| 光谱学与光谱分析 |
|
|
|
|
|
| Sensitive Bands Extraction and Prediction Model of Tomato Chlorophyll in Glass Greenhouse |
| DING Yong-jun1, ZHANG Jing-jing1, SUN Hong2, LI Xiu-hua3 |
1. College Information Engineering，Lanzhou City University, Lanzhou 730030, China
2. “Key Laboratory of Modern Precision Agriculture System Integration Research” Ministry of Education, China Agricultural University, Beijing 100083, China
3. College of Electrical Engineering, Guangxi University, Nanning 530004, China |
|
|
|
|
Abstract In order to predict the content of chlorophyll in tomato rapidly and accurately, this study, with spectrum technology, focused on the extraction of sensitive spectral bands of tomato chlorophyll in glass greenhouse environment and created an effective estimation model. During the period of cultivating tomatoes, leaf spectra were measured with an ASD FieldSpec HH spectrophotometer and chlorophyll content was measured with Type 752 UV-Vis spectrophotometer. Based on the original spectra, absorbance spectra, first derivative spectra and continuum removal spectra, spectral data was preprocessed, in which the effectiveness of spectral features of chlorophyll content of tomato was highlighted and spectral response characteristics of the absorbance spectra in the visible part was enhanced. It was shown that both the continuum removal spectra and the first derivative spectra have strong blue and red absorption valley and green reflection peak. In this paper, the original spectrum, absorbance spectrum, first derivative spectrum and continuum removal spectrum were analyzed and compared, and then optimal spectral feature parameters were extracted with methods of Inter-Correlation analysis and multivariate collinearity diagnosis. Sensitive bands from original spectrum are 639, 672, 696, 750 and 768 nm. Sensitive bands from absorbance spectrum are 638, 663, 750 and 763 nm. Sensitive bands from first derivative spectrum are 516, 559 and 778 nm. Sensitive bands from continuum removal spectrum are 436, 564, 591, 612, 635, 683 and 760 nm. The stepwise multiple regressions were used to develop the prediction models of the chlorophyll content of tomato leaf. The result showed that the prediction model, which used the values from continuum removal spectrum at 436, 564, 591, 612, 635, 683, 760 nm as input variables, had the best predictive ability. The calibration R-Square was 0.88 and the validation R-Square was 0.82.
|
|
Received: 2015-11-29
Accepted: 2016-03-25
|
|
|
|
Corresponding Authors:
DING Yong-jun
E-mail: dingyj@lzcu.edu.cn
|
|
[1] CHEN Shu-lin, BI Yin-li, QI Li-shuai(陈书琳，毕银丽，齐礼帅). Journal of China University of Mining & Technology (中国矿业大学学报), 2015, 44(1): 170.
[2] Casa R, Castaldi F, Pascucci S. The Journal of Agricultural Science, 2015, 153(5): 876.
[3] Huang J R, Sun J Y, Liao H J. Precision Agriculture，2015, 16(2): 148.
[4] Nguy-Robertson A, Peng Y, Arkebauer T. Communications in Soil Science and Plant Analysis，2015, 46(21): 2734.
[5] BI Jing-zhi, LIU Xiang-nan, ZHAO Dong(毕景芝，刘湘南，赵 冬). Journal of Applied Sciences (应用科学学报), 2014, 04: 394.
[6] DING Yong-jun, LI Min-zan, AN Deng-kui(丁永军，李民赞，安登奎). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(5): 244.
[7] Jianfeng W, Dongxian H, Jinxiu S. International Journal of Agricultural and Biological Engineering, 2015, 8(5): 73.
[8] Qian W, Hong S, Minzan L. International Journal of Agricultural and Biological Engineering，2014, 7(2): 138.
[9] Fan Xiaoxue, Zang Jie, Xu Zhigang. Acta Physiologiae Plantarum, 2013, 35(9): 2721.
[10] Li Z, Wang Q, Lü J. Applied Spectroscopy，2015, 69(6): 721.
[11] WANG Lu, LIN Qi-zhong, JIA Dong(王 璐，蔺启忠，贾 东). Journal of Remote Sensing(遥感学报), 2007, 11(6): 906.
[12] Mielke C, Boesche N K, Rogass C. Remote Sensing Letters, 2015, 6(2): 97.
[13] DENG Xiao-lei, LI Min-zan, ZHENG Li-hua(邓小蕾，李民赞，郑立华). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(14): 140.
|
| [1] |
ZHENG Hong-quan, DAI Jing-min*. Research Development of the Application of Photoacoustic Spectroscopy in Measurement of Trace Gas Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 1-14. |
| [2] |
CHENG Jia-wei1, 2，LIU Xin-xing1, 2*，ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
| [3] |
FAN Ping-ping，LI Xue-ying，QIU Hui-min，HOU Guang-li，LIU Yan*. Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 52-55. |
| [4] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
| [5] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
| [6] |
BAI Xi-lin1, 2, PENG Yue1, 2, ZHANG Xue-dong1, 2, GE Jing1, 2*. Ultrafast Dynamics of CdSe/ZnS Quantum Dots and Quantum
Dot-Acceptor Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 56-61. |
| [7] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
| [8] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
| [9] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
| [10] |
YANG Chao-pu1, 2, FANG Wen-qing3*, WU Qing-feng3, LI Chun1, LI Xiao-long1. Study on Changes of Blue Light Hazard and Circadian Effect of AMOLED With Age Based on Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 36-43. |
| [11] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [12] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
| [13] |
XU Qiu-yi1, 3, 4, ZHU Wen-yue3, 4, CHEN Jie2, 3, 4, LIU Qiang3, 4 *, ZHENG Jian-jie3, 4, YANG Tao2, 3, 4, YANG Teng-fei2, 3, 4. Calibration Method of Aerosol Absorption Coefficient Based on
Photoacoustic Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 88-94. |
| [14] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
| [15] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
|
|
|
|
