Generative Adversarial Networks (GANs) for Crop Simulation and Enhancement: Exploring Synthetic Data for Agricultural Research

Real-world data collection in agriculture is not only highly expensive but also faces challenges due to seasonal variations and limited generalization capabilities for capturing unusual disease patterns and environmental changes, as is required for deep learning models. We propose a new GAN-based model aimed at crop simulation and disease data augmentation. A class-conditional Deep Convolutional GAN will be designed to generate high-resolution, biologically plausible crop disease images by jointly learning visual structures and class-specific disease characteristics. The real samples in our dataset will be combined with these generated synthetic samples to increase training for crop disease classification. The multi-class plant dataset available in PlantVillage is used in the experimental scenario, with classes for healthy and diseased crops. Extensive ablation experiments confirm that the classifier's performance can be enhanced by GAN-based training compared to state-of-the-art deep learning architectures such as ResNet50, DenseNet121, EfficientNetB3, and VGG16. The accuracy of the proposed model, ResNet50 + GAN, is about 98.65%. This shows not only higher precision levels but also a higher recall rate with an F1-score computed accordingly. This validates the realism of the synthetic images developed. This is an implication of the usefulness of synthetic data in addressing data scarcity and class imbalance in the domain of intelligent agriculture.