Tomato Yield Prediction Using Random Forest Algorithm

This study explores the application of the Random Forest algorithm to forecast tomato yields based on climatic, soil, and crop management variables.With global food demand expected to rise by 70% by 2050, precision agriculture techniques, such as machine learning-based yield prediction, are crucial for enhancing resource allocation and improving productivity.The research engages a dataset comprising weather conditions, soil properties, and agronomic practices to train and authenticate a Random Forest model.Key findings indicate that temperature (32%) and rainfall (25%) are the most significant yield determinants, while the model achieves an R score of 0.95, indicating high predictive accuracy.The study enumerates the potential of Random Forest in empowering data-driven farming decisions, reducing waste, and enhancing food security.Recommendations include the implementation of predictive analytics by farmers especially the large-scale ones, policy interventions for climate-resilient agriculture (which can gain prominence with the right push in Nigeria), and future research integrating deep learning techniques for improved accuracy.