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http://dx.doi.org/10.30693/SMJ.2020.9.1.16

Multi-Tasking U-net Based Paprika Disease Diagnosis  

Kim, Seo Jeong (전북대학교 전자.정보공학부)
Kim, Hyong Suk (전북대학교 지능형로봇연구소)
Publication Information
Smart Media Journal / v.9, no.1, 2020 , pp. 16-22 More about this Journal
Abstract
In this study, a neural network method performing both Detection and Classification of diseases and insects in paprika is proposed with Multi-Tasking U-net. Paprika on farms does not have a wide variety of diseases in this study, only two classes such as powdery mildew and mite, which occur relatively frequently are made as the targets. Aiming to this, a U-net is used as a backbone network, and the last layers of the encoder and the decoder of the U-net are utilized for classification and segmentation, respectively. As the result, the encoder of the U-net is shared for both of detection and classification. The training data are composed of 680 normal leaves, 450 mite-damaged leaves, and 370 powdery mildews. The test data are 130 normal leaves, 100 mite-damaged leaves, and 90 powdery mildews. Its test results shows 89% of recognition accuracy.
Keywords
Multi-Tasking Learning; Deep Learning; Segmentation; Diagnosis of Paprika Diseases; Classification;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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