Improving Rice Nitrogen Nutrition Index Estimation Using UAV Images Combined with Meteorological and Fertilization Variables

Real-time and accurate monitoring of rice nitrogen status is essential for precision nitrogen management. Although unmanned aerial vehicle (UAV)-based spectral sensors have been widely used, existing estimation models that rely solely on crop phenotypes still suffer from limited accuracy and stability. In this study, the UAV vegetation indices (VIs), meteorological parameters (M) and fertilization (F) data were incorporated as input variables to establish rice N nutrition index (NNI) estimation models using three machine learning (ML) algorithms (adaptive boosting (AB), partial least squares (PLSR) and random forest (RF). The results showed that the models’ predictive accuracy ranked as follows based on input variable combinations: VI + M + F > VI + F > VI + M > VI. Among the three ML models, the RF algorithm demonstrated the best performance and achieved validation R2 values ranging from 0.94 to 0.95 across all growth stages. Both meteorology and fertilization factors benefited the model, with their incorporation greatly improving model accuracy. This demonstrated the potential to enhance the diagnosis of seasonal rice nitrogen status and provide guidance for in-season site-specific N management through consumer-grade UAV imagery and machine learning.