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

## TL;DR

This paper introduces a hybrid AI model combining satellite imagery and weather data to improve crop recommendations and weather forecasts in Egypt's Al-Sharkia region.

## Contribution

The novel hybrid framework integrates CNN, LSTM, and rule-based models for localized agricultural forecasting and crop advisories.

## Key findings

- The CNN model reduced training loss from 0.2362 to 6.87e-4 for agricultural land classification.
- The RNN-LSTM model achieved an RMS error of 0.19 for meteorological variable predictions.
- The hybrid model provides precise forecasts and customized agricultural advice using Sentinel-2 and NOAA data.

## Abstract

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.