AI-Powered Hyperlocal Drought Prediction and Multilingual Voice Alert System for Smallholder Farmers
Transforming satellite observations and artificial intelligence into actionable drought early warnings for climate-resilient agriculture.
AgroAlert Ghana is an AI-powered agricultural intelligence platform that provides hyperlocal drought prediction and multilingual early warning alerts for smallholder farmers.
The platform combines satellite remote sensing, climate reanalysis data, machine learning and accessible communication technologies to convert environmental observations into timely and actionable drought information.
Unlike conventional drought monitoring systems that provide regional summaries, AgroAlert Ghana is designed to generate community-level risk predictions and communicate them directly to farmers through voice calls and SMS, making climate information accessible even in areas with limited internet connectivity and low literacy levels.
Climate change continues to increase the frequency and severity of drought across sub-Saharan Africa.
In Ghana, prolonged dry spells threaten:
- Food security
- Farmer livelihoods
- National agricultural productivity
- Rural economic resilience
Many existing early warning systems are:
- designed for researchers rather than farmers
- focused on regional-scale predictions
- dependent on internet access
- inaccessible to farmers with limited literacy
AgroAlert Ghana addresses these gaps by combining artificial intelligence with multilingual communication technologies to deliver practical drought information directly to farming communities.
The platform integrates multiple Earth observation datasets including:
- Sentinel-2 NDVI imagery
- ERA5 weather reanalysis
- MODIS Land Surface Temperature
- Google Earth Engine processing
AgroAlert Ghana evaluates three machine learning approaches:
| Model | Purpose |
|---|---|
| Random Forest | Non-linear environmental prediction |
| LSTM | Temporal drought sequence modelling |
| Hybrid Ensemble | Validation-weighted combination of both models |
The hybrid model combines spatial environmental relationships with temporal drought patterns to improve prediction robustness.
Alerts are delivered using:
- 📞 AI-generated multilingual voice calls
- 📱 SMS notifications
- 🔁 Farmer verification feedback
Supported languages include:
- Twi
- Ewe
- Dagbani
- English
The platform includes a feedback mechanism that allows farmers to confirm drought conditions after receiving alerts.
These responses can be incorporated into future model retraining to improve prediction performance over time.
This project introduces:
- A hybrid Random Forest-LSTM drought prediction framework
- Hyperlocal drought forecasting for Ghana
- An illiteracy-first voice alert system
- Community-level drought risk assessment
- A feedback-driven machine learning pipeline
- An open and reproducible research workflow