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Öğe Automated Classification System Based on YOLO Architecture for Body Condition Score in Dairy Cows(Mdpi, 2024) Dandil, Emre; Cevik, Kerim Kursat; Boga, MustafaSimple Summary This study proposes an automatic classification system for determining body condition score in dairy cows using a deep learning architecture. An original dataset was created by categorizing images of different breeds from different farms into five body condition score classes: Emaciated, Poor, Good, Fat, and Obese. In the experimental analysis, the proposed deep learning model accurately classified 102 out of 126 cow images in the test set, achieving an average accuracy of 0.81 for all classes in Holstein and Simmental cows and an average area under the precision-recall curve of 0.87. The proposed body condition score classification system can help to accurately monitor rapid declines in body condition in dairy cows and serve as a tool for production decision-makers to reduce negative energy balance during early lactation.Abstract Body condition score (BCS) is a common tool used to assess the welfare of dairy cows and is based on scoring animals according to their external appearance. If the BCS of dairy cows deviates from the required value, it can lead to diseases caused by metabolic problems in the animal, increased medication costs, low productivity, and even the loss of dairy cows. BCS scores for dairy cows on farms are mostly determined by observation based on expert knowledge and experience. This study proposes an automatic classification system for BCS determination in dairy cows using the YOLOv8x deep learning architecture. In this study, firstly, an original dataset was prepared by dividing the BCS scale into five different classes of Emaciated, Poor, Good, Fat, and Obese for images of Holstein and Simmental cow breeds collected from different farms. In the experimental analyses performed on the dataset prepared in this study, the BCS values of 102 out of a total of 126 cow images in the test set were correctly classified using the proposed YOLOv8x deep learning architecture. Furthermore, an average accuracy of 0.81 was achieved for all BCS classes in Holstein and Simmental cows. In addition, the average area under the precision-recall curve was 0.87. In conclusion, the BCS classification system for dairy cows proposed in this study may allow for the accurate observation of animals with rapid declines in body condition. In addition, the BCS classification system can be used as a tool for production decision-makers in early lactation to reduce the negative energy balance.Öğe Body Condition Score (BCS) Classification with Deep Learning(IEEE, 2019) Cevik, Kerim Kursat; Boga, MustafaThe most important indicator of whether animals 'needs are met in livestock enterprises is the animals' body condition score (BCS) score. In dairy cattle BCS is based on scoring from 1 to 5 according to the external appearance of the animals. BCS is a subjective method based on visual or palpation method to determine the relationship between subcutaneous fat thickness and bone protrusions in pelvic region in back, waist and coccyx regions in cattle. Generally, BCS values in the enterprises are determined by a method based on expert knowledge and determined by observation. If the animal is above or below the desired BCS, at this stage, diseases resulting from metabolic problems, low yield or animal losses may be observed. With the regular control of this situation, the profitability of the enterprise may increase with the production of more health animals. For this purpose, it was aimed to determine the BCS score with a computer-aided software. Images from cattle were arranged in specific forms and classified by Convolutional Neural Networks (CNN). Of the 180 images, 75% were used for training and 25% for testing. In this study, system performance was increased by using pre-trained CNN architectures and the responses of different architectures to BCS classification problem were tested. As a result, it was seen that BCS scoring can be done more than 60% successfully by using CNN methods.Öğe Classifying Milk Yield Using Deep Neural Network(Zoological Soc Pakistan, 2020) Boga, Mustafa; Cevik, Kerim Kursat; Burgut, AykutThis study aim to describe the impact of the number of lactation, lactation days, age at first calving and breeding, and number of insemination (ratio) on cattle milk yield (last seven days in average). For this purpose, the milk yields of 156 Holstein Friesian cattle were investigated according to different age, lactation, calving and insemination associated parameters. Optimum values in literature were organized by an expert in establishing classification data. The expert determined the classes of the outputs data (average milk) through the input data (calving age, milking days, number of lactation and insemination). Applying deep neural networks, we established that average classification success of the system was 69.23% as a result of 6-Layers Cross-Verification Test which is commonly used in the literature for small datasets. In these datasets, it was found that 84 animals had GOOD, 39 animals carried POOR and 33 animals possessed MEDIUM milk yield. It was revealed that there is provided animal raising conditions by 53,84% (84/156*100); therefore, there is no professional farm management. Taken together, the finding show that there is a need of additional controlled management on animal raising and mistakes of the enterprise need to be recovered as early as possible.Öğe Deep Learning Based Egg Fertility Detection(Mdpi, 2022) Cevik, Kerim Kursat; Kocer, Hasan Erdinc; Boga, MustafaSimple Summary This study employs a Mask R-CNN technique along with the transfer learning model to accurately detect fertile and infertile eggs. It is a novel study that uses a single DL model to carry out detection, classification and segmentation of fertile and infertile eggs based on incubator images. This study investigates the implementation of deep learning (DL) approaches to the fertile egg-recognition problem, based on incubator images. In this study, we aimed to classify chicken eggs according to both segmentation and fertility status with a Mask R-CNN-based approach. In this manner, images can be handled by a single DL model to successfully perform detection, classification and segmentation of fertile and infertile eggs. Two different test processes were used in this study. In the first test application, a data set containing five fertile eggs was used. In the second, testing was carried out on the data set containing 18 fertile eggs. For evaluating this study, we used AP, one of the most important metrics for evaluating object detection and segmentation models in computer vision. When the results obtained were examined, the optimum threshold value (IoU) value was determined as 0.7. According to the IoU of 0.7, it was observed that all fertile eggs in the incubator were determined correctly on the third day of both test periods. Considering the methods used and the ease of the designed system, it can be said that a very successful system has been designed according to the studies in the literature. In order to increase the segmentation performance, it is necessary to carry out an experimental study to improve the camera and lighting setup prepared for taking the images.Öğe Developmental Hip Dysplasia Segmentation of Ultrasound Images(IEEE, 2016) Cevik, Kerim Kursat; Kocer, Hasan ErdincIn our study, Developmental Dysplasia of the Hip (DDH) is intended to automatically segmenting the ultrasound images for diagnosis. Initially, a filter is applied to the raw images. Seven different filters (Mean, Median, Gaussian, Wiener, Perona & Malik, Lee and Frost) are applied to the images and finally the output images are evaluated. Filtered DDH images were segmented and results are evaluated in the second part of the work. In the DDH diagnosis, the ilium and femoral regions are segmented by using Active Contour Models and Circular Hough Transform methods, respectively. When the segmentation process is analyzed, it is observed that the Wiener filters manage to increase the success rate due to their ability to remove speckle noise and ilium segmentation was performed with 94%. It is observed that Wiener filter was also success, besides when applied histogram equalization after filtering success rate is determined as 96% in the femoral region.Öğe Measuring the Effect of Filters on Segmentation of Developmental Dysplasia of the Hip(KOWSAR PUBL, 2016) Kocer, Hasan Erdinc; Cevik, Kerim Kursat; Sivri, Mesut; Koplay, MustafaBackground: Developmental dysplasia of the hip(DDH) can be detected with ultrasonography (USG) images. However, the accuracy of this method is dependent on the skill of the radiologist. Radiologists measure the hip joint angles without computer-based diagnostic systems. This causes mistakes in the diagnosis of DDH. Objectives: In this study, we aimed to automate segmentation of DDH ultrasound images in order to make it convenient for radiologic diagnosis by this recommended system. Materials and Methods: This experiment consisted of several steps, in which pure DDH and various noise-added images were formed. Then, seven different filters (mean, median, Gaussian, Wiener, Perona and Malik, Lee, and Frost) were applied to the images, and the output images were evaluated. The study initially evaluated the filter implementations on the pure DDH images. Then, three different noise functions, speckle, salt and pepper, and Gaussian, were applied to the images and the noisy images were filtered. In the last part, the peak signal to noise ratio (PSNR) and mean square error (MSE) values of the filtered images were evaluated. PSNR and MSE distortion measurements were applied to determine the image qualities of the original image and the output image. As a result, the differences in the results of different noise removal filters were observed. Results: The best results of PSNR values obtained in filtering were: Wiener (43.49), Perona and Malik (27.68), median (40.60) and Lee (35.35) for the noise functions of raw images, Gaussian noise added, salt and pepper noise added and speckle noise added images, respectively. After the segmentation process, it was seen that applying filtering to DDH USG images had low influence. We correctly segmented the ilium zone with the active contour model. Conclusion: Various filters are needed to improve the image quality. In this study, seven different filters were implemented and investigated on both noisy and noise-free images.Öğe Mobile applications to obtain minimum cost feed mixes(Tubitak Scientific & Technological Research Council Turkey, 2020) Boga, Mustafa; Cevik, Kerim Kursat; Onder, HasanIn this study, ration preparation software to minimize the cost of feed for ruminant livestock such as cattle, sheep, and goats for both milk and meat yield was developed for Web- and Android-based systems using genetic algorithms. To maximize accessibility on PCs, smartphones, and tablet PCs, we used Web- and Android-based software to find cheaper feed mixes that satisfy the nutritional requirements of ruminants. With this novel system, farmers and scientists can obtain low-cost feed mixes via the Web or smartphones, regardless of time or location. This application is useful for feed producers and farmers because they can use this software from any location and at any time. Users can input their new feed resources for preparing rations.Öğe Segmentation of the Ilium and Femur Regions from Ultrasound Images for Diagnosis of Developmental Dysplasia of the Hip(AMER SCIENTIFIC PUBLISHERS, 2016) Cevik, Kerim Kursat; Kocer, Hasan Erdinc; Andac, SeydaThe objective of the study is to evaluate the efficiency of applying filters on ultrasound images in order to increase the success rate of segmentation in the diagnosis of Developmental Dysplasia of the Hip (DDH). This research consists of several steps, in which pure DDH images are formed. Seven different filters (Mean, Median, Gaussian, Wiener, Perona and Malik, Lee and Frost) are applied to the images and finally the output images are evaluated. Initially, a filter is applied to the raw images. To assess the resulting images peak signal to noise ratio (PSNR) and mean square error (MSE) values are used. In the next section of the study, those seven different filters are applied to the raw images and segmentation is carried out and then the results are evaluated. In the DDH diagnosis, the ilium and femoral regions are segmented by using Active Contour Models and Circular Hough Transform methods, respectively. The results of the study show that applying Wiener filter to the iliac region results in 100% success, while the filter also achieves 90% success rate in the femoral region. In conclusion, the examining PSNR and MSE values show that the degree of filter's success varies according to the type of noise contained in the image. When the segmentation process is analyzed, it is observed that the Wiener filters manage to increase the success rate due to their ability to remove speckle noise.Öğe The effect of SOS Table learning environment on mobile learning tools acceptance, motivation and mobile learning attitude in English language learning(Routledge Journals, Taylor & Francis Ltd, 2022) Onal, Nezih; Cevik, Kerim Kursat; Senol, VeyselThe aim of this study is to demonstrate effectiveness of mobile game called SOS Table in the context of the subject Tenses in English within the framework of mobile learning tools acceptance, learning language motivation and mobile learning attitude of the students. The target group of the research consists of preparatory class students of a school of foreign languages in a state university in Turkey. The research was carried out with mixed method research. The quantitative paradigm-based section of this study was designed with a single-group pre-test and post-test model. In this model, three different scales were applied to the participants. The participants used the SOS Table mobile game developed by the researchers for 8 weeks. After the applications, semi-structured interviews were conducted with seven participants. The quantitative data of the study were analyzed with paired sampled t-Test and the qualitative data were subjected to thematic content analysis. The results of the study indicated that the mobile application named SOS Table increased both the mobile learning tools acceptance of the participants and motivation in English, and mediated the positive attitude development for mobile learning in English. The qualitative data obtained also supported these findings.
