Predicting Crop Yield Productivity Using Machine Learning Algorithms: A Comparison of Linear and Non-linear Approaches

Abstract

Predicting crop yield productivity is crucial for farmers and the agricultural sector to gain insights into crop productivity and returns. With advancements in technology and artificial intelligence, predicting crop yield using machine learning algorithms has become an important innovation. This study aimed to predict crop yield productivity using various machine learning algorithms and techniques. The dataset was sourced from Kaggle, preprocessed, and analyzed using linear algorithms such as Linear Regression, LASSO, and Ridge, as well as non-linear algorithms including SVR, KNN Regressor, and Polynomial Regression. Mean Squared Error (MSE) was computed to evaluate algorithms performance. Comparing the efficacy of linear versus non-linear algorithms on the dataset revealed that non-linear algorithms outperformed linear ones, indicating that the dataset's non-linear nature. Therefore, non-linear machine learning algorithms like SVR, KNN Regressor, and Polynomial Regression were recommended for better accuracy. Among these, KNN Regressor performed the best with an MSE of 0.00025, followed by SVR and Polynomial Regression with MSE values of 0.00142 and 0.0024, respectively.