Integrating UAV-based multispectral and thermal infrared imageries with machine learning for predicting water stress in winter wheat

Abstract

Assessing spatial and temporal variations in crop water stress is vital for precision irrigation. This study utilized Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MSS) and thermal band (TB) sensors to map the crop water stress index (CWSI) in wheat. A water deficit experiment was conducted on winter wheat under varying irrigation levels during late vegetative, reproductive, and maturation stages. CWSI was calculated using canopy temperature, ambient air temperature, and vapor pressure deficit (VPD). Six machine learning (ML) models—linear model (LM), random forest (RF), decision tree (DT), support vector machine (SVM), extreme gradient boosting (XGB), and artificial neural network (ANN)—were developed for pre-heading, post-heading, and seasonal datasets. The top five vegetation indices (VIs), selected using Recursive Feature Elimination (RFE), along with thermal data, were used as inputs to the ML models. Results showed that seasonal ML models outperformed those based only on pre-heading or post-heading data. Particularly, the RF model performed well, with respective R² and RMSE values of 0.87 and 0.09 for seasonal, 0.82 and 0.05 for pre-heading, and 0.93 and 0.06 for post-heading datasets. SHapley Additive exPlanations (SHAP) analysis identified Red Normalized Value (RNV), TB, and Green Red Vegetation Index (GRVI) as key predictors of CWSI in the RF model. CWSI maps effectively captured spatial variations in water stress, aligning with irrigation management practices. This study demonstrates the effectiveness of combining UAV remote sensing and ML for precision irrigation management.