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An automated cucumber inspection system based on neural network

Precision agriculture and smart farming have been gaining importance in recent years due to the coupled breakthrough of deep learning algorithms in machine vision. This paper aims to develop an end-to-end automatic agricultural food grading system based on its visual appearance. The target object co...

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Main Authors: Gan, Yee Siang, Luo, Shi Hao, Li, Chih Hsueh, Chung, Shih Wei, Liong, Sze Teng, Tan, Lit Ken
Format: Article
Published: John Wiley and Sons Inc 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/103221/
http://dx.doi.org/10.1111/jfpe.14069
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spelling my.utm.1032212023-10-24T09:48:53Z http://eprints.utm.my/103221/ An automated cucumber inspection system based on neural network Gan, Yee Siang Luo, Shi Hao Li, Chih Hsueh Chung, Shih Wei Liong, Sze Teng Tan, Lit Ken T Technology (General) Precision agriculture and smart farming have been gaining importance in recent years due to the coupled breakthrough of deep learning algorithms in machine vision. This paper aims to develop an end-to-end automatic agricultural food grading system based on its visual appearance. The target object considered herein is cucumber as it is one of the vegetables that can be grown in many countries around the world. Particularly, the developed system incorporates both the software and hardware components, in which the geometric properties of a moving cucumber on a conveyor belt can be computed. Concretely, an industrial camera is employed to capture the image of a cucumber. Then, three individual detection systems that perform the cucumber identification, geometry properties approximation, and defect detection, are designed. Finally, if the cucumber is found defective, the PLC motor control will be activated to separate the cucumber into an alternative container. As a result, the proposed algorithms yield promising performances when experimenting on a self-collected data set, namely “Cuc-70” that consists of a total of 4620 images. The cucumber identification generates an average WIoU of 93%, volume approximation accuracy of 98%, and defect detection WIoU of 92%. In addition, comprehensive analysis is conducted in order to validate the robustness of the proposed system and the compelling performance executed can be evidenced from the quantitative and qualitative results reported. In the future, this system can be integrated into online automatic sorting and grading for effective manufacturing and production. John Wiley and Sons Inc 2022 Article PeerReviewed Gan, Yee Siang and Luo, Shi Hao and Li, Chih Hsueh and Chung, Shih Wei and Liong, Sze Teng and Tan, Lit Ken (2022) An automated cucumber inspection system based on neural network. Journal of Food Process Engineering, 45 (9). n/a. ISSN 0145-8876 http://dx.doi.org/10.1111/jfpe.14069 DOI: 10.1111/jfpe.14069
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Gan, Yee Siang
Luo, Shi Hao
Li, Chih Hsueh
Chung, Shih Wei
Liong, Sze Teng
Tan, Lit Ken
An automated cucumber inspection system based on neural network
description Precision agriculture and smart farming have been gaining importance in recent years due to the coupled breakthrough of deep learning algorithms in machine vision. This paper aims to develop an end-to-end automatic agricultural food grading system based on its visual appearance. The target object considered herein is cucumber as it is one of the vegetables that can be grown in many countries around the world. Particularly, the developed system incorporates both the software and hardware components, in which the geometric properties of a moving cucumber on a conveyor belt can be computed. Concretely, an industrial camera is employed to capture the image of a cucumber. Then, three individual detection systems that perform the cucumber identification, geometry properties approximation, and defect detection, are designed. Finally, if the cucumber is found defective, the PLC motor control will be activated to separate the cucumber into an alternative container. As a result, the proposed algorithms yield promising performances when experimenting on a self-collected data set, namely “Cuc-70” that consists of a total of 4620 images. The cucumber identification generates an average WIoU of 93%, volume approximation accuracy of 98%, and defect detection WIoU of 92%. In addition, comprehensive analysis is conducted in order to validate the robustness of the proposed system and the compelling performance executed can be evidenced from the quantitative and qualitative results reported. In the future, this system can be integrated into online automatic sorting and grading for effective manufacturing and production.
format Article
author Gan, Yee Siang
Luo, Shi Hao
Li, Chih Hsueh
Chung, Shih Wei
Liong, Sze Teng
Tan, Lit Ken
author_facet Gan, Yee Siang
Luo, Shi Hao
Li, Chih Hsueh
Chung, Shih Wei
Liong, Sze Teng
Tan, Lit Ken
author_sort Gan, Yee Siang
title An automated cucumber inspection system based on neural network
title_short An automated cucumber inspection system based on neural network
title_full An automated cucumber inspection system based on neural network
title_fullStr An automated cucumber inspection system based on neural network
title_full_unstemmed An automated cucumber inspection system based on neural network
title_sort automated cucumber inspection system based on neural network
publisher John Wiley and Sons Inc
publishDate 2022
url http://eprints.utm.my/103221/
http://dx.doi.org/10.1111/jfpe.14069
_version_ 1781777664131989504
score 13.211869

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