Estimate the Pre-Flowering Specific Leaf Area of Rice Based on Vegetation Indices and Texture Indices Derived from UAV Multispectral Imagery

Rice ranks among the most significant staple crops worldwide. Precise and dynamic monitoring of specific leaf area (SLA) provides essential information for evaluating rice growth and yield. While previous remote sensing studies on SLA estimation have primarily focused on crops such as wheat and soybeans, studies on rice SLA remain limited. This study aims to evaluate the predictive potential of several machine learning algorithms for estimating rice SLA across different growth stages, planting densities, and nitrogen treatments at the pre-flowering stage. By utilizing UAV-based multispectral remote sensing data, a high-precision rice SLA monitoring model was developed. The feasibility of using vegetation indices (VIs), texture indices (TIs), and their combinations to predict rice SLA was explored. VIs and TIs were derived from UAV imagery, and the recursive feature elimination was conducted on these indices individually as well as their combined fusion (VIs + TIs). Four machine learning algorithms were employed to predict SLA values. The results indicate that random forest-based models utilizing VIs, TIs, and their fusion can all predict rice SLA effectively with high accuracy. Among these models, the RF model utilizing the combined variables (VIs + TIs) exhibited the highest performance, with R2 = 0.9049, RMSE = 0.0694 m2/g, RRMSE = 0.1042, and RPD = 3.2419. This study demonstrates that individual VIs can provide effective spectral information for SLA estimation, especially during the crucial pre-flowering growth phase of rice. The fusion of VIs and TIs enhances the model’s adaptability to complex field conditions by integrating both canopy biochemical and structural characteristics, thus improving model stability. This technology offers a swift and efficient approach for monitoring crop growth in the field, offering a theoretical foundation for subsequent crop yield estimation.