Li, J.; Yin, Z.; Li, D.; Zhao, Y. Negative Contrast: A Simple and Efficient Image Augmentation Method in Crop Disease Classification. Agriculture2023, 13, 1461.
Li, J.; Yin, Z.; Li, D.; Zhao, Y. Negative Contrast: A Simple and Efficient Image Augmentation Method in Crop Disease Classification. Agriculture 2023, 13, 1461.
Li, J.; Yin, Z.; Li, D.; Zhao, Y. Negative Contrast: A Simple and Efficient Image Augmentation Method in Crop Disease Classification. Agriculture2023, 13, 1461.
Li, J.; Yin, Z.; Li, D.; Zhao, Y. Negative Contrast: A Simple and Efficient Image Augmentation Method in Crop Disease Classification. Agriculture 2023, 13, 1461.
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 .
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
