Noise-robust Apple Disease Classification with Image Augmentation Methods

被引:0
|
作者
Kim, Jang-Yeon [2 ]
Kim, Tae-Kyeong [3 ]
Cho, Hyun-Chong [1 ]
机构
[1] Dept. of Electronics Engineering and Interdisciplinary, Graduate Program for BIT Medical Convergence, Kangwon National University, Korea, Republic of
[2] Dept. of Electronics Engineering Kangwon, National University, Korea, Republic of
[3] Dept. of Interdisciplinary, Graduate Program for BIT Medical Convergence, Kangwon National University, Korea, Republic of
基金
新加坡国家研究基金会;
关键词
Disease control - Fruits - Image classification - Salt and pepper noise;
D O I
10.5370/KIEE.2022.71.9.1302
中图分类号
学科分类号
摘要
When the apple disease occurs, accurate and rapid control must be carried out. If appropriate measures are not taken, the spread of the disease and secondary damage such as soil contamination caused by pesticides may occur. In this paper, the apple disease classification system that can classify the type of disease as well as normal from image is proposed. The apple disease classes consists of Marssonina blotch, Fire Blight, Valsa cacker, Alernaria blotch, and Bitter rot. Xception network was used to extract and learn features from image. Google's AutoAugment CIFAR-10 policy is used to increase apple disease data to increase network's classification performance. Then, in order to increase the reliability of data, the augmented data was selected by model trained only with original data. Gaussian, Salt-and-pepper, Speckle and Poisson noise were added to the test data to show good performance for noisy input data. We compared the performance of the model trained with original data and augmented data selected by threshold value 0.9. As a result, the proposed study showed a performance improvement of up to 6% in F1-Score. © 2022 Korean Institute of Electrical Engineers. All rights reserved.
引用
收藏
页码:1302 / 1307
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