Negative Contrast: A Simple And Efficient Image Augmentation Method In Crop Disease Classification

Abstract

Crop disease classification has always been a critical and persistent problem in the field of agricultural and forestry sciences, where often we do not have access to a sufficient number of samples to know the distribution of real-world samples. How to make full use of the existing data is the starting point of our thinking. To address this problem, this paper proposes a supervised image augmentation method Negative Contrast, which uses the contrast images of existing disease samples after removing disease areas as negative samples for image augmentation when samples are relatively scarce. Numerous experiments have shown that several classical models using this augmentation method have improved in disease classification of four crops, rice, wheat, corn, and soybean, with a maximum accuracy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, and thus reflects better generalization ability in real-world disease classification. Our dataset and codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean and https://github.com/hiter0/contrastaug .

Plant phenotyping relevance

作物病害画像から病害状態を推定する画像拡張手法そのものを提案・評価しており、植物病害フェノタイピング手法が中心である。

Code and data availability

The authors explicitly state that their Plant Real-World crop disease dataset (Kaggle) and their analysis/augmentation code (GitHub) are publicly available.