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Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market pri...

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Autores principales: Mukhiddinov, Mukhriddin, Muminov, Azamjon, Cho, Jinsoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9653939/
https://www.ncbi.nlm.nih.gov/pubmed/36365888
http://dx.doi.org/10.3390/s22218192
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author Mukhiddinov, Mukhriddin
Muminov, Azamjon
Cho, Jinsoo
author_facet Mukhiddinov, Mukhriddin
Muminov, Azamjon
Cho, Jinsoo
author_sort Mukhiddinov, Mukhriddin
collection PubMed
description Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.
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spelling pubmed-96539392022-11-15 Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning Mukhiddinov, Mukhriddin Muminov, Azamjon Cho, Jinsoo Sensors (Basel) Article Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning. MDPI 2022-10-26 /pmc/articles/PMC9653939/ /pubmed/36365888 http://dx.doi.org/10.3390/s22218192 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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.org/licenses/by/4.0/).
spellingShingle Article
Mukhiddinov, Mukhriddin
Muminov, Azamjon
Cho, Jinsoo
Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning
title Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning
title_full Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning
title_fullStr Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning
title_full_unstemmed Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning
title_short Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning
title_sort improved classification approach for fruits and vegetables freshness based on deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9653939/
https://www.ncbi.nlm.nih.gov/pubmed/36365888
http://dx.doi.org/10.3390/s22218192
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