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<p>The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. The species of grapevine leaves are important in terms of price and taste. In this study, deep learning-based classification is conducted by using images of grapevine leaves. For this purpose, images of 500 vine leaves belonging to 5 species were taken with a special self-illuminating system. Later, this number was increased to 2500 with data augmentation methods. The classification was conducted with a state-of-art CNN model fine-tuned MobileNetv2. As the second approach, features were extracted from pre-trained MobileNetv2′s Logits layer and classification was made using various SVM kernels. As the third approach, 1000 features extracted from MobileNetv2′s Logits layer were selected by the Chi-Squares method and reduced to 250. Then, classification was made with various SVM kernels using the selected features. The most successful method was obtained by extracting features from the Logits layer and reducing the feature with the Chi-Squares method. The most successful SVM kernel was Cubic. The classification success of the system has been determined as 97.60%. It was observed that feature selection increased the classification success although the number of features used in classification decreased.</p><p>Keywords: Deep learning, Transfer learning, SVM, Grapevine leaves, Leaf identification</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>Abstract: Images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. A total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.</p><p>Relevant Information:<br>Seven different types of dry beans were used in this research, taking into account the features such as form, shape, type, and structure by the market situation. A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. Bean images obtained by computer vision system were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.</p><p>Attribute Information:<br>1.) Area (A): The area of a bean zone and the number of pixels within its boundaries.<br>2.) Perimeter (P): Bean circumference is defined as the length of its border.<br>3.) Major axis length (L): The distance between the ends of the longest line that can be drawn from a bean.<br>4.) Minor axis length (l): The longest line that can be drawn from the bean while standing perpendicular to the main axis.<br>5.) Aspect ratio (K): Defines the relationship between L and l.<br>6.) Eccentricity (Ec): Eccentricity of the ellipse having the same moments as the region.<br>7.) Convex area (C): Number of pixels in the smallest convex polygon that can contain the area of a bean seed.<br>8.) Equivalent diameter (Ed): The diameter of a circle having the same area as a bean seed area.<br>9.) Extent (Ex): The ratio of the pixels in the bounding box to the bean area.<br>10.)Solidity (S): Also known as convexity. The ratio of the pixels in the convex shell to those found in beans.<br>11.)Roundness (R): Calculated with the following formula: (4piA)/(P^2)<br>12.)Compactness (CO): Measures the roundness of an object: Ed/L<br>13.)ShapeFactor1 (SF1)<br>14.)ShapeFactor2 (SF2)<br>15.)ShapeFactor3 (SF3)<br>16.)ShapeFactor4 (SF4)<br>17.)Class (Seker, Barbunya, Bombay, Cali, Dermosan, Horoz and Sira)</p> Read More

<p>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>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> Read More