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http://dx.doi.org/10.46670/JSST.2020.29.6.433

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

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)
Publication Information
Journal of Sensor Science and Technology / v.29, no.6, 2020 , pp. 433-439 More about this Journal
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
Near infrared; Spectrometer; Deep Neural Network; Brix(Sugar Content); LabVIEW;
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Times Cited By KSCI : 2  (Citation Analysis)
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