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

Abstract

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.

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

“…They were second only to Ridge in tuber yield and tuber set prediction. Both RF and Adaboost are tree-based ensemble learning techniques, and they are widely used in the field of crop yield prediction [ 5 , 42 ]. However, in this study, they were found to be inferior to Ridge, PLSR, and SVR in dealing with a small sample size with high dimensional features.…”

“…Unlike the traditional yield assessment method, the advanced remote sensing technique is based on a non-destructive and efficient approach which has great potential for estimating the crop production and monitoring the growth status over the growing season. In the past few decades, satellite data have been broadly used in crop yield prediction with various spatial and spectral resolution [ 5 , 6 , 7 ], but their adoption in precision farming is hampered by the nonnegligible cloud contamination and the poor spatial resolution [ 8 ]. Recently, the development of sensors promoted the application of unmanned aerial vehicles (UAVs) in precision agriculture [ 9 , 10 , 11 ].…”

Using Machine Learning and Feature Selection for Alfalfa Yield Prediction

“…Once again, R 2 was their metric of choice, and their success with similar models such as random forest helped motivate the current work to apply ML to a related problem [23]. Wang et al [9] used ML to predict yields for winter wheat in the CONUS in their 2020 paper, where they combined multiple sources of data including satellite imagery, climate data, and soil maps to train a support vector machine (SVM), AdaBoost model, deep neural network (DNN), and a random forest with positive results measured in R 2 and mean absolute error (MAE), such as the current work, as well as root mean squared error (RMSE) [9]. Our work adopted a simpler approach but used fewer varieties of data, all of which were publicly available, whereas ours did not require processing image data.…”

“…Our work extends and generalizes this approach by reporting R and R 2 , trying some different models, and using more accessible datasets with simpler features. Other previous work in this area generally used more complex data collection techniques, such as unmanned aerial vehicles (UAVs) [7], remote sensors [8], and satellite imagery [9]. Our primary contributions are as follows:…”

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

“…Several authors have also tackled the estimation of plant growth-related traits by data fusion from different sensors [11][12][13][14] for the computation of crop surface models based on image mosaicing methods [15][16][17][18]. Other approaches rely on the computation of individual aerial images.…”