Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 4 (2026): Volume 10, Issue 4, April 2026 | Pages: 134-143
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 13-04-2026
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).
CNN, machine learning, deep learning, vgg16, vgg19, fruit classification Iraq
Abdulqader Faris Abdulqader. (2026). A Comparative Study of Fruit Images Classification Using VGG16 and VGG19. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(4), 134-143. Article DOI https://doi.org/10.47001/IRJIET/2026.104019
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