Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables

Abstract

Food waste is a global concern and is the focus of this research. Currently, no method in the state of the art classifies multiple fruits and vegetables and their level of ripening. The objective of the study is to design and develop an intelligent system based on deep learning techniques to classify between types of fruits and vegetables, and also to evaluate the level of ripeness of some of them. The system consists of two models using the MobileNet V2 architecture. One algorithm is for the classification of 32 classes of fruits and vegetables, and another is for the determination of the ripeness of 6 classes of them. The overall intelligent system is the union of the two models, predicting first the class of fruit or vegetable and then its ripeness. The fruits and vegetables classification model achieved 97.86% accuracy, 98% precision, 98% recall, and 98% F1-score, while the ripeness assessment model achieved 100% accuracy, 98% precision, 99% recall, and 99% F1-score. According to the results, the proposed system is able to classify between types of fruits and vegetables and evaluate their ripeness. To achieve the best performance indicators, it is necessary to obtain the appropriate hyperparameters for the artificial intelligence models, in addition to having an extensive database with well-defined classes.

Keywords

• artificial intelligence
• computer vision
• deep learning
• food waste
• fruit
• ripeness detection