LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis

• URL: http://arxiv.org/abs/2010.06499v1
• Date: Mon, 12 Oct 2020 02:33:49 GMT
• Title: LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis
• Authors: Quan Huu Cap, Hiroki Tani, Hiroyuki Uga, Satoshi Kagiwada and Hitoshi Iyatomi
• Abstract summary: Leaf Artifact-Suppression Super Resolution (LASSR) is specifically designed for diagnosing leaf disease. LASSR can generate much more pleasing, high-quality images compared to the state-of-the-art ESRGAN model.
• Score: 2.449909275410288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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