title: [Grey analysis of NIR spectra and prediction of nitrogen content in jujube leaves].
abstract: Jujube was chosen as the object in the present research. Spectra data of jujube leaves were collected during the period of budding, branch leaf, flowering and coloring. The nitrogen contents of jujube leaf samples were determined by Kjeldahl analysis method. Grey relation analysis between spectral reflectance and nitrogen content of jujube leaves was done based on Grey theory. It was found that the gray relation between spectral reflectance and nitrogen content of jujube leaves at 560, 678 and 786 nm was high. Nine kinds of vegetation index based on spectra data of NIR786, R678 and G570 were calculated. The gray relation of nine kinds of vegetation index was NDVI>GRVI>NDGI>GNDVI>CNDVI>RVI>GDVI>DVI>SAVI. NDVI, GRVI, NDGI, GNDVI and CNDVI were chosen to build prediction models of nitrogen content of jujube leaves. Spectra data of 560, 678 and 786 nm were also used to build prediction models of nitrogen content of jujube leaves. LS-SVM and GM(1, N) were used to build prediction module. The prediction R2 and verification R2 of LS-SVM module were 0.805 and 0.704 respectively when five kinds of vegetation index were used as input of prediction module. When when Spectra data of 560, 678 and 786 nm were used as input, the prediction R2 and verification R2 of LS-SVM prediction model were 0.772 and 0.685 respectively. The prediction R2 and verification R2 of GM(1, N) module were 0.927 7 and 0.895 8 respectively when spectra data of 560, 678 and 786 nm were used as input. The results of prediction GM(1, N) module which used five kinds of vegetation index as input were 0.547 6 and 0.489 7. From those results it was observed that grey theory only needed little information to build prediction module with high precision, so that it could be used in precision management of jujube plants.
authors.affiliation: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China. weiwei810311@163.com
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Sun
authors.affiliation: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
authors.forename: Li-Hua
authors.initials: LH
authors.lastname: Zheng
authors.affiliation: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
authors.forename: Min-Zan
authors.initials: MZ
authors.lastname: Li
mesh_terms: D008962:Models, Theoretical; D009584:Nitrogen / Q000032:analysis*; D018515:Plant Leaves / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared; D031957:Ziziphus / Q000737:chemistry*
chemical_list: D009584:Nitrogen