Super-Resolution for Practical Automated Plant Disease Diagnosis System

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$\times$ 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 interpolation method of 65.6% and shows a small gap (3% lower) between the original result of 95.5%.