Scalable software solution (web/mobile) for farmers

Prototype built in Google Colab (ML backend) + planned UI (web/app) integration

At least 10% increase in productivity through insights

Achieved yield prediction & optimization with ML (LightGBM + feature optimization), showing potential for >10% improvement

Accurate yield predictions using climate, soil, and input data

Implemented LightGBM regression model with evaluation (MAE, RMSE, R²) → reliable yield prediction

Data-driven recommendations for best farming conditions

Built parameter optimization (rainfall, pesticide use, avg temp) → suggests conditions for max yield

Multi-language support (regional languages)

Currently UI planned → Python backend + streamlit/gradio UI (extendable to local languages later)

Deployable web/mobile app for many farmers

Current working ML pipeline in Colab, scalable to cloud/web app deployment