Estimation of Rice Grain Protein Content Using Fusion Imagery from UAV-based Multi-Sensors

Grain protein content (GPC) is a crucial indicator for evaluating rice quality and its commercial value. Establishing a rapid and non-destructive method for estimating rice GPC was established, so as to provide theoretical foundations and technical support for smart breeding and precision crop management.

This study employed a drone equipped with both an RGB camera and a multispectral camera to collect RGB and multispectral imagery, along with ground-measured grain protein content (GPC) data, from the heading to maturity stages of 522 rice breeding material accessions from 2022 to 2023. The Gram-Schmidt image fusion method was applied to process the RGB and multispectral images for generating fused images. Spectral and texture features extracted from the original multispectral images were combined with fused image features, and three machine learning regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Regression (GBR)—were employed to construct GPC estimation models.

The R-band of the RGB images contained richer image information. Vegetation indices derived from the fused R-band exhibited higher correlations with GPC than those calculated from the original multispectral data. The mean texture (Mean) appeared most frequently in texture index construction (accounting for 63.16%), with the MEA₅₆₀-MEA₈₄₀ index showing certain correlations with GPC across different rice types (Huaian conventional japonica: |r²|=0.28; Rugao hybrid japonica: |r²|=0.20). Using a combination of multispectral image features, texture features, and fused image features as input parameters, the GPC estimation models for rice breeding materials achieved higher accuracy at the heading stage (R2 cal=0.64) and maturity stage (R2 cal=0.70) than at the filling stage model (R2 cal=0.53). Incorporating fused image features improved GPC estimation accuracy (ΔR2 cal=0.08-0.26) over using original image features. The interannual model of RF outperformed those of XGBoost and GBR in accuracy (RF: R2 val=0.74, RMSE=0.21%; XGBoost: R2 val=0.58, RMSE=0.23%; GBR: R2 val=0.42, RMSE=0.23%).

The integration of UAV image fusion technique and machine learning methods could effectively enhance the estimation accuracy of the grain protein content (GPC) in rice breeding materials. These findings provided a theoretical reference and practical approaches for the precise estimation of rice quality parameters on a large scale.