Maize Yield Prediction using Artificial Neural Networks based on a Trial Network Dataset

Abstract

The prediction of grain yield is important for sowing, cultivar positioning, crop management, and public policy. This study aims to predict maize productivity by applying an artificial neural network and by building models of multilayer perceptrons (MLPs) using public data and maize experimental networks. The dataset included parameters of climate, soil water balance, and agronomic characteristics from maize hybrids of an experimental network of two agricultural years. The climatic and soil balance water parameters were divided according to the maize plant development stages. Six databases were obtained by combining the imputation of missing data with the agronomic characteristics of the maize hybrids, the climatic parameters/soil water balance, and the complete database with both. Hyper parameterization of the models was obtained using GridSearch and k-fold cross-validation. The models with imputation were more accurate than those without it. The model with climate data/soil water balance and the complete model with imputation presented the smallest errors of 71 kg ha−1. In all the models, cultivars, locations, and their interactions were important, and different climatic conditions had the greatest weight in predicting productivity. It was concluded that the MLP models performed adequately and captured the non-linear effects of the interaction between the environment and maize hybrids. Climatic and soil balance water parameters at different stages of maize plant development explain the productivity of maize hybrids more than the agronomic characteristics of the cultivars.