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The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits. [ABSTRACT FROM AUTHOR]

Improving the accuracy of photovoltaic (PV) power forecasting is crucial to ensure more effective use of energy resources. Improvements are especially important for regions for which historical solar radiation data do not exist. This paper proposes a cyber-secure forecasting model called federated deep learning (FDL) to forecast PV power generation in various regions across Iran. The training process in each client is done by a convolutional neural network (CNN). Then, a generalizable global supermodel is generated based on the features extracted in each client, which has the ability to generalize and forecast for regions where there is no training data. Preserve data privacy and ideal performance against cyber-attacks are prominent features of the proposed method. The use of the proposed method is illustrated with a case study for Iran. The proposed FDL network is designed with 9 clients and three different scenarios were developed to test the robustness of the suggested method. In the first scenario, the PV power generation forecasting is done via the proposed technique and other conventional methods. The performance accuracy (R 2) of the generated global supermodel in this scenario for PV power generation forecasting in the regions of Khomein, Meybod, Varzaneh, Taleghan, and Shiraz are obtained as 0.981, 0.989, 0.986, 0.983, and 0.987 respectively. However, it was observed that other conventional deep learning-based models such as CNN and long short-term memory were not able to provide any forecasting for these regions. The second scenario models the scaling attack as a specific pattern of false data injection attack, to evaluate the performance of forecasting models against the data integrity attack. In the third scenario, cyber-attack detection is performed based on data visualization and image processing procedures. The results presented in different scenarios emphasize the high accuracy and generalizability of the global cyber-secure supermodel in PV power generation forecasting in different regions of Iran. [ABSTRACT FROM AUTHOR]

This research paper reports the process and results of a project to automatically classify historical and non-historical buildings using airborne and satellite imagery. The case study area is the center of Yazd, the most important historical site in Iran. New computational scientific methods and accessibility to satellite images have created more opportunities to work on automated historical architecture feature recognition. Building on this, a convolutional neural network (CNN) is the main method for the classification task of the project. The most distinctive features of the historical houses in Iran are central courtyards. Based on this characteristic, the objective of the research is recognizing and labeling the houses as historical buildings by a CNN model. As a result, the trained model is tested by a validation dataset and has an accuracy rate of around 98%. In Sum, the reported project is one of the first works on deep learning methods in historical Iranian architecture study and one of the first efforts to use automated remote sensing techniques for recognizing historical courtyard houses in aerial images. [ABSTRACT FROM AUTHOR]

An accurate earthquake-induced damage assessment from very high resolution satellite images requires a joint use of spectral and spatial information. The spatial relations between pixels in damaged areas should properly be included in the classification model in order to exploit the spatial information. Morphological Profiles (MPs) and Attribute Profiles (APs) are the state-of-the-art image processing methods that produce a set of filtered images, which are able to highlight specific patterns representing a specific land cover class. In this study, the MPs and the APs are used to create additional spatial features for two different very high resolution post-event satellite images, acquired from the City of Bam in Iran and the City of Port-au-Prince in Haiti. These contextual features are then analyzed by means of a feature selection algorithm, called Minimum Redundancy Maximum Relevance, to find the most relevant features contributing the damage class the most. A final feature subset of selected features is analyzed using two different classifiers, which are k-nearest neighbors and support-vector machines. The results show that the use of a proper configuration of those profiles can significantly improve the classification accuracy and the quality of the thematic map, but generalization of the classification model is limited especially for the larger areas. [ABSTRACT FROM AUTHOR]

Background: Automated image segmentation is an essential step in quantitative image analysis. This study assesses the performance of a deep learning-based model for lung segmentation from computed tomography (CT) images of normal and COVID-19 patients. Methods: A descriptive-analytical study was conducted from December 2020 to April 2021 on the CT images of patients from various educational hospitals affiliated with Mashhad University of Medical Sciences (Mashhad, Iran). Of the selected images and corresponding lung masks of 1,200 confirmed COVID-19 patients, 1,080 were used to train a residual neural network. The performance of the residual network (ResNet) model was evaluated on two distinct external test datasets, namely the remaining 120 COVID-19 and 120 normal patients. Different evaluation metrics such as Dice similarity coefficient (DSC), mean absolute error (MAE), relative mean Hounsfield unit (HU) difference, and relative volume difference were calculated to assess the accuracy of the predicted lung masks. The Mann- Whitney U test was used to assess the difference between the corresponding values in the normal and COVID-19 patients. P<0.05 was considered statistically significant. Results: The ResNet model achieved a DSC of 0.980 and 0.971 and a relative mean HU difference of -2.679% and -4.403% for the normal and COVID-19 patients, respectively. Comparable performance in lung segmentation of normal and COVID-19 patients indicated the model's accuracy for identifying lung tissue in the presence of COVID-19-associated infections. Although a slightly better performance was observed in normal patients. Conclusion: The ResNet model provides an accurate and reliable automated lung segmentation of COVID-19 infected lung tissue. A preprint version of this article was published on arXiv before formal peer review (https://arxiv.org/abs/2104.02042). [ABSTRACT FROM AUTHOR]

One of the most important issues in forest management is preservation and development of biodiversity. This study aims at estimating richness, evenness and species diversity of woody plants in Arasbaran Protected Area (East Azerbaijan Province, Iran) using Landsat 8 OLI satellite images based on Multiple Linear Regression (MLR) and Classification and Regression Trees (CART) models. This study was performed in an elevation range of 1,000 - 1,850 m. A total of 130 samples were selected based on physiographic factors. In each sampling plot, number and type of dominant tree species in the canopy were recorded. Statistical modeling was carried out by calibrating MLR and CART models. The R-squared values of MLR models were in the range of 0.10 - 0.24 for predicting richness, evenness and Shannon and Simpson's species diversity indices based on greenness as the only predictor variable (other variables were excluded from MLR models). The R-squared values of CART models were equal to 0.21, 0.42, 0.41 and 0.42, respectively. Validation of the results indicated that the CART model had a relatively better performance compared to MLR model. Overall, both methods could not estimate species richness and diversity very precisely based on Landsat 8 OLI Satellite data in the region suggesting the necessity to use high-resolution satellite data for the best evaluation of forests diversity indices in mountainous forests. [ABSTRACT FROM AUTHOR]

Saffron classification based on machine vision techniques as well as the expert's opinion is an objective and nondestructive method that can increase the accuracy of this process in real applications. The experts in Iran classify saffron into three classes Pushal, Negin, and Sargol based on apparent characteristics. Four hundred and forty color images from saffron for the three different classes were acquired, using a mobile phone camera. Twenty‐one color features and 99 textural features were extracted using image analysis. Twenty‐two classifiers were employed for classification using mentioned features. The support vector machine and Ensemble classifiers were better than other classifiers. Our results showed that the mean classification accuracy was up to 83.9% using the Quadratic support vector machine and Subspace Discriminant classifier. [ABSTRACT FROM AUTHOR]

• Red color level of image background at wheat booting can simulate relative yield. • Images were taken from heterogeneous stands of cultivar mixtures with 6 replicates. • The new index (BRL) was more efficient compared with some other vegetation criteria. • The similarities between patterns of RGY & BRL was acceptable even in the mixtures. • It is suggested that the non-vegetation image segments may provide valuable data. Digital cameras are increasingly utilized as a reliable and readily available remote sensing tool in the emerging technology of high throughput phenotyping and agronomic field assessments. In almost every image analysis of green canopies, image segmentation into the vegetation and non-vegetation parts using thresholding is a fundamental step. Therefore, only the vegetation parts of the high resolution images are considered for estimations and the data recorded in the remained pixels will be ignored. In the present study, an accidental observation is described, in which the red color level of the non-vegetation segments of the images taken at the wheat booting phase could simulate the relative pattern of final grain yield. An image archive of heterogeneous wheat canopies (i.e. cultivar mixtures), captured from experimental plots of a field study conducted at the School of Agriculture, Shiraz University, Iran, was analyzed. It was observed that the red color levels of the image background (BRL, mostly composed of soil pixels) were accurately simulating the final grain yields of the treatments, (i.e. very precise mimicry of relative grain yields –RGY– in monocultures, and exact determination of curve extrema and also the direction of fluctuations, in the case of mixtures). Compared with other colors and image-derived criteria evaluated, BRLs had the pattern most similar to the relative grain yields. Moreover, assessments showed the importance of using higher number of replicates in analyses (i.e. 6 vs 3 replications). This observation suggests the red color level of the image-background as a potential novel index for the prediction of wheat grain yield during the preanthesis period; and also the ability of the non-vegetation image segments to provide valuable data for crop remote sensing and image processing studies. [ABSTRACT FROM AUTHOR]

• Wind power generation forecasting based on Deep Federated Learning. • A cyber-secure supermodel for wind power forecasting in critical regions. • A novel Image Processing approach was applied for cyber-attack detection. • Performance of the proposed model was evaluated under a false data injection attack. • The presented results confirmed the superiority of the proposed model. Accurate wind power forecasting is one of the most important operations within the economic dispatch problem to increase the performance of power and energy systems. Accordingly, this study proposes a cyber-resilient hybrid approach based on the Federated Learning and Convolutional Neural Network (CNN) procedure for short-term wind power generation forecasting in different regions of Iran. Generalizability, data independence, forecasting for regions where no training data is available, and preserving the security and privacy of data are prominent features of the proposed method. The federated network was designed with an architecture of 9 clients to perform the training process and extract the salient features from the data associated with each region in each client via the CNN technique. Then, the generalized global supermodel is produced based on the extracted features in each client to forecast the wind power in new and unknown regions such as Mahshahr, Bojnord, and Lootak that had no training data available and had no effect on global supermodel generation. Various scenarios were developed to test the robustness of the suggested methodology. In the first scenario, wind power forecasting is performed based on the suggested technique. In this scenario, the accuracy of the generalized supermodel to forecast wind power generation in each of the Mahshahr, Bojnord, and Lootak regions is 84%, 85%, and 74%, respectively. The second scenario models the scaling attack by changing the wind speed parameters to evaluate the performance of forecasting models against the data integrity attack. In this scenario, an evaluation of the forecast results based on various performance metrics is conducted highlighting the accuracy reduction of the forecast model, due to the damage caused by cyber-attacks on the input data. In the third scenario, the detection of cyber-attack is done based on the image processing-based technique. The presented results emphasize the accurate performance and high generalizability of the cyber-resilient global supermodel in forecasting wind power in various regions of Iran. [ABSTRACT FROM AUTHOR]

• Classification of wheat varieties using image processing. • Extraction of texture feature using GLCM, GLRM and LBP algorithms. • The unsupervised classification results confirmed by supervised methods. • The features extracted from the GLRM algorithm had the best results by SVM model. Wheat is a major crop in Iran and plays an essential role in satisfying human nutritional needs. Depending on the geographical location as well as the climatic conditions of each region, different varieties of this crop can be cultivated. The purity of the seeds used for field cultivation has a substantial impact on both field efficiency and flour quality as the final product. To increase the seed supplied for cultivation in seed supply centers, the use of new non-destructive automatic methods for this purpose is indispensable. In this study, 7 varieties of wheat in the East Azerbaijan Province were investigated by employing a machine vision system. By extracting texture features using Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Pattern (LBP) algorithms and classifying using principal component analysis (PCA) as the unsupervised method, along with the Support Vector Machine (SVM) and Artificial Neural Network (ANN) as supervised methods, the system accuracy was surveyed. Results indicated that modeling using a combination of all extracted features (125 features) yielded the best results by the ANN model, which the Correct Classification Rate (CCR) was obtained 100% and 95.04% for training and testing datasets, respectively. The testing dataset CCR was improved using 20 selected features by the chi-square test feature selection method up to 98.10%, which was better than the modeling results with all extracted features. Results also revealed that by using image processing and texture features extraction methods, it was possible to identify the classification of different wheat varieties with over 95% accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • The soil surface crack morphometry was investigated using image processing. • The freeze-thaw cycle effect was assessed on the soil crack pattern in control and treated plots. • Cyanobacterization improved soil cracking patterns and rheological properties. The present study was conducted with the aim of feasibility study of inoculation of soil cyanobacteria in improving soil crack patterns and controlling surface runoff under freeze-thaw conditions in erosion sensitive area of Badranlou in North Khorasan Province, Northeastern Iran. This study identified the dominant and safe cyanobacteria available in the micro-bank of the soil and inoculated them on prepared small experimental plots of 0.5 m × 0.5 m in three replications subjected to rainfall simulation. The crack pattern morphometry of the soil surface was comparatively studied using an image processing technique. Samples inoculated with cyanobacteria were compared to controls. The results showed that cyanobacterization influences the soil cracking patterns. So that a few deep and large cracks in the control treatment were significantly improved to shallow, small, frequent, and well-connected crevices with a larger area (226%; p < 0.04), longer length (101%; p < 0.05), and smaller width (70%; p < 0.03). The positive effects of changes in crack properties on runoff resulted from a simulated rain having 70 mm h−1 intensity and 30 min duration on the plots with a slope of 20% were also found. Compared to the control soil, the results showed that the soil cyanobacteria treatment by secreting polysaccharides and creating a spider-like system on the soil surface caused agglutination of aggregates and improved rheological properties of the soil, leading to increase runoff start time (274%; p < 0.01) and decrease runoff volume (-147%; p < 0.001). The results suggested that modifying soil surface conditions through inoculation of the soil cyanobacteria can be an innovative method to improve soil rheologic indices. These results would improve the understanding of soil cracks morphometry and the use of microorganisms in reducing the detrimental effects of extreme climatic conditions on soil hydrologic behaviors. [ABSTRACT FROM AUTHOR]

[Display omitted] • This research constrains new prospective altered places in Taftan area, Iran. • A conventional hybrid method of band ratio in RGB images (BR-FCC) detects altered rocks. • Applying the new hybrid method of constrained energy minimization in RGB images (CEM-FCC) detects alteration zones. • The CEM-FCC helps to identify alteration zones based on the characteristic assemblage minerals and to prioritize the alteration zones. Remote sensing data provide a useful piece of information for initial identification of alteration zones, lithological units and structures in relation to mineralization, saving time and cost. In this research, silicic, argillic and propylitic alteration zones with gold mineralization in Taftan area in Sistan and Baluchestan province (Iran) were identified by thermal infrared (TIR), shortwave infrared (SWIR) and visible-near infrared (VNIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat-8 data. Detecting altered and mineralized zones of epithermal gold were conducted using a conventional hybrid method of band ratio in false coloured composite (BR-FCC), and a new hybrid method including, constrained energy minimization in false coloured composite (CEM-FCC). The hybrid methods of the BR-FCC (BRs in RGB) identified the silicic area and places, containing clay and secondary iron minerals. Furthermore, the advanced argillic, argillic and propylitic alteration zones detected and derived in altered areas specifically to the northwest of Taftan, in Kharestan and Bidester deposits using the hybrid methods of the CEM-FCC (CEMs in RGB). Results of image processing showed two new prospective altered places to the northeast of Kharestan deposit and to the northwest of Taftan volcano. [ABSTRACT FROM AUTHOR]

Two case studies are presented that apply a new quantitative micro-facies analysis method to multiple thin section photographs from the carbonate and sandstone reservoirs of two giant fields: (1) the Permo-Triassic carbonate reservoir formation from the South Pars gas-condensate field (offshore Iran); and (2) the Fasila sand reservoir of Middle Pliocene age from the Shah Deniz Field (offshore Azerbaijan). An essential part of comprehensive microfacies analysis of reservoir rocks is to reliably identify, characterize and quantify microscopic features including matrix components, mineral grains, rock fabrics, porosity, fossil contents and the products of diagenesis. This can be achieved by a combination of image processing techniques to assist in distinguishing paleoenvironments and depositional settings of porous and permeable formations. These techniques, applied to the case studies, are shown to effectively improve reservoir characterization for both carbonate and clastic reservoirs Image processing algorithms have to overcome several hurdles to achieve consistent and repeatable microfacies analysis and this is best conducted by exploiting several algorithms focus on specific recognition tasks. Initially, a histogram equalization algorithm is applied to adjust the quality of thin section images to make them consistently comparable with reference photographs. Microfacies analysis is then conducted on the adjusted images in steps to distinguish matrix texture, grain and pore-space boundaries and micro-fossil components. This is achieved by applying multiple functional image processing algorithms and sensitivity analysis sequentially targeting specific microfacies characterization objectives. Accurate grain size is quantified by a customized Graphical User Interface (GUI) algorithm. Pore detection and quantified two-dimensional porosity volumes are calculated for multiple thin-section images with a K-means clustering of two measurements extracted from color image space analysis. Color image data is also used to specifically distinguish the different minerals constituting the rock matrix, cement, and the form of the pore space in two dimensions. This enables these features to be consistently visualized and displayed in scatter plots that determine the precise microfacies to which each thin section belongs. Applying these techniques to the two case studies demonstrates how they can objectively and quantitatively provide microfacies analysis for carbonate and silicate reservoirs. • Quantifying reservoir microfacies analysis by combining image processing algorithms. • Edge detection /color adjustment algorithms characterize porosity and grain forms. • Image arithmetic and gamma corrections help to better resolve key features. • 2D Porosity distributions quantified by K-means clustering in L*A*B* color space. • Nearest-neighbor classifier facilitates segmental analysis of user-defined features. [ABSTRACT FROM AUTHOR]

Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars. [ABSTRACT FROM AUTHOR]