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All Journal International Journal of Intelligent Systems and Applications in Engineering
Sabanci, Kadir
Advanced Technology and Science (ATScience)
Computer Science & IT

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The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery Unlersen, Muhammed Fahri; Sabanci, Kadir
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.05552

Abstract

In this study, the Japanese Oak and Pine Wilt in forested areas of Japan was classified into two group as diseased trees and all other land cover area according to the 6 attributes in the spectral data set of the forest. The Wilt Data Set which was obtained from UCI machine learning repository database was used. Weka (Waikato Environment for Knowledge Analysis) software was used for classification of areas in the forests. The classification success rates and error values were calculated and presented for classification data mining algorithms just as Multilayer Perceptron (MLP) and k-Nearest Neighbor (kNN). In MLP neural networks the classification performance for various numbers of neurons in the hidden layer was presented. The highest success rate was obtained as 86.4% when the number of neurons in the hidden layer was 10. The classification performance of kNN method was calculated for various counts of neighborhood. The highest success rate was obtained as 72% when the count of neighborhood number was 2.
Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks Sabanci, Kadir; Koklu, Murat; Unlersen, Muhammed Fahri
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.74573

Abstract

Quality is one of the important factors in agricultural products marketing. Grading machines have great role in quality control systems. The most efficient method used in grading machines today is image processing. This study aims to do the grading of high valued agricultural product of our land called pistachio that has two different types namely Siirt and Long type of pistachios by image processing methods and artificial neural networks. Photos of Siirt and long type of pistachios are taken by a Webcam with CCD sensor. These photos were converted to gray scale in Matlab. Afterwards, these photos were converted to binary photo format using Otsu’s Method. Then this data was used to train multi-layered neural network to complete grading.  Matlab was used for both image processing and artificial neural networks. Successes of the grading with image processing and artificial neural networks for mixed type pistachios Siirt and Long were researched.
Co-Authors Koklu, Murat Unlersen, Muhammed Fahri