Modelling of Intra-Field Winter Wheat Crop Growth Variability Using in Situ Measurements, Unmanned Aerial Vehicle-Derived Vegetation Indices, Soil Properties, and Machine Learning Algorithms

Abstract

Crop growth and yield often vary, not only between farms, but also at the sub-field level. These variations can stem from sub-field heterogeneities of soil and plant biophysical parameters. This means that soil and plant biophysical data can be used to predict intra-field crop growth and yield variability. This study used soil properties and vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery as predictor variables, and monthly measurements of crop height (cm) as a response variable to predict crop growth rate in two winter wheat farms in South Africa. These datasets were analyzed using two regression models including Gaussian process regression (GPR) and ensemble learning that uses least-squares boosting (LSboost) and bagging (Bag) in MATLAB. The results showed that soil properties, particularly Ca, Mg, K and clay, were more important than VIs in predicting actual crop growth. Furthermore, GPR (R2 = 0.68 to 0.75, RMSE = 15.85 to 18.38 cm) performed slightly better than LSboost-Bag-ER (R2 = 0.64 to 0.70 and RMSE = 17.26 to 19.34 cm) in predicting crop growth. These findings are useful for crop agronomic management.

Keywords

• wheat
• UAV
• vegetation indices
• soil properties
• Gaussian process regression
• least-squares boosting and bagging regression