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

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 ad

SUBMITTER: Bi L 

PROVIDER: S-EPMC7746658 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature