The seedling condition of wheat reflects the growth status and health level of the seedlings, which is an important basis for yield prediction and field management. Traditional seedling condition evaluation methods relying on manual expertise have limitations in large-scale field applications, such as low efficiency, strong subjectivity, and difficulty in generalization. This study used UAVs equipped with RGB sensors, combined with ground-measured agronomic parameters, to explore a comprehensive wheat seedling condition assessment method that integrated multimodal remote sensing features, so as to provide a technical pathway for large-scale and cross-regional monitoring.

Multi-site and multi-altitude UAV flight tests were designed in Jiangsu Province to capture UAV imagery and to simultaneously collect agronomic parameters, such as tiller number and canopy coverage. Based on vegetation indices and texture features, crop spectral and structural information was extracted. Feature selection was performed using Information Value (IV) and GINI coefficients. Various machine learning models, including random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT), were developed to assess the classification accuracy of seedling condition. The optimal image resolution was determined based on the local variance coefficient to enhance the stability and cross-regional adaptability of the application.

The enhanced green-red difference index (EXGR) showed the best accuracy in identifying canopy coverage during the seedling stage (Pixel Accuracy (PA)=0.69, Specificity (S)=0.83). The green-red ratio index (GRRI) exhibited a significant correlation with tiller number (R²=0.58, relative root mean square error (rRMSE)=0.28). The Random Forest algorithm, which integrates agronomic parameters and remote sensing features, achieved the highest accuracy in seedling condition grade classification (PA=0.85, R=0.86). Tiller number and texture information (E_energy) contributed the most to the seedling condition grade classification (IV>0.70). A flight altitude of (35±5) m was found to be an important reference for obtaining high-quality UAV data (local variance=0.17).

This study constructed a comprehensive wheat seedling condition assessment framework integrating agronomic parameters and remote sensing features, which demonstrated the feasibility and efficiency of portable UAV RGB imagery combined with machine learning methods for cross-regional seedling condition grade monitoring. This method could provide data support and methodological references for regional-scale dynamic seedling condition assessment, crop management strategy formulation, and food security assurance.