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http://dx.doi.org/10.14578/jkfs.2020.109.1.23

A Study on the Performance of Deep learning-based Automatic Classification of Forest Plants: A Comparison of Data Collection Methods  

Kim, Bomi (Chungnam Forest Environment Research Institute)
Woo, Heesung (School of Forestry Sciences and Landscape Architecture, Kyungpook National University)
Park, Joowon (School of Forestry Sciences and Landscape Architecture, Kyungpook National University)
Publication Information
Journal of Korean Society of Forest Science / v.109, no.1, 2020 , pp. 23-30 More about this Journal
Abstract
The use of increased computing power, machine learning, and deep learning techniques have dramatically increased in various sectors. In particular, image detection algorithms are broadly used in forestry and remote sensing areas to identify forest types and tree species. However, in South Korea, machine learning has rarely, if ever, been applied in forestry image detection, especially to classify tree species. This study integrates the application of machine learning and forest image detection; specifically, we compared the ability of two machine learning data collection methods, namely image data captured by forest experts (D1) and web-crawling (D2), to automate the classification of five trees species. In addition, two methods of characterization to train/test the system were investigated. The results indicated a significant difference in classification accuracy between D1 and D2: the classification accuracy of D1 was higher than that of D2. In order to increase the classification accuracy of D2, additional data filtering techniques were required to reduce the noise of uncensored image data.
Keywords
deep learning; image detection; classification; identification; tree species;
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