| 光谱学与光谱分析 |
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| Investigation of the Hyperspectral Image Characteristics of Wheat Leaves under Different Stress |
| ZHANG Dong-yan1, 2, ZHANG Jing-cheng1, 2, ZHU Da-zhou1, WANG Ji-hua1, 2, LUO Ju-hua1, ZHAO Jin-ling1, HUANG Wen-jiang1* |
1. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
2. Institute of Remote Sensing & Information Technique, Zhejiang University, Hangzhou 310029, China |
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Abstract The diagnosis of growing status and vigor of crops under various stresses is an important step in precision agriculture. Hyperspectral imaging technology has the advantage of providing both spectral and spatial information simultaneously, and has become a research hot spot. In the present study, auto-development of the pushbroom imaging spectrometer (PIS) was utilized to collect hyperspectral images of wheat leaves which suffer from shortage of nutrient, pest and disease stress. The hyperspectral cube was processed by the method of pixel average step by step to highlight the spectral characteristics, which facilitate the analysis based on the differences of leaves reflectance. The results showed that the hyperspectra of leaves from different layers can display nutrient differences, and recognize intuitively different stress extent by imaging figures. With the 2 nanometer spectral resolution and millimeter level spatial resolution of PIS, the number of disease spot can be qualitatively calculated when crop is infected with diseases, and, the area of plant disease could also be quantitatively analyzed; when crop suffered from pest and insect, the spectral information of leaves with single aphid and aphids can be detected by PIS, which provides a new means to quantitatively detect the aphid destroying of wheat leaf. The present study demonstrated that hyperspecral imaging has a great potential in quantitative and qualitative analysis of crop growth.
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Received: 2010-08-21
Accepted: 2010-11-16
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Corresponding Authors:
HUANG Wen-jiang
E-mail: huangwj@nercita.org.cn
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[1] Inoue Y, Penuelas J. International Journal of Remote Sensing, 2001, 22(18), 3883.
[2] HUANG Hui, WANG Wei, PENG Yan-kun(黄 慧, 王 伟, 彭彦昆). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(7): 1811.
[3] WANG Wei, PENG Yan-kun, MA Wei(王 伟, 彭彦昆, 马 伟). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 5(41): 172.
[4] ZHANG Dong-yan, SONG Xiao-yu, MA Zhi-hong(张东彦, 宋晓宇, 马智宏). Scientia Agriculture Sinica(中国农业科学), 2010, 43(11): 2239.
[5] TAN Hai-zhen, LI Shao-kun, WANG Ke-ru(谭海珍, 李少昆, 王克如). Acta Agronomica Sinica(作物学报), 2008, 34(10): 1812.
[6] Christian Nansen, Tulio Macedo, Rand Wwanson. International Journal of Remote Sensing, 2009, 30(10): 2447.
[7] TIAN You-wen, LI Tian-lai, ZHANG Lin(田有文, 李天来, 张 琳). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(5): 202.
[8] CHAI A-li, LIAO Ning-fang, TIAN Li-xun(柴阿丽, 廖宁放, 田立勋). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(5): 1357.
[9] CAI Jian-rong, WANG Jian-hei, CHEN Quan-sheng(蔡健荣, 王建黑, 陈全胜). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(1): 127.
[10] Agelisi. Plant Pathology(植物病理学). Beijing: China Agricultural Press(北京：中国农业出版社), 2009.
[11] RUILIANG P U. International Journal of Remote Sensing, 2009, 30(11): 2759.
[12] ZHAO Chun-jiang, HUANG Wen-jiang, WANG Ji-hua(赵春江, 黄文江, 王纪华). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2006, 22(6): 104.
[13] WANG Ji-hua, ZHAO Chun-jiang, HUANG Wen-jiang(王纪华, 赵春江, 黄文江). Basis and Application of Quantitative Remote Sensing in Agriculture(农业定量遥感基础与应用). Beijing: Science Press(北京：科学出版社), 2008. 141.
[14] Zhang Jingheng, Wang Ke, Bailey J S. Pedosphere, 2006, 16(1): 108.
[15] WANG Yuan-yuan, LI Gui-cai, ZHANG Li-jun(王圆圆, 李贵才, 张立军). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(4): 1070.
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