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논문 기본 정보

자료유형
학술저널
저자정보
Donggun Lee (Kyungpook National University) Jooseon Oh (Chonnam National University) Youngtae Choi (Kyungpook National University) Donggeon Lee (Kyungpook National University) Hongjeong Lee (Kyungpook National University) Sung-Bo Shim (Kyungpook National University) Yushin Ha (Kyungpook National University)
저널정보
충남대학교 농업과학연구소 Korean Journal of Agricultural Science Korean Journal of Agricultural Science Vol.50 No.3
발행연도
2023.9
수록면
415 - 424 (10page)

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초록· 키워드

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The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and nonmarketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called ‘You Only Look Once (YOLO)’ to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.

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Abstract
Introduction
Materials and Methods
Results and Discussion
Conclusion
References

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