Apple quality identification and classification by image processing based on convolutional neural networks

Abstract This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on convolutional neural networks (CNN) which aimed at accurate...

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Autores principales:	Yanfei Li, Xianying Feng, Yandong Liu, Xingchang Han
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4efa033dc89d4bc28309ec3df5b0aad8
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spelling	oai:doaj.org-article:4efa033dc89d4bc28309ec3df5b0aad82021-12-02T16:45:54ZApple quality identification and classification by image processing based on convolutional neural networks10.1038/s41598-021-96103-22045-2322https://doaj.org/article/4efa033dc89d4bc28309ec3df5b0aad82021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96103-2https://doaj.org/toc/2045-2322Abstract This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on convolutional neural networks (CNN) which aimed at accurate and fast grading of apple quality. Specific, complex, and useful image characteristics for detection and classification were captured by the proposed model. Compared with existing methods, the proposed model could better learn high-order features of two adjacent layers that were not in the same channel but were very related. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. The overall accuracy of the proposed model tested using an independent 300 apple dataset was 95.33%. The results showed that the training accuracy, overall test accuracy and training time of the proposed model were better than Google Inception v3 model and traditional imaging process method based on histogram of oriented gradient (HOG), gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The proposed model has great potential in Apple’s quality detection and classification.Yanfei LiXianying FengYandong LiuXingchang HanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yanfei Li Xianying Feng Yandong Liu Xingchang Han Apple quality identification and classification by image processing based on convolutional neural networks
description	Abstract This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on convolutional neural networks (CNN) which aimed at accurate and fast grading of apple quality. Specific, complex, and useful image characteristics for detection and classification were captured by the proposed model. Compared with existing methods, the proposed model could better learn high-order features of two adjacent layers that were not in the same channel but were very related. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. The overall accuracy of the proposed model tested using an independent 300 apple dataset was 95.33%. The results showed that the training accuracy, overall test accuracy and training time of the proposed model were better than Google Inception v3 model and traditional imaging process method based on histogram of oriented gradient (HOG), gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The proposed model has great potential in Apple’s quality detection and classification.
format	article
author	Yanfei Li Xianying Feng Yandong Liu Xingchang Han
author_facet	Yanfei Li Xianying Feng Yandong Liu Xingchang Han
author_sort	Yanfei Li
title	Apple quality identification and classification by image processing based on convolutional neural networks
title_short	Apple quality identification and classification by image processing based on convolutional neural networks
title_full	Apple quality identification and classification by image processing based on convolutional neural networks
title_fullStr	Apple quality identification and classification by image processing based on convolutional neural networks
title_full_unstemmed	Apple quality identification and classification by image processing based on convolutional neural networks
title_sort	apple quality identification and classification by image processing based on convolutional neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4efa033dc89d4bc28309ec3df5b0aad8
work_keys_str_mv	AT yanfeili applequalityidentificationandclassificationbyimageprocessingbasedonconvolutionalneuralnetworks AT xianyingfeng applequalityidentificationandclassificationbyimageprocessingbasedonconvolutionalneuralnetworks AT yandongliu applequalityidentificationandclassificationbyimageprocessingbasedonconvolutionalneuralnetworks AT xingchanghan applequalityidentificationandclassificationbyimageprocessingbasedonconvolutionalneuralnetworks
_version_	1718383451965489152

Apple quality identification and classification by image processing based on convolutional neural networks

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