@inproceedings{86fad1865b3845b2b73fdfc270cae483,
title = "Artificially Ripeness Identification of Indonesian Banana Cultivar Using Convolution Neural Network",
abstract = "Detection of bananas that ripen naturally and artificially using digital images is quite difficult because they physically have almost the same characteristics. Feature identification requires very detailed characteristic information so that a diverse feature extraction process is needed, but not necessarily the extracted ones are the optimal features. In this study, the convolution neural network (CNN) method was used and the best features were learned automatically during the classification process. Two CNN-based methods, VGG based model and ResNet18, were tested to obtain the optimal identification. The results of testing on six Indonesian banana cultivars showed that VGG based model produced better accuracy of 88.67\%. However, ResNest18 achieved convergence faster.",
keywords = "artificial ripeness, banana, ResNet18, VGG based model",
author = "Candra Dewi and Shiryu Ueno and Kunihito Kato",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 8th International Conference on Sustainable Information Engineering and Technology, SIET 2023 ; Conference date: 24-10-2023 Through 25-10-2023",
year = "2023",
month = oct,
day = "24",
doi = "10.1145/3626641.3626668",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "107--111",
booktitle = "SIET 2023 - Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology",
}