@inproceedings{278b4a1846ef48d6813ad083f64c1477,
title = "Classifying Olive Fruits Based on Produced Oil Quality: A Benchmark Dataset and Strong Baselines",
abstract = "Obtaining the highest quality olive oil (OO) during the milling process is greatly desirable. Since the quality of the produced oil depends mainly on the olive fruits (OF), it is important to manually check each batch of OF before milling them in addition to performing lab tests to verify the quality of the produced OO. The goal of this work is to automate the process of classifying OF based on whether they produce extra virgin OO (EVOO) or not. We collect a large dataset of more than 11K OF images and label them as positive/negative based on whether they produced EVOO or not. We then fine-tune several state-of-the-art deep learning models on this dataset. The results show that most pretrained models are very accurate for this dataset leading the suggestion that we use the most efficient one.",
keywords = "Deep Learning, Extra Virgin Olive Oil, Olive Fruit Classification, Transfer Learning",
author = "Mahmoud Ghandour and Raffi Al-Qurran and Mahmoud Al-Ayyoub and Ali Shatnawi and Mohammad Alsmirat and Fumie Costen",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th International Conference on Information and Communication Systems, ICICS 2021 ; Conference date: 24-05-2021 Through 26-05-2021",
year = "2021",
month = may,
day = "24",
doi = "10.1109/ICICS52457.2021.9464577",
language = "English",
series = "2021 12th International Conference on Information and Communication Systems, ICICS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "495--501",
editor = "Mohammad Alsmirat and Abdallah Almaaitah and Yaser Jararweh and Mauri, \{Jaime Lloret\}",
booktitle = "2021 12th International Conference on Information and Communication Systems, ICICS 2021",
address = "United States",
}