Version 1
: Received: 20 July 2023 / Approved: 21 July 2023 / Online: 24 July 2023 (03:09:22 CEST)
How to cite:
Apostolopoulos, I. D.; Tzani, M.; Aznaouridis, S. A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features. Preprints2023, 2023071552. https://doi.org/10.20944/preprints202307.1552.v1
Apostolopoulos, I. D.; Tzani, M.; Aznaouridis, S. A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features. Preprints 2023, 2023071552. https://doi.org/10.20944/preprints202307.1552.v1
Apostolopoulos, I. D.; Tzani, M.; Aznaouridis, S. A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features. Preprints2023, 2023071552. https://doi.org/10.20944/preprints202307.1552.v1
APA Style
Apostolopoulos, I. D., Tzani, M., & Aznaouridis, S. (2023). A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features. Preprints. https://doi.org/10.20944/preprints202307.1552.v1
Chicago/Turabian Style
Apostolopoulos, I. D., Mpesi Tzani and Sokratis Aznaouridis. 2023 "A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features" Preprints. https://doi.org/10.20944/preprints202307.1552.v1
Abstract
Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial Intelligence can aid in assessing the quality of the fruit using images. This paper presents a general machine-learning model for assessing fruit quality using deep image features. The model leverages the learning capabilities of the recent successful networks for image classification called Vision Transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and learned to distinguish between good and rotten fruit images. The general model demonstrated impressive results in accurately identifying the quality of various fruits such as Apples (with a 99.50% accuracy), Cucumbers (99%), Grapes (100%), Kakis (99.50%), Oranges (99.50%), Papayas (98%), Peaches (98%), Tomatoes (99.50%), and Watermelons (98%). However, it showed slightly lower performance in identifying Guavas (97%), Lemons (97%), Limes (97.50%), mangoes (97.50%), Pears (97%), and Pomegranates (97%).
Keywords
Fruit Quality; Machine Learning; Deep Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
