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http://dx.doi.org/10.9723/jksiis.2019.24.4.001

LeafNet: Plants Segmentation using CNN  

Jo, Jeong Won (군산대학교 컴퓨터정보통신공학부)
Lee, Min Hye (군산대학교 컴퓨터정보통신공학부)
Lee, Hong Ro (군산대학교 컴퓨터정보통신공학부)
Chung, Yong Suk (제주대학교 식물자원환경전공)
Baek, Jeong Ho (농촌진흥청 국립농업과학원)
Kim, Kyung Hwan (농촌진흥청 국립농업과학원)
Lee, Chang Woo (군산대학교 컴퓨터정보통신공학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.24, no.4, 2019 , pp. 1-8 More about this Journal
Abstract
Plant phenomics is a technique for observing and analyzing morphological features in order to select plant varieties of excellent traits. The conventional methods is difficult to apply to the phenomics system. because the color threshold value must be manually changed according to the detection target. In this paper, we propose the convolution neural network (CNN) structure that can automatically segment plants from the background for the phenomics system. The LeafNet consists of nine convolution layers and a sigmoid activation function for determining the presence of plants. As a result of the learning using the LeafNet, we obtained a precision of 98.0% and a recall rate of 90.3% for the plant seedlings images. This confirms the applicability of the phenomics system.
Keywords
Deep learning; Segmentation; Plant phenomics; Phenomics system; CNN;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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