This study investigates the application of modified Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic RGB and infrared (IR) datasets for precision agriculture, particularly targeting the detection of Raphanus Raphanistrum (wild radish). Traditional WGAN models face challenges such as vanishing gradients and poor convergence, which hinder their effectiveness in generating high-quality synthetic data. To address these issues, this work proposes modifications that include replacing fully connected layers with convolutional and transposed convolutional layers, combined with batch normalization, to improve the fidelity of generated images and training stability. The experimental results demonstrate that the modified WGAN-GP produces superior synthetic images compared to other GAN variants, especially in maintaining structural similarity for RGB datasets. However, generating high-quality IR images remains challenging due to inherent spectral complexities, with consistently lower SSIM (Structural Similarity Index) scores across models. This study highlights the importance of architectural modifications and auxiliary learning strategies in enhancing GAN performance, especially for complex agricultural datasets. The findings suggest that future work should focus on integrating attention mechanisms and advanced loss functions to further improve model stability and the quality of generated synthetic data. These advancements are vital for the broader adoption of GANs in precision agriculture, enabling enhanced data augmentation for machine learning applications that support effective crop monitoring and management.