High-Precision Crop Yield Prediction Model Combining GANs and Random Forest

Abstract Crop yield prediction algorithms are now much more accurate and useful thanks to recent developments in deep learning. In order to analyse crop yield, this study explores the combination of Random Forest methods with Generative Adversarial Networks (GANs). In order to overcome data scarcity and class imbalance, GANs are used for data augmentation, producing realistic synthetic samples that strengthen deep learning models. Various agro-climatic and soil datasets are used to forecast yield using Random Forest, an ensemble machine learning technique. According to comparative analyses, Random Forest outperforms conventional regression models and has great generalization across areas and crops4568, regularly achieving high predictive accuracy (R2 > 0.95). A potent foundation for precision agriculture is provided by the complementary application of Random Forest for prediction and GANs for data enrichment, allowing better precision in crop yield analysis. With proper hyperparameter tuning, RF can achieve very high accuracy (R² up to 0.99 in some studies), making it a preferred choice for practical yield forecasting.