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http://dx.doi.org/10.6109/jkiice.2020.24.8.993

CNN-Based Toxic Plant Identification System  

Park, SungHyun (Department of Computer Engineering, Paichai University)
Lim, Byeongyeon (Department of Computer Engineering, Paichai University)
Jung, Hoekyung (Department of Computer Engineering, Paichai University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.8, 2020 , pp. 993-998 More about this Journal
Abstract
The technology of interiors is currently developing around the world. According to various studies, the use of plants to create an environment in the home interior is increasing. However, households using furniture are designed as environment-friendly environment interiors, and in Korea and abroad, plants are used for home interiors. Unexpected accidents are occurring. As a result, there were books and broadcasts about the dangers of specific plants, but until now, accidents continue to occur because they do not properly recognize the dangers of specific plants. Therefore, in this paper, we propose a toxic plant identification system based on a multiplicative neural network model that identifies common toxic plants commonly found in Korea. We propose a high efficiency model. Through this, toxic plants can be identified with higher accuracy and safety accidents caused by toxic plants.
Keywords
Toxic Plants; Deep Learning; Image Classification; CNN;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
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