Maize yield prediction based on artificial intelligence using spatio-temporal data

Abstract

The aim of this study was to predict maize yield by artificial intelligence using spatio-temporal training date. Counter-propagation artificial neural networks (CP-ANNs), xy-fused networks (XY-Fs), supervised Kohonen networks (SKNs), extreme gradient boosting (XGBoost) and support-vector machine (SVM) were used for predicting maize yield in 5 vegetation pariods. Input variables for modelling were: soil parameters (pH, P2O5, K2O, Zn, Clay content, ECa, draught force, Cone index) and micro-relief averages and meteorological parameters for the 63 treatment units. The best performing method (XGBoost) attained 92.1 and 95.3% of accuracy on the training and the test set.