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[作物与杂草幼苗期的光谱分析]

[Spectrum analysis of crop and weeds at seedling].

作者信息

Mao Wen-hua, Wang Yue-qing, Wang Yi-ming, Zhang Xiao-chao

机构信息

College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2005 Jun;25(6):984-7.

Abstract

The infestation information on field weeds is the basis of variable spraying herbicides. It was found that the method using the spectral characteristics of plant is superior in real-time respect. The Fourier transform infrared spectrum technique was applied to measure the reflectance of wheat and weeds in the range from 700 to 1100 nm. The discrimination analysis was done using the SPSS software. Firstly, the source spectrum data were compressed and normalized. Secondly, the characteristic wavelengths were selected by using stepwise method. Thirdly, the discrimination model was set up to use the selected wavelengths as the variables for detecting wheat and weeds. It was shown by the result of discrimination analysis that the correct classification rate of wheat and weeds detection with the selected wavelength points achieved 97%. In addition, the selected wavelength points were marked in the "red edge" of reflectance within some range, and the rate of correct classification increased with the increase in the numbers of the selected wavelength points. According to the selected wavelength points, the proper filters were chosen to perform the multi-spectral images captured and processed with the machine vision system.

摘要

田间杂草的侵染信息是变量喷施除草剂的依据。研究发现,利用植物光谱特征的方法在实时性方面更具优势。应用傅里叶变换红外光谱技术测量小麦和杂草在700至1100纳米范围内的反射率。使用SPSS软件进行判别分析。首先,对源光谱数据进行压缩和归一化处理。其次,采用逐步法选择特征波长。第三,建立判别模型,将所选波长作为检测小麦和杂草的变量。判别分析结果表明,利用所选波长点检测小麦和杂草的正确分类率达到了97%。此外,所选波长点在一定范围内的反射率“红边”处被标记出来,正确分类率随着所选波长点数量的增加而提高。根据所选波长点,选择合适的滤光片,通过机器视觉系统对采集到的多光谱图像进行处理。

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