Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network

Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, t...

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Autores principales:	Nguyen Minh Trieu, Nguyen Truong Thinh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dragon fruit fruits AI SVM CNN sorting Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/762570380c7b4262b6dafec0263d33b8
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spelling	oai:doaj.org-article:762570380c7b4262b6dafec0263d33b82021-11-25T16:31:28ZQuality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network10.3390/app1122105582076-3417https://doaj.org/article/762570380c7b4262b6dafec0263d33b82021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10558https://doaj.org/toc/2076-3417Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, the classification of dragon fruit is carried manually, lead to low-quality classification high labor costs. Therefore, this study describes an automatic dragon fruit classifying system using non-destructive measurements, based on a convolutional neural network (CNN). This classifying system uses a combination of a model of machine learning and image processing using a convolutional neural network to identify the external features of dragon fruits; the fruits are then classified and evaluated by groups. The dragon fruit is recognized by the system, which extracts the objects combined with the signal obtained from the loadcell to calculate and determine dragon fruit in each group. The training data are collected from the dragon fruit processing system, with a dataset of images obtained from more than 1287 dragon fruits, to train the model. In this system, the classification of the processing speed and accuracy are the two most important factors. The results show that the classification system achieves high efficiency. The system is effective with existing dragon fruit types. In Vietnamese factories, the processing speed of the system increases the sorting capacity of export packing facilities to six times higher than that of the manual method, with an accuracy of more than 96%.Nguyen Minh TrieuNguyen Truong ThinhMDPI AGarticledragon fruitfruitsAISVMCNNsortingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10558, p 10558 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dragon fruit fruits AI SVM CNN sorting Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	dragon fruit fruits AI SVM CNN sorting Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Nguyen Minh Trieu Nguyen Truong Thinh Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network
description	Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, the classification of dragon fruit is carried manually, lead to low-quality classification high labor costs. Therefore, this study describes an automatic dragon fruit classifying system using non-destructive measurements, based on a convolutional neural network (CNN). This classifying system uses a combination of a model of machine learning and image processing using a convolutional neural network to identify the external features of dragon fruits; the fruits are then classified and evaluated by groups. The dragon fruit is recognized by the system, which extracts the objects combined with the signal obtained from the loadcell to calculate and determine dragon fruit in each group. The training data are collected from the dragon fruit processing system, with a dataset of images obtained from more than 1287 dragon fruits, to train the model. In this system, the classification of the processing speed and accuracy are the two most important factors. The results show that the classification system achieves high efficiency. The system is effective with existing dragon fruit types. In Vietnamese factories, the processing speed of the system increases the sorting capacity of export packing facilities to six times higher than that of the manual method, with an accuracy of more than 96%.
format	article
author	Nguyen Minh Trieu Nguyen Truong Thinh
author_facet	Nguyen Minh Trieu Nguyen Truong Thinh
author_sort	Nguyen Minh Trieu
title	Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network
title_short	Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network
title_full	Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network
title_fullStr	Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network
title_full_unstemmed	Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network
title_sort	quality classification of dragon fruits based on external performance using a convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/762570380c7b4262b6dafec0263d33b8
work_keys_str_mv	AT nguyenminhtrieu qualityclassificationofdragonfruitsbasedonexternalperformanceusingaconvolutionalneuralnetwork AT nguyentruongthinh qualityclassificationofdragonfruitsbasedonexternalperformanceusingaconvolutionalneuralnetwork
_version_	1718413144230985728

Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network

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