MMDGAN: A Fusion Data Augmentation Method for Tomato-Leaf Disease Identification
Tomato disease control is of great significance to ensure crop production and tomato disease classification study is an essential tool for doing so. In this paper, we propose a new data augmentation method based on deep threshold multi-feature extraction convolution GAN with Mixed Attention and Markovian Discriminator (MMDGAN) for tomato disease leaf classification. Firstly, in the generator of MMDGAN, a deep threshold multi-feature extraction module was proposed to improve the feature extraction of tomato disease leaves. Then, a mixed attention mechanism combined cross attention module with fused features-highlighting module was proposed to coordinate the overall generation of images. Finally, for the discriminator, Markov discriminator was used to strengthen the similarity judgment of local texture of images. Based on the open datasets PlantVillage, the Frechet Inception Distance (FID) score of healthy tomato leaf image, Leaf Mold, Leaf Curl and Spider Mite generated by MMDGAN were 159.3010, 164.4744, 230.3825 and 254.9866 respectively. Thereafter, a B-ARNet model is trained on synthetic and real images using transfer learning to classify the four categories of tomato diseases. The proposed method achieved an accuracy of 97.12%, with and F1 value of 97.78%. The proposed approach shows its superiority over the existing methodologies.
L. Zhang et al., "MMDGAN: A Fusion Data Augmentation Method for Tomato-Leaf Disease Identification," Applied Soft Computing, vol. 123, article no. 108969, Elsevier, Jul 2022.
The definitive version is available at https://doi.org/10.1016/j.asoc.2022.108969
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Deep threshold multi-feature extraction module; Markovian discriminator; Mixed attention; MMDGAN data augmentation; Tomato leaf disease
International Standard Serial Number (ISSN)
Article - Journal
© 2023 Elsevier, All rights reserved.
01 Jul 2022
Education Department of Hunan Province, Grant kq2014160