evaluate the proposed method qualitatively and quantitatively. Results: The data in this study included 100 samples of actinic keratosis images and 100 samples of squamous cell carcinoma. The results of the present study showed that using the genetic algorithm method together with the support vector machine method could help identify the type of skin cancer with 99.7 ± 0.4% accuracy. Conclusion: The effect of different tissue features in diagnosing the type of lesion showed an increase in the amount and variety of features extracted from the samples would lead to better training and more accurate analysis of the system.

Currently, to obtain the dynamic properties of structures in structural engineering, experimental relationships, mathematical models and computer software aids are mainly used. These dynamic properties depend on many details about material behavior and the compound of the structures, which could not all be taken into account in analytical models. Therefore, there are always differences between the analytical models and the actual structures. So, seismic tests on structures is the most reliable method for obtaining these properties, so far, various methods have been developed in the world to preform these seismic experiments on the experimental structures such as image processing method. In this paper, the image processing method utilized through the dynamic properties of structures for mass change detection in the structure. For this purpose, a cantilever beam oscillated and the recorded displacements are considered as the inputs of the system identification. By deriving these displacements twice, the acceleration of the structure obtained and by processing these accelerations, natural frequency and shape mode of structure have been extracted and compared with the results of the accelerometer sensors embedded on the structure. Finally, mode shape curvature method utilized and the location of the sensors on the structure has been detected with good accuracy.

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.

Several histogram equalization methods for enhancing the color images of Rosa Damascena flowers and some thresholding methods for segmentation of the flowers were examined. Images were taken outdoors at different times of day and light conditions. A factorial experiment in the form of a Completely Randomized Design with two factors of histogram equalization method at 8 levels and thresholding method at 15 levels, was implemented. Histogram equalization methods included: CHE, BBHE, BHEPL-D, DQHEPL, DSIHE, RMSHE, RSIHE, and no histogram equalization (NHE) as the control. Thresholding method levels were: Huang, Intermodes, Isodata, Li, maximum entropy, mean, minimum, moments, Otsu, percentile, Renyi’s entropy, Shanbhag, Yen, constant, and global basic thresholding method. The effect of these factors on the properties of the segmented images such as the Percentage of Incorrectly Segmented Area (PISA), Percentage of Overlapping Area (POA), Percentage of Undetected Area (PUA), and Percentage of Detected Flowers (PDF) was investigated. Results of histogram equalization analysis showed that DQHEPL and NHE have the statistically significant lowest PUA (11.13% and 8.32%, respectively), highest POA (89.35% and 92.07%, respectively), and highest PDF (61.88% and 64.94%, respectively). Thresholding methods had a significant effect on PISA, PUA, POA, and PDF. The highest PDF belonged to constant, minimum, and Intermodes (75.07%, 73.08% and 74.30%, respectively) They also had the lowest PISA (0.35%, 1.29%, and 1.85%, respectively) and PUA (33.72%, 23.09%, and 15.56%, respectively). These methods had the highest POA (80.73%, 76.70%, and 84.67%, respectively). Hence, they are suitable methods for segmentation of Rosa Damascena flowers in color images.

Studying vegetation status is one of the most important environmental issues. Using contemporary and field methods in this regard will cost a lot of time and money. Remote sensing technology and satellite image processing is a solution that can facilitate such difficult processes. In this study, remote sensing technology will be integrated with web geographic services to provide users with adequate analysis of the growth of the plants and their changes based on vegetation indices in the shortest possible time. Important technologies used for this purpose are ENVI IDL, Google Map API, JavaScript, and ASP.NET. The service was successfully implemented and was employed for Landsat satellite images. In order to evaluate the performance of the service at different points and with a different number of images, the timeliness of the NDVI vegetation index was prepared. The processing time evaluation also showed that by increasing the number of satellite images the processing time will increase linearly.

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.

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]

Kichi is a local sweet bread that is cooked in the west of Iran, especially in Chaharmahal and Bakhtiari Province. This product is traditionally produced and has a low shelf life and hard texture. Therefore, the aim of this study was to investigate the effect of xanthan gum (0, 0.1, 0.3 and 0.5%) to improve the quality of Kichi sweet bread. Also mean size area, porosity, fractal dimension, and color parameters of Kichi bread were evaluated. The results showed that addition of xanthan led to improving WHC. The results of the texture analysis showed that softer samples were obtained in the low concentrations of xanthan gum (0.1% based on flour) and gumminess in this sample was significantly less than other samples (p < /em>

Figures in scientific documents are rich source of information. The first step in retrieving information from such figures is to build a valid figure database. To this end, we developed a system for generating figure database from scholarly Persian documents, in large scale. The first step is to parse files and extract figures and their corresponding descriptions. There are two general approaches for extracting figures from documents, one is based on image processing methods and another one is based on processing the file primitives. The focus of this paper is on later one. This approach is shown to be a better choice for the search engines because of its speed and scalability properties. We propose a structure based method that extracts the figures and their descriptions by analyzing the file layout. This information is saved in a database with a specific structure and is indexed for retrieval in the search engine. The proposed algorithm was implemented in Python programming language. As a benchmark we used the basic method in the literature which is based on the processing PDF file. We employed the proposed method in a case study on Iran scientific information database (Ganj). In this regard, 150 scientific documents were randomly chosen from Ganj database and analyzed using two mentioned methods. Based on our experimental results, the proposed method is more efficient than the basic method especially for Persian documents. There many unanswered challenges for Persian documents when using the basic method. The number of noise images resulted from the basic method is high and Persian text extracted is not well organized. Our proposed method overcomes some of these drawbacks and is recommended for generating figure database from scientific Persian documents. The proposed method is able to correctly extract about 40% of the images with their corresponding descriptions which is 10% better than the basic method.

Single point mooring terminals are, as the name implies, facilities of small horizontal dimensions, to which large vessels are moored by means of a bow hawser or by any other means which allows the vessel to rotate 360 around the mooring point. Generally, the single point mooring terminal can have two functions. Primarily, it affords a safe mooring to the vessels. Secondly, it can form a link in the chain for the transport of oil. In this study, with the feasibility of 3D image processing, the SPM translational motion is evaluated. This translational motion includes Surge, Sway, and Heave. In order to ensure the results of the analysis of SPM translational motion, verification is carried out. In the following, by constructing SPM and situating it in reaction condition with regular wave, translational motion is obtained. Finally, the results indicate that image processing is acceptable in translational motion assessment of SPM.

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 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]

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]

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]