Deep-learning-based defective bean inspection with GAN-structured automated labeled data augmentation in coffee industry

Yung Chien Chou, Cheng Ju Kuo, Tzu Ting Chen, Gwo Jiun Horng, Mao Yuan Pai, Mu En Wu, Yu Chuan Lin, Min Hsiung Hung, Wei Tsung Su, Yi Chung Chen, Ding Chau Wang, Chao Chun Chen

Research output: Contribution to journalArticle

Abstract

In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80%.

Original languageEnglish
Article number4166
JournalApplied Sciences (Switzerland)
Volume9
Issue number19
DOIs
Publication statusPublished - 2019 Oct 1

Fingerprint

coffee
Coffee
learning
inspection
Inspection
industries
labor
automation
augmentation
Labeling
marking
Automation
Personnel
Industry
robot arms
Robotic arms
Defects
agriculture
defects
Agriculture

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Cite this

Chou, Yung Chien ; Kuo, Cheng Ju ; Chen, Tzu Ting ; Horng, Gwo Jiun ; Pai, Mao Yuan ; Wu, Mu En ; Lin, Yu Chuan ; Hung, Min Hsiung ; Su, Wei Tsung ; Chen, Yi Chung ; Wang, Ding Chau ; Chen, Chao Chun. / Deep-learning-based defective bean inspection with GAN-structured automated labeled data augmentation in coffee industry. In: Applied Sciences (Switzerland). 2019 ; Vol. 9, No. 19.
@article{e64dc0217f0c4c23a9cff2068821eeb3,
title = "Deep-learning-based defective bean inspection with GAN-structured automated labeled data augmentation in coffee industry",
abstract = "In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80{\%}.",
author = "Chou, {Yung Chien} and Kuo, {Cheng Ju} and Chen, {Tzu Ting} and Horng, {Gwo Jiun} and Pai, {Mao Yuan} and Wu, {Mu En} and Lin, {Yu Chuan} and Hung, {Min Hsiung} and Su, {Wei Tsung} and Chen, {Yi Chung} and Wang, {Ding Chau} and Chen, {Chao Chun}",
year = "2019",
month = "10",
day = "1",
doi = "10.3390/app9194166",
language = "English",
volume = "9",
journal = "Applied Sciences (Switzerland)",
issn = "2076-3417",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "19",

}

Deep-learning-based defective bean inspection with GAN-structured automated labeled data augmentation in coffee industry. / Chou, Yung Chien; Kuo, Cheng Ju; Chen, Tzu Ting; Horng, Gwo Jiun; Pai, Mao Yuan; Wu, Mu En; Lin, Yu Chuan; Hung, Min Hsiung; Su, Wei Tsung; Chen, Yi Chung; Wang, Ding Chau; Chen, Chao Chun.

In: Applied Sciences (Switzerland), Vol. 9, No. 19, 4166, 01.10.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Deep-learning-based defective bean inspection with GAN-structured automated labeled data augmentation in coffee industry

AU - Chou, Yung Chien

AU - Kuo, Cheng Ju

AU - Chen, Tzu Ting

AU - Horng, Gwo Jiun

AU - Pai, Mao Yuan

AU - Wu, Mu En

AU - Lin, Yu Chuan

AU - Hung, Min Hsiung

AU - Su, Wei Tsung

AU - Chen, Yi Chung

AU - Wang, Ding Chau

AU - Chen, Chao Chun

PY - 2019/10/1

Y1 - 2019/10/1

N2 - In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80%.

AB - In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80%.

UR - http://www.scopus.com/inward/record.url?scp=85073267483&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073267483&partnerID=8YFLogxK

U2 - 10.3390/app9194166

DO - 10.3390/app9194166

M3 - Article

AN - SCOPUS:85073267483

VL - 9

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

SN - 2076-3417

IS - 19

M1 - 4166

ER -