MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization

Dasen Li, Zhendong Yin (), Yanlong Zhao, Wudi Zhao and Jiqing Li
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Dasen Li: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Zhendong Yin: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Yanlong Zhao: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Wudi Zhao: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Jiqing Li: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Agriculture, 2023, vol. 13, issue 6, 1-15

Abstract: Tomato disease classification based on images of leaves has received wide attention recently. As one of the best tomato disease classification methods, the convolutional neural network (CNN) has an immense impact due to its impressive performance. However, better performance is verified by independent identical distribution (IID) samples of tomato disease, which breaks down dramatically on out-of-distribution (OOD) classification tasks. In this paper, we investigated the corruption shifts, which was a vital component of OOD, and proposed a tomato disease classification method to improve the performance of corruption shift generalization. We first adopted discrete cosine transform (DCT) to obtain the low-frequency components. Then, the weight of the feature map was calculated by multiple low-frequency components, in order to reduce the influence of high-frequency variation caused by corrupted perturbation. The proposed method, termed as a multiple low-frequency attention network (MLFAnet), was verified by the benchmarking of ImageNet-C . The accuracy result and generalization performance confirmed the effectiveness of MLFAnet. The satisfactory generalization performance of our proposed classification method provides a reliable tool for the diagnosis of tomato disease.

Keywords: tomato disease classification; out of distribution; corruption shifts generalization; frequency component; attention mechanism (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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