VGU RESEARCH REPOSITORY
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https://epub.vgu.edu.vn/handle/dlibvgu/1865| Title: | Improving coffee harvest efficiency a deep learning approach to coffee fruit maturity detection | Authors: | Phan Chi Tho | Keywords: | Object detection;Computer vision system;Coffee fruit;Smart agriculture | Issue Date: | 2024 | 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. |
URI(1): | https://epub.vgu.edu.vn/handle/dlibvgu/1865 | Rights: | Attribution-NonCommercial 4.0 International |
| Appears in Collections: | Computer Science (CS) |
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| File | Description | Size | Format | Existing users please Login |
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| Improving coffee harvest efficiency_ A deep learning approach to coffee fruit maturity detection.pdf | 31.26 MB | Adobe PDF |
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