融合无人机光谱与纹理信息的玉米氮素营养估测

doi:10.3864/j.issn.0578-1752.2024.16.005

Abstract

Abstract:

【Objective】Crop nitrogen nutrition status is a key indicator to characterize the green degree and health status of maize canopy. In order to compare the accuracy of single spectral index model and texture information fusion model in maize nitrogen nutrition estimation model, this investigated the accuracy and reliability of maize nitrogen nutrition estimation model based on UAV multispectral information and texture information fusion. 【Method】 Matrice-300 RTK multi-rotor aircraft equipped with MS600 Pro multi-spectral sensor was used to obtain multi-spectral images of maize tasseling-silking stages under six nitrogen levels in two years. By extracting vegetation index and texture features, the correlation between vegetation index, single texture feature, combined texture index and fusion information of vegetation index and texture index, was comprehensively analyzed. The vegetation index, normalized difference texture index (NDTI) and their combined parameters with the largest amount of information were selected. Four nitrogen nutrition parameters of maize leaf nitrogen content (LNC), plant nitrogen content (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) were compared and estimated by multiple stepwise regression (MSR), random forest (RF), support vector machine (SVM), and grey wolf optimized convolutional neural network ( GWO-CNN ). 【Result】 (1) There were differences in the original spectral reflectance of maize under different nitrogen treatments, and the differences in the red band R (660 nm), blue band B (450 nm) and near-infrared band NIR (840 nm) were significant. (2) The vegetation indices (EVI, GARI, REOSAVI, SIPI, and MCARI), single texture features (var₄₅₀, var₆₆₀, mean₈₄₀, dis₇₂₀, and hom₈₄₀) and combined texture index NDTI extracted from UAV multispectral images could be used for LNC, PNC, LNA and PNA estimation of maize in VT-R1 stage. The GWO-CNN model based on vegetation index had better estimation effect on LNC, PNC, LNA and PNA than single texture feature and texture index model, and its R² were 0.831, 0.761, 0.826 and 0.770, respectively. (3) The accuracy of GWO-CNN model with vegetation index and texture index for LNC, PNC, LNA and PNA estimation was significantly higher than that of vegetation index and texture index, and its R² was 0.921, 0.901, 0.917 and 0.892, respectively, which was 9.77%, 15.54%, 9.92% and 13.68% higher than that of single spectral information optimal estimation model. 【Conclusion】 Fusion of multi-spectral vegetation index and texture index could effectively improve the estimation accuracy of maize nitrogen nutrition, and better evaluate the distribution of maize nitrogen distribution, which provided new ideas for precise maize nitrogen fertilizer management based on UAV platform at field scale.

Key words: maize, nitrogen, multi-spectral, vegetation index, texture features

YUN BinYuan, XIE TieNa, LI Hong, YUE Xiang, LÜ MingYue, WANG JiaQi, JIA Biao. Nitrogen Nutrition Estimation of Maize Based on UAV Spectrum and Texture Information[J].Scientia Agricultura Sinica, 2024, 57(16): 3154-3170.

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年份 Year	pH	有机质 OM (g·kg^-1)	全氮 Total N (g·kg^-1)	全磷 Total P (g·kg^-1)	碱解氮 Avail. N (mg·kg^-1)	速效磷 Avail. P (mg·kg^-1)	速效钾 Avail. K (mg·kg^-1)
2021	7.71	12.56	0.73	0.51	36.00	15.37	99.54
2022	8.14	14.15	0.80	0.60	37.65	16.31	108.41

光谱波段 Band	中心波长 Center band-length (nm)	波宽 Band width (nm)	灰板反射率 Gray plate reflectivity (%)
红Red (R)	660	10	51.2
绿Green (G)	555	20	51.2
蓝Blue (B)	450	20	51.2
近红外Near infrared reflectance (NIR)	840	40	51.0
红边Red edge (RE)	720	10	51.2

指数Index	公式Formula	来源 Reference
归一化植被指数 NDVI	(NIR−R)/(NIR+R)	[13]
改进比值植被指数 MSR	(NIR/R−1) /(NIR/R+1)^0.5	[13]
增强型植被指数 EVI	2.5(NIR−R)/(NIR+6R−7.5B+1)	[11]
归一化绿色植被指数NGI	G/(NIR+G+RE)	[17]
绿色大气阻力植被指数 GARI	NIR−[G−1.7(B−R)]/ NIR+[G−1.7(B−R)]	[8]
结构不敏感色素指数 SIPI	(NIR−B)/(NIR−R)	[18]
修正型叶绿素吸收反射率植被指数 MCARI	[(NIR−RE) −0.2(NIR−R) ]( NIR/RE)	[8]
红边重归一化差值植被指数RERDVI	(NIR−RE)/(NIR+RE)	[19]
红边优化土壤调节植被指数REOSAVI	(1+0.16)( NIR−RE) /(NIR+RE+0.16)	[19]

指数Index	公式Formula
均值Mean (mean)	$\text{mean=}\sum{_{i,j}^{N-\text{1}}i{{P}_{i,j}}}$
方差Variance (var)	$\text{var=}\sum{_{i,j=0}^{N-\text{1}}i{{P}_{i,j}}}{{(i-mean)}^{2}}$
同质性Homogeneity (hom)	$\text{hom=}\sum{_{i,j=0}^{N-\text{1}}i}\frac{{{P}_{i,j}}}{1+{{(i-j)}^{2}}}$
对比度Contrast (con)	$\text{con=}\sum{_{i,j=0}^{N-\text{1}}i}{{P}_{i,j}}{{(i-j)}^{2}}$
相异性Dissimilarity (dis)	$~\text{dis=}\sum{_{i,j=0}^{N-\text{1}}i}{{P}_{i,j}}\|i-j\|$
墒Entropy (ent)	$\text{ent=}\sum{_{i,j=0}^{N-\text{1}}i}{{P}_{i,j}}(-\ln {{P}_{i,j}})$
二阶矩Second moment (sm)	$\text{sm=}\sum{_{i,j=0}^{N-\text{1}}i}{{P}_{i,j}}^{2}$
自相关Correlation (cor)	$\operatorname{corr}=\sum_{i, j=0}^{N-1} i P_{i, j}\left[\frac{(i-\text { mean })(j-\text { mean })}{\sqrt{\operatorname{var}_{i} \times \operatorname{var}_{j}}}\right]$

模型 Model	LNC		PNC		LNA		PNA
模型 Model	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
MSR	0.771	0.998	0.664	0.734	0.759	7.314	0.701	12.677
RF	0.795	0.876	0.747	0.848	0.783	6.294	0.751	11.858
SVM	0.802	0.917	0.721	0.391	0.798	7.286	0.759	12.387
GWO-CNN	0.831	0.723	0.761	0.336	0.826	6.299	0.770	11.387

Nitrogen Nutrition Estimation of Maize Based on UAV Spectrum and Texture Information

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参数 Parameter	纹理指数Texture Parameter	MSR	RF	SVM	GWO-CNN
LNC	NDTI（hom₇₂₀，con₇₂₀）NDTI（var₇₂₀，mean₈₄₀）NDTI（dis₆₆₀，hom₈₄₀）	0.670	0.717	0.723	0.755
PNC	NDTI（con₇₂₀，hom₇₂₀）NDTI（var₇₂₀，mean₈₄₀）NDTI（dis₆₆₀，hom₈₄₀）	0.662	0.691	0.711	0.737
LNA	NDTI（hom₇₂₀，con₇₂₀）NDTI（var₆₆₀，mean₈₄₀）NDTI（dis₆₆₀，hom₈₄₀）	0.674	0.709	0.719	0.753
PNA	NDTI（dis₆₆₀，hom₈₄₀）NDTI（var₆₆₀，mean₈₄₀）NDTI（dis₇₂₀，hom₇₂₀）	0.656	0.703	0.713	0.731

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模型 Model	LNC		PNC		LNA		PNA
模型 Model	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
MSR	0.545	0.519	0.510	1.095	0.548	8.133	0.510	14.606
RF	0.574	0.823	0.536	1.087	0.563	7.251	0.529	12.358
SVM	0.571	0.749	0.542	0.987	0.559	8.288	0.532	13.487
GWO-CNN	0.599	0.648	0.569	0.996	0.597	7.287	0.563	12.397

模型 Model	LNC		PNC		LNA		PNA
模型 Model	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
MSR	0.881	0.768	0.872	0.889	0.884	4.378	0.860	7.256
RF	0.893	0.787	0.895	0.745	0.897	3.362	0.874	6.643
SVM	0.901	0.672	0.899	0.772	0.902	3.048	0.881	6.783
GWO-CNN	0.921	0.559	0.901	0.659	0.917	2.856	0.892	6.135