基于SD-SVD-Burg的玉米叶片铜铅污染甄别与程度诊断

Discrimination of copper and lead pollution and diagnosis of pollution degree in maize leaves based on SD-SVD-Burg

为研究一种快速甄别作物受重金属污染的元素类别和所受污染程度的方法,于2017年设置不同梯度铜(Cu)、铅(Pb)胁迫下的玉米盆栽实验,对玉米的紫谷、绿峰和红边3个光谱特征区间的高光谱数据进行光谱一阶微分和奇异值分解处理,并结合Burg算法绘制功率谱密度曲线,同时利用2014年采集的光谱数据作为验证组检验该模型的稳定性。结果表明:健康玉米叶片与不同浓度Cu、Pb胁迫下玉米叶片光谱信号的功率谱密度曲线的波峰数及波峰坡度均不相同。功率谱曲线平均功率和玉米叶片中Cu、Pb含量的相关系数最高可达0.9958,证明该方法在对玉米进行污染元素种类辨别和污染程度诊断方面具有可行性,不同年份Cu与Pb胁迫下绿峰功率谱曲线平均功率与玉米叶片中Cu、Pb含量的相关系数分别为0.9213和0.9915,进一步验证该算法在玉米Cu、Pb污染诊断方面具有稳定性与普适性。

In order to study a method for rapid screening of elements and degree of heavy metal contamination in crops,a potted plant experiment of maize under different gradient of copper and lead stress was set up in 2017.Hyperspectral data of the three spectral characteristic intervals of purple valley,green peak,and red edge of maize were processed by spectral first-order differential and singular value decomposition.Power spectral density curve was plotted by combining Burg algorithm,and the spectral data collected in 2014 were used as a validation group to test the stability of the model.Results showed that the peak number and slope of the spectral density curve of spectral signals of maize leaves were different between healthy maize leaf under different Cu and Pb concentrations.The average power of the power spectrum curve and content of Cu and Pb in maize leaf had the highest correlation coefficient of 0.9958,and this proved that the method of differentiating and diagnosing the types and levels of maize pollution elements is feasible.The correlation coefficients between the average power of green peak power spectrum curve and content of Cu and Pb in maize leaves in different years under Cu and Pb stress were 0.9213 and 0.9915,respectively,further verifying the stability and universality of the algorithm in the diagnosis of Cu and Pb pollution in maize.