A Neural Meta-Model for Predicting Winter Wheat Crop Yield
Yogesh Bansal, David Lillis and M-Tahar Kechadi
Machine Learning, 2024.
Abstract
This study presents the development and evaluation of machine learning models to predict winter wheat crop yield using heterogeneous soil and weather data sets. A concept of an error stabilisation stopping mechanism is introduced in an LSTM model specifically designed for heterogeneous datasets. The comparative analysis of this model against an LSTM model highlighted its superior predictive performance. Furthermore, weighted regression models were developed to capture environmental factors using agroclimatic indices. Finally, a neural meta model was built by combining the predictions of several individual models. The experimental results indicated that a neural meta model with an MAE of 0.82 and RMSE of 0.983 tons/hectare demonstrated a notable performance, highlighting the importance of incorporating weighted regression models based on agroclimatic indices. This study shows the potential for improved yield prediction through the proposed model and the subsequent development of a meta model.