秋玉米SPAD值的光谱估算模型研究

doi:10.16035/j.issn.1001-7283.2019.05.028

Abstract

Abstract:

By studying the correlation between SPAD value of different autumn maize leaves varieties and first derivative spectrum, the sensitive wavelengths and first derivative spectrum parameters of 8 autumn maize varieties were screened out, and linear, exponential, polynomial and first derivative spectrum parameters’ prediction models of SPAD value of different autumn maize varieties were established, and RMSE and RE of modeling set and verification set were calculated. The results showed that higher correlation between the SPAD value and the first derivative spectrum of different autumn maize varieties, which reached more than 0.8. The sensitive bands of different autumn maize varieties in first derivative spectrum are between 650-680nm. The SPAD value prediction model based on the sensitive wavelength performed well, especially by the polynomial model, but its estimation accuracy was different among the varieties. Among the 8 varieties of autumn maize, the prediction model of Zhengda 999 had the best performance, its modeling set RMSE and RE were 2.762 and 3.643%, respectively, and its verification set RMSEv and REv were 3.322 and 4.518%, respectively.

Key words: Autumn maize, SPAD value, Spectrum, Estimation model

Hua Yuhui,Gao Zhiqiang. Hyperspectral Estimation of SPAD Values in Different Varieties of Autumn Maize[J].Crops, 2019, 35(5): 173-179.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Table 1

Coefficient of determination of first derivative spectrum and spectrum parameters with SPAD value of different autumn maize varieties"

双兴玉2号Shuangxingyu No.2		正大999 Zhengda 999		湘农玉27号Xiangnongyu No.27		兴玉818 Xingyu 818
参数Parameter	R²	参数Parameter	R²	参数Parameter	R²	参数Parameter	R²
R′₄₉₁	0.453^**	R′₄₈₃	0.346^**	R′₄₉₀	0.317^**	R′₄₉₉	0.053^*
R′₆₂₅	0.403^**	R′₆₃₅	0.387^**	R′₆₂₈	0.384^**	R′₆₂₄	0.352^**
R′₆₇₁	0.729^**	R′₆₇₁	0.906^**	R′₆₇₃	0.861^**	R′₆₆₇	0.721^**
R′₇₂₈	0.301^**	R′₇₁₄	0.112^*	R′₇₃₈	0.564^**	R′₇₃₉	0.101^*
NDSI (R′₄₉₁,R′₆₂₅)	0.003	NDSI (R′₄₈₃,R′₆₃₅)	0.277^**	NDSI (R′₄₉₀,R′₆₂₈)	0.000	NDSI (R′₄₉₉,R′₆₂₄)	0.006
NDSI (R′₄₉₁,R′₆₇₁)	0.681^**	NDSI (R′₄₈₃,R′₆₇₁)	0.237^**	NDSI (R′₄₉₀,R′₆₇₃)	0.489^**	NDSI (R′₄₉₉,R′₆₆₇)	0.654^**
NDSI (R′₄₉₁,R′₇₂₈)	0.325^**	NDSI (R′₄₈₃,R′₇₁₄)	0.188	NDSI (R′₄₉₀,R′₇₃₈)	0.299^**	NDSI (R′₄₉₉,R′₇₃₉)	0.362^**
NDSI (R′₆₂₅,R′₆₇₁)	0.005	NDSI (R′₆₃₅,R′₆₇₁)	0.084	NDSI (R′₆₂₈,R′₆₇₃)	0.013	NDSI (R′₆₂₄,R′₆₆₇)	0.012
NDSI (R′₆₂₅,R′₇₂₈)	0.413^**	NDSI (R′₆₃₅,R′₇₁₄)	0.229^**	NDSI (R′₆₂₈,R′₇₃₈)	0.411^**	NDSI (R′₆₂₄,R′₇₃₉)	0.400^**
NDSI (R′₆₇₁,R′₇₂₈)	0.681^**	NDSI (R′₆₇₁,R′₇₁₄)	0.787^**	NDSI (R′₆₇₃,R′₇₃₈)	0.443^**	NDSI (R′₆₆₇,R′₇₃₉)	0.734^**
RSI (R′₄₉₁,R′₆₂₅)	0.022	RSI (R′₄₈₃,R′₆₃₅)	0.001	RSI (R′₄₉₀,R′₆₂₈)	0.001	RSI (R′₄₉₉,R′₆₂₄)	0.001
RSI (R′₄₉₁,R′₆₇₁)	0.089	RSI (R′₄₈₃,R′₆₇₁)	0.000	RSI (R′₄₉₀,R′₆₇₃)	0.055	RSI (R′₄₉₉,R′₆₆₇)	0.080
RSI (R′₄₉₁,R′₇₂₈)	0.056	RSI (R′₄₈₃,R′₇₁₄)	0.001	RSI (R′₄₉₀,R′₇₃₈)	0.049	RSI (R′₄₉₉,R′₆₃₉)	0.011
RSI (R′₆₂₅,R′₆₇₁)	0.642^**	RSI (R′₆₃₅,R′₆₇₁)	0.413^**	RSI (R′₆₂₈,R′₆₇₃)	0.607^**	RSI (R′₆₂₄,R′₆₆₇)	0.644^**
RSI (R′₆₂₅,R′₇₂₈)	0.335^**	RSI (R′₆₃₅,R′₇₁₄)	0.251^**	RSI (R′₆₂₈,R′₇₃₈)	0.461^**	RSI (R′₆₂₄,R′₇₃₉)	0.330^**
RSI (R′₆₇₁,R′₇₂₈)	0.017	RSI (R′₆₇₁,R′₇₁₄)	0.706^**	RSI (R′₆₇₃,R′₇₃₈)	0.450^**	RSI (R′₆₆₇,R′₇₃₉)	0.002
DSI (R′₄₉₁,R′₆₂₅)	0.280^**	DSI (R′₄₉₁,R′₆₂₅)	0.016	DSI (R′₄₉₀,R′₆₂₈)	0.158^*	DSI (R′₄₉₉,R′₆₂₄)	0.246^**
DSI (R′₄₉₁,R′₆₇₁)	0.503^**	DSI (R′₄₉₁,R′₆₇₁)	0.861^**	DSI (R′₄₉₀,R′₆₇₃)	0.702^**	DSI (R′₄₉₉,R′₆₆₇)	0.568^**
DSI (R′₄₉₁,R′₇₂₈)	0.222^**	DSI (R′₄₉₁,R′₇₁₄)	0.040	DSI (R′₄₉₀,R′₇₃₈)	0.552^**	DSI (R′₄₉₉,R′₇₃₉)	0.101^*
DSI (R′₆₂₅,R′₆₇₁)	0.453^**	DSI (R′₆₂₅,R′₆₇₁)	0.827^**	DSI (R′₆₂₈,R′₆₇₃)	0.793^**	DSI (R′₆₂₄,R′₆₆₇)	0.643^**
DSI (R′₆₂₅,R′₇₂₈)	0.151^*	DSI (R′₆₂₅,R′₇₁₄)	0.043	DSI (R′₆₂₈,R′₇₃₈)	0.532^**	DSI (R′₆₂₄,R′₇₃₉)	0.060
DSI (R′₆₇₁,R′₇₂₈)	0.062	DSI (R′₆₇₁,R′₇₁₄)	0.008	DSI (R′₆₇₃,R′₇₃₈)	0.466^**	DSI (R′₆₆₇,R′₇₃₉)	0.035
临奥1号B3 Lin′ao No.1 B3		湘农玉32号Xiangnongyu No.32		洛玉1号Luoyu No.1		田玉335 Tianyu 335
参数Parameter	R²	参数Parameter	R²	参数Parameter	R²	参数Parameter	R²
R′₅₄₉	0.345^**	R′₅₅₃	0.332^**	R′₅₃₉	0.801^**	R′₅₅₁	0.325^**
R′₆₂₄	0.598^**	R′₆₃₃	0.206^**	R′₆₁₀	0.853^**	R′₆₃₄	0.471^**
R′₆₇₁	0.608^**	R′₆₇₇	0.524^**	R′₆₅₄	0.819^**	R′₆₇₇	0.217^**
R′₇₃₉	0.752^**	R′₇₂₉	0.136^*	R′₇₄₀	0.883^**	R′₇₂₃	0.545^**
NDSI (R′₅₄₉,R′₆₂₄)	0.003	NDSI (R′₅₅₃,R′₆₃₃)	0.000	NDSI (R′₅₃₉,R′₆₁₀)	0.001	NDSI (R′₅₅₁,R′₆₃₄)	0.591^**
NDSI (R′₅₄₉,R′₆₇₁)	0.127^*	NDSI (R′₅₅₃,R′₆₇₇)	0.274^**	NDSI (R′₅₃₉,R′₆₅₄)	0.021	NDSI (R′₅₅₁,R′₆₇₇)	0.359^**
NDSI (R′₅₄₉,R′₇₃₉)	0.004	NDSI (R′₅₅₃,R′₇₂₉)	0.116^*	NDSI (R′₅₃₉,R′₇₄₀)	0.420^**	NDSI (R′₅₅₁,R′₇₂₃)	0.591^**
NDSI (R′₆₂₄,R′₆₇₁)	0.011	NDSI (R′₆₃₃,R′₆₇₇)	0.248^**	NDSI (R′₆₁₀,R′₆₅₄)	0.717^**	NDSI (R′₆₃₄,R′₆₇₇)	0.037
NDSI (R′₆₂₄,R′₇₃₉)	0.042	NDSI (R′₆₃₃,R′₇₂₉)	0.056	NDSI (R′₆₁₀,R′₇₄₀)	0.436^**	NDSI (R′₆₃₄,R′₇₂₃)	0.082
NDSI (R′₆₇₁,R′₇₃₉)	0.662^**	NDSI (R′₆₇₇,R′₇₂₉)	0.733^**	NDSI (R′₆₅₄,R′₇₄₀)	0.735^**	NDSI (R′₆₇₇,R′₇₂₃)	0.813^**
RSI (R′₅₄₉,R′₆₂₄)	0.005	RSI (R′₅₅₃,R′₆₃₃)	0.000	RSI (R′₅₃₉,R′₆₁₀)	0.014	RSI (R′₅₅₁,R′₆₃₄)	0.007
RSI (R′₅₄₉,R′₆₇₁)	0.010	RSI (R′₅₅₃,R′₆₇₇)	0.428^**	RSI (R′₅₃₉,R′₆₅₄)	0.586^**	RSI (R′₅₅₁,R′₆₇₇)	0.007
RSI (R′₅₄₉,R′₇₃₉)	0.009	RSI (R′₅₅₃,R′₇₂₉)	0.126^*	RSI (R′₅₃₉,R′₇₄₀)	0.426^**	RSI (R′₅₅₁,R′₇₂₃)	0.007
RSI (R′₆₂₄,R′₆₇₁)	0.678^**	RSI (R′₆₃₃,R′₆₇₇)	0.389^**	RSI (R′₆₁₀,R′₆₅₄)	0.737^**	RSI (R′₆₃₄,R′₆₇₇)	0.455^**
RSI (R′₆₂₄,R′₇₃₉)	0.435^**	RSI (R′₆₃₃,R′₇₂₉)	0.049	RSI (R′₆₁₀,R′₇₄₀)	0.552^**	RSI (R′₆₃₄,R′₇₂₃)	0.087
RSI (R′₆₇₁,R′₇₃₉)	0.020	RSI (R′₆₇₇,R′₇₂₉)	0.517^**	RSI (R′₆₅₄,R′₇₄₀)	0.004	RSI (R′₆₇₇,R′₇₂₃)	0.731^**
DSI (R′₅₄₉,R′₆₂₄)	0.475^**	DSI (R′₅₅₃,R′₆₃₃)	0.029	DSI (R′₅₃₉,R′₆₁₀)	0.655^**	DSI (R′₅₅₁,R′₆₃₄)	0.069
DSI (R′₅₄₉,R′₆₇₁)	0.768^**	DSI (R′₅₅₃,R′₆₇₇)	0.832^**	DSI (R′₅₃₉,R′₆₅₄)	0.793^**	DSI (R′₅₅₁,R′₆₇₇)	0.856^**
DSI (R′₅₄₉,R′₇₃₉)	0.366^**	DSI (R′₅₅₃,R′₇₂₉)	0.368^**	DSI (R′₅₃₉,R′₇₄₀)	0.256^**	DSI (R′₅₅₁,R′₇₂₃)	0.350^**
DSI (R′₆₂₄,R′₆₇₁)	0.705^**	DSI (R′₆₃₃,R′₆₇₇)	0.813^**	DSI (R′₆₁₀,R′₆₅₄)	0.000	DSI (R′₆₃₄,R′₆₇₇)	0.814^**
DSI (R′₆₂₄,R′₇₃₉)	0.325^**	DSI (R′₆₃₃,R′₇₂₉)	0.373^**	DSI (R′₆₁₀,R′₇₄₀)	0.136^*	DSI (R′₆₃₄,R′₇₂₃)	0.351^**
DSI (R′₆₇₁,R′₇₃₉)	0.212^**	DSI (R′₆₇₇,R′₇₂₉)	0.157^*	DSI (R′₆₅₄,R′₇₄₀)	0.130^*	DSI (R′₆₇₇,R′₇₂₃)	0.045

Table 1

Table 2

Table 3

Table 4

Modeling and testing results under different models of SPAD value of different autumn maize varieties"

品种Variety	模型 Model	建模集 Modeling set		验证集 Verification set
品种Variety	模型 Model	RMSE	RE (%)	RMSEv	REv (%)
双兴玉2号	一元线性Linear	4.825	6.976	4.114	5.845
Shuangxingyu No.2	指数Exponential	4.936	6.860	4.403	5.833
	多项式Polynomial	4.773	6.814	3.924	5.803
	NDSI (R′₄₉₁,R′₆₇₁)	5.031	7.618	4.327	5.615
正大999	一元线性Linear	2.851	3.666	3.112	4.050
Zhengda 999	指数Exponential	2.776	3.632	3.245	4.360
	多项式Polynomial	2.762	3.643	3.322	4.518
	DSI (R′₄₉₁,R′₆₇₁)	2.749	3.619	4.151	5.066
湘农玉27号	一元线性Linear	4.849	7.298	5.350	6.569
Xiangnongyu No.27	指数Exponential	4.393	6.642	7.285	8.478
	多项式Polynomial	4.272	6.433	8.613	9.695
	DSI (R′₆₂₈,R′₆₇₃)	5.051	7.782	7.501	9.127
兴玉818	一元线性Linear	5.100	6.701	4.421	6.097
Xingyu 818	指数Exponential	5.310	6.720	4.724	6.449
	多项式Polynomial	5.019	6.779	4.620	6.128
	NDSI (R′₆₆₇,R′₇₃₉)	5.132	6.635	4.432	6.004
临奥1号B3	一元线性Linear	4.240	5.134	4.172	5.357
Lin′ao No.1 B3	指数Exponential	4.304	5.120	4.110	5.060
	多项式Polynomial	4.237	5.110	4.202	5.457
	DSI (R′₅₄₉,R′₆₇₁)	4.361	5.126	4.190	5.553
湘农玉32号	一元线性Linear	3.880	5.055	3.795	4.564
Xiangnongyu No.32	指数Exponential	3.758	4.894	3.757	4.537
	多项式Polynomial	3.672	4.805	3.949	4.658
	DSI (R′₅₅₃,R′₆₇₇)	3.897	4.858	3.717	4.342
洛玉1号	一元线性Linear	4.179	5.202	9.062	9.308
Luoyu No.1	指数Exponential	4.138	5.140	7.529	8.258
	多项式Polynomial	4.123	5.061	7.465	8.098
	DSI (R′₅₃₉,R′₆₅₄)	4.570	6.303	5.722	7.731
田玉335	一元线性Linear	3.703	4.473	4.008	5.118
Tianyu 335	指数Exponential	3.867	4.559	4.240	5.443
	多项式Polynomial	3.629	4.603	3.923	4.913
	DSI (R′₅₅₁,R′₆₇₇)	3.630	4.379	4.054	5.163

Table 4

References 27

[1]	余蛟洋, 常庆瑞, 由明明 , 等. 基于高光谱和BP神经网络模型苹果叶片SPAD值遥感估算. 西北林学院学报, 2018,33(2):157-164.
[2]	孙小香, 王芳东, 郭熙 , 等. 基于水稻冠层高光谱的叶片SPAD值估算模型研究. 江西农业大学学报, 2018,40(3):444-453.
[3]	艾天成, 李方敏, 周治安 , 等. 作物叶片叶绿素含量与SPAD值相关性研究. 湖北农学院学报, 2000,20(1):6-8.
[4]	李哲, 张飞, 陈丽华 , 等. 光谱指数的植物叶片叶绿素含量估算模型. 光谱学与光谱分析, 2018,38(5):1533-1539.
[5]	赵士诚, 何萍, 仇少君 , 等. 相对SPAD值用于不同品种夏玉米氮肥管理的研究. 植物营养与肥料学报, 2011,17(5):1091-1098. doi: 10.11674/zwyf.2011.1059
[6]	孙红, 赵毅, 张猛 , 等. 玉米拔节期冠层叶绿素含量多光谱图像检测. 农业工程学报, 2015(S2):186-192.
[7]	徐新刚, 赵春江, 王纪华 , 等. 基于可见光-近红外新光谱特征和最优组合原理的大麦叶片氮含量监测. 红外与毫米波学报, 2013,32(4):351-358,365.
[8]	吴启侠, 李晋波, 朱建强 , 等. 淹水胁迫下棉花叶片SPAD高光谱估算模型研究. 棉花学报, 2017(6):579-588.
[9]	王磊 . 玉米营养光谱诊断技术研究. 北京:中国农业科学院, 2007.
[10]	李振 . 基于高光谱玉米氮素营养与生长指标的监测. 济南:山东农业大学, 2012.
[11]	He R, Li H, Qiao X , et al. Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress. International Journal of Remote Sensing, 2018,39(12):4059-4076.
[12]	Li L, Ren T, Ma Y , et al. Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters. Computers and Electronics in Agriculture, 2016,126:21-31.
[13]	张春雷 . 玉米成为我国第一大粮食作物品种. 农产品市场周刊, 2012(47):12.
[14]	李媛媛 . 基于地物光谱仪与成像光谱仪耦合的玉米生长信息监测研究. 杨凌:西北农林科技大学, 2017.
[15]	贺英, 邓磊, 毛智慧 , 等. 基于数码相机的玉米冠层SPAD遥感估算. 中国农业科学, 2018,51(15):66-77.
[16]	王丽凤, 张长利, 赵越 , 等. 高光谱成像技术的玉米叶片氮含量检测模型. 农机化研究, 2017,39(11):140-147.
[17]	付彦博, 王治国, 耿庆龙 , 等. 基于光谱分析不同温度下棉花叶片SPAD值含量估测. 新疆农业科学, 2017,54(3):409-416.
[18]	陈春玲, 金彦, 曹英丽 , 等. 基于GA-BP神经网络高光谱反演模型分析玉米叶片叶绿素含量. 沈阳农业大学学报, 2018,49(5):626-632.
[19]	于雷, 章涛, 朱亚星 , 等. 基于IRIV算法优选大豆叶片高光谱特征波长变量估测SPAD值. 2018,34(16):148-154.
[20]	高鑫, 高聚林, 于晓芳 , 等. 基于不同玉米品种叶片高光谱的SPAD值估测模型研究. 玉米科学, 2016(2):108-114.
[21]	高佳, 崔海岩, 史建国 , 等. 花粒期光照对夏玉米光合特性和叶绿体超微结构的影响. 应用生态学报, 2018,29(3):883-890.
[22]	武倩雯, 熊黑钢, 靳彦华 , 等. 基于多个高光谱参数的玉米叶片叶绿素含量估测模型. 干旱地区农业研究, 2016,34(1):202-205.
[23]	李敏夏, 张林森, 李丙智 , 等. 苹果叶片高光谱特性与叶绿素含量和SPAD值的关系. 西北林学院学报, 2010,25(2):35-39.
[24]	李俊霞, 杨俐苹, 白由路 , 等. 不同品种玉米氮含量与叶片光谱反射率及SPAD值的相关性. 中国土壤与肥料, 2015(3):34-39.
[25]	李媛媛, 常庆瑞, 刘秀英 , 等. 基于高光谱和BP神经网络的玉米叶片SPAD值遥感估算. 农业工程学报, 2016,32(16):135-141.
[26]	Chen Z Q, Wang L, Bai Y L , et al. Hyperspectral prediction model for maize leaf SPAD in the whole growth period. Spectroscopy and Spectral Analysis, 2013,33(10):2838-2842.
[27]	王飞, 宋希云, 刘树堂 , 等. 水肥耦合下夏玉米不同生育时期的高光谱特性与反演模型研究. 植物生理学报, 2014,50(3):358-363.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	Wang Haitao,Liu Cunjing,Tang Liyuan,Zhang Sujun,Li Xinghe,Cai Xiao,Zhang Xiangyun,Zhang Jianhong. Status and Developmental Tendency of Hybrid Cotton in Hebei Province[J]. Crops, 2019, 35(5): 1 -8 .
[2]	Meng Fanlai,Guo Huachun. Effects of Enhanced UV-B on Photosynthetic Characteristics and UV-Absorbing Compounds of Sweet Potato[J]. Crops, 2019, 35(5): 114 -119 .
[3]	Ren Yongfeng,Lu Zhanyuan,Zhao Peiyi,Gao Yu,Liu Guanghua,Li Yanfang. Effects of Different Planting Methods on Water Utilization and Yield of Potato in Dryland[J]. Crops, 2019, 35(5): 120 -124 .
[4]	Shi Liran,Hao Hongbo,Cui Haiying,Li Mingzhe. Effects of Shading on Photosynthetic Characteristics and Rapid Chlorophyll Fluorescence Kinetic Characteristics of Foxtail Millet[J]. Crops, 2019, 35(5): 125 -128 .
[5]	Li Chunxi,Li Sisi,Shao Yun,Ma Shouchen,Liu Qing,Weng Zhengpeng,Li Xiaobo. Effects of Organic Materials Returning on Enzyme Activities and Soil Carbon and Nitrogen Content in Wheat Field under Nitrogen-Reducing Conditions[J]. Crops, 2019, 35(5): 129 -134 .
[6]	Liang Xiaohong,Zhang Ruidong,Huang Minjia,Liu Jing,Cao Xiong. Interaction of Film Mulching and Nitrogen Application on Yield, Water and Nitrogen Use Efficiency of Sorghum[J]. Crops, 2019, 35(5): 135 -142 .
[7]	Chen Li,Zhang Luxin,Wu Feng,Li Zhen,Long Xingzhou,Yang Yurui,Yin Baozhong. Effects of Wheat-Maize Double Crops Rotational Tillage on Soil Characteristics and Crop Yield in Hebei Plain[J]. Crops, 2019, 35(5): 143 -150 .
[8]	Jiang Lina,Zhang Yawen,Zhu Yalin,Zhao Lingxiao. Effects of Nitrogen Application on Dry Matter Accumulation, Transport and Yield in Different Wheat Varieties[J]. Crops, 2019, 35(5): 151 -158 .
[9]	Dong Zhiqiang,Wang Mengmeng,Li Hongyi,Xue Xiaoping,Pan Zhihua,Hou Yingyu,Chen Chen,Li Nan,Li Manhua. Applicability Assessment of WOFOST Model of Growth and Yield of Summer Maize in Shandong Province[J]. Crops, 2019, 35(5): 159 -165 .
[10]	Wang Jinsong,Dong Erwei,Jiao Xiaoyan,Wu Ailian,Bai Wenbin,Wang Lige,Guo Jun,Han Xiong,Liu Qingshan. Effects of Different Planting Patterns on Yield and Nutrient Absorption of Sorghum Jinnuo 3[J]. Crops, 2019, 35(5): 166 -172 .

品种 Variety	模型Model
品种 Variety	一元线性模型 Linear model	R²	指数模型 Exponential model	R²	多项式模型 Polynomial model	R²
双兴玉2号Shuangxingyu No.2	y=38660x+46.774	0.731	y=46.66e^704.24x	0.737	y=-19533118x²+48050.806x+46.482	0.737
正大999 Zhengda 999	y=34262x+36.096	0.861	y=39.224e^591.41x	0.860	y=14888708x²+15133.378x+41.232	0.886
湘农玉27号Xiangnongyu No.27	y=77172x+30.210	0.907	y=34.932e^1336.9x	0.908	y=92891892x²+11627.991x+39.552	0.913
兴玉818 Xingyu 818	y=54077x+54.495	0.721	y=53.95e^913.14x	0.710	y=-30808871x²+62332.558x+54.681	0.730
临奥1号B3 Lin′ao No.1 B3	y=38823x+49.368	0.800	y=49.831e^634.56x	0.797	y=-4207729x²+41629.656x+49.108	0.801
湘农玉32号Xiangnongyu No.32	y=30562x+39.597	0.801	y=42.769e^487.93x	0.853	y=16960342x²+4942.054x+47.636	0.843
洛玉1号Luoyu No.1	y=111055x+77.22	0.869	y=80.587e^1984x	0.842	y=96303678x²+148021.766x+80.146	0.825
田玉335 Tianyu 335	y=33687x+34.605	0.883	y=38.05e^580.92x	0.879	y=-12646105x²+52181.083x+29.068	0.888

Hyperspectral Estimation of SPAD Values in Different Varieties of Autumn Maize

RichHTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 7

References 27

Related Articles 3

Metrics

Comments

Recommended 10

品种Variety	光谱参数模型 Spectum paramter model	R²
双兴玉2号Shuangxingyu No.2	y=-10.951x+57.295	0.681
正大999 Zhengda 999	y=31517.7x+38.273	0.861
湘农玉27号Xiangnongyu No.27	y=86746.221x+18.798	0.793
兴玉818 Xingyu 818	y=136.176x+190.901	0.734
临奥1号B3 Lin′ao No.1 B3	y=31211.09x+54.037	0.768
湘农玉32号Xiangnongyu No.32	y=26016.52x+54.037	0.832
洛玉1号Luoyu No.1	y=530.47.443x+91.222	0.793
田玉335 Tianyu 335	y=29300.613x+35.924	0.856

[1]	Kainan Zhao, Xuhong Chang, Demei Wang, Zhiqiang Tao, Yushuang Yang, Ruiqi Ma, Yingjie Zhu, Zheli Xu, Baojun Zhang, Guangcai Zhao. Effects of Tridimensional Uniform Sowing and Fertilizer on Grain and Physiological Characteristics of Winter Wheat [J]. Crops, 2019, 35(1): 103-110.
[2]	Chen Yingying,Wangxu Yiling,Zhu Yuhan,Wu Wei,Liu Tao,Sun Chengming. Hyperspectral Estimation of Nitrogen Content in Rice Panicle [J]. Crops, 2018, 34(5): 116-120.
[3]	Zhongnan Li,Kewei Wang,Yueren Wang,Shenghui Wu,Guangfa Li. Genetic Analysis on Chlorophyll SPAD Value of Seedling Leaf in Maize Variety Xianyu 335 [J]. Crops, 2016, 32(4): 101-101.