Wheat yield prediction using a regression approach - Kuzdas, Vojtěch (2022)
This bachelor thesis explores the applicability of linear and tree regression models in predicting the true yield of wheat crops in the South Moravian and Ústí nad Labem regions in the Czech Republic. Thesis pairs Sentinel-2 satellite imagery with single-season post-harvest yield data into a 10x10m grid. Yield data are split into percentile and manually filtered datasets. Two vegetation indices, NDVI and EVI, are extracted from satellite imagery and used as the primary independent variables in model training. Some models use additional post-harvest variables as independent variables. Simple and multiple linear regression and tree regression with bagging are done to predict the true yield. All models across all datasets are compared using standard R2 and MSE statistics. Models based on heavily filtered datasets achieve R2 values in the upper range of 0.92. Models based on less filtered datasets achieve R2 values of 0.65. However, the industrial applicability of constructed models requires further discussion due to potential issues (e.g. overfitting). Tree-based models showed better performance over linear regression models. Although perceived promisingly, tree-bagging did not prove to be effective on the given data.