Negative Contrast: A Simple and Efficient Image Augmentation Method in Crop Disease Classification

Jiqing Li, Zhendong Yin (), Dasen Li and Yanlong Zhao
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Jiqing 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
Dasen Li: 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

Agriculture, 2023, vol. 13, issue 7, 1-14

Abstract: Crop disease classification constitutes a significant and longstanding challenge in the domain of agricultural and forestry sciences. Frequently, there is an insufficient number of samples to accurately discern the distribution of real-world instances. Leveraging the full potential of the available data is the genesis of our approach. To address this issue, we propose a supervised image augmentation technique—Negative Contrast. This method employs contrast images of existing disease samples, devoid of disease areas, as negative samples for image augmentation, particularly when the samples are relatively scarce. Numerous experiments demonstrate that the employment of this augmentation method enhances the disease classification performance of several classical models across four crops—rice, wheat, corn, and soybean, with an accuracy improvement reaching up to 30.8%. Furthermore, the comparative analysis of attentional heatmaps reveals that models utilizing negative contrast focus more accurately and intensely on the disease regions of interest, thereby exhibiting superior generalization capabilities in real-world crop disease classification.

Keywords: crop disease classification; crop disease dataset; image augmentation (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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