A hybrid deep learning and rule-based model for smart weather forecasting and crop recommendation using satellite imagery.

The effective management of meteorological forecasting data is crucial for enhancing agricultural sustainability and precision, especially considering climate change. This study presents an innovative framework that integrates multispectral image analysis, advanced weather forecasting, and rule-based models to improve agricultural practices in Egypt's Al-Sharkia region, specifically targeting rice and wheat cultivation. The framework employs artificial intelligence and sophisticated data processing techniques to analyze information from satellites, remote sensing devices, and meteorological stations, delivering accurate weather predictions and climate forecasts. The Convolutional Neural Network (CNN) model classified agricultural land into appropriate categories, exhibiting exceptional performance with a reduction in training loss from 0.2362 to 6.87e-4. The Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) model demonstrated significant predictive accuracy, achieving a root mean square (RMS) error of 0.19 in forecasting critical meteorological variables. In contrast to prior research that utilizes solely remote sensing or meteorological data, this study introduces an innovative hybrid framework that amalgamates CNN-based image analysis, LSTM-based weather prediction, and rule-based crop advisories. This comprehensive method provides precise, localized forecasts and customized agricultural advice, facilitating informed decisions regarding crop selection, planting schedules, and resource allocation. This thorough methodology, validated by Sentinel-2 and NOAA data, aims to reduce crop losses, decrease operational costs, and encourage sustainable agricultural practices in response to climate change problems.