Resumen
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.