中国农业科学2026，Vol.59Issue(1)：41-56,16.DOI:10.3864/j.issn.0578-1752.2026.01.004
基于无人机多源影像融合的水稻籽粒蛋白质含量估测
Estimation of Rice Grain Protein Content Using Fusion Imagery from UAV-based Multi-Sensors
摘要
Abstract
[Objective]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.[Method]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.[Result]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%).[Conclusion]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.关键词
无人机/多源影像/特征融合/机器学习/水稻/籽粒蛋白质含量/无损估测Key words
unmanned aerial vehicle(UAV)/multi-source imagery/feature fusion/machine learning/rice/grain protein content(GPC)/non-destructive estimation引用本文复制引用
费耀莹,朱艳,曹卫星,郑恒彪,王迪,唐伟杰,郭彩丽,张小虎,邱小雷,程涛,姚霞,江冲亚..基于无人机多源影像融合的水稻籽粒蛋白质含量估测[J].中国农业科学,2026,59(1):41-56,16.基金项目
国家重点研发计划(2022YFD2001100)、钟山育种实验室项目(ZSBBL-KY2023-05)、江苏省青年科技人才托举工程(JSTJ-2024-429) （2022YFD2001100）
