FORECASTING AGRICULTURAL TASKS USING MACHINE LEARNING
Keywords:
machine learning methods, yield modeling, database, random forest, model accuracy estimation.Abstract
This article visualizes the results of forecasting using advanced machine learning algorithms. Model accuracy estimates are obtained using machine learning methods. We have studied the forecasting process to extend the power of the RF method for corn yield forecasting with other most advanced machine learning algorithms.
References
1. Ali, A. M., Abouelghar, M. A., Belal, A.-A., Saleh, N., Younes, M., Selim, A., et al. (2022). Crop yield prediction using multi sensors remote sensing (re-view article). Egypt. J. Remote Sens. Space Sci. 25, 711–716. doi:10.1016/j.ejrs.2022.04.006
2. Archontoulis, S. V., Castellano, M. J., Licht, M. A., Nichols, V., Baum, M., Huber, I., et al. (2020). Predicting crop yields and soil-plant nitrogen dy-namics in the US Corn Belt. Crop Sci. 60 (2), 721–738. doi:10.1002/csc2.20039
3. Arnault, J., Rummler, T., Baur, F., Lerch, S., Wagner, S., Fersch, B., et al. (2018). Precipitation sensitivity to the uncertainty of terrestrial water flow in WRF-Hydro: An ensemble analysis for central Europe. J. Hydrometeorol. 19 (6), 1007–1025. doi:10.1175/jhm-d-17-0042.1