光谱学与光谱分析 |
|
|
|
|
|
Monitoring of Cnaphalocrocis Medinalis Guenee Based on Canopy Reflectance |
SUN Hong1, LI Min-zan1*, ZHOU Zhi-yan2, LIU Gang1, LUO Xi-wen2 |
1. Key Laboratory of Modern Precision Agriculture System Integration Research of Ministry of Education, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Key Technology on Agricultural Machine and Equipment,Ministry of Education, South China Agricultural University, Guangzhou 510642, China |
|
|
Abstract The canopy reflectance of rice was measured in the filed in order to monitor the damaged region caused by Cnaphalocrocis medinalis Guenee. The characteristics of canopy spectral reflectance were analyzed in contrast region and damaged regions. When rice plant was damaged by Cnaphalocrocis medinalis Guenee, the chlorophyll absorption was decreased in the band of 600-700 nm. The canopy reflectance of moderate damage region was lower than that of the contrast region, while the reflectance of severe damage region rice was higher near 550 nm. The canopy reflectance of Cnaphalocrocis medinalis Guenee damaged rice was fluctuant and exhibited the significant peak in the NIR band of 750-770nm. Meanwhile, red edge inflection point as one of the most important spectral parameters was analyzed at different damage levels based on the first derivative of reflectance spectra. The analysis results indicated that red edge inflection position moved to direction of blue light (short wavelength) with the affection severity increasing. Then the modified reflectance of rice canopy was calculated based on zero-mean calculation and standard deviation. It was easy to find the degree of deviation from the average of samples and distinguish the damaged region from experiment plots. The canopy modified reflectance was gently in the contrast region, but changed violently in the affected regions in the band of 750-950 nm. The analysis of Cnaphalocrocis medinalis Guenee affected regions illustrated that the Cnaphalocrocis medinalis Guenee was increased with the increase in severity. The vegetation index was applied in detection of Cnaphalocrocis medinalis Guenee damaged regions because of the composition of multi-wavelength information. The wavelengths 762 and 774 nm were chosen to build detection parameters of Cnaphalocrocis medinalis Guenee such as NIR-RVI, NIR-DVI, NIR-NDVI and KI. The results indicated that the NIR-NDVI could be used to identify the damaged region with contrast region efficiently. The accurate rate of 25 verification samples selected randomly reached 70%. The preliminary studies on rice Cnaphalocrocis medinalis Guenee damaged regions provided a new method to detect the affected regions in the wide area.
|
Received: 2009-04-18
Accepted: 2009-07-22
|
|
Corresponding Authors:
LI Min-zan
E-mail: limz@cau.edu.cn
|
|
[1] LIU Zhan-yu, HUANG Jing-feng, TAO Rong-xiang, et al(刘占宇, 黄敬峰, 陶荣祥, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(9): 2157. [2] WU Shu-wen, WANG Chao-ren, CHEN Xiao-bin, et al(吴曙雯, 王潮人, 陈晓斌, 等). Journal of Shanghai Jiaotong University·Agricultural Science(上海交通大学学报·农业科学版), 2002, 20(1): 73. [3] PENG Wang-lu(彭望琭). Introduction of Remote Sensing(遥感概论). Beijing: Higher Education Press(北京:高等教育出版社),2002. 307. [4] WANG Ji-hua, ZHAO Chun-jiang, HUANG Wen-jiang(王纪华,赵春江,黄文江). Semote Sensing Quantitative Theory and Application in Agriculture(农业定量遥感基础与应用). Beijing: Science Press(北京:科学出版社), 2008. 373. [5] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai(严衍禄,赵龙莲,韩东海). Near-Infrared Spectrum Analysis and Application(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京:中国轻工业出版社), 2005. 523. [6] Beverly S Ausmus, James W Hilty. Remote Sensing of Environment, 1971, 2: 77. [7] Hamed Hamid Muhammed, Anders Larsolle. Biosystems Engineering, 2003, 86(2): 125. [8] Hamed Hamid Muhammed. Biosystems Engineering, 2005, 91(1): 9. [9] Moshou D, Bravo C, Oberti R, et al. Real-Time Imaging, 2005, 11(2): 75. [10] LIU Xing-ku, LI Zhao-hua(刘兴库,李兆华). Journal of Northeast Forestry University(东北林业大学学报), 1993, 21(2): 106. [11] WU Ji-you, NI Jian(吴继友, 倪 健). Remote Sensing of Environment(环境遥感), 1995, 10(4): 250. [12] HUANG Mu-yi, WANG Ji-hua, HUANG Wen-jiang, et al(黄木易, 王纪华, 黄文江, 等). Transaction of the Chinese Society of Agricultural Engineering(农业工程学报), 2003, 19(6): 154. [13] LIU Liang-yun, HUANG Mu-yi, HUANG Wen-jiang, et al(刘良云, 黄木易, 黄文江, 等). Journal of Remote Sensing(遥感学报). 2004, 8(3): 275. [14] YANG Ke-ming,GUO Da-zhi(杨可明, 郭达志). Geography and Geo-Information Science(地理与地理信息科学), 2006, 22(4): 31. [15] WANG Yuan-yuan, CHEN Yun-hao, LI Jing, et al(王圆圆, 陈云浩, 李 京, 等). Remote Sensing for Land & Resources(国土资源遥感), 2007, 1: 57. [16] QIU Bai-jing, CHEN Guo-ping, CHENG Qi-wen(邱白晶, 陈国平, 程麒文). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2008, 39(9): 92. [17] Xu H R,Ying Y B,Fu X P, et al. Biosystems Engineering , 2007, 96(4): 447. [18] 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. [19] CHEN Bing, LI Shao-kun, WANG Ke-ru, et al. Agricultural Sciences in China,2008,(7): 561. [20] TIAN You-wen, ZHANG Chang-shui, LI Cheng-hua(田有文, 张长水, 李成华). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2004, 35(3): 95. [21] GE Jing, SHAO Lu-shou, DING Ke-jian, et al(葛 婧,邵陆寿,丁克坚,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2008, 39(1): 114. [22] LI Min-zan(李民赞). Spectral Analysis Technology and Application(光谱分析技术及其应用). Beijing: Science Press(北京: 科学出版社), 2006. 122.
|
[1] |
LI Shu-jie1, LIU Jie1, DENG Zi-ang1, OU Quan-hong1, SHI You-ming2, LIU Gang1*. Study of Germinated Rice Seeds by FTIR Spectroscopy Combined With Curve Fitting[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1832-1840. |
[2] |
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*. Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1956-1964. |
[3] |
YU Zhi-rong, HONG Ming-jian*. Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1735-1740. |
[4] |
WANG Yi-ya1, WANG Yi-min1*, GAO Xin-hua2. The Evaluation of Literature and Its Metrological Statistics of X-Ray Fluorescence Spectrometry Analysis in China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1329-1338. |
[5] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
[6] |
TAN Yang1, WU Xiao-hong2, 3*, WU Bin4, SHEN Yan-jun1, LIU Jin-mao1. Qualitative Analysis of Pesticide Residues on Chinese Cabbage Based on GK Improved Possibilistic C-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1465-1470. |
[7] |
TANG Guang-tong1, YAN Hui-bo1, WANG Chao-yang1, LIU Zhi-qiang1, LI Xin1, YAN Xiao-pei1, ZHANG Zhong-nong2, LOU Chun2*. Experimental Investigation on Hydrocarbon Diffusion Flames: Effects of Combustion Atmospheres on Flame Spectrum and Temperature[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1654-1660. |
[8] |
YU Yue, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, ZHANG Lei, SUI Yuan-yuan*. Hyperspectral Inversion Model for SPAD of Rice Leaves Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1092-1097. |
[9] |
ZHANG Dian-kai1, LI Yan-hong1*, ZI Chang-yu1, ZHANG Yuan-qin1, YANG Rong1, TIAN Guo-cai2, ZHAO Wen-bo1. Molecular Structure and Molecular Simulation of Eshan Lignite[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1293-1298. |
[10] |
SONG Hong-yan, ZHAO Hang, YAN Xia, SHI Xiao-feng, MA Jun*. Adsorption Characteristics of Marine Contaminant Polychlorinated Biphenyls Based on Surface-Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 704-712. |
[11] |
ZHOU Jun1, 2, YANG Yang2, YAO Yao2, LI Zi-wen3, WANG Jian3, HOU Chang-jun1*. Application of Mid-Infrared Spectroscopy in the Analysis of Key Indexes of Strong Flavour Chinese Spirits Base Liquor[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 764-768. |
[12] |
YU Feng-hua1, 2, ZHAO Dan1, GUO Zhong-hui1, JIN Zhong-yu1, GUO Shuang1, CHEN Chun-ling1, 2*, XU Tong-yu1, 2. Characteristic Analysis and Decomposition of Mixed Pixels From UAV Hyperspectral Images in Rice Tillering Stage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 947-953. |
[13] |
LI Xue-ping1, 2, 3, ZENG Qiang1, 2, 3*. Development and Progress of Spectral Analysis in Coal Structure Research[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 350-357. |
[14] |
GAO Le-le1, ZHONG Liang1, DONG Hai-ling1, LAI Yu-qiang5, LI Lian1,3*, ZANG Heng-chang1, 2, 3, 4*. Characterization of Moisture Absorption Process of Stevia and Rapid Determination of Rebaudioside a Content by Using Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 415-422. |
[15] |
HAN Yu1, SONG Shao-zhong2*, ZHANG Jia-huan3, TAN Yong1*, LIU Chun-yu1, ZHOU Yun-quan1, QU Guan-nan1, HAN Yan-li4, ZHANG Jing3, HU Yu3, MENG Wei-shi3, LIU Huan-jun5, ZHANG Yi-xiang1, LI Jia-yi1. Research on Soybean Bacterial Disease Markers Based on Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 459-463. |
|
|
|
|