Super-Resolution for Practical Automated Plant Disease Diagnosis System
Quan Huu Cap, Hiroki Tani, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi, Iyatomi

TL;DR
This paper introduces a super-resolution preprocessing technique that significantly enhances the accuracy of automated plant disease diagnosis from low-resolution images, approaching the performance of high-resolution images.
Contribution
It demonstrates the effectiveness of super-resolution methods in recovering detailed symptoms and improving diagnostic accuracy in practical plant disease detection systems.
Findings
Super-resolution images closely resemble natural images with 4× upscaling.
The proposed method boosts disease classification accuracy by 26.9%.
Diagnostic accuracy approaches that of original high-resolution images.
Abstract
Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of symptoms are lost. In this paper, we first propose an effective pre-processing method for improving the performance of automated plant disease diagnosis systems using super-resolution techniques. We investigate the efficiency of two different super-resolution methods by comparing the disease diagnostic performance on the practical original high-resolution, low-resolution, and super-resolved cucumber images. Our method generates super-resolved images that look very close to natural images with 4 upscaling factors and is capable of recovering the lost detailed symptoms, largely boosting the diagnostic performance. Our model improves the disease classification accuracy by 26.9% over the bicubic…
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Taxonomy
TopicsPlant Pathogens and Fungal Diseases · Image Processing Techniques and Applications · Advanced Image Processing Techniques
