|
|
|
|
|
|
Estimation of Total Nitrogen Content in Fresh Tea Leaves Based on
Wavelet Analysis |
WANG Fan1, 2, CHEN Long-yue2, 3, DUAN Dan-dan1, 2, 4*, CAO Qiong1, 4, ZHAO Yu1, LAN Wan-rong5 |
1. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
2. Qingyuan Academy of Smart Agriculture, Qingyuan 511500, China
3. Nongxin Technology (Guangzhou) limited Liability Company, Guangzhou 510000, China
4. Hunan Agricultural University,Changsha 410125, China
5. Jiangmen Agricultural Technology Service Center, Jiangmen 529000, China
|
|
|
Abstract Tea is one of the most popular beverages globally, which is greatly affected by the content of nitrogen (N) in quality. Due to the complicated and time-consuming method for determining N content in fresh tea leaves by traditional chemical analysis, this paper proposes a means for N content prediction by hyperspectral technique. The wavelet coefficients extracted by continuous wavelet transform (CWT) technology are used to estimate N content by different decomposition layers of CWT. Moreover, the predictive effects of models built by different wavelength selection algorithms are also discussed. Several 151 hyperspectral data of tea samples were collected from tea gardens in the Yingde City of Guangdong Province. The original spectra data are preprocessed by smoothing (SG), detrending (Detrending), first derivative (1st), multiple scattering correction (MSC), and standard normal variable transformation (SNV) while comparing with CWT. Then, continuous wavelet multi-scale analysis is applied to process the original spectrum for generating wavelet coefficients, and Pearson correlation analysis was also performed. Next, three kinds of methods, including successive projections algorithm (SPA), competitive adaptive weighted sampling (CARS) and variable combination population analysis (VCPA), are adopted to optimize the variable space of the spectral data after CWT transformation. At last, quantitative models of N content prediction are established and compared by PLSR with characteristic variables selected by the three above mentioned methods as input. The overall results show that the continuous wavelet analysis algorithm can improve the model’s efficiency for estimating the N content of the fresh tea leaves by hyperspectral data. Furthermore, it has better performance than other conventional spectral preprocessing methods significantly. With continuous wavelet decomposition, the precision of the model for N content prediction gradually decreases with the increase of the decomposition scale.There is a good correlation between the spectrum after the continuous wavelet transforms on the scale of 1~6 and the N in fresh tea leaves,which shows that the small-scale continuous wavelet algorithm can be well applied to monitor N content in fresh tea leaves. The model established by CWT (1scale)-VCPA method has the best performance, andthe number of variables is reduced by 99.34% compared to the full band. The R2 of the calibration model and prediction model respectively, are 0.95 and 0.90. Compared with the traditional spectral processing method, the accuracy is improved by 11%. It is proved that the combination of CWT-VCPA can obviously reduce the spectral dimension and improve the accuracy of the model. This research achieves an efficient way for N content prediction of tea, which provides a technical basis and reliable reference for other components evaluation of tea.
|
Received: 2021-06-22
Accepted: 2022-01-17
|
|
Corresponding Authors:
DUAN Dan-dan
E-mail: duandd@nercita.org.cn
|
|
[1] WANG Fan, ZHAO Chun-jiang,XU Bo, et al(王 凡,赵春江,徐 波,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2020, 36(24):273.
[2] Chen Q S, Chen M, Liu Y, et al. Journal of Food Science and Technology, 2018, 55: 4363.
[3] Yamashita H, Sonobe R, Hirono Y, et al. Scientific Reports,2020,(10): 17360.
[4] Hazarika A K, Chanda S, Sabhapondit S, et al. Journal of Food Science and Technology, 2018, 55: 4867.
[5] Thiele-Bruhn S, Emmerling C, Harbich M, et al. Journal of Near Infrared Spectroscopy, 2016, 24(3): 255.
[6] Sun J, Zhou X, Hu Y, et al. Computers and Electronics in Agriculture, 2019, 160: 153.
[7] Hui C, Zan L, Chao T, et al. Vibrational Spectroscopy, 2018, 99: 178.
[8] Li H, Zhu J, Jiao T, et al. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, 2020, 243: 118765.
[9] Al-Kaf H A G, Alduais N A M, Saad A M H Y, et al. IEEE Access, 2020, 8: 1.
[10] Wang Y J, Jin S S, Li M H, et al. Computers and Electronics in Agriculture, 2020, 175: 105538.
[11] Wang G, Fang Q, Teng Y, et al. International Journal of Applied Earth Observation and Geoinformation, 2016, 53: 48.
[12] Thiele-Bruhn S, Emmerling C, Harbich M, et al. Journal of Near Infrared Spectroscopy, 2016, 24(3): 255.
[13] Yun Y H, Wang W T, Deng B C, et al. Analytica Chimica Acta, 2015, 862: 14.
[14] Qi X, Jiang J, Cui X, et al. Food Analytical Methods, 2019, 13(2): 1.
[15] XUE Li-hong, CAO Wei-xing, LUO Wei-hong, et al(薛利红,曹卫星,罗卫红,等). Chinese Journal of Plant Ecology(植物生态学报), 2004,(2): 172.
[16] Liu W W, Li M J, Zhang M Y, et al. Ecosystem Health and Sustainability, 2020, 6(1): 1726211.
[17] AN Si-yu, ZHANG Lei, SHANG Xian-zhao, et al(安思宇,张 磊,尚献召,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(1): 206.
|
[1] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[3] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[5] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[6] |
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. |
[7] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[8] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[9] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[10] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[11] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
[12] |
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. |
[13] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
[14] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
[15] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
|
|
|
|