Detection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting

Automatic grading method of pomelo fruit according to the shape and size is urgently needed in the industry since the work mainly depends on artificial judgment currently. In this research, a method, which detected the vertical and horizontal size of pomelo by using contour coordinate transformation...

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Autores principales:	LI Yan, SHEN Jie, XIE Hang, GAO Guangyin, LIU Jianxiong, LIU Jie
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	pomelo contour fruit shape detection back propagation neural network coordinate system conversion image processing fruit shape discriminant model Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/2efb31e0909f4b9bba221b3a0566c27d
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spelling	oai:doaj.org-article:2efb31e0909f4b9bba221b3a0566c27d2021-11-17T07:52:00ZDetection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting2096-809410.12133/j.smartag.2021.3.1.202102-SA007https://doaj.org/article/2efb31e0909f4b9bba221b3a0566c27d2021-03-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-1-86.shtmlhttps://doaj.org/toc/2096-8094Automatic grading method of pomelo fruit according to the shape and size is urgently needed in the industry since the work mainly depends on artificial judgment currently. In this research, a method, which detected the vertical and horizontal size of pomelo by using contour coordinate transformation fitting, fruit shape feature extraction and direction angle compensation algorithm, while it determined the shape defects based on fruit shape index, was proposed. The image acquisition system was self-designed and built up with a CMOS camera, a dot matrix LED light source, a plane mirror, the computer, a box and brackets. The image data containing whole surface information of Shatian pomelo samples with different sizes and shapes were collected by this system. The G-B component grayscale image was chosen for denoising and segmentation. The Laplacian edge detection algorithm was implemented to extract the edge pixels of the fruit. The polynomial fitting method was applied to converse the rectangular coordinates to polar coordinates so that the fruit shape description was simplified. The characteristic point polar angle value was used to compensate the random direction of the vertical and horizontal diameters of the sample. Then the vertical and horizontal diameters of fruit were calculated after classifying the sample shapes into the spherical and the pear-like categories. For the involved 168 pomelo samples, the average error, maximum absolute error and average relative error of the vertical diameters were 2.23 mm, 7.39 mm and 1.6% respectively, while these parameters of the horizontal diameters were 2.21 mm, 7.66 mm and 1.4% respectively. The fruit shape discriminant model was established by using BP neural network algorithm based on the seven features extracted from the fitting function and verified by independent validation set including 3 peak heights, 3 peak widths and 1 trough value difference. The total recognition rate of shape identification was 83.7%. The results illustrated that the method had the potential to measuring the pomelo size and shape for grading fast and non-destructively.LI YanSHEN JieXIE HangGAO GuangyinLIU JianxiongLIU JieEditorial Office of Smart Agriculturearticlepomelo contourfruit shape detectionback propagation neural networkcoordinate system conversionimage processingfruit shape discriminant modelAgriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 1, Pp 86-95 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	pomelo contour fruit shape detection back propagation neural network coordinate system conversion image processing fruit shape discriminant model Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	pomelo contour fruit shape detection back propagation neural network coordinate system conversion image processing fruit shape discriminant model Agriculture (General) S1-972 Technology (General) T1-995 LI Yan SHEN Jie XIE Hang GAO Guangyin LIU Jianxiong LIU Jie Detection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting
description	Automatic grading method of pomelo fruit according to the shape and size is urgently needed in the industry since the work mainly depends on artificial judgment currently. In this research, a method, which detected the vertical and horizontal size of pomelo by using contour coordinate transformation fitting, fruit shape feature extraction and direction angle compensation algorithm, while it determined the shape defects based on fruit shape index, was proposed. The image acquisition system was self-designed and built up with a CMOS camera, a dot matrix LED light source, a plane mirror, the computer, a box and brackets. The image data containing whole surface information of Shatian pomelo samples with different sizes and shapes were collected by this system. The G-B component grayscale image was chosen for denoising and segmentation. The Laplacian edge detection algorithm was implemented to extract the edge pixels of the fruit. The polynomial fitting method was applied to converse the rectangular coordinates to polar coordinates so that the fruit shape description was simplified. The characteristic point polar angle value was used to compensate the random direction of the vertical and horizontal diameters of the sample. Then the vertical and horizontal diameters of fruit were calculated after classifying the sample shapes into the spherical and the pear-like categories. For the involved 168 pomelo samples, the average error, maximum absolute error and average relative error of the vertical diameters were 2.23 mm, 7.39 mm and 1.6% respectively, while these parameters of the horizontal diameters were 2.21 mm, 7.66 mm and 1.4% respectively. The fruit shape discriminant model was established by using BP neural network algorithm based on the seven features extracted from the fitting function and verified by independent validation set including 3 peak heights, 3 peak widths and 1 trough value difference. The total recognition rate of shape identification was 83.7%. The results illustrated that the method had the potential to measuring the pomelo size and shape for grading fast and non-destructively.
format	article
author	LI Yan SHEN Jie XIE Hang GAO Guangyin LIU Jianxiong LIU Jie
author_facet	LI Yan SHEN Jie XIE Hang GAO Guangyin LIU Jianxiong LIU Jie
author_sort	LI Yan
title	Detection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting
title_short	Detection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting
title_full	Detection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting
title_fullStr	Detection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting
title_full_unstemmed	Detection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting
title_sort	detection and grading method of pomelo shape based on contour coordinate transformation and fitting
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/2efb31e0909f4b9bba221b3a0566c27d
work_keys_str_mv	AT liyan detectionandgradingmethodofpomeloshapebasedoncontourcoordinatetransformationandfitting AT shenjie detectionandgradingmethodofpomeloshapebasedoncontourcoordinatetransformationandfitting AT xiehang detectionandgradingmethodofpomeloshapebasedoncontourcoordinatetransformationandfitting AT gaoguangyin detectionandgradingmethodofpomeloshapebasedoncontourcoordinatetransformationandfitting AT liujianxiong detectionandgradingmethodofpomeloshapebasedoncontourcoordinatetransformationandfitting AT liujie detectionandgradingmethodofpomeloshapebasedoncontourcoordinatetransformationandfitting
_version_	1718425850902216704

Detection and Grading Method of Pomelo Shape Based on Contour Coordinate Transformation and Fitting

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