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

摘要/Abstract

摘要：

【目的】作物氮素营养状况是表征玉米冠层绿色程度和健康状态的关键指标，为比较玉米氮素营养估测模型中单一光谱指数模型与融合纹理信息模型精度的差异，探究基于无人机多光谱与纹理信息融合的玉米氮素营养估测模型的精准性与可靠性。【方法】采用Matrice 300 RTK多旋翼飞行器搭载MS600 Pro多光谱传感器，获取2年间6个氮素水平下玉米抽雄-吐丝期的多光谱影像数据，提取植被指数和纹理特征信息，综合分析植被指数、单纹理特征、组合纹理指数及植被指数和纹理指数融合的相关性，优选信息量最大的植被指数、归一化差值纹理指数（NDTI）及其组合信息，利用多元逐步回归（MSR）、随机森林（RF）、支持向量机（SVM）和灰狼优化的卷积神经网络（GWO-CNN）对比估测玉米叶片氮含量（LNC）、植株氮含量（PNC）、叶片氮积累（LNA）和植株氮积累（PNA）4个氮素营养参数。【结果】（1）不同氮素处理下玉米原始光谱反射率之间存在差异，红波段R（660 nm）、蓝波段B（450 nm）和近红外波段NIR（840 nm）波段差异较为显著。（2）基于无人机多光谱影像提取的植被指数（EVI、GARI、REOSAVI、SIPI和MCARI）、单纹理特征（var₄₅₀、var₆₆₀、mean₈₄₀、dis₇₂₀和hom₈₄₀）和组合纹理指数NDTI均可用于VT-R1阶段玉米LNC、PNC、LNA及PNA估测，其中基于植被指数的GWO-CNN模型对LNC、PNC、LNA和PNA的估测效果优于单纹理特征和纹理指数模型，R²分别为0.831、0.761、0.826和0.770。（3）融合植被指数和纹理指数的GWO-CNN模型对LNC、PNC、LNA和PNA估测精度明显高于植被指数和纹理指数，R²分别为0.921、0.901、0.917和0.892，较单一光谱信息最优估测模型精度R²分别提高了9.77%、15.54%、9.92%和13.68%。【结论】融合多光谱的植被指数和纹理指数能够有效提高玉米氮素营养估测精度，较好地评估玉米氮素分布情况，为田块尺度下基于无人机平台的玉米氮肥精准管理提供新思路。

关键词: 玉米, 氮素, 多光谱, 植被指数, 纹理信息

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

运彬媛, 谢铁娜, 李虹, 岳翔, 吕明玥, 王佳琦, 贾彪. 融合无人机光谱与纹理信息的玉米氮素营养估测[J]. 中国农业科学, 2024, 57(16): 3154-3170.

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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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2024.16.005

https://www.chinaagrisci.com/CN/Y2024/V57/I16/3154

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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