Fruit Defect Detection Using CNN Models with Real and Virtual Data

Renzo Pacheco, Paula González, Luis Chuquimarca, Luis Chuquimarca, Boris Vintimilla, Sergio Velastin, Sergio Velastin

2023

Abstract

The present study seeks to evaluate different CNN models in order to compare their performance in recognizing a range of defects in apples and mangoes to ensure the quality of the production of these foods. Using the CNN models, InceptionV3, MobileNetV2, VGG16 and DenseNet121, which were trained with a dataset of real and synthetic images of apples and mangoes covering fruit in acceptable quality condition and with defects: rot, bruises, scabs and black spots. Training was performed with variations on the hyper-parameters and the metric is accuracy. The MobileNetV2 model achieved the highest accuracy in training and testing, obtaining 97.50% for apples and 92.50% for mangoes, making it the most suitable model for defect detection in these fruits. The InceptionV3 and DenseNet121 models presented accuracy values above 90%, while the VGG16 model obtained the poorest performance by not exceeding 80% accuracy for any of the fruits. The trained models, especially MobileNetV2, are capable of recognizing a range of defects in the fruits under study with a high degree of accuracy and are suitable for use in the development of automation applications for the quality assessment process of apples and mangoes.

Download


Paper Citation


in Harvard Style

Pacheco R., González P., Chuquimarca L., Vintimilla B. and Velastin S. (2023). Fruit Defect Detection Using CNN Models with Real and Virtual Data. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 272-279. DOI: 10.5220/0011679800003417


in Bibtex Style

@conference{visapp23,
author={Renzo Pacheco and Paula González and Luis Chuquimarca and Boris Vintimilla and Sergio Velastin},
title={Fruit Defect Detection Using CNN Models with Real and Virtual Data},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={272-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011679800003417},
isbn={978-989-758-634-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Fruit Defect Detection Using CNN Models with Real and Virtual Data
SN - 978-989-758-634-7
AU - Pacheco R.
AU - González P.
AU - Chuquimarca L.
AU - Vintimilla B.
AU - Velastin S.
PY - 2023
SP - 272
EP - 279
DO - 10.5220/0011679800003417
PB - SciTePress