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<p>Citation Request :<br>KOKLU, M., KURSUN, R., TASPINAR, Y. S., and CINAR, I. (2021). Classification of Date Fruits into Genetic Varieties Using Image Analysis. Mathematical Problems in Engineering, Vol.2021, Article ID: 4793293, DOI:10.1155/2021/4793293<br><a href="https://www.hindawi.com/journals/mpe/2021/4793293/">https://www.hindawi.com/journals/mpe/2021/4793293/</a></p><p>Abstract: A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color, length, diameter, and shape. The external appearance of the fruits is a major determinant of the fruit type. Determining the variety of fruits by looking at their external appearance may necessitate expertise, which is time-consuming and requires great effort. The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai by using three different machine learning methods. In accordance with this purpose, 898 images of seven different date fruit types were obtained via the computer vision system (CVS). Through image processing techniques, a total of 34 features, including morphological features, shape, and color, were extracted from these images. First, models were developed by using the logistic regression (LR) and artificial neural network (ANN) methods, which are among the machine learning methods. Performance results achieved with these methods are 91.0% and 92.2%, respectively. Then, with the stacking model created by combining these models, the performance result was increased to 92.8%. It has been concluded that machine learning methods can be applied successfully for the classification of date fruit types.</p> Read More

<p>Citation Request : KOKLU, M., SARIGIL, S., &amp; OZBEK, O. (2021). The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.). Genetic Resources and Crop Evolution, 68(7), 2713-2726. Doi: https://doi.org/10.1007/s10722-021-01226-0 https://link.springer.com/article/10.1007/s10722-021-01226-0 https://link.springer.com/content/pdf/10.1007/s10722-021-01226-0.pdf DATASET: https://www.muratkoklu.com/datasets/ Abstract: Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. This study was carried out on the two most important and quality types of pumpkin seeds, ‘‘Urgup_Sivrisi’’ and ‘‘Cercevelik’’, generally grown in Urgup and Karacaoren regions in Turkey. However, morphological measurements of 2500 pumpkin seeds of both varieties were made possible by using the gray and binary forms of threshold techniques. Considering morphological features, all the data were modeled with five different machine learning methods: Logistic Regression (LR), Multilayer Perceptrons (MLP), Support Vector Machine (SVM) and Random Forest (RF), and k-Nearest Neighbor (k-NN), which further determined the most successful method for classifying pumpkin seed varieties. However, the performances of the models were determined with the help of the 10 kfold cross-validation method. The accuracy rates of the classifiers were obtained as LR 87.92 percent, MLP 88.52 percent, SVM 88.64 percent, RF 87.56 percent, and k-NN 87.64 percent. Keywords Pumpkin seed Logistic regression, Multilayer peceptrons, Random forest, Classification, Support vector machine, Thresholding DATASET: https://www.muratkoklu.com/datasets/</p> Read More

<p>Article Download (PDF): <a href="https://dergipark.org.tr/tr/download/article-file/1227592">https://dergipark.org.tr/tr/download/article-file/1227592</a></p><p>ABSTRACT: In this study, machine vision system was developed in order to distinguish between two different variety of raisins (Kecimen and Besni) grown in Turkey. Firstly, a total of 900 pieces raisin grains were obtained, from an equal number of both varieties. These images were subjected to various preprocessing steps and 7 morphological feature extraction operations were performed using image processing techniques. In addition, minimum, mean, maximum and standard deviation statistical information was calculated for each feature. The distributions of both raisin varieties on the features were examined and these distributions were shown on the graphs. Later, models were created using LR, MLP, and SVM machine learning techniques and performance measurements were performed. The classification achieved 85.22% with LR, 86.33% with MLP and 86.44% with the highest classification accuracy obtained in the study with SVM. Considering the number of data available, it is possible to say that the study was successful.</p> Read More

<p>Article Download (PDF):<br>1: <a href="https://www.mattioli1885journals.com/index.php/progressinnutrition/article/view/9686/9178">https://www.mattioli1885journals.com/index.php/progressinnutrition/article/view/9686/9178</a><br>2: <a href="https://doi.org/10.3390/electronics11070981">https://doi.org/10.3390/electronics11070981</a></p><p>ABSTRACT: In order to keep the economic value of pistachio nuts which have an important place in the agricultural economy, the efficiency of post-harvest industrial processes is very important. To provide this efficiency, new methods and technologies are needed for the separation and classification of pistachios. Different pistachio species address different markets, which increases the need for the classification of pistachio species. In this study, it is aimed to develop a classification model different from traditional separation methods, based on image processing and artificial intelligence which are capable to provide the required classification. A computer vision system has been developed to distinguish two different species of pistachios with different characteristics that address different market types. 2148 sample image for these two kinds of pistachios were taken with a high-resolution camera. The image processing techniques, segmentation and feature extraction were applied on the obtained images of the pistachio samples. A pistachio dataset that has sixteen attributes was created. An advanced classifier based on k-NN method, which is a simple and successful classifier, and principal component analysis was designed on the obtained dataset. In this study; a multi-level system including feature extraction, dimension reduction and dimension weighting stages has been proposed. Experimental results showed that the proposed approach achieved a classification success of 94.18%. The presented high-performance classification model provides an important need for the separation of pistachio species and increases the economic value of species. In addition, the developed model is important in terms of its application to similar studies.<br>Keywords: Classification, Image processing, k nearest neighbor classifier, Pistachio species</p><ol><li>SINGH D, TASPINAR YS, KURSUN R, CINAR I, KOKLU M, OZKAN IA, LEE H-N., (2022). Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models, Electronics, 11 (7), 981. <a href="https://doi.org/10.3390/electronics11070981">https://doi.org/10.3390/electronics11070981</a>. (Open Access)</li></ol><p>ABSTRACT: Pistachio is a shelled fruit from the anacardiaceae family. The homeland of pistachio is the Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and exported in Turkey. Since the prices, tastes, and nutritional values of these types differs, the type of pistachio becomes important when it comes to trade. This study aims to identify these two types of pistachios, which are frequently grown in Turkey, by classifying them via convolutional neural networks. Within the scope of the study, images of Kirmizi and Siirt pistachio types were obtained through the computer vision system. The pre-trained dataset includes a total of 2148 images, 1232 of Kirmizi type and 916 of Siirt type. Three different convolutional neural network models were used to classify these images. Models were trained by using the transfer learning method, with AlexNet and the pre-trained models VGG16 and VGG19. The dataset is divided as 80% training and 20% test. As a result of the performed classifications, the success rates obtained from the AlexNet, VGG16, and VGG19 models are 94.42%, 98.84%, and 98.14%, respectively. Models’ performances were evaluated through sensitivity, specificity, precision, and F-1 score metrics. In addition, ROC curves and AUC values were used in the performance evaluation. The highest classification success was achieved with the VGG16 model. The obtained results reveal that these methods can be used successfully in the determination of pistachio types.<br>Keywords: pistachio; genetic varieties; machine learning; deep learning; food recognition</p> Read More

<p>Article Download (PDF):<br>1: <a href="https://www.mattioli1885journals.com/index.php/progressinnutrition/article/view/9686/9178">https://www.mattioli1885journals.com/index.php/progressinnutrition/article/view/9686/9178</a><br>2: <a href="https://doi.org/10.3390/electronics11070981">https://doi.org/10.3390/electronics11070981</a></p><p>ABSTRACT: In order to keep the economic value of pistachio nuts which have an important place in the agricultural economy, the efficiency of post-harvest industrial processes is very important. To provide this efficiency, new methods and technologies are needed for the separation and classification of pistachios. Different pistachio species address different markets, which increases the need for the classification of pistachio species. In this study, it is aimed to develop a classification model different from traditional separation methods, based on image processing and artificial intelligence which are capable to provide the required classification. A computer vision system has been developed to distinguish two different species of pistachios with different characteristics that address different market types. 2148 sample image for these two kinds of pistachios were taken with a high-resolution camera. The image processing techniques, segmentation and feature extraction were applied on the obtained images of the pistachio samples. A pistachio dataset that has sixteen attributes was created. An advanced classifier based on k-NN method, which is a simple and successful classifier, and principal component analysis was designed on the obtained dataset. In this study; a multi-level system including feature extraction, dimension reduction and dimension weighting stages has been proposed. Experimental results showed that the proposed approach achieved a classification success of 94.18%. The presented high-performance classification model provides an important need for the separation of pistachio species and increases the economic value of species. In addition, the developed model is important in terms of its application to similar studies.<br>Keywords: Classification, Image processing, k nearest neighbor classifier, Pistachio species</p><ol><li>SINGH D, TASPINAR YS, KURSUN R, CINAR I, KOKLU M, OZKAN IA, LEE H-N., (2022). Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models, Electronics, 11 (7), 981. <a href="https://doi.org/10.3390/electronics11070981">https://doi.org/10.3390/electronics11070981</a>. (Open Access)</li></ol><p>ABSTRACT: Pistachio is a shelled fruit from the anacardiaceae family. The homeland of pistachio is the Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and exported in Turkey. Since the prices, tastes, and nutritional values of these types differs, the type of pistachio becomes important when it comes to trade. This study aims to identify these two types of pistachios, which are frequently grown in Turkey, by classifying them via convolutional neural networks. Within the scope of the study, images of Kirmizi and Siirt pistachio types were obtained through the computer vision system. The pre-trained dataset includes a total of 2148 images, 1232 of Kirmizi type and 916 of Siirt type. Three different convolutional neural network models were used to classify these images. Models were trained by using the transfer learning method, with AlexNet and the pre-trained models VGG16 and VGG19. The dataset is divided as 80% training and 20% test. As a result of the performed classifications, the success rates obtained from the AlexNet, VGG16, and VGG19 models are 94.42%, 98.84%, and 98.14%, respectively. Models’ performances were evaluated through sensitivity, specificity, precision, and F-1 score metrics. In addition, ROC curves and AUC values were used in the performance evaluation. The highest classification success was achieved with the VGG16 model. The obtained results reveal that these methods can be used successfully in the determination of pistachio types.<br>Keywords: pistachio; genetic varieties; machine learning; deep learning; food recognition</p> Read More

<p>Data Set Name: Rice Dataset (Commeo and Osmancik)<br>Abstract: A total of 3810 rice grain's images were taken for the two species (Cammeo and Osmancik), processed and feature inferences were made. 7 morphological features were obtained for each grain of rice.</p><p>Source:<br>Ilkay CINAR<br>Graduate School of Natural and Applied Sciences,<br>Selcuk University, Konya, TURKEY<br><a href="mailto:ilkay_cinar@hotmail.com">ilkay_cinar@hotmail.com</a></p><p>Murat KOKLU<br>Faculty of Technology,<br>Selcuk University, Konya, TURKEY.<br><a href="mailto:mkoklu@selcuk.edu.tr">mkoklu@selcuk.edu.tr</a></p><p>Relevant Information: In order to classify the rice varieties (Cammeo and Osmancik) used, preliminary processing was applied to the pictures obtained with computer vision system and a total of 3810 rice grains were obtained. Furthermore, 7 morphological features have been inferred for each grain. A data set has been created for the properties obtained.</p><p>Attribute Information:</p><ol><li>Area: Returns the number of pixels within the boundaries of the rice grain.</li><li>Perimeter: Calculates the circumference by calculating the distance between pixels around the boundaries of the rice grain.</li><li>Major Axis Length: The longest line that can be drawn on the rice grain, i.e. the main axis distance, gives.</li><li>Minor Axis Length: The shortest line that can be drawn on the rice grain, i.e. the small axis distance, gives.</li><li>Eccentricity: It measures how round the ellipse, which has the same moments as the rice grain, is.</li><li>Convex Area: Returns the pixel count of the smallest convex shell of the region formed by the rice grain.</li><li>Extent: Returns the ratio of the region formed by the rice grain to the bounding box pixels</li><li>Class: Commeo and Osmancik.</li></ol><p>Relevant Papers / Citation Requests / Acknowledgements:<br>Cinar, I. and Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, vol.7, no.3 (Sep. 2019), pp.188-194. <a href="https://doi.org/10.18201/ijisae.2019355381">https://doi.org/10.18201/ijisae.2019355381</a>.</p> Read More

<p>Feature extraction processes were performed based on the image processing techniques using morphological, shape and color features for five different rice varieties of the same brand. A total of 75 thousand pieces of rice grain were obtained, including 15 thousand pieces of each variety of rice. Pre-processing operations were applied to the images and made available for feature extraction. A total of 106 features were inferred from the images; 12 morphological features and 4 shape features obtained using morphological features and 90 color features obtained from five different color spaces (RGB, HSV, L<i>a</i>b*, YCbCr, XYZ). In addition, for the 106 features obtained, features were selected by ANOVA, X2 and Gain Ratio tests and useful features were determined. In all tests, out of 106 features, the 5 most effective and specific features were obtained roundness, compactness, shape factor 3, aspect ratio and eccentricity. The color features were listed in different order following these features.</p> Read More

<p>Data Set Name: Rice Dataset (Commeo and Osmancik)<br>Abstract: A total of 3810 rice grain's images were taken for the two species (Cammeo and Osmancik), processed and feature inferences were made. 7 morphological features were obtained for each grain of rice.</p><p>Source:<br>Ilkay CINAR<br>Graduate School of Natural and Applied Sciences,<br>Selcuk University, Konya, TURKEY<br><a href="mailto:ilkay_cinar@hotmail.com">ilkay_cinar@hotmail.com</a></p><p>Murat KOKLU<br>Faculty of Technology,<br>Selcuk University, Konya, TURKEY.<br><a href="mailto:mkoklu@selcuk.edu.tr">mkoklu@selcuk.edu.tr</a></p><p>DATASET: <a href="https://www.muratkoklu.com/datasets/">https://www.muratkoklu.com/datasets/</a></p><p>Relevant Information: In order to classify the rice varieties (Cammeo and Osmancik) used, preliminary processing was applied to the pictures obtained with computer vision system and a total of 3810 rice grains were obtained. Furthermore, 7 morphological features have been inferred for each grain. A data set has been created for the properties obtained.</p><p>Attribute Information:</p><ol><li>Area: Returns the number of pixels within the boundaries of the rice grain.</li><li>Perimeter: Calculates the circumference by calculating the distance between pixels around the boundaries of the rice grain.</li><li>Major Axis Length: The longest line that can be drawn on the rice grain, i.e. the main axis distance, gives.</li><li>Minor Axis Length: The shortest line that can be drawn on the rice grain, i.e. the small axis distance, gives.</li><li>Eccentricity: It measures how round the ellipse, which has the same moments as the rice grain, is.</li><li>Convex Area: Returns the pixel count of the smallest convex shell of the region formed by the rice grain.</li><li>Extent: Returns the ratio of the region formed by the rice grain to the bounding box pixels</li><li>Class: Commeo and Osmancik.</li></ol><p>Relevant Papers / Citation Requests / Acknowledgements:<br>Cinar, I. and Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, vol.7, no.3 (Sep. 2019), pp.188-194. <a href="https://doi.org/10.18201/ijisae.2019355381">https://doi.org/10.18201/ijisae.2019355381</a>.</p> Read More