SEABEM, the Stacked Ensemble Algorithms Biomass Estimator Model, is a web application with a stacked ensemble of Machine Learning (ML) algorithms running on the backend to predict cover crop biomass for locations in Sub-Saharan. The SEABEM model was developed using a previously developed database of crop growth and yield that included site characteristics such as latitude, longitude, soil texture (sand, silt, and clay percentages), temperature, and precipitation. The goal of SEABEM is to provide global farmers, mainly small-scale African farmers, the knowledge they need before practicing and benefiting from cover crops while avoiding the expensive and time-consuming operations that come with blind on-site experimentation. The results were derived from comparing ten different ML algorithms, demonstrating the dominance of ensemble models. The top-performing models - Gradient Boost Regressor, Extra Trees Regressor, and Random Forest Regressor - were stacked together into one model to power the SEABEM web application. As the project is open-sourced on a GitHub repository, the GitHub community is available for others to improve the project. The SEABEM web application is also accessible and valuable to anyone worldwide as its development came from global data
