County-scale crop yield prediction by integrating crop simulation with machine learning models

Abstract

Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield prediction complex. Building upon a previous work in which we coupled crop modeling with machine learning (ML) models to predict maize yields for three US Corn Belt states, here, we expand the concept to the entire US Corn Belt (12 states). More specifically, we built five new ML models and their ensemble models, considering the scenarios with and without crop modeling variables. Additional input values in our models are soil, weather, management, and historical yield data. A unique aspect of our work is the spatial analysis to investigate causes for low or high model prediction errors. Our results indicated that the prediction accuracy increases by coupling crop modeling with machine learning. The ensemble model overperformed the individual ML models, having a relative root mean square error (RRMSE) of about 9% for the test years (2018, 2019, and 2020), which is comparable to previous studies. In addition, analysis of the sources of error revealed that counties and crop reporting districts with low cropland ratios have high RRMSE. Furthermore, we found that soil input data and extreme weather events were responsible for high errors in some regions. The proposed models can be deployed for large-scale prediction at the county level and, contingent upon data availability, can be utilized for field level prediction.

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

“…3 and the corresponding graphs are presented in Figure 7. 1.0 0.75 −0.06 XGBoost [34] 0.99 0.75 −0.13 RF [34] 1.12 0.68 −0.14 LR [34] 1.12 0.68 0.03 OWE [34] 0 LightGBM [34] 1.0 0.75 −0.06 XGBoost [34] 0.99 0.75 −0.13 RF [34] 1.12 0.68 −0.14 LR [34] 1.12 0.68 0.03 OWE [34] 0.99 0.75 −0.06 LSTM Model with Adam [54] 0.02 0.96 MLP alone 0. The above table demonstrates that the intended MLP with SMO has outperformed with minimal RMSE and higher R2 values among the models that are being considered for statistical analysis.…”

“…Some recent studies have shown that the accuracy and transparency of crop production forecasts might be improved by combining ML algorithms with crop simulation models. For three US Corn Belt states, RMSE was lowered from 20% to 8% when crop simulation results were included in an ML model, as found by Shahhosseini et al [34]. Table 1 summarizes some of the research conducted in the previous two years to predict agricultural yields.…”

Machine Learning-based Extrapolation of Crop Cultivation Cost

“…Yulita et al [22] examined COVID-19 data in West Java and discovered that the Gaussian processes method outperformed the least median squared linear regression method when it was applied to predicting the new COVID patients. LightGBM, Lasso regression, random forest, linear regression, and XGBoost-and their ensemble models were developed by Sajid et al [23] and demonstrated the models' predictive power. The outcomes showed that combining crop modelling with ML improves prediction accuracy.…”

Ensemble of BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone

“…hyperspectral) data in alfalfa (Feng et al 2020), winter wheat (Li et al 2022) and soybean (Yoosefzadeh-Najafabadi et al 2021a). Within maize, ensembling has been found to be effective for models predicting yield at a high level -predicting yield of counties rather than plots -using machine learning (Shahhosseini et al 2020), machine learning coupled with crop modeling (Sajid et al 2022), and deep learning (Shahhosseini et al 2021b).…”