|
|
|
|
|
|
Analyze on the Response Characteristics of Leaf Vegetables to Particle Matters Based on Hyperspectral |
KONG Li-juan, YU Hai-ye, CHEN Mei-chen, PIAO Zhao-jia, LIU Shuang, DANG Jing-min, ZHANG Lei, SUI Yuan-yuan* |
School of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China |
|
|
Abstract Particle matter (PM) pollution not only affects human life but also influences the photosynthesis, growth, yield as well as quality of the plant. In this paper, Pakchoi, oilseed rape(Brassica napus) and Italian lettuce which were at harvest periods were investigated in the simulated particulate pollution environment. The hyperspectral and photosynthetic data of leaf were obtained and their response mechanism to particle matters was studied by hyperspectral technique and plant phenotypic. The spectral and photosynthetic characteristics of leaf vegetables to particle matters were analyzed. The results showed that under the influence of particle matter only, the changing trend of hyperspectral response curves did not alter with the kinds of leaf vegetables, while reflectance value was very different. The leaf showed higher reflectivity within the visible region. The red edge position was “blue shift”. Oilseed rape was the most sensitive to PM, and Pakchoi had the strongest ability to absorb PM. The correlations between the net photosynthetic rate and the original spectra, the first derivative spectra, the ten hyperspectral characteristic parameters and the four vegetation indices were compared. The sensitive bands were extracted by the correlation analysis method. The characteristic wavelengths were extracted by original spectrum, first derivative (FD), multiple scatter correction (MSC) and correlation analysis method, and the net photosynthetic rate quantitative inversion model was established by ln logarithm operation, polynomial function and several combination methods. Characteristic parameters were got such as Dr, SDr, SDr/SDb, SDr/Sdy for Italian lettuce, SDr, Dy, NIRRP, (SDr-SDy)/(SDr+SDy) for oilseed rape and λr, SDy, (SDr-SDy)/(SDr+SDy) for Pakchoi. Moverover, the pretreatment methods were FD, second derivative (SD), Savitzky-Golay smooth (SG) and MSC. Also, four modeling methods were classical least squares(CLS), principal component regression (PCR), stepwise multiple linear regression(SMLR)and partial least squares (PLS). The inversion model of the net photosynthetic rate of three kinds of leaves during the collecting period was established with correlated coefficients as an evaluation index of model. The combination of FD+SG+PLS was finally determined the best method for the net photosynthetic rate of Pakchoi and Italian lettuce inversion model, and the combination of FD+SG+MSC+SMLR was finally determined the best method for the net photosynthetic rate of Brassica napus inversion model. The results could provide references for the model modification of leaf vegetables under particle matter pollution environment. This study may provide the theoretical basis for the diagnosis and analysis of physiological changes of leaf vegetables under particle matter pollution using hyperspectral technology, and bring novel ideas for disease identification and early warning of facility agricultural vegetables.
|
Received: 2019-12-25
Accepted: 2020-05-12
|
|
Corresponding Authors:
SUI Yuan-yuan
E-mail: suiyuan@jlu.edu.cn
|
|
[1] Kanniah K D, Beringer J, Tapper N J, et al. Theoretical and Applied Climatology, 2010, 100(3/4): 423.
[2] Leonard R J, McArthur C, Hochuli D F. Urban Frestry & Urban Greening, 2016,20: 249.
[3] Perini K, Ottelé M, Giulini S, et al. Ecological Engineering, 2017, 100: 268.
[4] Nguyen T, Yu X X, Zhang Z M, et al. Journal of Environmental Sciences, 2015, 27(1): 33.
[5] Dzierzanowski K, Popek R, Gawrońska H, et al. International Journal of Phytoremediation, 2011, 13(10): 1037.
[6] Weerakkody U, Dover J W, Mitchell P, et al. Urban Forestry & Urban Greening, 2018, 30: 98.
[7] HE Yong, LIU Fei, LI Xiao-li, et al(何 勇, 刘 飞, 李晓丽, 等). Spectroscopy and Imaging Technology in Agriculture(光谱及成像技术在农业中的应用). Beijing:Science Press(北京:科学出版社),2016. 1.
[8] SUN Jun, ZHOU Xin, MAO Han-ping, et al(孙 俊, 周 鑫, 毛罕平, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(12): 323.
[9] WANG Lin-lin, YU Hai-ye, ZHANG Lei, et al(王琳琳, 于海业, 张 蕾, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(14): 279.
[10] Hwang H J, Yook S J, Ahn K H. Atmospheric Environment,2011, 45(38): 6987.
[11] Guo Li, Ma Shuli, Zhao Dongsen, et al. Journal of the Air & Waste Management Association, 2019, 69(8): 6987.
[12] Wen Xin, Zhang Pingyu, Liu Daqian. Chinese Geographical Science, 2018, 28(5): 810.
[13] Przybysz A, Sbø, Hanslin H M, et al. Science of the Total Environment, 2014, 481: 360.
[14] LI Yuan-xi, CHEN Xi-yun, LUO Da, et al(李苑溪, 陈锡云, 罗 达, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(2): 546. |
[1] |
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. |
[2] |
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. |
[3] |
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. |
[4] |
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. |
[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] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
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. |
|
|
|
|