Yazar "Kurtulmus, Ferhat" seçeneğine göre listele

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    Classification of chestnuts according to moisture levels using impact sound analysis and machine learning
    (Springer, 2018) Kurtulmus, Ferhat; Oztufekci, Sencer; Kavdir, Ismail
    In this study, a prototype system was designed, built and tested to classify chestnuts using impact sound signals and machine learning methods according to moisture contents. Briefly, the system consisted of a shotgun microphone, a sliding platform, an impact surface, a triggering system, a sound device and a computer. Sound signal data were acquired from 2028 chestnut samples with three different moisture levels. Acoustic signals from chestnut samples were filtered to alleviate negative effects of unwanted noise. Four machine learning classifiers using three different feature sets obtained from two feature groups applying feature reduction methods were trained and tested to classify pairs of chestnut moisture group categories as 35% versus 45%, 35% versus 55%, 45% versus 55% (classification with two outputs) and 35% versus 45% versus 55% (classification with three outputs), respectively. The highest classification success (88%) was achieved for the classification application category of 35 versus 55%.
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    Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers
    (Springer, 2018) Kavdir, Ismail; Buyukcan, M. Burak; Kurtulmus, Ferhat
    Green olives (Olea europaea L. cv. Ayvalik') were classified based on their surface features such as existence of bruise and fly-defect using two NIR spectrometer readings of reflectance and transmittance, and classifiers such as artificial neural networks (ANN) and statistical (Ident and Cluster). Spectral readings were performed in the ranges of 780-2500 and 800-1725nm for reflectance and transmittance modes, respectively. Original spectral readings were used as input features to the classifiers. Diameter correction was applied on reflectance spectra used in ANN classifier expecting improved classification results. ANN classifier performed better in general compared to statistical classifiers. Classification performance in detecting bruised olives using diameter corrected reflectance features and ANN classifier was 99% while it was 98% for Ident and Cluster classification approaches using regular reflectance features. Classification between solid and fly-defected olives was performed with success rates of 93% using reflectance features and 58% using transmittance features with ANN classifier while statistical classifiers of Ident and Cluster performed between 52 and 78% success rates using the same spectral readings. ANN classifier resulted 92% classification success for the classification application considering three output classes of solid, bruised and fly-defected olives using reflectance features while it performed 57.3% success rate using transmittance features.
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    Classification of pepper seeds using machine vision based on neural network
    (Chinese Acad Agricultural Engineering, 2016) Kurtulmus, Ferhat; Alibas, Ilknur; Kavdir, Ismail
    Pepper is widely planted and used all over the world as fresh vegetable and spice. Genetic and morphological information of pepper are stored through seeds. Determination of seed variety is crucial for correctly identifying genetic materials. Pepper varieties cannot be easily classified even by an expert eye due to the very small size of seeds and visual similarities. Hence, more advanced technologies are required to determine the variety of a pepper seed. A classification method was proposed to discriminate pepper seed based on neural networks and computer vision. Image acquisition was conducted using an office scanner at a resolution of 1200 dpi. Image features representing color, shape, and texture were extracted and used to classify pepper seeds. By calculating features from different color components, a feature database was constructed. Effective features were selected using sequential feature selection with different criterion functions. As a result of the feature selection procedure, the number of the features was significantly reduced from 257 to 10. Cross validation rules were applied to obtain a reliable classification model by preventing overfitting. Different numbers of neurons in the hidden layer and various training algorithms were investigated to determine the best multilayer perceptron model. The best classification performance was obtained using 30 neurons in the hidden layer of the network. With this network, an accuracy rate of 84.94% was achieved using the sequential feature selection and the training algorithm of resilient back propagation in classifying eight pepper seed varieties.
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    Detecting corn tassels using computer vision and support vector machines
    (Pergamon-Elsevier Science Ltd, 2014) Kurtulmus, Ferhat; Kavdir, Ismail
    An automated solution for maize detasseling is very important for maize growers who want to reduce production costs. Quality assurance of maize requires constantly monitoring production fields to ensure that only hybrid seed is produced. To achieve this cross-pollination, tassels of female plants have to be removed for ensuring all the pollen for producing the seed crop comes from the male rows. This removal process is called detasseling. Computer vision methods could help positioning the cutting locations of tassels to achieve a more precise detasseling process in a row. In this study, a computer vision algorithm was developed to detect cutting locations of corn tassels in natural outdoor maize canopy using conventional color images and computer vision with a minimum number of false positives. Proposed algorithm used color informations with a support vector classifier for image binarization. A number of morphological operations were implemented to determine potential tassel locations. Shape and texture features were used to reduce false positives. A hierarchical clustering method was utilized to merge multiple detections for the same tassel and to determine the final locations of tassels. Proposed algorithm performed with a correct detection rate of 81.6% for the test set. Detection of maize tassels in natural canopy images is a quite difficult task due to various backgrounds, different illuminations, occlusions, shadowed regions, and color similarities. The results of the study indicated that detecting cut location of corn tassels is feasible using regular color images. (C) 2014 Elsevier Ltd. All rights reserved.