International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

A Comparative Study of Fruit Images Classification Using VGG16 and VGG19

Abstract

Fruit classification is important topic, it is used in fruit industry and supermarket. It is reduced time and workers' effort in marketing. The results of previous studies explained that vgg16 and vgg19 models are outperform other CNNs models (Alexnet, resident50 and googlenet) in fruit classification. Fruit images dataset is used. It is contained 1000 images divided into five classes they are banana, grape, apple, mango and strawberry. Each class has 200 images. The study highlights how to modifying them by fine tuning their hyper parameters. The results showed that vgg16 is out performing than vgg19 after modified. Because it has accuracy of 0.92% and complexity of 122 million floating point operations (FLOP), where vgg19 has accuracy of 0.88% and complexity of 137 million million floating point operations (FLOP). 

Country : Iraq

1 Abdulqader Faris Abdulqader

  1. Department of Pharmacy, Al-Noor University, Nineveh, Iraq

IRJIET, Volume 10, Issue 4, April 2026 pp. 134-143

doi.org/10.47001/IRJIET/2026.104019

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