Crop Yield Optimization: Strengthen Food Security & Economic Planning
A model that predicts land fertility and annual yields, analyzing complex patterns and correlations with soil profile dataThe product can empower farmers and policymakers to design more informed land-use strategies. This helps ensure economic sustainability and long-term food security, as the world faces a variety of pressing challenges related to food and land supply in the 21st century.
A Random Forest regressor and MLP Neural Network was used to uncover statistical patterns in a large, complex dataset. The model is very robust, providing accurate yield predictions based on available soil data.
This project hopes to use a new data-driven, incorporating modern statistics and machine learning models to more capably address a critical global problem,
It can provide farmers and government agencies a powerful tool to manage resources and develop sustainable land-use strategies.
The goal is to help maximize agricultural output while addressing the economic and social impacts of increasing population and diminishing farmlands.
