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Development of an automated fruit classification system by using computer vision and deep learning
Thi-Thoa Mac1, Huy-Anh Bui2, Duc-Vinh Pham1
1School of Mechanical Engineering, Hanoi University of Science and Technology, No 1 Dai Co Viet, Hanoi, Viet Nam.
Hardwarex
|April 30, 2026
Summary
This study introduces an automated fruit classification and warehouse system using YOLOv8 for Industry 4.0. The integrated system achieves high accuracy in fruit identification and optimizes warehouse operations.
Area of Science:
- Computer Vision and Deep Learning
- Industrial Automation
- Smart Warehouse Systems
Background:
- Industry 4.0 drives demand for advanced automation in manufacturing.
- Computer vision and deep learning are crucial for accurate industrial classification and quality inspection.
- Smart warehouses enhance efficiency in product storage and logistics.
Purpose of the Study:
- To develop an automated import/export system for industrial applications.
- To integrate the YOLOv8 network for precise fruit classification (kumquats, longans, cherry tomatoes).
- To implement a smart warehouse system for optimized storage and real-time data tracking.
Main Methods:
- Utilized the YOLOv8 deep learning network for object detection and classification.
- Developed an automated warehouse system for fruit handling.
- Integrated a personal web platform for import/export data recording.
- Employed a multi-threading mechanism for real-time data processing.
Main Results:
- Achieved high accuracy (approximately 98%) in classifying various fruits.
- Demonstrated an optimized automated import/export process.
- Enabled real-time tracking of warehouse input and output volumes.
Conclusions:
- The proposed automated system effectively integrates computer vision and smart warehousing for industrial optimization.
- YOLOv8 demonstrates strong performance in fruit classification within an automated system.
- The system offers a scalable solution for enhancing efficiency in industrial production lines and warehouses.
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