|
|
|
|
|
|
Research on Optimal Near-Infrared Band Selection of Chlorophyll (SPAD) 3D Distribution about Rice Plant |
ZHANG Jian1, LI Yong1, XIE Jing2*, LI Zong-nan1 |
1. College of Resources & Environmental, Huazhong Agricultural University, Wuhan 430070, China
2. College of Basic Sciences, Huazhong Agricultural University, Wuhan 430070, China |
|
|
Abstract Whether the chlorophyll 3D distribution of crop is obtained accurately really attracts attention of scientific research and production field, such as crop nutrition, cultivation and breeding. In this study, the research object is the rice plant. The transformed ordinary SLR camera with different near infrared filters was used to acquire the multispectral images of rice plant in multi-view. Five kinds of vegetation indexes were calculated by combination image based on different bands and different channels. Then the optimal rice plant chlorophyll (SPAD value) prediction model was built between vegetation index and measured SPAD value. The research results showed that the prediction model with the quadratic function between GNDVI vegetation index and measured SPAD value can analyze chlorophyll content (SPAD value) of rice plant well, R2=0.758, RMSE=1.532. The GNDVI vegetation index was constructed by the R channel of near-infrared 760nm band and the G channel of visible light band. On this basis, the rice 3D model with texture information was built by multi-angle imaging 3D modeling method. Meanwhile, the optimal prediction model was applied to the integrated texture map of rice, and then the chlorophyll 3D distribution of rice was obtained. So rapid nondestructive detection of rice growth condition and chlorophyll nutrient situation can be realized.
|
Received: 2016-04-27
Accepted: 2016-08-16
|
|
Corresponding Authors:
XIE Jing
E-mail: xiejing625@mail.hzau.edu.cn
|
|
[1] LIU Ren-jie, FANG Jun-long, LI Min-zan, et al(刘仁杰, 房俊龙, 李民赞, 等). Journal of Agricultural Mechanization Research(农机化研究), 2016, 4: 141.
[2] ZHAO Chun-jiang, LU Sheng-lian, GUO Xin-yu, et al(赵春江, 陆声链, 郭新宇, 等). Scientia Agricultura Sinica(中国农业科学), 2015, 17: 3415.
[3] ZHANG Yan-chao, ZHUANG Zai-chun, XIAO Yu-zhao, et al(张艳超, 庄载椿, 肖宇钊, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 17: 207.
[4] Bakhshipour A, Jafari A, Hosseini S M. American-Eurasian J Agric & Environ, 2012, 12(10): 1288.
[5] Clark R, Maccurdy R, Jung J K, et al. Plant Physiology, 2011, 156(2): 455.
[6] Paproki A, Sirault X, Berry S, et al. BMC Plant Biology, 2012, 12(1): 63.
[7] Zou X, Zou H, Lu J. Machine Vision and Applications, 2012, 23(1): 43.
[8] Zhang D, Wang X, Ma W, et al. Intelligent Automation & Soft Computing, 2012, 18(8): 1111.
[9] Zou X, Shi J, Hao L, et al. Analytica Chimica Acta, 2011, 706(1): 105.
[10] WU Qian, SUN Hong, LI Min-zan, et al(吴 倩, 孙 红, 李民赞,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(1): 178.
[11] Zhang J, Yang C, Song H, et al. Remote Sensing, 2016, 8(3).
[12] ZHANG Yan-nan, NIU Jian-ming, ZHANG Qing, et al(张艳楠, 牛建明, 张 庆, 等). Acta Prataculturae Sinica(草业学报), 2012, 1: 229.
[13] LI Min-zan(李民赞). Technique and Application of Spectral Analysis(光谱分析技术及其应用). Beijing: Science Press(北京:科学出版社), 2006. 176.
[14] CHEN Xiao-ling(陈晓玲). Romote Sensing of Environment: Models and Applications(环境遥感模型与应用). Wuhan: Wuhan University Press (武汉: 武汉大学出版社), 2008. 131.
[15] Nijland W, Jong R D, Jong S M D, et al. Agricultural & Forest Meteorology, 2014, 184(1): 98.
[16] ZHANG Zhi-gang, XIONG Yun-hai, WANG Guang-ming, et al(张志刚, 熊运海, 王光明, 等). Guizhou Agricultural Sciences(贵州农业科学), 2000, 2: 18.
|
[1] |
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. |
[2] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
[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] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[5] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[6] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
[7] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[8] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[9] |
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. |
[10] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[11] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[12] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[13] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[14] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[15] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
|
|
|
|