Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Advanced Chemical Sensor Technologies · Date Palm Research Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory of Artificial Vision and Thermography/Mechatronics, Faculty of Engineering, Autonomous University of Queretaro, Campus San Juan del Rio, San Juan del Rio 76807, Queretaro, Mexico
Artificial Intelligence Systems Applied to Biomedical and Mechanical Models, Faculty of Engineering, Autonomous University of Queretaro, Campus San Juan del Rio, San Juan del Rio 76807, Queretaro, Mexico
Faculty of Engineering, Autonomous University of Queretaro, Cerro de las Campanas S/N, Santiago de Queretaro 76010, Queretaro, Mexico
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