Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3950
Title: Convolutional neural network-based apple images classification and image quality measurement by light colors using the color-balancing approach
Authors: Büyükarıkan, Birkan
Ülker, Erkan
Keywords: Convolutional neural network
Color balancing
Classification
Light colors
Image quality
Constancy
Issue Date: 2023
Publisher: Springer
Abstract: The appearance of an object is affected by the color and quality of the light on the surface and the location of the lighting source. Color-balancing methods can solve the problems caused by light changes. Color-balancing models increase the visibility of the image by changing color and clarity. The study aims to examine the images of physiological disorders in apples' classification performances of images in different light colors with color-balancing models with pre-trained CNN models. Physiological disorders were classified with 0.949 accuracies in the ResNet50V2 model and sharpness data set in the green light color. With the proposed approaches, there was an increase in performance compared to the original data set. The best success in all light colors is in the sharpness data set type. In addition, the quality of the images was measured using MSE, PSNR, and SSIM. PSNR increased in the warm and cold white sharpness data set type and green light CLAHE data set type. Finally, experimental studies have shown that color balancing significantly affects classification success.
URI: https://doi.org/10.1007/s00530-023-01084-z
https://hdl.handle.net/20.500.13091/3950
ISSN: 0942-4962
1432-1882
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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