Defect green coffee bean detection using image recognition and supervised learning
Addressing the quality of green coffee bean is an important process to define its quality and market price for any industry that processing it. Normally, the evaluation that is carried out in determining the quality of green coffee is by visual inspection where it has limitations, and it is prone to...
Saved in:
| Main Author: | |
|---|---|
| Format: | Academic Exercise |
| Language: | en en |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/33344/1/DEFECT%20GREEN%20COFFEE%20BEAN%20DETECTION%20USING%20IMAGE%20RECOGNITION%20AND%20SUPERVISED%20LEARNING.24pages.pdf https://eprints.ums.edu.my/id/eprint/33344/2/DEFECT%20GREEN%20COFFEE%20BEAN%20DETECTION%20USING%20IMAGE%20RECOGNITION%20AND%20SUPERVISED%20LEARNING.pdf https://eprints.ums.edu.my/id/eprint/33344/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Addressing the quality of green coffee bean is an important process to define its quality and market price for any industry that processing it. Normally, the evaluation that is carried out in determining the quality of green coffee is by visual inspection where it has limitations, and it is prone to error. Therefore, in this research project, the process will be conducted by using an image classifier with the model of a machine learning algorithm which the candidates comprise of Support Vector Machine, k-Nearest Neighbour and Decision Tree. k-nearest neighbour has the highest F1-score (0.51) than the other two algorithms (Support Vector Machine: 0.50, and Decision Tree: 0.48). The model was integrated as web application with Flask where user can upload the image and the system will return result with precision and prediction. This integrated web application is tested with functionality test and integration test which it succeeded both successfully fulfilling each criterion tested. |
|---|