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Interferometric synthetic aperture radar (InSAR) decorrelation that creates great challenges to phase unwrapping has been a critical issue for mapping large earthquake deformation. Some studies have proposed a 'remove-and-return model' solution to tackle this problem, but it has not been fully validated yet, and therefore has rarely been applied to real earthquake scenarios. In this study, we use the 2023 M w 7.8 and 7.6 earthquake doublet in Turkey and Syria as a case example to develop an iterative modelling method for InSAR-based coseismic mapping. We first derive surface deformation fields using Sentinel-1 offset tracking and Sentinel-2 optical image correlation, and invert them for an initial coseismic slip model, based on which we simulate InSAR coseismic phase measurements. We then remove the simulated phase from the actual Sentinel-1 phase and conduct unwrapping. The simulated phase is added back to the unwrapped phase to produce the final phase measurements. Comparing to the commonly used unwrapping method, our proposed approach can significantly improve coherence and reduce phase gradients, enabling accurate InSAR measurements. Combining InSAR, offset tracking and optical image correlation, we implement a joint inversion to obtain an optimal coseismic slip model. Our model shows that slip on the Çardak Fault is concentrated on a ∼100 km segment; to both ends, slip suddenly diminished. On the contrary, rupture on the East Anatolian Fault Zone propagated much longer as its geometry is fairly smooth. The iterative coseismic modelling method is proven efficient and can be easily applied to other continental earthquakes. [ABSTRACT FROM AUTHOR]

Quality control of hazelnuts is a major concern in many regions across the world, but particularly in Turkey as the world's largest hazelnut producer. Using image processing and deep learning techniques, this study intended to detect and classify healthy hazelnuts and hazelnuts infected with the Brown Marmorated Stink Bug. Infected hazelnut samples were collected from the 2021 production period by experts. A Guppy Pro CCD camera-based image acquisition system was used to capture hazelnut images. A total of 400 RGB hazelnut images were captured to train machine learning models. Image segmentation process was carried out to subtract hazelnut images from the background using the Thresholding technique. Moment features were extracted from RGB and l*a*b* spaces to be used to train traditional machine learning models. Furthermore, the most relevant and discriminative feature set was selected using the Boruta feature selection method. Traditional machine learning models including Random Forest, Support Vector Machine, Logistic Regression, Naive Bayes, and Decision Tree were trained twice, once with all features and another with the selected feature set only. The overall accuracy, statistical characteristics of the confusion matrix, and model training time were all calculated to evaluate and compare models performances. As a result, threshold value of 50 was determined from the gray level histogram and was able to separate hazelnut image from the background perfectly. Only seven moment features were identified as the most discriminative features out of 24 features. The SVM model with all feature vectors had the greatest classification accuracy of 98.75 %. When only the selected features were employed, the performance of Random Forest and Logistic Regression models improved to 97.5 and 96.25 %, respectively. [ABSTRACT FROM AUTHOR]

Multiple choice exams appear as the most common method used to measure student success in education systems in Turkey and around the world. Evaluation of multiple choice exams is usually done by optical form reading systems using expensive optical forms and optical scanners. The most important reason for using optical form reading systems is to reduce the error rate to zero in the evaluation process. In this study, an alternative system is proposed for the evaluation of multiple choice exams. The designed system is a web-based software with high accuracy on evaluating that contains optical form design module, session planning module and evaluation module. The form in which the designed template will be printed on A4 paper instead of a special optical form and the system in which standard scanners will be used instead of the optical scanner, uses the image processing algorithms in the OpenCV library in the evaluation process which is an intermediate library developed in C#. The proposed system is coded to run in parallel to speed up the evaluation process. In order to determine the performance of the proposed system, the optical forms filled by 208 students studying at Karamanoğlu Mehmetbey University, Department of Computer Engineering were evaluated. The accuracy rate of the system has been determined as 99.97%. It has been determined that 1 optical evaluation time, calculated by dividing the total time obtained by running in parallel, by the number of evaluated optical forms, varies between 1.7 seconds and 15 seconds, depending on the scanning resolution. [ABSTRACT FROM AUTHOR]

This study develops a novel approach for identifying buildings that were damaged in the aftermath of the Kahramanmaraş earthquakes on February 6, 2023, which were among the most devastating in the history of Türkiye. The approach involves using two pre-event and one post-event Sentinel-1 and Sentinel-2 images to detect changes in the varying-sized and shaped buildings following the earthquakes. The approach is based on the hypothesis that the radiometric characteristics of building pixels should change after an earthquake, and these changes can be detected by analysing the spectral distance between the building pixel vectors before and after the earthquake. The proposed approach examines the changes in building pixel vectors on pre-event and post-event Sentinel-2 MultiSpectral Instrument images. It also incorporates the backscattering features of Sentinel-1 Synthetic Aperture Radar images, as well as the variance image, a feature that is derived from a Grey-Level Co-occurrence Matrix, and the Normalized Difference Built-up Index image, which were derived from the optical data. The approach was tested on three sites, two of which were in Kahramanmaraş and the third in Hatay city. The results showed that the proposed method was able to accurately identify damaged and undamaged buildings with an overall accuracy of 75%, 84.4%, and 73.8% in test sites 1, 2, and 3, respectively. These findings demonstrate the potential of the proposed approach to effectively identify damaged buildings in post-disaster situations. [ABSTRACT FROM AUTHOR]

SUMMARY: Large continental earthquakes are infrequent, but they are important for understanding active tectonics. Many pre-1990, magnitude greater than 7 earthquakes have not been well documented due to a lack of data. The declassification of KeyHole (KH) imagery provides opportunities for investigating those historical events. In this study, we present new geodetic observations of surface deformation from the 1976 November 24 Ms 7.3 Chaldiran earthquake in eastern Turkey. We use various historical KH imagery and modern Pleiades stereo imagery to determine the coseismic deformation, providing new constraints on the 1976 rupture. Based on the image measurements, we resolve the coseismic slip at depth. The inverted slip from a simple elastic dislocation model, ∼4.0 m at shallow depth of the Hidirmentes segment, is larger than the surface displacement, around 3 m from previous field surveys, suggesting that the coseismic deformation is a combination of slip on the primary fault with 30 ± 16 per cent off-fault deformation. We also measure cumulative offsets at two sites with well-preserved features, and find a constant ratio of horizontal to vertical offsets throughout the past few earthquake cycles. Both the observed constant slip ratio in this study and offset clusters from previous field measurements support that the Chaldiran fault exhibits a characteristic slip behaviour in the late Quaternary. By combining all the geodetic and geological observations in the broader region, we also present a brief analysis of the slip rate of the Chaldiran fault. [ABSTRACT FROM AUTHOR]

The east-west extended Sivas Basin in central-eastern Anatolia is a foreland basin that formed after the obduction of the Tethyan ophiolite during the late Cretaceous and is also a north-verging fold-and-thrust belt. The basement rocks of the basin represent mainly sedimentary (Mesozoic platform-type carbonates) and late Cretaceous Divriği ophiolitic complex. As the ophiolitic rocks are affected by intense tectonic processes, field-based mapping studies require long processes and costs. The study tests to reveal the lithological features of the ophiolitic complex outcropping around the Ulaş district of the Sivas Province using remote sensing methods and techniques. Due to arid climatic conditions and rare vegetation cover in the region, almost all outcrops of the basin rocks can be separated by spectral enhancement methods easily. Band ratio (BR), spectral indices (SI), decorrelation stretch (DS), principal component analysis (PCA), and support vector machine (SVM) on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used in this research. BR, PCA, DC, and SI techniques clearly distinguish the Divriği Ophiolitic Complex from the basement and cover sedimentary rocks. SVM distinguishes the chromite-bearing dunites from the other ophiolite-related rock units. According to image analysis performances, it has been observed that the rocks of the Divriği ophiolitic complex can be differentiated in more detail compared to a 1/100.000 scaled geological map of the region. [ABSTRACT FROM AUTHOR]

Image/sketch completion is a core task that addresses the problem of completing the missing regions of an image/sketch with realistic and semantically consistent content. We address one type of completion which is producing a tentative completion of an aerial view of the remnants of a building structure. The inference process may start with as little as 10% of the structure and thus is fundamentally pluralistic (e.g., multiple completions are possible). We present a novel pluralistic building contour completion framework. A feature suggestion component uses an entropy-based model to request information from the user for the next most informative location in the image. Then, an image completion component trained using self-supervision and procedurally generated content produces a partial or full completion. In our synthetic and real-world experiments for archaeological sites in Turkey, with up to only 4 iterations, we complete building footprints having only 10–15% of the ancient structure initially visible. We also compare to various state-of-the-art methods and show our superior quantitative/qualitative performance. While we show results for archaeology, we anticipate our method can be used for restoring highly incomplete historical sketches and for modern day urban reconstruction despite occlusions. [ABSTRACT FROM AUTHOR]

3D Modelling of gravity data is generally performed as a multi-objective optimisation, trying to minimise observed-calculated data misfit while providing models with certain properties, such as smooth or sharp boundaries. We demonstrated that controlling the model properties in global optimisation schemes is possible through basic image processing filters, and developed a Particle Swarm Optimisation (PSO) algorithm that benefits from Gaussian and Percentile filters to avoid ambiguous boundaries that are generally seen in 3D smooth inversions of gravity data. The effectiveness of the algorithm is shown on a synthetic model consisting of two dipping structures with anomalous density contrasts. Thereafter, the algorithm is implemented to recover subsurface density distribution from a field data set. The field data is collected at the south-western part of the Sinanpaşa graben, Turkey. Due to the lack of previous geophysical studies in the area, 3D Euler decomposition, tilt angle, and 3D smooth inversion methods are also implemented to help interpretation and to compare to the model recovered using the PSO algorithm. The developed approach is observed to be resulted with a model, which is more compatible with the known geology of the region. [ABSTRACT FROM AUTHOR]

The Ministry of National Education of Turkey equipped all schools with interactive boards (IBs) within the scope of the FATİH project. The need for appropriate materials has arisen for the effective use of IBs in educational activities. Three‐dimensional (3D) technologies are also among these materials. It has been determined in the study that IBs have the necessary hardware to run stereoscopic 3D (S3D) training materials, but the panel has not got an S3D imaging feature. Therefore, only the anaglyph S3D imaging method can be applied to IBs. Thus, an anaglyph S3D training material was prepared for the interaction of the skeletal system and interactive 3D material design for IBs with its effects in education was investigated. Besides, S3D imaging methods with features of IBs were examined. A Likert‐type scale was developed to measure the usability of the training material on IBs and the material was evaluated by 20 experts. The data were analyzed by the SPSS statistical program and the results were interpreted. According to the results, educational material seems to be positive in terms of image characteristics, content, navigation, and ease of use, font sizes were moderate for readability, the feedback process and the help menu were moderately effective. [ABSTRACT FROM AUTHOR]

This paper reports a study which utilizes deep learning for automated detection of the symptoms of diseases on vine leaves. Vine fruits or grapes are very important and have existed in Syria and surrounding areas (e.g., Turkey) for many years. Quality of vine fruits is also very important in grape production as it is consumed in these areas every day. The aim of this study is to improve diseasedetection accuracy in vine leaves and to develop a system to help Syrian and Turkish farmers and agricultural engineers to maintain the quality of grape production. In this study, over 1000 images of vine leaves have been collected from vine yards in Syria and the internet. These images are processed using MATLAB 2018B, Deep Learning Toolbox including convolutional neural networks (CNNs) with AlexNet, GoogleNet and ResNet-18. A standard transfer learning (TL) algorithm is also used with CNNs, whereas a multiclass support vector machine (SVM) is used with AlexNet, whilst GPU and CUDA are used for accelerating the process of the disease detection for vine leaves. A software system has been created that enables the automatic and efficient detection of nine types of leaf diseases and the identification of healthy leaves. Experimental studies showed that the total detection accuracy of this system reaches 92.5%, 87.4% and 85.0%, 85.1% when AlexNet+TL, ResNet-18+TL, GoogleNet+TL and AlexNet+SVM are used respectively. This smart agricultural application can provide early identification of the symptoms of grape diseases on leaves and thus help maintain the quality of vine fruits. [ABSTRACT FROM AUTHOR]

The Late Cretaceous Pınarbaşı Ophiolite (PO), located at central Turkey is represented by distinct rock types (e.g., harzburgite, dunite, pyroxenite, gabbro) that can be observed in many ophiolites around the world. In this study, the classification of rock types of PO in the Eastern Tauride Belt (Turkey) was made using the advanced spaceborne thermal emission and reflection radiometer and Landsat‐8 satellite images. The developed colour–band ratio composite images together with principal component analysis and minimum noise fraction transformations implemented to the remote sensing data efficiently delineate the spatial distribution of pyroxenites, metamorphic sole rocks, and gabbros in the study area. In addition, a body of serpentinized dunites, pyroxenites, metamorphic sole rocks, and listwaenites that has not been recognized before was identified within PO using remote sensing techniques. The promising results of the image processing were verified by detailed field observations and petrographic analysis. The satellite‐based remote sensing approaches of this work can be applicable for mapping ophiolitic rock types in PO in particular as well as other ophiolites from similar geographical regions around the world. [ABSTRACT FROM AUTHOR]

Hazelnut is one of the most popular nuts consumed by people; it has different cultivars in Turkey. The aim of the current study was to characterize some physicomechanical characteristics, shape features, color, and biochemical properties of 6 standard and 3 local hazelnut cultivars grown in Turkey. The shape and size properties of the samples were determined using image processing techniques as an alternative to conventional measurement methods. Additionally, principal component analysis (PCA) was used to classify the hazelnut samples in terms of the biochemical parameters of the hazelnut cultivars. According to the findings, the highest crude oil (63.25%) and lowest protein contents (13.63%) were observed in the Kalınkara cultivar. Oleic and linoleic acids were the major fatty acids for all hazelnut samples. While local Devedişi and standard Çakıldak cultivars had the highest oleic acid levels, the highest linoleic acid level was observed for the Dağ fındığı cultivar. The cultivars of Foşa had the highest Zn and Mn, while the highest Cu was found in the Tombul cultivar. The greatest surface and projected areas were calculated for the Kara fındık and Dağ fındığı samples, while the greatest hardness value was measured for the Devedişi cultivar. PCA revealed some positive and negative correlations between the physicomechanical and biochemical parameters. The present analyses revealed significant correlations between hardness and internal shell b* values and between Cu content and internal L*. Such correlations should be taken into consideration in food processing applications and machine design for these hazelnut cultivars. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) is commonly used in Earth Sciences to study surface displacements or construct high resolution topographic maps. Recent satellites such as those of the Sentinel-1 constellation allow to derive dense deformation maps with millimetric precision with high revisit frequency. However, InSAR is still limited by interferometric coherence. Interferometric phase noise resulting from a loss of coherence, due to changes in scattering properties between repeated SAR acquisitions, may lead to unwrapping errors, which then in turn lead to centimetric errors in time-series reconstruction. We present an algorithm based on interferometric phase closure to automatically correct unwrapping errors. We describe the algorithm and highlight its performances with two case studies, in Lebanon with Envisat satellite data and in Central Turkey with Sentinel-1 data. The first data set is particularly affected by unwrapping errors because of long spatial (500 m) and temporal baseline interferograms (6 yr) and decorrelation due, in particular, to vegetation. The second data set contains unwrapping errors because of temporal changes in the scattering properties of the ground. For these two examples, the algorithm allows the correction of almost all detectable unwrapping errors, without requiring visual inspection or manual deletions. Our algorithm is efficient especially on large data sets, such as with Sentinel-1 constellation, where interferometric phase is redundant and improves eventually the reconstruction of time-series. [ABSTRACT FROM AUTHOR]

Copyright of Yuzuncu Yil Universitesi Journal of Agricultural Sciences (YYU J Agr Sci) is the property of Yuzuncu Yil University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Neutrosophic set, initiated by Smarandache, is a novel tool to deal with vagueness considering the truth-membership T, indeterminacy-membership I and falsity-membership F satisfying the condition 0 ≤ T + I + F ≤ 3 . It can be used to characterize the uncertain information more sufficiently and accurately than intuitionistic fuzzy set. Neutrosophic set has attracted great attention of many scholars that have been extended to new types and these extensions have been used in many areas such as aggregation operators, decision making, image processing, information measures, graph and algebraic structures. Because of such a growth, we present an overview on neutrosophic set with the aim of offering a clear perspective on the different concepts, tools and trends related to their extensions. A total of 137 neutrosophic set publication records from Web of Science are analyzed. Many interesting results with regard to the annual trends, the top players in terms of country level as well as institutional level, the publishing journals, the highly cited papers, and the research landscape are yielded and explained in-depth. The results indicate that some developing economics (such as China, India, Turkey) are quite active in neutrosophic set research. Moreover, the co-authorship analysis of the country and institution, the co-citation analysis of the journal, reference and author, and the co-occurrence analysis of the keywords are presented by VOSviewer software. [ABSTRACT FROM AUTHOR]

Remote sensing satellite images plays a significant role in mapping land use/land cover LULC. Machine learning ML provide robust functions for satellite image classification. The objective of this paper is to extend the capability of GIS specialists in geospatial area with minimum knowledge in computer science to easily perform ML satellite image classification. A framework consisting 7 stages established. Tools of steps developed in two programing environments, which are ArcGIS for geospatial datasets structuring and Anaconda for ML training and classification. During the development, authors constrained to reduce the complexity of big data of satellite images and limited memory of computers to make tools available for implementation in PC. In addition, automation and improving the performance accuracy. TensorFlow-Keras library employed to perform the classification using neural networks. A case study using RASAT satellite image in Ankara-Turkey utilized to perform the analysis. The developed classifier gained 80% performance accuracy. The complete RASAT satellite image processed and smoothly classified based on blocks methods. The developed tools successfully tested and applied in geospatial area and can be effectively execute in PC by GIS specialist. [ABSTRACT FROM AUTHOR]

Inversion of electromagnetic induction data is often realized using smoothing operators for regularizing the process. These operators are applied using derivative matrices, penalizing differences between neighboring mesh cells. The resulting smoothing effect is similar to that of low-pass Gaussian filters and may cause difficulties due to the smoothed boundaries between structures. To prevent such ambiguities, non-linear rank filters are applied widely in image processing applications to filter low frequency variations while keeping boundaries. Correspondingly, we have defined a non-linear rank order smoothing operator for regularization. The effects of the defined parameters are demonstrated on synthetic models and the operator is observed to be able to provide better defined boundaries. The implementation of the operator on field data is realized using single-frequency VLF-R data collected on profiles traversing the surface rupture of the North Anatolian Fault in Kocaeli, Turkey. In the study area, ERT data are also collected on a single profile, and its smooth inversions are realized individually and jointly with VLF-R data for comparisons. Inversions show that the recovered subsurface structure using the rank order smoothing is closer to that indicated by the ERT data and previous studies. According to the recovered models, the clayey fillings in the fault rupture are acting as a barrier affecting the horizontal fluid flow in the near-surface and the resulting water accumulations around the fault rupture may cause susceptibility for earthquake induced damages. • VLF-R data is inverted using a smoothing operator based on rank order filters. • The operator is observed to be able to provide models with better defined boundaries. • VLF-R data collected on the rupture of the North Anatolian Fault is inverted. • Inversions suggest, the clayey fills in the rupture causes water accumulations around the fault. [ABSTRACT FROM AUTHOR]

Image processing techniques (band rationing, color composite, Principal Component Analyses) are widely used by many researchers to describe various mines and minerals. The primary aim of this study is to use remote sensing data to identify iron deposits and gossans located in Kaman, Kırşehir region in the central part of Anatolia, Turkey. Capability of image processing techniques is proved to be highly useful to detect iron and gossan zones. Landsat ETM+ was used to create remote sensing images with the purpose of enhancing iron and gossan detection by applying ArcMap image processing techniques. The methods used for mapping iron and gossan area are 3/1 band rationing, 3/5 : 1/3 : 5/7 color composite, third PC and PC4 : PC3 : PC2 as RG B which obtained result from Standard Principal Component Analysis and third PC which obtained result from Developed Selected Principal Component Analyses (Crosta Technique), respectively. Iron-rich or gossan zones were mapped through classification technique applied to obtained images. Iron and gossan content maps were designed as final products. These data were confirmed by field observations. It was observed that iron rich and gossan zones could be detected through remote sensing techniques to a great extent. This study shows that remote sensing techniques offer significant advantages to detect iron rich and gossan zones. It is necessary to confirm the iron deposites and gossan zones that have been detected for the time being through field observations. [ABSTRACT FROM AUTHOR]

Background Dynamic magnetic resonance imaging (dMRI) is an imaging tool that can be used to evaluate and stage pelvic organ prolapse (POP). Greater understanding of the incidental detection of POP in asymptomatic patients is needed. Purpose To evaluate the prevalence of dMRI-detected POP in pre-and postmenopausal women who were imaged for reasons unrelated to pelvic floor dysfunction. Material and Methods A total of 227 women who had diagnoses that did not include POP underwent abdominal/pelvic dMRI. Patients with a positive gynecological examination for or a clinical history of POP ( n = 11), hysterectomy ( n = 4), or gynecologic-oncology surgery ( n = 2) were excluded, as well as patients who were unable to strain during MRI ( n = 11). A total of 199 patients without visible prolapse were enrolled in the study. An H-line, M-line, pubococcygeal line (PCL), and mid-pubic line (MPL) were used to detect and grade prolapse. Results The prevalence of dMRI-identified POP was higher in postmenopausal subjects. The PCL led to a greater frequency of prolapse detection than the MPL. The frequency of middle compartment descent was similar regardless of whether the PCL or MPL was used as a reference line. There was a higher incidence of prolapse in the posterior compartment. Using an H-line and PCL as references, the anterior and posterior compartments were found to significantly differ between pre- and postmenopausal subjects. The MRI parameters that were used to define POP were not correlated with parity, vaginal birth, BMI, or fetal birth weight. With respect to the MPL, age was correlated with both the presence of an elongated H-line and with descent. Conclusion Dynamic MRI identified incidental pelvic organ prolapse in asymptomatic patients. The prevalence of dMRI-detected POP was higher in postmenopausal women without visible prolapse. These findings suggest the need for further studies to identify how to modify the currently used dMRI thresholds for postmenopausal women. [ABSTRACT FROM AUTHOR]

Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels' important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %. [ABSTRACT FROM AUTHOR]

In this study, a novel system was created to localize cancerous regions for stomach images which were taken with computed tomography(CT). The aim was to determine the coordinates of cancerous regions which spread in the stomach area in the color space with using this system. Also, to limit these areas with a high accuracy ratio and to feedback to the user of this system were the other objectives. This integration was performed with using energy mapping, analysis methods and multiple image processing methods and the system which was consisted from these advanced algorithms was appeared. For this work, in the range of 25-40 years and when gender discrimination was insignificant, 30 volunteer patients were chosen. During the formation of the system, to exalt the accuracy to the maximum level, 2 main stages were followed up. First, in the system, advanced image processing methods were processed between each other and obtained data were studied. Second, in the system, FFT and Log transformations were used respectively for the first two cases, then these transformations were used together for the third case. For totally three cases, energy distribution and DC energy intensity analysis were done and the performance of this system was investigated. Finally, with using the system's unique algorithms, a non-invasive method was achieved to detect the gastric cancer and when FFT and Log transformation were used together, the maximum success rate was obtained and this rate was calculated as 83,3119 %. [ABSTRACT FROM AUTHOR]

The need for timely, accurate, and interoperable geospatial information is steadily increasing. In this context, process-based image processing systems will be the initial segment for future’s automatic systems. A process-based system is believed to be a good approach for agricultural purpose because agricultural activities are carried out according to a periodic (annual) cycle. Therefore, a process-based image analysis procedure was designed for routine crop classification for an agricultural region in Kırklareli, Turkey. The process tree developed uses a multi-temporal image data set as an input and gives the final crop classification as an output by using an incremental rule set. The test data set was composed of five images of Satellite Pour l’Observation de la Terre 4 (SPOT 4) data acquired in 2007. Basically, image objects were first extracted and then classified. A rule set was structured depending on class definitions. A decision-based process was executed and formed a multilevel image classification system. The final classification was obtained by merging classes from the appropriate levels where they were extracted. To evaluate the success of the application the accuracy of the classification was assessed. The overall accuracy and kappa index of agreement was found to be 80% and 0.78, respectively. At the end of the study, problems of segmentation and classification operations were discussed and solution approaches were outlined. To assess the process in terms of its scope for automation, the efficiency and success of the rule set were also discussed. [ABSTRACT FROM AUTHOR]

Human activities in many parts of the world have greatly affected natural areas. Therefore, monitoring and forecasting of land-cover changes are important components for sustainable utilization, conservation, and development of these areas. This research has been conducted on Igneada, a legally protected area on the northwest coast of Turkey, which is famous for its unique, mangrove forests. The main focus of this study was to apply a land use and cover model that could quantitatively and graphically present the changes and its impacts on Igneada landscapes in the future. In this study, a Markov chain-based, stochastic Markov model and cellular automata Markov model were used. These models were calibrated using a time series of developed areas derived from Landsat Thematic Mapper (TM) imagery between 1990 and 2010 that also projected future growth to 2030. The results showed that CA Markov yielded reliable information better than St. Markov model. The findings displayed constant but overall slight increase of settlement and forest cover, and slight decrease of agricultural lands. However, even the slightest unsustainable change can put a significant pressure on the sensitive ecosystems of Igneada. Therefore, the management of the protected area should not only focus on the landscape composition but also pay attention to landscape configuration. [ABSTRACT FROM AUTHOR]

Copyright of Erwerbs-Obstbau is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, supplementary information on the morphology of the siphonostomatoid copepod Caligus quadratus Shiino, 1954 (Copepoda: Caligidae) is given based on the new material collected from the gills of the Atlantic bluefin tuna, Thunnus thynnus (Linnaeus, 1758) caught in the Gulf of Antalya, Turkey. The morphology of C. quadratus is re-examined for the first time by adopting a recently developed visualisation technique by Kamanli et al. [1]. Appendages of Congo red stained specimens of C. quadratus were dissected and scanned using confocal laser scanning microscope (CLSM) and the CLSM images were processed using Dristhi software programme. 3D reconstructions of some confusing appendages were visualised using Drishti. Line drawing was used to depict the habitus of both female and male C. quadratus. Key diagnostic characters of C. quadratus are presented together with the newly observed additional taxonomic characters. In addition, previously misinterpreted and simply overlooked features in the previous descriptions of C. quadratus are also re-described. This is the first report of C. quadratus from the Mediterranean Sea and the Atlantic bluefin tuna constitutes a new host record for this caligid copepod. • Atlantic bluefin tuna constitutes a new host record for Caligus quadratus. • It is the first record of Caligus quadratus in the Mediterranean Sea. • Supplementary information on the morphology of Caligus quadratus were presented. • A new technique, CLSM and Drishti 3D software, was used to describe C. quadratus. [ABSTRACT FROM AUTHOR]

Pedestrians confront risky situations at midblock sections due to the unyielding behavior of drivers. Thus, pedestrians have to wait for an appropriate gap to cross. This research investigates pedestrians' gap acceptance and crossing speed for midblock crossings by image processing methods in Izmir, Turkey. A total of 498 pedestrians have been tracked at two midblock crossings. The data were collected for one hour at each midblock crossing during the evening peak hour between 5.00-6.00 p.m. Three synchronized cameras were used to record pedestrian crossings. Then, by using image processing, vehicle and pedestrian trajectories have been obtained. Two cameras were mounted on telescopic tripods reaching up to 9 m, and the third camera was used to identify pedestrians' gender better. The parameters extracted from the recordings are; pedestrians' gender, group size, whether they carried items or not, and their accepted/rejected gaps. Pedestrian and item detection has been performed by YOLOv3 and YOLACT models. The accepted and rejected time gaps were extracted for pedestrians, excluding the pedestrians who crossed between stopped vehicles and crossed when an approaching vehicle did not exist within 100 m from the midblock crossing. Raff's method was used to estimate the critical gap using accepted/rejected gaps. The critical gaps ranged between 4.1 s and 6.2 s. The 15th percentile crossing speeds were found to be similar, ranging between 0.78 m/s and 0.80 m/s., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

There is a growing scholarly interest in monitoring and assessment of the impacts of land use and land cover changes (LULCC) on the environment. Recent technological developments and tools of geographic information systems (GIS) and remote sensing (RS) facilitate the researches on this interest. This research aims at monitoring LULCC in Bandırma, Turkey over 30 years. This research utilizes GIS and RS techniques. Landsat satellite images of the years 1987, 2003, and 2019 are used in supervised classification methods with the maximum likelihood technique. The classified images show artificial surfaces, agricultural areas, forests, wetlands, and water bodies based on the first level classes of the Coordination of Information on the Environment (CORINE) land cover legend. Accuracy assessment is performed both before and after the correction process by using ArcGIS (10.6 Edition) software. The final overall Kappa values are above 0.95 for 3 years. These images are compared by using the Land Change Modeler tool of IDRISI (Selva Edition) software. The results represent that forest areas are increased and that wetlands are decreased in both 1987-2003 and 2003-2019 periods. Agricultural lands are decreased in the later period. Comparison maps demonstrate that land change from agricultural to artificial occurs (i) around existing settlements; (ii) along transportation axes; and (iii) in the locations of large urban land uses such as industrial areas, mines, and wind energy plants. The land change trend analysis shows that change from agricultural to artificial is the largest in the middle and southeastern parts of the area neighboring the Manyas Lake Ramsar conservation site.

Stomach cancer is a type of cancer that is hard to detect at an early stage because it gives almost no symptoms at the beginning. Stomach cancer is an increasing incidence of cancer both in the World as well as in Turkey. The most common method used worldwide for gastric cancer diagnosis is endoscopy. However, definitive diagnosis is made with endoscopic biopsy results. Diagnosis with endoscopy is a very specific and sensitive method. With high-resolution endoscopy it is possible to detect mild discolorations, bulges and structural changes of the surface of the mucosa. However, because the procedures are performed with the eye of a doctor, it is possible that the cancerous areas may be missed and / or incompletely detected. Because of the fact that the cancerous area cannot be completely detected may cause the problem of cancer recurrence after a certain period of surgical intervention. In order to overcome this problem, a computerized decision support system (CDS) has been implemented with the help of specialist physicians and image processing techniques. The performed CDS system works as an assistant to doctors of gastroenterology, helping to identify the cancerous area in the endoscopic images of the scaffold, to take biopsies from these areas and to make a better diagnosis. We believe that gastric cancer will be helpful in determining the area and biopsy samples taken from the patient will be useful in determining the area. It is therefore considered a useful model. [ABSTRACT FROM AUTHOR]

This article describes methods used to determine the severity of Dry Eye Syndrome (DES) based on Oxford Grading Schema (OGS) automatically by developing and applying a decider model. The number of dry punctate dots occurred on corneal surface after corneal fluorescein staining can be used as a diagnostic indicator of DES severity according to OGS; however, grading of DES severity exactly by carefully assessing these dots is a rather difficult task for humans. Taking into account that current methods are also subjectively dependent on the perception of the ophtalmologists coupled with the time and resource intensive requirements, enhanced diagnosis techniques would greatly contribute to clinical assessment of DES. Automated grading system proposed in this study utilizes image processing methods in order to provide more objective and reliable diagnostic results for DES. A total of 70 fluorescein-stained cornea images from 20 patients with mild, moderate, or severe DES (labeled by an ophthalmologist in the Keratoconus Center of Yildirim Beyazit University Ataturk Training and Research Hospital) used as the participants for the study. Correlations between the number of dry punctate dots and DES severity levels were determined. When automatically created scores and clinical scores were compared, the following measures were observed: Pearson’s correlation value between the two was 0.981; Lin’s Concordance Correlation Coefficients (CCC) was 0.980; and 95% confidence interval limites were 0.963 and 0.989. The automated DES grade was estimated from the regression fit and accordingly the unknown grade is calculated with the following formula: Gpred = 1.3244 log(Ndots) - 0.0612. The study has shown the viability and the utility of a highly successful automated DES diagnostic system based on OGS, which can be developed by working on the fluorescein-stained cornea images. Proper implemention of a computationally savvy and highly accurate classification system, can assist investigators to perform more objective and faster DES diagnoses in real-world scenerios. [ABSTRACT FROM AUTHOR]

Background and Objective: One of the first stages in the identification of an individual is gender determination. Through gender determination, the search spectrum can be reduced. In disasters such as accidents or fires, which can render identification somewhat difficult, durable teeth are an important source for identification. This study proposes a smart system that can automatically determine gender using 3D digital maxillary tooth plaster models., Methods: The study group was composed of 40 Turkish individuals (20 female, 20 male) between the ages of 21 and 24. Using the iterative closest point (ICP) algorithm, tooth models were aligned, and after the segmentation process, models were transformed into depth images. The local discrete cosine transform (DCT) was used in the process of feature extraction, and the random forest (RF) algorithm was used for the process of classification., Results: Classification was performed using 30 different seeds for random generator values and 10-fold cross-validation. A value of 85.166% was obtained for average classification accuracy (CA) and a value of 91.75% for the area under the ROC curve (AUC)., Conclusions: A multi-disciplinary study is performed here that includes computer sciences, medicine and dentistry. A smart system is proposed for the determination of gender from 3D digital models of maxillary tooth plaster models. This study has the capacity to extend the field of gender determination from teeth., (Copyright © 2017 Elsevier B.V. All rights reserved.)