Bayesian Multi-modeling of Deep Neural Nets for Probabilistic Crop Yield Prediction

Abstract

An imperative aspect of agricultural planning is accurate yield prediction. Artificial Intelligence (AI) techniques, such as Deep Learning (DL), have been recognized as effective means for achieving practical solutions to this problem. However, these approaches most often provide deterministic estimates and do not account for the uncertainties involved in model predictions. This study presents a framework that employs the Bayesian Model Averaging (BMA) and a set of Copula functions to integrate the outputs of multiple deep neural networks, including the 3DCNN (3D Convolutional Neural Network) and ConvLSTM (Convolutional Long Short-Term Memory), and provides a probabilistic estimate of soybean crop yield over a hundred counties across three states in the United States. The results of this study show that the proposed approach produces more accurate and reliable soybean crop yield predictions than the 3DCNN and ConvLSTM networks alone while accounting for the models’ uncertainties.