2020/10/12 by Quan Huu Cap, Cap, Quan Huu, Hiroki Tani +7
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Phytoplasmas and Hemiptera pathogens #Plant Pathogens and Fungal Diseases #Smart Agriculture and AI #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2010.06499
openalex publication_date 2020/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The collection of high-resolution training data is crucial in building robust plant disease diagnosis systems, since such data have a significant impact on diagnostic performance. However, they are very difficult to obtain and are not always available in practice. Deep learning-based techniques, and particularly generative adversarial networks (GANs), can be applied to generate high-quality super-resolution images, but these methods often produce unexpected artifacts that can lower the diagnostic performance. In this paper, we propose a novel artifact-suppression super-resolution method that is specifically designed for diagnosing leaf disease, called Leaf Artifact-Suppression Super Resolution (LASSR). Thanks to its own artifact removal module that detects and suppresses artifacts to a considerable extent, LASSR can generate much more pleasing, high-quality images compared to the state-of-the-art ESRGAN model. Experiments based on a five-class cucumber disease (including healthy) discrimination model show that training with data generated by LASSR significantly boosts the performance on an unseen test dataset by nearly 22% compared with the baseline, and that our approach is more than 2% better than a model trained with images generated by ESRGAN.