Nondestructive testing method for rape water stress with multiple
features information fusion based on PCABP method
1.Institute of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2.Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, Jiangsu 212013, China
Abstract： The canopy spectral reflectance, the multispectral image and the canopy temperature were fused to quantitatively analyze the rape moisture content based on nondestructive testing of rape water stress. Stepwise regression method was used to extract the features of moisture content from different sensors. The water stress index (CWSI) was obtained by detecting canopyair temperature difference and environment temperature and humidity to compensate light influence. The results show that the spectral features at wavelength of 960,1 450,1 650 nm, the features of image mean value at 560， 960， 810 nm and the image ratios at 960 nm to 810 nm are highly correlated with the rape moisture content during the whole growth period of rope. The principal component analysis (PCA) was applied to transform and reduce dimensions for feature space, and the prediction model of moisture content of rape was built by BP neural network. The results show that more information can be integrated to achieve the quantitative analysis of water stress of rape. The proposed model precision is obviously higher than that of single detection method.
张晓东, 李立, 毛罕平, 高洪燕, 苏辰. 基于PCA-BP多特征融合的油菜水分胁迫无损检测[J]. 江苏大学学报（自然科学版）, 2016, 37(2): 174-182.
ZHANG Xiao-Dong, LI Li, MAO Han-Ping, GAO Hong-Yan, SU Chen. Nondestructive testing method for rape water stress with multiple
features information fusion based on PCABP method[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2016, 37(2): 174-182.
2016, Vol. 37 
