Maize leaf disease image enhancement algorithm using TFEGAN

• Published in: Crop protection Vol. 182; p. 106734
• Main Authors: Yang, Zaichun, Fang, Shundong, Huang, Hongxu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Generative adversarial network; Image denoising; Maize disease image enhancement; Two-way extraction module; Maize disease image enhancement; Two-way extraction module; Generative adversarial network; Image denoising
• ISSN: 0261-2194; 1873-6904
• DOI: 10.1016/j.cropro.2024.106734

Maize leaf diseases have a profound impact on maize crop yield. However, due to practical constraints, gathering a substantial number of maize disease images is time-consuming and labor-intensive. Furthermore, low-light conditions and noise interference in maize leaf disease images present significant challenges for image generation. Therefore, this paper proposes TFEGAN, a novel algorithm designed to generate maize leaf disease images. TFEGAN leverages a Two-Way Extraction Module (TWEM) to enhance the perceptual domain of the model and effectively reuse maize leaf disease features. The algorithm employs two well-trained Generative Adversarial Networks (GANs) to enhance low-light and noisy maize leaf disease images. The low-light enhancement component utilizes a double discriminator and self-regularization to balance global and local enhancement while constraining the distance between the input image and the denoising component. Moreover, the denoising part employs a strong coordinated attention mechanism and an environmental encoder to learn noise regions and their surrounding structures, thus generating better local images and enabling more accurate evaluation by the discriminant network. The generators of both networks are encapsulated by two discriminators and combined with a bi-directional feature extraction structure to generate normal maize leaf disease images. Experimental evaluations demonstrate the effectiveness of TFEGAN in improving the quality of maize leaf disease images under low-light and noisy conditions, showcasing its potential for practical applications. Overall, this paper presents a promising solution to the challenges of low-light and noise interference in maize leaf disease images, utilizing bidirectional feature extraction and GANs. •A mixed maize disease image enhancement method is proposed.•The two-way feature extraction module retains local and global details.•The strong coordinated self-attention preserves key disease features.