Utilizing machine learning and vegetation indices for spring wheat yield forecasting

Abstract

The results of research on the development and assessment of the accuracy of predictive models of spring wheat yield based on the use of remote sensing data and machine learning methods are presented. Yield data of spring wheat variety Novosibirskaya 31 obtained in a field experiment in the central forest-steppe of the Novosibirsk region in 2019–2022 were used in this work. Both qualitative predictors (the level of agrotechnologies intensification) and quantitative predictors (atmospheric precipitation in critical phases of wheat plant development and indicators of vegetation indices characterizing the condition of crops) were taken into account when creating the models. The use of various methods of intellectual data analysis, as well as the combination of parametric and non-parametric approaches in the study provided a sufficiently high accuracy of spring wheat yield forecasting. The methods used to predict spring wheat yield included linear regression, nonlinear Regression Splines based model, decision tree (CART), Random Forest, Adaptive Boosting (AdaBoost) and Gradient boosting. It was found that the models based on random forest, gradient and adaptive boosting algorithms were characterized by the highest predictive capabilities of crop yield depending on the emerging conditions of vegetation and controlling influence (R2 = 0.74–0.80). The development of predictive yield models using remote sensing and machine learning represent a certain scientific novelty and practical significance for effective management of crop productivity in changing soil-climatic and economic conditions. Predictive modeling is faced with multilevel environmental uncertainty and high variability of the resulting indicators on a particular land plot. In this regard, the multilevel approach may represent a promising solution for effective forecasting of spring wheat yield.