Examining the Difference Between Image Size, Background Color, Gray Picture and Color Picture in Leave Classification with Deep Learning

Abstract

In academic studies, there are many factors that change depending on the changes in the parameters of the process, such as the processing time, the required processing power, as well as the success. In the methods used for classification, recognition, and detection, the changes in the data received as input may affect the result, as well as the variables specific to the methods used. Convolutional neural networks, whose use is increasing day by day in processes such as classification and recognition using images, learn and classify the characteristics of data sets in different image sizes, including color, gray or black and white images, with filters and functions on the layers in the model. Many different parameters such as layers in the created model and filters and functions in these layers can be changed. As a result of these changes, the most suitable number of layers, the optimum values for the parameters and functions in these layers are determined for the data set used. There are studies focused on optimizing many different structures, such as reproducing the images in the used data set or determining the best by testing different parameters in the classification method. In this study, while the changes were made in the leaf images with a fixed background in the determined leaf data set, the model used in leaf classification with convolutional neural network was kept constant. It is aimed to examine the pictures used for 3 different image sizes, the gray picture or color picture difference and the changes caused by the background color.