Identification of Key Rice Growth Stages from Single-Date UAV RGB Imagery Using Stacking Ensemble Learning

The timely identification of key rice growth stages supports water management, crop monitoring, and yield estimation. This study evaluated whether single-date unmanned aerial vehicle (UAV) RGB imagery can identify early tillering, booting, heading, and milk-ripe rice without a continuous image time series. UAV imagery and synchronous field observations were collected from 64 fields in the Zhanghe Irrigation District, China, during 2022–2023. Four visible-light indices and 12 gray-level co-occurrence matrix (GLCM) texture features were used with k-nearest neighbors (KNN), a support vector machine (SVM), random forest (RF), a gradient boosting decision tree (GBDT), and corresponding stacking ensembles. Stacking-RF achieved the highest test performance, with 91.7% accuracy and a macro-F1 of 0.917, exceeding KNN, the best individual model, by 6.8 percentage points and 0.067, respectively. Early tillering and milk-ripe were identified more accurately than booting and heading. Visible-light indices supplied most of the discriminative information, while texture features contributed complementary canopy-structure information. The results show that an RGB-only, single-date input can support a rapid within-site growth-stage assessment. However, the sample-level split, single study area, and absence of radiometric calibration limit inference to new fields, years, and regions.