Journal Article

Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

Feng, Luwei, Wang, Yumiao, Du, Qingyun, Zhang, Zhou, Runge, Troy

2020

Overview

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.

• Publisher: MDPI AG
• Subject: Agricultural production / Algorithms / Artificial neural networks / Cereal crops / Climatic data / CONUS / Crop management / Crop yield / Data sources / Economic forecasting / empirical models / Food / Food security / grain crops / grain yield / Growing season / Learning algorithms / Machine learning / meteorological data / Model accuracy / multi-source data / Neural networks / Physiology / prediction / Prediction models / Regression analysis / Regression models / Remote sensing / Researchers / Satellite imagery / Satellites / soil map / Soil maps / Statistical analysis / Support vector machines / Triticum aestivum / United States / Vegetation / Wheat / Winter / Winter wheat / world markets / World population / yield forecasting / yield prediction