# Forecasting Corn Yield with Machine Learning Ensembles

arXiv: https://arxiv.org/abs/2001.09055 · HTML: https://mlsanity.com/paper/2001.09055/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2020.

## Built on by (most cited first, 10 of 10)

- [Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena](https://mlsanity.com/paper/2206.05487/) (2022)
- [Corn Yield Prediction with Ensemble CNN-DNN](https://mlsanity.com/paper/2105.14351/) (2021)
- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [Foundation Models for Environmental Science: A Survey of Emerging Frontiers](https://mlsanity.com/paper/2504.04280/) (2025)
- [A Survey of Foundation Models for Environmental Science](https://mlsanity.com/paper/2503.03142/) (2025)
- [Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review](https://mlsanity.com/paper/2306.04566/) (2023)
- [Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction](https://mlsanity.com/paper/2502.06062/) (2025)
- [An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions](https://mlsanity.com/paper/2108.00952/) (2021)
- [Corn Yield Prediction Model with Deep Neural Networks for Smallholder Farmer Decision Support System](https://mlsanity.com/paper/2401.03768/) (2024)
- [Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning](https://mlsanity.com/paper/2504.18451/) (2025)

## Abstract (arXiv)

The emerge 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. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine learning 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 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 ensemble model. Results from the scenario of having partial in-season weather knowledge reveal that decent yield forecasts can be made as early as June 1st. To find the marginal effect of each input feature on the forecasts made by the proposed ensemble model, a methodology is suggested that is the basis for finding feature importance for the ensemble model. The findings suggest that weather features corresponding to weather in weeks 18-24 (May 1st to June 1st) are the most important input features.