Rainfall Prediction from Environmental Sounds: Combining Audio Signals with Visual Data for Precision Irrigation

Abstract

Rainfall prediction plays a crucial role in agricultural management and irrigation strategies. Traditional methods of weather forecasting rely heavily on meteorological data, which may not always be accessible or timely. This study explores a novel approach to predicting rainfall by combining environmental audio signals with visual data. We investigate the relationship between sound patterns produced by the environment before and during rainfall and integrate these findings with visual data such as cloud cover and humidity levels. The proposed system aims to enhance precision irrigation by providing more accurate and timely rainfall predictions, ultimately improving water management and crop yield