基于无人机多源影像融合的水稻籽粒蛋白质含量估测

doi:10.3864/j.issn.0578-1752.2026.01.004

Abstract

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.

Key words: unmanned aerial vehicle (UAV), multi-source imagery, feature fusion, machine learning, rice, grain protein content (GPC), non-destructive estimation

FEI YaoYing, WANG Di, TANG WeiJie, GUO CaiLi, ZHANG XiaoHu, QIU XiaoLei, CHENG Tao, YAO Xia, JIANG ChongYa, ZHU Yan, CAO WeiXing, ZHENG HengBiao. Estimation of Rice Grain Protein Content Using Fusion Imagery from UAV-based Multi-Sensors[J].Scientia Agricultura Sinica, 2026, 59(1): 41-56.

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Figures/Tables 17

Fig. 1

Table 1

Table 2

Table 3

Spectral parameters used in this study"

光谱参数 Spectral parameter	名称 Name	公式 Formula	参考文献 Reference
NDRE	归一化差值红边指数 Normalized difference red-edge index	$\mathrm{NDRE}=\frac{\mathrm{R}_{840}-\mathrm{R}_{717}}{\mathrm{R}_{840}+\mathrm{R}_{717}}$	[22]
CI_rededge	红边叶绿素指数 Red-edge chlorophyll index	$\mathrm{CI}_{\text {rededge }}=\frac{\mathrm{R}_{840}}{\mathrm{R}_{717}}-1$	[23]
CI_green	绿度叶绿素指数 Green chlorophyll index	$\mathrm{CI}_{\text {green }}=\frac{\mathrm{R}_{717}}{\mathrm{R}_{560}}-1$	[23]
NDYI	归一化差值黄度指数 Normalized difference yellowness index	$\mathrm{NDYI}=\frac{\mathrm{R}_{560}-\mathrm{R}_{475}}{\mathrm{R}_{560}+\mathrm{R}_{475}}$	[24]
EVI2	增强型植被指数2 Enhanced vegetation index 2	$\mathrm{EVI} 2=\frac{2.5 \times\left(\mathrm{R}_{840}-\mathrm{R}_{717}\right)}{\mathrm{R}_{840}+6 \times \mathrm{R}_{668}-7.5 \times \mathrm{R}_{475}+1}$	[25]
MCARI	修正型叶绿素吸收反射指数 Modified chlorophyll absorption reflectance index	$M C A R I=\left[\left(R_{717}-R_{668}\right)-0.2 \times\left(R_{717}-R_{560}\right)\right] \times\left(\frac{R_{717}}{R_{668}}\right)$	[26]
SAVI	土壤调节植被指数 Soil adjusted vegetation index	$\mathrm{SAVI}=\frac{1.5 \times\left(\mathrm{R}_{840}-\mathrm{R}_{668}\right)}{\mathrm{R}_{840}+\mathrm{R}_{668}+0.5}$	[27]
DATT	达特指数 Datt vegetation index	$\mathrm{DATT}=\frac{\mathrm{R}_{840}-\mathrm{R}_{717}}{\mathrm{R}_{840}-\mathrm{R}_{668}}$	[28]
DVI	差值植被指数 Difference vegetation index	$\mathrm{DVI}=\mathrm{R}_{840}-\mathrm{R}_{668}$	[29]
GNDVI	绿度归一化差值植被指数 Green normalized difference vegetation index	$\text { GNDVI }=\frac{R_{840}-R_{560}}{R_{840}+R_{560}}$	[30]
OSAVI	优化型土壤调节植被指数 Optimized soil adjusted vegetation index	$\text { OSAVI }=\frac{R_{840}-R_{668}}{R_{840}+R_{668}+0.16}$	[31]
SIPI	结构不敏感色素指数 Structure-intensive pigment index	$\text { SIPI }=\frac{R_{840}-R_{475}}{R_{840}+R_{668}}$	[32]
SRPI	简单比值色素指数 Simple ratio pigment index	$\mathrm{SRPI}=\frac{\mathrm{R}_{475}}{\mathrm{R}_{668}}$	[32]

Table 3

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Table 6

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Table 7

Table 8

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地点 Site	播种时间 Sowing time	品种 Variety	株行距 plant and row spacing	面积 Area (m²)	小区个数 Number of plots
如皋 Rugao	2022-05-15	常规粳稻 Conventional Japonica rice 杂交粳稻 Hybrid Japonica rice	20 cm×20 cm	3.60	240
如皋 Rugao	2023-05-10	常规粳稻 Conventional Japonica rice 杂交粳稻 Hybrid Japonica rice	20 cm×30 cm	5.20	240
淮安 Huaian	2023-05-10	常规粳稻 Conventional Japonica rice	20 cm×20 cm	10.75	297

相机 Camera	影像大小 Image size (pixel)	波段 Band	视场角 Field of view	飞行高度 Flight height (m)	像素大小 Pixel size (cm)	实物图 Product photo
禅思P1 DJI Zenmuse P1	8192×5460	R（620-750 nm）、G（500-570 nm）、B（450-495 nm）	38°×31°	30	0.38
RedEdge-MX	1280×960	475±20、560±20、668±10、717±40、840±10 nm	47.2°×20.9°	30	2.17

模型 Model	年份 Year	类型 Type	数据集描述 Dataset description	样本数量 Number
机器学习 Machine learning	2023	常规粳稻 Conventional Japonica Rice	训练集 Training set	105
		常规粳稻 Conventional Japonica Rice	验证集 Validation set	45
		杂交粳稻 Hybrid Japonica Rice	训练集 Training set	80
		杂交粳稻 Hybrid Japonica Rice	验证集 Validation set	34
交叉验证 Cross validation	2023	常规粳稻 Conventional Japonica Rice	训练集 Training set	103
		常规粳稻 Conventional Japonica Rice	验证集 Validation set	44
		杂交粳稻 Hybrid Japonica Rice	训练集 Training set	60
		杂交粳稻 Hybrid Japonica Rice	验证集 Validation set	26
年际验证 Inter-annual validation	2022	杂交粳稻 Hybrid Japonica Rice	训练集 Training set	200
年际验证 Inter-annual validation	2023	杂交粳稻 Hybrid Japonica Rice	验证集 Validation set	200

类型 Type	地点 Site	年份 Year	均值 Mean	最大值 Max	最小值 Min	中位数 Median	标准差 Standard deviation	变异系数 Coefficient of variation (%)
常规粳稻 Conventional Japonica rice	如皋 Rugao	2022	6.64	7.80	5.67	6.62	0.58	0.09
	淮安 Huaian	2023	7.11	8.02	6.07	7.14	0.47	0.07
	如皋 Rugao	2023	8.61	10.41	6.97	8.49	0.98	0.11
杂交粳稻 Hybrid Japonica rice	如皋 Rugao	2022	7.04	10.38	5.40	6.83	0.97	0.14
杂交粳稻 Hybrid Japonica rice	如皋 Rugao	2023	9.08	11.97	6.70	9.12	1.21	0.13

时期 Period	波段 Band	峰值信噪比 PSNR	结构相似性指数 SSIM	归一化均方根误差NRMSE	空间频率 SF	标准差 SD	平均梯度 AG
抽穗期 Heading	红 Red	12.09	0.70	0.26	32.06	31.39	18.36
抽穗期 Heading	绿 Green	8.44	0.59	0.39	15.04	14.43	8.42
灌浆初期 Early filling	红 Red	9.86	0.62	0.36	16.12	15.75	8.85
灌浆初期 Early filling	绿 Green	8.92	0.41	0.32	16.34	15.82	8.80
灌浆中期 Middle filling	红 Red	12.09	0.61	0.26	32.06	31.39	18.36
灌浆中期 Middle filling	绿 Green	9.73	0.49	0.33	13.01	12.84	7.29
成熟期 Maturity	红 Red	10.05	0.67	0.34	18.61	17.44	10.64
成熟期 Maturity	绿 Green	9.55	0.58	0.32	18.31	15.25	9.31

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

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生育时期 Period	参数 Parameter	R²	RF		XGBoost		GBR
生育时期 Period	参数 Parameter	R²	淮安 Huaian	如皋 Rugao	淮安 Huaian	如皋 Rugao	淮安 Huaian	如皋 Rugao
抽穗开花期 Heading	植被指数、纹理指数 Vegetation index、Texture index	建模R2 cal Calibration R2 cal	0.54	0.49	0.55	0.68	0.48	0.34
	植被指数、纹理指数 Vegetation index、Texture index	验证R2 val Validation R2 val	0.29	0.39	0.54	0.67	0.38	0.08
	融合指数、纹理指数 Fusion index、Texture index	建模R2 cal Calibration R2 cal	0.56	0.78	0.64	0.64	0.63	0.60
	融合指数、纹理指数 Fusion index、Texture index	验证R2 val Validation R2 val	0.41	0.73	0.33	0.55	0.42	0.46
灌浆初期 Early filling	植被指数、纹理指数 Vegetation index、Texture index	建模R2 cal Calibration R2 cal	-	0.44	-	0.79	-	0.26
	植被指数、纹理指数 Vegetation index、Texture index	验证R2 val Validation R2 val	-	0.42	-	0.74	-	0.21
	融合指数、纹理指数 Fusion index、Texture index	建模R2 cal Calibration R2 cal	-	0.76	-	0.62	-	0.41
	融合指数、纹理指数 Fusion index、Texture index	验证R2 val Validation R2 val	-	0.74	-	0.58	-	0.40
灌浆中期 Middle filling	植被指数、纹理指数 Vegetation index、Texture index	建模R2 cal Calibration R2 cal	0.42	0.42	0.46	0.80	0.30	0.13
	植被指数、纹理指数 Vegetation index、Texture index	验证R2 val Validation R2 val	0.28	0.32	0.20	0.65	0.18	0.08
	融合指数、纹理指数 Fusion index、Texture index	建模R2 cal Calibration R2 cal	0.44	0.78	0.53	0.73	0.45	0.62
	融合指数、纹理指数 Fusion index、Texture index	验证R2 val Validation R2 val	0.33	0.74	0.37	0.66	0.31	0.49
成熟期 Maturity	植被指数、纹理指数 Vegetation index、Texture index	建模R2 cal Calibration R2 cal	0.31	0.47	0.32	0.75	0.30	0.42
	植被指数、纹理指数 Vegetation index、Texture index	验证R2 val Validation R2 val	0.26	0.44	0.28	0.72	0.23	0.14
	融合指数、纹理指数 Fusion index、Texture index	建模R2 cal Calibration R2 cal	0.46	0.76	0.70	0.62	0.48	0.53
	融合指数、纹理指数 Fusion index、Texture index	验证R2 val Validation R2 val	0.29	0.75	0.51	0.51	0.33	0.37

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