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유광현(Gwanghyun Yu),이재원(Jaewon Lee),보호앙트롱(Vo Hoang Trong),당탄부(Dang Thanh Vu),후이트완녁(Huy Toan Nguyen),이주환(JooHwan Lee),신도성(Dosung Shin),김진영(Jinyoung Kim) 한국정보기술학회 2019 한국정보기술학회논문지 Vol.17 No.12
Weeds are a major object which is very harmful to crops. To remove the weeds effectively, we have to classify them accurately and use herbicides. As computing technology has developed, image-based machine learning methods have been studied in this field, specially convolutional neural network(CNN) based models have shown good performance in public image dataset. However, CNN with numerous training parameters and high computational amount. Thus, it works under high hardware condition of expensive GPUs in real application. To solve these problems, in this paper, a hierarchical architecture based deep-learning model is proposed. The experimental results show that the proposed model successfully classify 21 species of the exotic weeds. That is, the model achieve 97.2612% accuracy with a small number of parameters. Our proposed model with a few parameters is expected to be applicable to actual application of network based classification services.