Evaluation of an Ensemble Technique for Prediction of Crop Yield

Crop yield prediction plays a crucial role in agricultural management and decision-making processes. Traditional approaches to crop yield prediction often face limitations in terms of accuracy and robustness due to the complex and dynamic nature of agricultural systems. Machine learning is an emerging technology to understand practical and real world use cases for agricultural production. Machine learning is a supporting tool for the agricultural production which helps to make decisions on what crops to be cultivated, crop yield prediction and crop management practices. In this research paper, we propose ensemble techniques to improve crop yield prediction accuracy. Ensemble methods such as bagging, boosting, and stacking that combine multiple models to improve their predictive power which have shown promising results in various domains. We collected a comprehensive dataset consisting of historical crop yield data, weather information, and soil characteristics. The data underwent pre-processing steps, including cleaning, normalization, and feature engineering. We developed an ensemble model architecture, selecting appropriate base models and training them using a validation process. To evaluate the effectiveness of an ensemble technique for predicting crop yield, several factors should be considered such as data, ensemble composition, evaluation metrics, generalizability and interpretability. Important parameters related to climatic conditions such as rainfall, humidity, soil type and temperature were taken into consideration for crop yield prediction. From literature review, it is understanding that Decision Tree, Random Forest and Neural Networks are the algorithms mostly used. The proposed work compared Random Forest and Boosting algorithms based on the score like Mean Squared Error (MSE), Mean Absolute Error (MAE) and R2 score to improve the weak learner for most expected outcome. Finally concluded that ensemble of Random Forest with Gradient Boosting Regressor achieved more accuracy and most expected outcome. At the same time, Mean Squared Error(MSE), Mean Absolute Error(MAE) were smaller in the proposed work. The results demonstrated that the ensemble technique consistently outperformed individual models, achieving higher prediction accuracy and reducing prediction errors. Our findings suggest that ensemble techniques are promising for improving crop yield prediction, offering more robust and accurate insights for agricultural planning and decision-making.