What question did this study set out to answer?

The aim is to improve weather forecasting for small and medium-scale farmers using synthetic data generation.

February 27, 2026Open Access

AgroGan - Synthetic Weather Visualization & Prediction for farmers

Key Points

The aim is to improve weather forecasting for small and medium-scale farmers using synthetic data generation.
Utilized deep learning techniques to generate synthetic weather data.
Employed Generative Adversarial Networks to learn complex weather patterns.
Created visual representations of synthetic weather data for better interpretation by farmers.
Synthetic weather data showed enhanced accuracy for localized forecasting.
Improved visualization made it easier for farmers to understand weather patterns.
Generated insights support informed decisions on irrigation and crop planning.

Abstract

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

AgroGan - Synthetic Weather Visualization & Prediction for farmers

Key Points

Abstract

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