光谱学与光谱分析 |
|
|
|
|
|
Research of Influence Factors on Spectral Recognition for Cotton Leaf Infected by Verticillium wilt |
CHEN Bing1, 2, WANG Fang-yong1, HAN Huan-yong1, LIU Zheng1, XIAO Chun-hua2, ZOU Nan2 |
1. Cotton Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding, Ministry of Agriculture, Shihezi 832000, China 2. Key Laboratory of Oasis Ecology Agriculture of Xinjiang Corps, Shihezi University, Shihezi 832003, China |
|
|
Abstract Through carrying out spectral test experiment, the influence factors of spectrum test were analyzed, the influence degree of various factors in spectral recognition was explicated and the method of spectra test was optimized for cotton leaf infected by verticillium wilt. The results indicated that under different severity levels, the shape and value of reflectance of disease symptoms part were Significantly higher than healthy part on cotton leaf, compared with the black board as baseboard, the spectral values of disease leaves were slightly higher in visible light wavebands and significantly higher in others wavebands than healthy leaves on white baseboard. Different position of leaf on cotton plant has different effect degree to the recognition of disease, the effect of stem leaf was more obvious than that of else leaf, the identical leaf position was less influenced by disease than that of others. The effect of healthy leaf was smaller than disease leaf. The reflectance of leaf back was higher than front in visible light waveband, from high to flat, and then low in near infrared waveband, and from high to low to in short infrared waveband. Test time and cotton varieties had less influence on recognizing disease by spectra, and the effect of the same condition was acceptable. Test site had no effect on disease recognition by spectra. The effect of each factor was different for recognizing disease leaf by spectra, and this study will provide reference for the researchers of crop disease diagnosis by spectra.
|
Received: 2013-04-15
Accepted: 2013-08-10
|
|
Corresponding Authors:
CHEN Bing
E-mail: zyrcb@126.com
|
|
[1] Chen B, Li S K, Wang K R, et al. International Journal of Remote Sensing, 2012, 33(9): 2706. [2] PU Rui-liang, GONG Peng(蒲瑞良, 宫 鹏). Haper-Spectral Remote Sensing and Application(高光谱遥感及其应用). Beijing: Higher Education Press(北京: 高等教育出版社), 2000. [3] Congalton R G, Green K. Aassessing the Accuracy of Remotely Sensed Data: Principle and Practices. Lew is Publishers, 1999. [4] Li X, Strahler A H. IEEE Transactions On Geoscience And Remote Sensing, 1992, 30(2): 276. [5] HE Ting, CHENG Ye, WANG Jing(何 挺, 程 烨, 王 静). China Land Science(中国土地科学), 2002, 16(5): 30. [6] Williams T L, Andrzejewski D, Lay J O, et al. Journal of the American Society for Mass Spectrometry, 2003, 14(4): 342. [7] Wang Yanrong,Yun Shipeng. Plant Ecology and Geobotany Journal, 1990, 14 (3): 258. [8] Mandal S M, Pati B R, Ghosh A K, et al. European Journal of Mass Spectrometry, 2007, 13(2): 165. [9] BAI Jun-hua, LI Shao-kun, SUI Xue-yan, et al(柏军华, 李少昆, 隋学艳, 等). Journal of Shihezi University: Natural Science(石河子大学学报·自然科学版), 2005, 23(1): 53. [10] FAN Jing-chao, ZHOU Guo-min(樊景超, 周国民). Journal of Anhui Agriculture Science(安徽农业科学), 2011, 39(1):464. [11] CHEN Bing, LI Shao-kun, WANG Ke-ru, et al(陈 兵, 李少昆, 王克如, 等). Scientia Agricultura Sinica(中国农业科学), 2007, 40(12): 2709. |
[1] |
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. |
[2] |
WANG Yu-ye1, 2, LI Hai-bin1, 2, JIANG Bo-zhou1, 2, GE Mei-lan1, 2, CHEN Tu-nan3, FENG Hua3, WU Bin4ZHU Jun-feng4, XU De-gang1, 2, YAO Jian-quan1, 2. Terahertz Spectroscopic Early Diagnosis of Cerebral Ischemia in Rats[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 788-794. |
[3] |
XUE Wen-dong1*, CHEN Ben-neng1, HONG De-ming1, YANG Zhen-hai1, LIU Guo-kun2. Research on Raman Spectrum Recognition Method Based on Improved
Reverse Matching[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 753-759. |
[4] |
HAN Min-jie, WANG Xiang-you, XU Ying-chao*, CUI Ying-jun, LÜ Dan-yang. Research on the Factors Influencing the Non-Destructive Detection of
Potatoes by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 37-42. |
[5] |
YI Can-can1, 2, 3, 5*, TUO Shuai1, 2, 3, TU Shan1, 2, 3, 4, ZHANG Wen-tao5. UMAP-Assisted Fuzzy C-Clustering Method for Recognition of
Terahertz Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2694-2701. |
[6] |
LÜ Jia-nan, LI Jun-sheng*, HUANG Guo-xia, YAN Liu-juan, MA Ji. Spectroscopic Analysis on the Interaction of Chrysene With Herring Sperm DNA and Its Influence Factors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 210-214. |
[7] |
ZHAI Wen-yu, CHEN Lei*, XU Yi-xuan, KONG Xiang-yu. Analysis of Impact Factors and Applications by Using Spectral Absorption Depth for Quantitative Inversion of Carbonate Mineral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2226-2232. |
[8] |
GUO Wei1, QIAO Hong-bo1, ZHAO Heng-qian2,3*, ZHANG Juan-juan1, PEI Peng-cheng1, LIU Ze-long2,3. Cotton Aphid Damage Monitoring Using UAV Hyperspectral Data Based on Derivative of Ratio Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1543-1550. |
[9] |
HU Qi-feng, CAI Jian. Research of Terahertz Time-Domain Spectral Identification Based on Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 94-99. |
[10] |
QUE Hua-li1, 2, YANG Wen-liang1, XIN Xiu-li1, MA Dong-hao1, ZHANG Xian-feng1, ZHU An-ning1*. Ammonia Volatilization from Farmland Measured by Laser Absorption Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 885-890. |
[11] |
XU Zhi-niu, HU Yu-hang, ZHAO Li-juan*, FAN Ming-yue. Fast and Highly Accurate Brillouin Frequency Shift Extracted Algorithm Based on Modified Quadratic Polynomial Fit[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 842-848. |
[12] |
ZHOU Hao, YANG Zheng. The Reversible Ammonium Detection Based on the Coupled Microfluidic Chipand the Investigation of the Impact Factors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3749-3754. |
[13] |
LAI Wen-hao1, ZHOU Meng-ran1*, LI Da-tong1, WANG Ya2, HU Feng1, ZHAO Shun3, GU Yu-lin1. Application of Unsupervised Learning AE and MVO-DBSCAN Combined with LIF in Mine Water Inrush Recognition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2437-2442. |
[14] |
GAO Bin1, ZHAO Peng-fei1, LU Yu-xin1, FAN Ya1, ZHOU Lin-hua1*, QIAN Jun2, LIU Lin-na2, ZHAO Si-yan2, KONG Zhi-feng3. Study on Recognition and Classificationof Blood Fluorescence Spectrum with BP Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(10): 3136-3143. |
[15] |
CHEN Bing1, WANG Gang4, LIU Jing-de1*, MA Zhan-hong2, WANG Jing3, LI Tian-nan1, 2. Extraction of Photosynthetic Parameters of Cotton Leaves under Disease Stress by Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1834-1838. |
|
|
|
|