| 基于拉曼光谱分析寒地水稻叶瘟病害植株特征 |
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| 引用本文： | 谭峰,才巧玲,孙雪成,马志欣,侯召龙.基于拉曼光谱分析寒地水稻叶瘟病害植株特征[J].农业工程学报,2015,31(4):191-196. |
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| 作者姓名： | 谭峰 才巧玲 孙雪成 马志欣 侯召龙 |
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| 作者单位： | 黑龙江八一农垦大学信息技术学院，大庆 163319,黑龙江八一农垦大学信息技术学院，大庆 163319,黑龙江八一农垦大学信息技术学院，大庆 163319,黑龙江八一农垦大学信息技术学院，大庆 163319,黑龙江八一农垦大学信息技术学院，大庆 163319 |
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| 基金项目： | 黑龙江省自然科学基金项目（F201329）；国家科技支撑计划项目（2014BAD06B01）；黑龙江八一农垦大学研究生创新科研项目（755） |
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| 摘 要： | 稻叶瘟是影响寒地水稻产量的重要病害之一。为了减少病害受灾程度,增加早期检测的手段,该文利用拉曼光谱仪对正常水稻与感染稻叶瘟的水稻叶片进行拉曼光谱采集,指认出了水稻叶片的特征频率。通过对无病害叶片与病害叶片官能团的拉曼特性谱峰和特征频率偏移的对比分析,指出稻叶瘟水稻叶片的特征谱峰和稻瘟病敏感谱线为1 800~2 600 cm-1的频谱区域,分析得到984和994 cm-1的双峰连线的斜率以及828和851 cm-1的双峰连线的斜率随着病害程度的增加而逐渐增大。通过对随机抽取的50个拉曼光谱样本的分析,得到2 000~2 300 cm-1散射截面随着病害程度的加重而增加,说明散射截面的变化与稻瘟病害存在良好对应关系。研究表明拉曼光谱分析为早期检测水稻稻叶瘟病提供了一种有效的手段。
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| 关 键 词： | 光谱分析 病害 作物 拉曼光谱 特征频率 稻叶瘟 早期检测 |
| 收稿时间： | 2014/11/24 0:00:00 |
| 修稿时间： | 2/5/2015 12:00:00 AM |
Analyzing plant characteristics of rice suffering leaf blast in cold area based on Raman spectrum |
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| Tan Feng,Cai Qiaoling,Sun Xuecheng,Ma Zhixin and Hou Zhaolong.Analyzing plant characteristics of rice suffering leaf blast in cold area based on Raman spectrum[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(4):191-196. |
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| Authors: | Tan Feng Cai Qiaoling Sun Xuecheng Ma Zhixin and Hou Zhaolong |
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| Institution: | Information Technology of Heilongjiang Bayi Agricultural University, Daqing 163319, China,Information Technology of Heilongjiang Bayi Agricultural University, Daqing 163319, China,Information Technology of Heilongjiang Bayi Agricultural University, Daqing 163319, China,Information Technology of Heilongjiang Bayi Agricultural University, Daqing 163319, China and Information Technology of Heilongjiang Bayi Agricultural University, Daqing 163319, China |
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| Abstract: | Abstract: Raman spectroscopy has been widely applied in some areas, such as agricultural products, food, and so on. In chemical molecular structure analysis and appraisal, it has some merits of simple pretreatment, nondestructive, rapid detection, etc. We all know that the Raman spectra of some substances with similar molecular structures may have significant differences. We can take advantage of this characteristic to effectively distinguish some substances with other similar molecular structures. Rice leaf blast is one of the most serious diseases that affect the yield of rice in cold area. In order to reduce the impact caused by the disease and increased early detection methods, Raman spectroscopy was used to collect spectroscopy of normal rice leaves and abnormal rice leaves and identified the characteristic frequency of the rice leaf. Through analysis of the spectral peaks and characteristic frequency offset of normal rice and abnormal rice functional groups, we found that the spectral peaks and the sensitive spectrum lines ranged from 1 800 to 2 600 cm-1. The slopes of bimodal lines between 984 and 994 cm-1, and between 828 and 851cm-1 were increased as the degree of disease increased. In random samples of 20 Raman spectra, the correct recognition ratio reached 75%. Collected Raman spectra have certain fluorescence background, and the process of baseline correction to the background has great influence on the Raman band peak height, but it has little influence on the peak area relatively. Therefore we can divide it by the peak maxima in the calculation of the spectral intensity. Finally, 50 samples of Raman spectra were randomly selected for analysis. We can get 1 005, 1 527cm-1 in the vicinity of the scattering cross section size and 2 000-2 300cm-1 within the scope of the multiple spectral sum of peak scattering cross section size. We can see the change from the first 25 samples which are not very large. But with the aggravation of the rice disease extent, the scattering cross section values increased obviously. It showed that the Raman spectra of the rice leaf without disease were very small in the 2 000-2 300 cm-1 range. Because in the acquisition process of the Raman spectrum of rice leaf, the total energy of the light source is conserved, the increase in rice blast disease sensitive band scattering intensity of spectral peak intensity will correspondingly weakened other characteristic functional groups. So, the influence of ratio of rice blast sensitive band scattering intensity and scattering peaks characteristic functional groups were more obvious in rice blast disease of Raman spectral lines. The scattering cross section of 2 000-2 300 cm-1 increased with the increase of the degree of disease through the analysis of 50 randomly selected samples of Raman spectra, which showed a good relationship between the changes of the scattering cross section and the rice blast disease. Raman spectroscopy provides an effective method for the early detection of rice leaf blast. |
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| Keywords: | Spectrum analysis diseases crops raman spectra characteristic frequency rice leaf blast early detection |
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