Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set.

Luning Bi,Guiping Hu

doi:10.3389/fpls.2020.583438

Abstract

Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.2% using classic data augmentation and 22% using synthetic samples without LSR.

Highlights

With the increasing global population, the demand for agriculture production is rising
The results showed that the quality of the synthetic images of Wasserstein Generative Adversarial Network (WGAN)-GP with label smoothing regularization (LSR) was better when ε was between 0.20 and 0.25
The training effectiveness of WGAN-GP-LSR can be illustrated by Figure 5

Summary

Introduction

With the increasing global population, the demand for agriculture production is rising. Plant diseases cause substantial management issues and economic losses in the agricultural industry (Abu-Naser et al, 2010). It has been reported that at least 10% of global food production is lost due to plant disease (Strange and Scott, 2005). Early detection, timely mitigation, and disease management are essential for agriculture production (Barbedo, 2018a). Plant disease inspection and classification have been carried out through optical observation of the symptoms on plant leaves by human with some training or experience. Plant disease recognition has known to be time-consuming and error-prone. Due to the large number of cultivated plants and their complex physiological symptoms, even experts with rich experience often fail to diagnose specific diseases and lead to mistaken disease treatments and management (Ferentinos, 2018)

Objectives

Methods

Results

Conclusion