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Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network
Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655355/ https://www.ncbi.nlm.nih.gov/pubmed/36360096 http://dx.doi.org/10.3390/foods11213483 |
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author | Nithya, R. Santhi, B. Manikandan, R. Rahimi, Masoumeh Gandomi, Amir H. |
author_facet | Nithya, R. Santhi, B. Manikandan, R. Rahimi, Masoumeh Gandomi, Amir H. |
author_sort | Nithya, R. |
collection | PubMed |
description | Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit’s surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%. |
format | Online Article Text |
id | pubmed-9655355 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96553552022-11-15 Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network Nithya, R. Santhi, B. Manikandan, R. Rahimi, Masoumeh Gandomi, Amir H. Foods Communication Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit’s surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%. MDPI 2022-11-02 /pmc/articles/PMC9655355/ /pubmed/36360096 http://dx.doi.org/10.3390/foods11213483 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 | Communication Nithya, R. Santhi, B. Manikandan, R. Rahimi, Masoumeh Gandomi, Amir H. Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network |
title | Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network |
title_full | Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network |
title_fullStr | Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network |
title_full_unstemmed | Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network |
title_short | Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network |
title_sort | computer vision system for mango fruit defect detection using deep convolutional neural network |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655355/ https://www.ncbi.nlm.nih.gov/pubmed/36360096 http://dx.doi.org/10.3390/foods11213483 |
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