田间原位光谱的鲜烟叶成熟度判别模型的研究

在田间原位对烟叶成熟度进行判别,能够有效减少由于对成熟度判断错误而导致的烟叶损失率升高、质量下降的问题,而传统的人眼结合叶龄的田间成熟度判别方法缺少客观性,因此提出采用光谱特征参数结合支持向量机的方法对田间原位烟叶成熟度进行判别。以专家评定并在田间原位进行测量的五个成熟度等级共351个烟叶反射光谱作为试验样品,五个成熟度等级分别为M1,M2,M3,M4,M5。通过对反射光谱的分析发现,不同成熟度烟叶的光谱在可见光波段能够得到区分,而在近红外波段区分不明显,因此在可见光波段进行分析建模。分别采用可见光范围内的连续光谱(350~780nm)、特征波段(496~719nm)、光谱特征参数(绿峰幅值、绿峰位置、红边幅值、蓝边幅值、红边面积、蓝边面积、红边位置、蓝边位置)作为输入变量,采用支持向量机方法(supportvector machine,SVM)建立烟叶成熟度判别模型。结果表明,应用可见光光谱特征参数作为输入变量所建立的模型的正确识别率达到98.85%,而应用可见光连续谱、可见光特征波段作为输入变量的正确识别率分别为90.80%和93.10%。因此使用可见光光谱特征参数建立支持向量机的鲜烟叶成熟度判别模型对田间原位烟叶成熟度进行判别是可行的。

Study on the Determination of the Maturity Level of Tobacco Leaf Based on In-Situ Spectral Measurement

Discriminating the maturity levels of tobacco leaf with in‐situ measurement can effectively reduce loss rate and quality decline due to misjudgment of the maturity levels of tobacco leaf .In the meantime ,the regular way we use to determine the ma‐turity levels of tobacco ,which is depend on tobacco leaf age and judgment of tobacco grower ,lacks of objectivity .So this paper proposed a method to identify maturity levels of tobacco leaf by using spectral feature parameters combined with the method of support vector machine (SVM ) .In this paper ,a total of 351 tobacco leaf samples collected in 5 maturity levels including imma‐ture (M1) ,unripe (M2) ,mature (M3) ,ripe (M4) ,and mellow (M5) determined by experts were scanned by field spectro‐scope(ASD FieldSpec3) with in‐situ measurement for getting their reflectance spectrum .Through spectral analysis we found that the spectrum of tobacco leaf with different levels of maturity can be distinguished in visible band but not easily be distinguished in near‐infrared band ,so we use the tobacco leaf spectrum in visible band as the sensitive bands to analyze and model .To find the most suitable input variables for modeling ,we use continuous spectrum (350 ~ 780 nm) ,feature band (496 ~ 719 nm) and spec‐tral feature parameters (the reflectance of green peak ,location of green peak ,first order differential value of red‐edge and blue‐edge ,red‐edge and blue‐edge area ,location of red‐edge and blue‐edge) in visible region as the input variables ,and using these three kinds of input variables in the method of SVM to establish a discriminant model for identifying maturity levels of tobacco leaf .The result shows that ,the model using spectral feature parameters gains the accuracy rate of 98.85% .While the accuracy rates of other two models were 90.80% and 93.10% ,respectively .The conclusion was drawn that using spectral feature param‐eters in visible spectrum as the input variables in SVM can improve the model performance .It is feasible to use this method to i‐dentify maturity level of tobacco leaf with in‐situ measurement .