糜子叶片氮含量和籽粒蛋白质含量高光谱监测研究

doi:10.3864/j.issn.0578-1752.2019.15.004

摘要/Abstract

摘要：

【目的】本研究以叶片氮含量为切入点,探求糜子籽粒蛋白质含量的最佳光谱预测模型,为糜子优质生产的管理调控提供理论依据。【方法】结合2017年和2018年2年的氮肥运筹试验数据和光谱数据,通过“光谱特征信息—叶片氮含量—籽粒蛋白质含量”这一研究思路,以叶片氮含量为中间链接点将光谱模型和籽粒蛋白质含量链接,建立基于高光谱糜子籽粒蛋白质含量监测模型。【结果】利用支持向量机（SVM）构建的糜子全生育期叶片氮含量监测模型要优于逐步多元线性回归（SMLR）和偏最小二乘法（PLS）,并且原始光谱反射率（R）的SVM模型效果优于一阶导数（1ST）模型,建模集和验证集的R ²分别为0.928、0.924;RMSE相对较小,分别为0.19、0.12;RPD都大于2,分别为3.71、6.07。开花期、灌浆期和成熟期的叶片氮含量和籽粒蛋白质含量均达到极显著正相关,相关系数分别为0.48、0.66和0.73。灌浆期R-SVM模型能准确的监测糜子籽粒蛋白质含量,决定系数R ²为0.798,均方根误差RMSE为0.14,预测残差RPD为1.65。【结论】建立基于灌浆期糜子籽粒蛋白质含量的高光谱R-SVM监测模型,有助于指导糜子优化田间管理、种植业结构调整和籽粒品质分级,为高光谱技术在糜子优质高产栽培和精准农业发展提供技术基础。

关键词: 糜子, 叶片氮含量, 籽粒蛋白质含量, 高光谱, 模型

Abstract:

【Objective】The objective of the study was to explore the best spectral prediction model of protein content in the grain of broomcorn millet based on leaf nitrogen content, which provided theoretical basis for the management and regulation of high-quality production of broomcorn millet.【Method】Using experimental data and spectral data of nitrogen application in 2017 and 2018, the predicting models on grain protein content were constructed based on hyperspectral by linking the spectral models and grain protein content with leaf nitrogen content as intersection in broomcorn millet. 【Result】The support vector machine (SVM) which constructed monitoring model of leaf nitrogen content at full growth period was superior to stepwise multiple linear regression (SMLR) and partial least square (PLS), and R-SVM was superior to 1ST-SVM, the R ² of calibration set and validation set were 0.928 and 0.924, respectively, RMSE were 0.19 and 0.12, respectively, and RPD were 3.71 and 6.07, respectively. Leaf nitrogen content and grain protein content at heading, filling and maturing stages were significantly positively correlated, and their correlation coefficients were 0.48, 0.66 and 0.73, respectively. The R-SVM at filling stage could monitor the grain protein content accurately of broomcorn millet.【Conclusion】Establishing monitoring model of R-SVM based on grain protein content in broomcorn millet at filling stage, which could help to guide the field management, adjustment of planting structure and grain quality grading, and to provide technical basis for hyperspectral technology in the development of high quality and high yield cultivation and precision agriculture.

Key words: broomcorn millet, leaf nitrogen content, grain protein content, hyperspectral, model

王君杰,陈凌,王海岗,曹晓宁,刘思辰,田翔,秦慧彬,乔治军. 糜子叶片氮含量和籽粒蛋白质含量高光谱监测研究[J]. 中国农业科学, 2019, 52(15): 2593-2603.

WANG JunJie,CHEN Ling,WANG HaiGang,CAO XiaoNing,LIU SiChen,TIAN Xiang,QIN HuiBin,QIAO ZhiJun. Effects of Hyperspectral Prediction on Leaf Nitrogen Content and the Grain Protein Content of Broomcorn Millet[J]. Scientia Agricultura Sinica, 2019, 52(15): 2593-2603.

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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2019.15.004

https://www.chinaagrisci.com/CN/Y2019/V52/I15/2593

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样本类型 Type of sample	样本数 Number of sample	全距 Total distance	极小值 Mix	极大值 Max	均值 Mean	标准差 Standard deviation
建模集 Calibration set	122	2.900	1.090	3.990	2.974	0.724
验证集 Validation set	39	2.660	1.270	3.930	3.007	0.722

预处理 Pretreatment	光谱特征 Spectral characteristics (nm)
R	418	697	2274
1ST	534	683	2084

模型类型 Model type	建模集 Calibration set			验证集 Validation set			变量个数 Number of variable
模型类型 Model type	决定系数 R²	均方根误差 RMSE	预测残差 RPD	决定系数 R²	均方根误差 RMSE	预测残差 RPD	变量个数 Number of variable
R-SMLR	0.754	0.85	0.85	0.790	0.44	1.63	3
1ST-SMLR	0.858	0.27	2.65	0.882	0.15	4.65	3
R-PLS	0.829	0.30	2.42	0.894	0.14	5.05	4
1ST-PLS	0.957	0.15	4.80	0.885	0.14	5.09	4
R-SVM	0.928	0.19	3.71	0.924	0.12	6.07	3
1ST-SVM	0.900	0.23	3.14	0.911	0.12	5.87	2

生育时期 Growth stage	拔节期 Jointing	抽穗期 Booting	开花期 Heading	灌浆期 Filling	成熟期 Maturity	籽粒蛋白质含量 GPC
拔节期Jointing	1.00
抽穗期Booting	0.55**	1.00
开花期Heading	0.40*	0.31	1.00
灌浆期Filling	0.45**	0.35*	0.58**	1.00
成熟期Maturity	0.26	0.07	0.63**	0.61**	1.00
籽粒蛋白质含量 GPC	0.10	0.22	0.48**	0.66**	0.73**	1.00

生育时期 Growth stage	拟合方程 Equation	决定系数 R²
开花期 Heading	y=2.5509x+5.1857	0.631
灌浆期 Filling	y=1.8327x+8.8819	0.872
成熟期 Maturity	y=1.1921x+11.789	0.900