A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features

Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and economy. High-quality fruits are more appealing, nutritious, safe, boosting consumer satisfaction revenue for producers. Artificial intelligence can aid assessing of fruit using images. This paper presents general machine learning model deep image features. leverages capabilities recent successful networks classification called vision transformers (ViT). The ViT built trained with combination various datasets taught to distinguish between good rotten images based on their visual appearance not predefined attributes. demonstrated impressive results accurately identifying fruits, such as apples (with 99.50% accuracy), cucumbers (99%), grapes (100%), kakis (99.50%), oranges papayas (98%), peaches tomatoes watermelons (98%). However, it showed slightly lower performance guavas (97%), lemons limes (97.50%), mangoes pears pomegranates (97%).