| 光谱学与光谱分析 |
|
|
|
|
|
| Prediction of Chlorophyll Content of Greenhouse Tomato Using Wavelet Transform Combined with NIR Spectra |
| DING Yong-jun, LI Min-zan*, ZHENG Li-hua, ZHAO Rui-jiao, LI Xiu-hua, AN Deng-kui |
| Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China |
|
|
|
|
Abstract In quantitative analysis of spectral data, noises and background interference always degrade the accuracy of spectral feature extraction. The wavelet transform is multi-scale decomposition used to reduce the noise and improve the analysis precision. On the other hand, the wavelet transform denoising is often followed by destroying the efficiency information. The present research introduced two indexes to control the scale of decomposition, the smoothness index (SI) and the time shift index (TSI). When the parameters satisfied TSI <0.01 and SI>0.100 4, the noise of spectral characteristic was reduced. In the meanwhile, the reflection peaks of biochemical components were reserved. Through analyzing the correlation between denoised spectrum and chlorophyll content, some spectral characteristics parameters reflecting the changing tendency of chlorophyll content were chosen. Finally, the partial least squares regression (PLSR) was used to develop the prediction model of the chlorophyll content of tomato leaf. The result showed that the predictiong model, which used the values of absorbance at 366, 405, 436, 554, 675 and 693 nm as input variables, had higher predictive ability (calibration coefficient was 0.892 6, and validation coefficient was 0.829 7) and better potential to diagnose tomato growth in greenhouse.
|
|
Received: 2011-01-15
Accepted: 2011-04-08
|
|
|
|
Corresponding Authors:
LI Min-zan
E-mail: limz@cau.edu.cn
|
|
[1] Blackburm G A. Journal of Experimental Botany, 2007, 58(4): 855.
[2] Impron I, Hemming S, Bot G. Biosystems Engineering, 2008, 99(4): 553.
[3] Donald T K, David H C，Roman M M. Photochemistry and Photobiology, 2005, 81(1): 1047.
[4] Kavdir I, Lu R, Ariana D. Postharvest Biology and Technology, 2007, 44(2): 165.
[5] TIAN You-wen, LI Tian-lai, ZHANG Lin(田有文，李天来，张 琳). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(5): 202.
[6] LIU Fei, WANG Li, HE Yong(刘 飞，王 莉，何 勇). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2008, 28(4): 272.
[7] YANG Hao-yu, YU Hai-ye, ZHANG Lei(杨昊谕，于海业，张 蕾). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2009, 40(10): 169.
[8] Polder G, van der Heijden G W. Postharvest Biology and Technology, 2004, 34(2): 117.
[9] XU H R, Ying Y B. Biosystems Engineering, 2007, 96(4): 447.
[10] LIU Zhe-gang, TANG Guo-xin(刘则刚，唐国新). Journal of Tianjin University(天津大学学报), 2004, 37(6): 535.
[11] LI Min-zan(李民赞). Spectral Analysis Technology and Application(光谱分析技术及其应用). Beijing: Science Press(北京: 科学出版社), 2006. 45.
[12] Blackburn G A, Ferwerda J G. Remote Sensing of Environment, 2008, 112(4): 1614.
[13] PAN Rui-chi, WANG Zhi-guan, DONG Yu-de(潘瑞炽, 汪止馆, 董愚得). Plant Physiology(植物生理学). Beijing: Higher Education Press(北京: 高等教育出版社), 1958. 145.
|
| [1] |
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. |
| [2] |
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. |
| [3] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [4] |
LI He1, WANG Yu2, FAN Kai2, MAO Yi-lin2, DING Shi-bo3, SONG Da-peng3, WANG Meng-qi3, DING Zhao-tang1*. Evaluation of Freezing Injury Degree of Tea Plant Based on Deep
Learning, Wavelet Transform and Visible Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 234-240. |
| [5] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
| [6] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
| [7] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [8] |
LIANG Jin-xing1, 2, 3, XIN Lei1, CHENG Jing-yao1, ZHOU Jing1, LUO Hang1, 3*. Adaptive Weighted Spectral Reconstruction Method Against
Exposure Variation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3330-3338. |
| [9] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [10] |
WANG Lin, WANG Xiang*, ZHOU Chao, WANG Xin-xin, MENG Qing-hui, CHEN Yan-long. Remote Sensing Quantitative Retrieval of Chlorophyll a and Trophic Level Index in Main Seagoing Rivers of Lianyungang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3314-3320. |
| [11] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
| [12] |
HUANG Chao1, 2, ZHAO Yu-hong1, ZHANG Hong-ming2*, LÜ Bo2, 3, YIN Xiang-hui1, SHEN Yong-cai4, 5, FU Jia2, LI Jian-kang2, 6. Development and Test of On-Line Spectroscopic System Based on Thermostatic Control Using STM32 Single-Chip Microcomputer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2734-2739. |
| [13] |
ZHENG Yi-xuan1, PAN Xiao-xuan2, GUO Hong1*, CHEN Kun-long1, LUO Ao-te-gen3. Application of Spectroscopic Techniques in Investigation of the Mural in Lam Rim Hall of Wudang Lamasery, China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2849-2854. |
| [14] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
| [15] |
WANG Jing-yong1, XIE Sa-sa2, 3, GAI Jing-yao1*, WANG Zi-ting2, 3*. Hyperspectral Prediction Model of Chlorophyll Content in Sugarcane Leaves Under Stress of Mosaic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2885-2893. |
|
|
|
|
