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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: 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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