Data Acquisition and Image Augmentation using Adversarial Networks

It is possible to avoid challenges caused by overfitting, and the performance of machine learning algorithms can improve when there is a large amount of data. The improved training data diversity that is offered by data augmentation, which does not require the collection of fresh data, is beneficial to the algorithms that are used in machine learning. In this paper, we collect the data from the associated paddy leaf dataset, however, it is found that the collected data is insufficient for conducting the training and testing of a classifier. In order to increase the samples for training and testing to predict the leaf disease using a machine or a deep learning classifier, it is essential to increase the number of instances for efficient classification. In regards to this, the study uses Generative Adversarial Networks (GANs) to increase the images required for training and testing using image cropping, flipping, color transformation, rotation and noise injection on the collected datasets. The simulation is conducted in python for the generation of images from the input datasets and we store these augmented images for further processing.