CROP YIELD PREDICTION USING SELECTED MACHINE LEARNING ALGORITHMS

Abstract

Agriculture is paramount to global food security, and predicting crop yields is crucial for policy and planning. However, predicting these yields is challenging due to the myriad of influencing factors, from soil quality to climate conditions. While traditional methods relied on historical data and farmer experience, recent advancements have witnessed a shift towards machine learning (ML) for improved accuracy. This study explored the application of machine learning (ML) techniques in predicting crop yields using data from Nigeria. Previous efforts lacked transferability across crops and localities; this research aimed to devise modular and reusable workflows. Using data from the Agricultural Performance Survey of Nigeria, this study evaluated the performance of different machine learning algorithms, including Linear Regression, Support Vector Regressor, K-Nearest neighbor, and Decision Tree Regressor. Results revealed the Decision Tree Regressor as the superior model for crop yield prediction, achieving a prediction accuracy of 72%. The findings underscore the potential of integrating ML in agricultural planning in Nigeria where agriculture significantly impacts the economy. Further research is encouraged to refine these models for broader application across varying agroecological zones.