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http://dx.doi.org/10.9717/kmms.2021.24.6.745

Exotic Weed Image Recognition System Based on ResNeXt Model  

Kim, Min-Soo (Dept. of Electronics Convergence Eng., Kwangwoon University)
Lee, Gi Yong (Dept. of Electronics Convergence Eng., Kwangwoon University)
Kim, Hyoung-Gook (Dept. of Electronics Convergence Eng., Kwangwoon University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.6, 2021 , pp. 745-752 More about this Journal
Abstract
In this paper, we propose a system that recognizes weed images using a classifier based on ResNeXt model. On the server of the proposed system, the ResNeXt model extracts the fine features of the weed images sent from the user and classifies it as one of the most similar weeds out of 21 species. And the classification result is delivered to the client and displayed on the smartphone screen through the application. The experimental results show that the proposed weed recognition system based on ResNeXt model is superior to existing methods and can be effectively applied in the real-world agriculture field.
Keywords
Exotic Weeds; Weed Image Recognition; Cardinality; ResNeXt;
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