Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application

Abstract

Accurate prediction of crop yield and dry matter as well as optimized water and nitrogen management can favor rational decision-making for farming systems. Combining high-performance computing with innovative technologies of big data processing, machine learning (ML) advances data-intensive science and provides an important supporting frame for crop yield prediction. This paper evaluated the performance of five ML algorithms, including linear regression (LR), decision tree (DT), support vector machine (SVM), ensemble learning (EL), and Gaussian process regression (GPR), for winter wheat (Triticum aestivum L.) yield and dry matter prediction using data collected from previous studies conducted within the last twenty years in the North China Plain (NCP). In addition, winter wheat yield and dry matter were explored using the best algorithm, while polynomial functions were proposed that could describe the relationship of water and nitrogen application with winter wheat yield and dry matter. Results confirmed that the GPR model outperformed all other models for predicting the yield (R2 = 0.87) and dry matter (R2 = 0.86) of winter wheat. The prediction errors of the GPR model for maximum yield and dry matter of winter wheat were 5.8 % and 1.1 %, respectively. The yield and dry matter of winter wheat in the NCP could be predicted by the GPR model and polynomial functions, and the optimal water and nitrogen application for maximum yield and dry matter could be obtained. The results provide insight into site-specific crop management.