Enhancing model performance in agricultural image analysis faces challenges due to limited datasets, biological variability, and uncontrolled environments. Deep learning models require large, realistic datasets, which are often difficult to obtain. Data augmentation, especially through Generative Adversarial Networks (GANs), has become essential in farming applications, generating synthetic images to improve model training and reduce the need for extensive image collection. This review explores various GAN approaches for image augmentation in farming, investigating their challenges and limitations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 128 publications were analyzed to identify research trends and gaps in GAN applications within the farming industry. Key applications of GANs include plant classification, weed detection, animal detection and behavior recognition, animal health and disease analysis, plant disease detection, phenotyping, and fruit quality assessment. Persistent issues like limited training datasets, occlusion challenges, and imbalanced data hinder model performance across these applications. Recognizing these challenges is critical for enhancing the efficiency and effectiveness of farming operations. Finally, this review concludes with insights and future directions to foster progress in this field.