A Low-Cost Deep-Learning-Based System for Grading Cashew Nuts

Author:

Pham Van-Nam1ORCID,Do Ba Quang-Huy1ORCID,Tran Le Duc-Anh2,Nguyen Quang-Minh3,Do Van Dinh4ORCID,Nguyen Linh5ORCID

Affiliation:

1. Faculty of Electrical Engineering, Hanoi University of Industry, Hanoi 100000, Vietnam

2. School of Electrical & Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam

3. School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam

4. Faculty of Electrical Engineering, Sao Do University, Hai Duong 170000, Vietnam

5. Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia

Abstract

Most of the cashew nuts in the world are produced in the developing countries. Hence, there is a need to have a low-cost system to automatically grade cashew nuts, especially in small-scale farms, to improve mechanization and automation in agriculture, helping reduce the price of the products. To address this issue, in this work we first propose a low-cost grading system for cashew nuts by using the off-the-shelf equipment. The most important but complicated part of the system is its “eye”, which is required to detect and classify the nuts into different grades. To this end, we propose to exploit advantages of both the YOLOv8 and Transformer models and combine them in one single model. More specifically, we develop a module called SC3T that can be employed to integrate into the backbone of the YOLOv8 architecture. In the SC3T module, a Transformer block is dexterously integrated into along with the C3TR module. More importantly, the classifier is not only efficient but also compact, which can be implemented in an embedded device of our developed cashew nut grading system. The proposed classifier, called the YOLOv8–Transformer model, can enable our developed grading system, through a low-cost camera, to correctly detect and accurately classify the cashew nuts into four quality grades. In our grading system, we also developed an actuation mechanism to efficiently sort the nuts according to the classification results, getting the products ready for packaging. To verify the effectiveness of the proposed classifier, we collected a dataset from our sorting system, and trained and tested the model. The obtained results demonstrate that our proposed approach outperforms all the baseline methods given the collected image data.

Publisher

MDPI AG

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Developed a Tomato-Condition Classification System using Image Processing and YOLO Technology;2024 9th International Conference on Integrated Circuits, Design, and Verification (ICDV);2024-06-06

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