Predictive modeling of crop yields: a comparative analysis of regression techniques for agricultural yield prediction.

Crop yield prediction plays a key role in modern agriculture, it enables farmers to make decisions about resource distribution, crop production management, and marketing business strategies. Regression models are extensively used for crop yield prediction. The performance of different regression techniques may vary depending on various factors such as the dataset, features, and modeling assumptions. In this paper, a comparative study was conducted to evaluate and compare the performance of different regression models for agriculture crop yield prediction. Collected a comprehensive dataset encompassing historical crop yield data, weather parameters and pesticides data features from various agricultural regions, then applied and compared various regression models, including Linear Regression (LR), K Nearest neighbor Regression (KNR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR),Linear Model Lasso Regression, Elasticnet Regression, Ridge Regression to predict crop yields for various crops. This study involved evaluating the performance of these regression models based on several performance metrics, including R² score, Root Mean Squared Error(RMSE), Mean Squared Error(MSE), Mean Absolute Error(MAE), Median Absolute Error(Median AE), Explain variance score and computing time. The results of our study provide insights into the comparative performance of different regression models for crop yield prediction in agriculture. Determined that the performance of the regression models varies crop type, area, and dataset used. Overall, the random forest regression model demonstrated the best performance in terms of R2, followed by K Nearest neighbor with hyper parameter tuning and decision tree regression. However, the choice of the most suitable regression model may also depend on other factors such as the interpretability and computational efficiency requirements of the application. Our research findings contribute to the existing literature on crop yield prediction in agriculture and afford treasured information for farmers, policymakers, and researchers to make conversant conclusions about the selection of appropriate regression models for crop yield prediction in their specific contexts. Further research could explore the combination of different regression models or the integration of other ML techniques to better the R2and robustness of crop yield prediction models in agriculture. [ABSTRACT FROM AUTHOR]