International Conference on Computer and Knowledge Engineering

Home / 12th International Conference on Computer and Knowledge Engineering

Extreme Gradient Boosting (XGBoost) Regressor and Shapley Additive Explanation for Crop Yield Prediction in Agriculture

Authors :

Dennis A/L Mariadass¹ Ervin Gubin Moung² Maisarah Mohd Sufian³ Ali Farzamnia⁴

1- Faculty of Computing and Informatics Universiti Malaysia Sabah 2- Faculty of Computing and Informatics Univerisity Malaysia Sabah 3- Faculty of Computing and Informatics Universiti Malaysia Sabah 4- universiti malaysia sabah

Keywords :

crop yield،prediction،machine learning،xgboost،shapley،gradient boosting

Abstract :

The primary purpose of precision agriculture is to maximize crop yields while utilizing a limited amount of land resources. Apart from industrialization, which fuelled Malaysia's significant economy and development, the country's agriculture industry performs a major role in guaranteeing food security and safety, as well as long-term development and wealth creation. The nation's policymakers must rely on reliable crop yield predictions to acquire easy export and import evaluations to improve national food security. Machine Learning can help anticipate yields more accurately. This paper proposes to use the XGBoost model for annual crop yield prediction in Malaysia. Experiments on the generated yield dataset shows promising results with 0.98 R-Squared value and outperformed the state-of-art models. The performance of the proposed model is extensively analyzed using the Shapley Additive Explanation (SHAP) to identify the important attributes in the crop yield prediction. The predictions provided by machine learning algorithms will aid farmers in deciding what to grow because of this research.