# 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. artificial intelligence; computer vision; deep learning; food waste; fruit; ripeness detection; vegetables +
Footnote †: journal: Medical Imaging  
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**Citation:** [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables. **Applid**  
**Citation:** [PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON], [PERSON]; [PERSON] [PERSON] Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables. **Appl. Sci. 2023**, 13, 12504. [[https://doi.org/10.3390/app132212504](https://doi.org/10.3390/app132212504)]([https://doi.org/10.3390/app132212504](https://doi.org/10.3390/app132212504))  
**Cognition:** O 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. is high [1]. Microbiological changes are caused by microbiological organisms involved in ripening. Generally, these are bacteria, viruses, and fungi, but sometimes they accelerate the ripening process, since the concentration may be higher than it should be [2]. The ripening of fruits and vegetables is genetically programmed and therefore these types of changes are given in a progressive order. The variables involved in these processes are tissue softening, carbohydrates, pigmentation, pectin, organic acids, and volatiles [3]. Artificial Neural Networks (ANNs) are widely used in the food industry, but specifically for Convolutional Neural Networks (CNNs), one of the limitations is the lack of a substantial dataset to perform their training [4]. The detection of fruits and vegetables using AI is a topic of great interest today. A review of the state of the art shows that there are numerous works focused on the development of classification algorithms and neural networks for the identification of different types of fruits. These techniques are based on computer vision and range from binary to hyperspectral images [5; 6; 7]. In computer vision, the hyperspectral systems combine spectral information for each pixel [8]. Cutting-edge deep learning techniques have been applied to fruit and vegetable classification. In two previous studies [9; 10], researchers have used Faster R-CNN to develop fruit recognition systems that can be used in harvesting, smart farming, and packaging.