Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks

doi:10.3964/j.issn.1000-0593(2015)09-2596-06

CORC > 中国农业科学院 > 作物科学研究所

	Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks
	Cui Ri-xian 1; Liu Ya-dong 1; Fu Jin-dong 2
刊名	SPECTROSCOPY AND SPECTRAL ANALYSIS
	2015
卷号	35 期号:9 页码:2596-2601
关键词	Winter wheat Visible spectrum Color indices Canopy cover Above ground Biomass BP based artificial neural networks
ISSN号	1000-0593
DOI	10.3964/j.issn.1000-0593(2015)09-2596-06
通讯作者	Cui Ri-xian
英文摘要	The objective of this study was to evaluate the feasibility of using color digital image analysis and back propagation (BP) based artificial neural networks (ANN) method to estimate above ground biomass at the canopy level of winter wheat field. Digital color images of winter wheat canopies grown under six levels of nitrogen treatments were taken with a digital camera for four times during the elongation stage and at the same time wheat plants were sampled to measure above ground biomass. Canopy cover (CC) and 10 color indices were calculated from winter wheat canopy images by using image analysis program (developed in Microsoft Visual Basic). Correlation analysis was carried out to identify the relationship between CC, 10 color indices and winter wheat above ground biomass. Stepwise multiple linear regression and BP based ANN methods were used to establish the models to estimate winter wheat above ground biomass. The results showed that CC, and two color indices had a significant correlation with above ground biomass. CC revealed the highest correlation with winter wheat above ground biomass. Stepwise multiple linear regression model constituting CC and color indices of NDI and b, and BP based ANN model with four variables (CC, g, b and NDI) for input was constructed to estimate winter wheat above ground biomass. The validation results indicate that the model using BP based ANN method has a better performance with higher R-2 (0.903) and lower RMSE (61.706) and RRMSE (18.876) in comparation with the stepwise regression model.
学科主题	Spectroscopy
语种	中文
出版者	OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
WOS记录号	WOS:000361268300041
内容类型	期刊论文
源URL	[http://111.203.20.206/handle/2HMLN22E/4665]
专题	作物科学研究所
作者单位	1.Qingdao Agr Univ, Coll Agron & Plant Protect, Shandong Prov Key Lab Dryland Farming Tech, Qingdao 266109, Peoples R China 2.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Cui Ri-xian,Liu Ya-dong,Fu Jin-dong. Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2015,35(9):2596-2601.
APA	Cui Ri-xian,Liu Ya-dong,&Fu Jin-dong.(2015).Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks.SPECTROSCOPY AND SPECTRAL ANALYSIS,35(9),2596-2601.
MLA	Cui Ri-xian,et al."Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks".SPECTROSCOPY AND SPECTRAL ANALYSIS 35.9(2015):2596-2601.