VGU RESEARCH REPOSITORY
Please use this identifier to cite or link to this item:
https://epub.vgu.edu.vn/handle/dlibvgu/1865| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Phan Chi Tho | en_US |
| dc.date.accessioned | 2024-11-15T04:38:48Z | - |
| dc.date.available | 2024-11-15T04:38:48Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.uri | https://epub.vgu.edu.vn/handle/dlibvgu/1865 | - |
| dc.description.abstract | The research investigates various popular classification models, comparing their performance against a proposed custom Convolutional Neural Network (CNN) for classifying coffee fruit ripeness. Additionally, the latest object detection models, including YOLOv7, YOLOv8, and RT-DETR, are evaluated for their effectiveness in detecting coffee fruit. Comparative analysis shows that the proposed CNN achieves the highest classification accuracy of 92.58%, while YOLOv8 stands out as the most well-rounded model for object detection, with a mAP@50 score of 92.47%. This thesis demonstrates the potential for deep learning models, particularly CNNs, YOLO, and Transformer to significantly improve coffee harvesting efficiency. Future work should focus on real-world testing and optimizing models for practical deployment in agricultural settings. | en_US |
| dc.language.iso | en | en_US |
| dc.rights | Attribution-NonCommercial 4.0 International | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.subject | Object detection | en_US |
| dc.subject | Computer vision system | en_US |
| dc.subject | Coffee fruit | en_US |
| dc.subject | Smart agriculture | en_US |
| dc.title | Improving coffee harvest efficiency a deep learning approach to coffee fruit maturity detection | en_US |
| dc.type | Thesis | en_US |
| item.grantfulltext | restricted | - |
| item.fulltext | With Fulltext | - |
| item.languageiso639-1 | other | - |
| Appears in Collections: | Computer Science (CS) | |
Files in This Item:
| File | Description | Size | Format | Existing users please Login |
|---|---|---|---|---|
| Improving coffee harvest efficiency_ A deep learning approach to coffee fruit maturity detection.pdf | 31.26 MB | Adobe PDF |
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