Harnessing the power of diffusion models for plant disease image augmentation.

ABSTRACT:

Introduction

The challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergenc

SUBMITTER: Muhammad A 

PROVIDER: S-EPMC10669158 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature