Hephzibah Baby
Jaya Shailesh Kumar Jha
Sanika Dabir
Charusheela Pandit
- Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Khalapur, Maharashtra, India
- Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Khalapur, Maharashtra, India
- Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Khalapur, Maharashtra, India
- Assistant Professor, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Mumbai University, Khalapur, Maharashtra, India
Abstract
The visual inspection method for fruit grading is prone to judgment distortion among different individuals. There is a demand for an automated fruit classification machine to replace labor-intensive processes with an intelligent system for fruit quality classification. This study proposes a practical real-time fruit quality classification system that classifies the fruit’s appearance in order to decrease human effort costs in the fruit industry. For the sorting and classification of fruits, there are different parameters such as color, weight, size, shape, and density. Research on fruit quality classification based on color, size, and volume is nearing completion in the laboratory but has not yet been implemented in practical applications. The assessment of fruit quality remains unresolved. The study aims to develop a system capable of classifying fruits based on color, volume, size, shape, and density. This categorization system, utilizing image processing, integrates artificial intelligence components such as a camera, computer vision, and artificial neural network. The system employs captured fruit images to ascertain mass, volume, and surface defects on the fruit.
Keywords: Quality, artificial intelligence (AI), computer vision, YOLO, object detection, preprocessing
[This article belongs to International Journal of Computer Science Languages(ijcsl)]
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References
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| Volume | 01 |
| Issue | 02 |
| Received | October 16, 2023 |
| Accepted | December 11, 2023 |
| Published | January 8, 2024 |