AgroGan - Synthetic Weather Visualization & Prediction for farmers

Abstract: Agriculture in many regions continuesto rely heavily on weather conditions, makingaccurate and timely forecasting essential foreffective farm management. However,traditional weather prediction systems often failto provide localized, real-time, and visuallyinterpretable insights required by small andmedium-scale farmers. Limited access tohistorical weather data further restricts informeddecision-making related to irrigation, cropplanning, and harvesting .Recent advancementsin artificial intelligence have enabled thegeneration of synthetic weather data using deeplearning techniques. Generative AdversarialNetworks (GANs) are particularly effective inlearning complex weather patterns andproducing realistic visual representations. AGAN consists of a Generator that createssynthetic weather images and a Discriminator