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About Chapter 1 PDF
This chapter introduces the background and objectives of developing a deep convolutional neural network classifier to detect physical defects in green coffee beans. Specifically, it will aim to prepare an image dataset of different coffee bean defects, train a CNN model using the dataset, implement the trained model as a computer-based classifier, and test/evaluate the classifier's performance. The significance of the study is that it could help improve the traditional manual sorting process which is time-consuming and inconsistent, and assist coffee farmers through a more accurate classification system. The scope is limited to Liberica coffee beans and uses pre-trained CNN models with transfer learning to classify beans as broken, sour, pea berry, blacked, or none.
Detailed Information
| Author: | ['Dennis Kent Manuel'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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