Affiliation:
1. Department of Medical Physics, School of Medicine, University of Patras, 26504 Rio, Greece
2. Department of Electrical and Computer Technology Engineering, University of Patras, 26504 Rio, Greece
3. Department of Computer Engineering and Informatics, University of Patras, 26504 Rio, Greece
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 fruit using images. This paper presents a general machine learning model for assessing fruit quality using deep image features. This 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 taught to distinguish between good and rotten fruit images based on their visual appearance and not predefined quality attributes. 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%).
Subject
Industrial and Manufacturing Engineering
Reference52 articles.
1. Prasad, K., Jacob, S., and Siddiqui, M.W. (2018). Preharvest Modulation of Postharvest Fruit and Vegetable Quality, Elsevier.
2. Recent application of imaging techniques for fruit quality assessment;Pathmanaban;Trends Food Sci. Technol.,2019
3. Consumer behaviour and fruit quality: Supply chain management in an emerging industry;Mowat;Supply Chain Manag. Int. J.,2000
4. Application of Deep Learning in Food: A Review;Zhou;Compr. Rev. Food Sci. Food Saf.,2019
5. Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution;Melesse;Procedia Comput. Sci.,2022
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