Data Augmentation on Plant Leaf Disease Image Dataset Using Image Manipulation and Deep Learning Techniques

Abstract

A large volume of data will increase the performance of machine learning algorithms and avoid overfitting problems. Collecting a large amount of training data in the agricultural field for designing plant leaf disease detection and diagnosis model is a highly challenging task which takes more time and resources. Data augmentation increases the diversity of training data for machine learning algorithms without collecting new data. In this article, augmented plant leaf disease datasets was developed using basic image manipulation and deep learning based image augmentation techniques such as Image flipping, cropping, rotation, color transformation, PCA color augmentation, noise injection, Generative Adversarial Networks (GANs) and Neural Style Transfer (NST) techniques. Performance of the data augmentation techniques was studied using state-of-the-art transfer learning techniques, for instance, VGG16, ResNet, and InceptionV3. An extensive simulation shows that the augmented dataset using GAN and NST techniques achieves better accuracy than the original dataset using a basic image manipulation based augmented dataset. Furthermore, a combination of Deep learning, Color, and Position augmentation dataset gives the maximum classification performance than all other datasets.