Forecasting Corn Yield With Machine Learning Ensembles

作者：Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis · 发表于：Frontiers in Plant Science · 年份：2020 · DOI：10.3389/fpls.2020.01120 · 被引用次数：275 · 研究领域：Smart Agriculture and AI、Climate change impacts on agriculture、Forecasting Techniques and Applications

The emergence of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields. Recent research has shown that Machine learning (ML) can provide reasonable predictions, faster, and with higher flexibility compared to simulation crop modeling. However, a single machine learning model can be outperformed by a “committee” of models (machine learning ensembles) that can reduce prediction bias, variance, or both and is able to better capture the underlying distribution of the data. This paper provides a machine leaning based framework to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa) considering complete and partial in-season weather knowledge. Several ensemble models are designed using blocked sequential procedure to generate out-of-bag predictions. The forecasts are made in county-level scale and aggregated for agricultural district, and state level scales. Results show that ensemble models based on weighted average of the base learners (average ensemble, exponentially weighted average ensemble (EWA), and optimized weighted ensemble) outperform individual models. Specifically, the proposed ensemble model could achieve best prediction accuracy (RRMSE of 7.8%) and least mean bias error (-6.06 bu/acre) compared to other developed models. Comparing our proposed model forecasts with the literature demonstrates the superiority of forecasts made by our proposed ense...