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 language | English |
|---|---|
| Article number | 4166 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 9 |
| Issue number | 19 |
| DOIs | |
| Publication status | Published - 2019 Oct 1 |
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All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Instrumentation
- Engineering(all)
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes
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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 journal › Article
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 -