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Deep Learning Based Tree Recognition rate improving Method for Elementary and Middle School Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(12), pp.9-16
  • DOI : 10.9708/jksci.2019.24.12.009
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 10, 2019
  • Accepted : December 13, 2019
  • Published : December 31, 2019

Jung-Eun Choi 1,  Hwanseung Yong 1

1이화여자대학교

Accredited

ABSTRACT

The goal of this study is to propose an efficient model for recognizing and classifying tree images to measure the accuracy that can be applied to smart devices during class. From the 2009 revised textbook to the 2015 revised textbook, the learning objective to the fourth-grade science textbook of elementary schools was added to the plant recognition utilizing smart devices. In this study, we compared the recognition rates of trees before and after retraining using a pre-trained inception V3 model, which is the support of the Google Inception V3. In terms of tree recognition, it can distinguish several features, including shapes, bark, leaves, flowers, and fruits that may lead to the recognition rate. Furthermore, if all the leaves of trees may fall during winter, it may challenge to identify the type of tree, as only the bark of the tree will remain some leaves. Therefore, the effective tree classification model is presented through the combination of the images by tree type and the method of combining the model for the accuracy of each tree type. I hope that this model will apply to smart devices used in educational settings.

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This paper was written with support from the National Research Foundation of Korea.
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