Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
권호 표지
DOI QR코드

DOI QR Code

Implemented of non-destructive intelligent fruit Brix(sugar content) automatic measurement system

비파괴 지능형 과일 당도 자동 측정 시스템 구현

  • Lee, Duk-Kyu (Department of Electronics Engineering, The School of Information Technology, Kangwon Nathonal University) ;
  • Eom, Jinseob (Department of Electronics Engineering, The School of Information Technology, Kangwon Nathonal University)
  • 이덕규 (강원대학교 IT 대학 전자공학과) ;
  • 엄진섭 (강원대학교 IT 대학 전자공학과)
  • Received : 2020.11.18
  • Accepted : 2020.11.28
  • Published : 2020.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, the need for IoT-based intelligent systems is increasing in various fields. In this study, we implemented the system that automatically measures the sugar content of fruits without damage to fruit's marketability using near-infrared radiation and machine learning. The spectrums were measured several times by passing a broadband near-infrared light through a fruit, and the average value for them was used as the input raw data of the machine-learned DNN(Deep Neural Network). Using this system, he sugar content value of fruits could be predicted within 5 s, and the prediction accuracy was about 93.86%. The proposed non-destructive sugar content measurement system can predict a relatively accurate sugar content value within a short period of time, so it is considered to have sufficient potential for practical use.

Keywords

References

  1. https://www.nongmin.com/news/NEWS/POL/GOV/297778/view?site_preference=normal (retrieved on Sep. 5, 2018).
  2. S. J. Choi, Introduction of Graphical Programming LabVIEW, dongil publisher, Seoul, pp. 14, 1998.
  3. B. G. Lee, J. T. Lim, and J. S. You, "Utilization of Social Media Analysis using Big Data", J. Korea Contents Assoc., Vol. 13, No. 2, pp. 211-219, 2013. https://doi.org/10.5392/JKCA.2013.13.02.211
  4. D. K. Choi and J. O. Park, "The Application Method of Machine Learning for Analyzing User Transaction Tendency in Big Data environments", J. Korea Inst. Inf. Commun. Eng., Vol. 19, No. 10, pp. 2232-2240, 2015. https://doi.org/10.6109/jkiice.2015.19.10.2232
  5. https://en.wikipedia.org/wiki/Machine_learning/(retrievedon Oct. 2018).
  6. https://terms.naver.com/entry.nhn?docId=830577&cid=42344&categoryId=42344 (retrieved on Sep. 17, 2020).
  7. https://ko.dict.naver.com/#/entry/koko/c1cac885760f4530-ba1528eb2b4b1339 (retrieved on Sep. 17, 2020).
  8. https://terms.naver.com/entry.nhn?docId=5826803&cid=64656&categoryId=64656 (retrieved on Sep. 17, 2020).