光谱学与光谱分析 |
|
|
|
|
|
Study on SPAD Visualization of Pumpkin Leaves Based on Hyperspectral Imaging Technology |
ZHAO Yan-ru, YU Ke-qiang, LI Xiao-li, HE Yong* |
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
|
|
Abstract Visible/near-infrared (380~1 030 nm) hyperspectral imaging technique was used to realize SPAD visualization of pumpkin leaves in the present study. Downy mildew could be diagnosed rapidly according to significant positive correlation between downy mildew epidemic and chlorophyll content. Leaves uninfected and infected with different level downy mildew were used to acquire hyperspectral images and extract spectral information. Competitive adaptive reweighted sampling (CARS) was applied to select optimal wavelengths and finally 10 optimal wavelengths were obtained. Partial least squares regression (PLSR) was employed to establish SPAD prediction model. Results showed that, through the analysis of calibration of 48 samples and prediction of 23 samples, CARS-PLSR could obtain good results with RC=0.918, RMSECV=3.932; RCV=0.846, RMSECV=5.254; RP=0.881, and RMSEP=3.714. Regression model was gained based on the relationship between SPAD and spectral of pumpkin leaves. While SPAD of each pixel was calculated with PLSR regression equation, then SPAD distribution map of pumpkin was visualized using imaging processing technology. Final downy mildew infection could be diagnosed based on SPAD distribution map. This study provided a theoretical reference for effective monitoring plant growth and downy mildew epidemic.
|
Received: 2013-07-17
Accepted: 2013-10-25
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] LIU Fei, WANG Li, HE Yong, et al(刘 飞,王 莉,何 勇,等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2008, 28(4): 272. [2] QIU Zheng-jun, SONG Hai-yan, HE Yong, et al(裘正军,宋海燕,何 勇,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2007, 23(7): 150. [3] LIU Hui-ning, OU Zhi-yuan(刘会宁,欧志远). Northern Horticulture(北方园艺), 2009, 7: 127. [4] GENG Chang-xing, ZHANG Jun-xiong, CAO Zheng-yong, et al(耿长兴,张俊雄,曹峥勇,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2011, 3(42): 170. [5] Tian Y W, Zhang L. Physics Procedia, 2012, 33: 742. [6] Gabriel A, Leiva V, Lu R F, et al. Journal of Food Engineering, 2013, 115: 91. [7] Zhang X L, Liu F, He Y. Biosystems Engineering, 2013, 115: 56. [8] Shi J Y, Zou X B, Zhao J W. Scientia Horticulturae, 2012, 138: 190. [9] ElMasry G, Wang N, Vigneault C. Postharvest Biology and Technology, 2009, 52(1): 1. [10] Macho S, Rius A, Callao M P, et al. Analytica Chimica Acta, 2001, 445: 213. [11] ZHANG Hua-xiu, LI Xiao-ning, FAN Wei, et al(张华秀,李晓宁,范 伟,等). Journal of Instrumental Analysis(分析测试学报), 2010, 29(5): 430. [12] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimica Acta, 2009, 648: 77. [13] ZHAO Rui-jiao, LI Min-zan, YANG Ce, et al(赵瑞娇,李民赞,杨 策,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(11): 3103. [14] ZHOU Zhu, LI Xiao-yu, GAO Hai-long, et al(周 竹,李小昱,高海龙). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(2): 128. [15] Wu D, Sun D W. Talanta, 2013, 111(15): 39.
|
[1] |
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. |
[2] |
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. |
[3] |
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. |
[4] |
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. |
[5] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[6] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
[7] |
SUN Bang-yong1, YU Meng-ying1, YAO Qi2*. Research on Spectral Reconstruction Method From RGB Imaging Based on Dual Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2687-2693. |
[8] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
[9] |
LIU Gang1, LÜ Jia-ming1, NIU Wen-xing1, LI Qi-feng2, ZHANG Ying-hu2, YANG Yun-peng2, MA Xiang-yun2*. Detection of Sulfur Content in Vessel Fuel Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1697-1702. |
[10] |
LI Bin, HAN Zhao-yang, WANG Qiu, SUN Zhao-xiang, LIU Yan-de*. Research on Bruise Level Detection of Loquat Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799. |
[11] |
ZHOU Qi1, 2, WANG Jian-jun1, 2*, HUO Zhong-yang1, 2*, LIU Chang1, 2, WANG Wei-ling1, 2, DING Lin3. UAV Multi-Spectral Remote Sensing Estimation of Wheat Canopy SPAD Value in Different Growth Periods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1912-1920. |
[12] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
[13] |
ZHANG Fan1, WANG Wen-xiu1, ZHANG Yu-fan1, HU Ze-xuan1, ZHAO Dan-yang1, MA Qian-yun1, SHI Hai-yan2, SUN Jian-feng1*. Hyperspectral and Ensemble Learning Method for Rapid Identification of Black Spot in Yali Pear at Gley Stage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1541-1549. |
[14] |
GUO Feng1, ZHAO Dong-e1*, YANG Xue-feng1, CHU Wen-bo2, ZHANG Bin1, ZHANG Da-shun3MENG Fan-jun3. Research on Hyperspectral Image Recognition of Iron Fragments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 997-1003. |
[15] |
JIA Meng-meng, YIN Yong*, YU Hui-chun, YUAN Yun-xia, WANG Zhi-hao. Hyperspectral Imaging Combined With Feature Wavelength Screening for Monitoring the Quality Change of Tomato During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 969-975. |
|
|
|
|