Crop Yield Prediction Based on Machine Learning Techniques

Crop yield forecasting is a crucial component of agricultural research and development since it has a direct impact on food production and security. Due to the quick changes in soil and climatic conditions, the old techniques of estimating crop yields based on farmers' observations and experience have grown more difficult. In recent years, machine-learning approaches have been used to solve this problem. This study focuses on the use of several machine learning models for agricultural production prediction, including closest neighbor regression, nearest polynomial regression, random forest regression, gradient-boosted tree regression, and support vector regression. Effective feature selection methods were used to convert the raw data into a machine learning-friendly format. The results of this study demonstrated that, when compared to other strategies, the use of an ensemble approach in feature selection can produce more precise predictions. The ensemble technique creates a thorough and accurate forecast by fusing many datasources and machine learning algorithms. This study emphasizes the value of employing machine learning methods for predicting agricultural yields as well as the advantages of applying an ensemble approach to feature selection.