Research on detection methods of rice leaf area index based on multi-source UAV remote sensing data

This study proposes a multi-sensor fusion framework to address the limitations of single-sensor Unmanned Aerial Vehicle (UAV) systems in rice leaf area index (LAI) estimation. Three UAV platforms equipped with multispectral, RGB, or LiDAR sensors were employed to collect data during three critical rice growth stages. Ordinary Least Squares (OLS) regression models were first constructed to estimate LAI by using the single feature that exhibited high correlation with rice LAI. Results revealed that models based on Laser Penetration Index (LPI; R² = 0.649, RMSE = 0.876) and Height Percentile Area 0–50 (HPA0-50; R² = 0.601, RMSE = 0.933) derived from LiDAR sensor exhibited the best performance among all single features. Three machine learning algorithms (Gradient Boosting Regression, Random Forest, and Stacking) were further employed to construct LAI estimation models by fusing vegetation indices, colour indices, texture features, and spatial structural features extracted from three sensors. The Random Forest model based on multi-sensor fusion data achieved optimal performance (R² = 0.861, RMSE = 0.600), demonstrating a 32.66% accuracy improvement over the single-feature OLS model. Permutation importance analysis was conducted to quantify sensor contributions, revealing the LiDAR sensor as the dominant contributor (67.78% of total importance), followed by multispectral (27.46%) and RGB sensors (4.75%). It was also demonstrated that a single-LiDAR sensor achieved higher accuracy (R² = 0.812) for LAI estimation, much higher than the single-multispectral or single-RGB sensor, which benefited from spectral-texture fusion. These results demonstrated that multi-sensor fusion was an efficient approach to achieve higher accuracy for rice LAI estimation, with LiDAR-driven spatial structural features serving as the key role for high-accuracy LAI estimation.