CNN-YOLOv8 - Based Tomato Quality Inspection System - A Case Study in Vietnam
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 7 |
| Year of Publication : 2023 |
| Authors : Thi-Mai-Phuong Dao, Ngoc-Khoat Nguyen, Van-Kien Nguyen |
10.14445/23488379/IJEEE-V10I7P103
How to Cite?
Thi-Mai-Phuong Dao, Ngoc-Khoat Nguyen, Van-Kien Nguyen, "CNN-YOLOv8 - Based Tomato Quality Inspection System - A Case Study in Vietnam," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 7, pp. 31-40, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I7P103
Abstract:
Quality classification is one of the final stages of the process of consuming agricultural products not only in Vietnam but also in other countries having agriculture sector. It determines the quality and directly affects the price of agricultural products in the market. With the significant development of science and technology, many advanced techniques have been used, in which computer vision and artificial neural networks have been widely applied with undeniable achievements. This has helped increase the quality of agricultural products, improve sorting efficiency, and reduce operating costs. In the present study, the YOLOv8- based deep learning network model using a convolutional neural network is proposed to solve the problem of detecting several surface diseases on the tomatoes considered significant crops in tropical countries, e.g., Vietnam. The results of training the YOLOv8 network model with a dataset of 500 product images, including both good and bad features, mean Average Precision (mAP) value up to 99.5%, a precision of 96.3%, and recall of 96.1% demonstrate a statement: the YOLOv8 algorithm can be effectively applied in agricultural product quality inspection systems.
Keywords:
Agricultural inspection, Computer vision, YOLOv8, Convolutional Neural Network (CNN), Tomato quality.
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