盐碱胁迫下芸豆冠层NIR光谱特性分析及检测方法

Analysis and Detection Method of NIR Spectral Characteristics of Kidney Bean Canopy Under Saline-Alkali Stress

盐碱胁迫是影响芸豆产量和质量重要的逆境因子之一。农作物盐碱胁迫的研究一般是通过传统的化学研磨萃取法,其操作繁琐且存在破坏性和耗时长等不足,目前对于盐碱胁迫下芸豆冠层近红外光谱(NIR)特性提取以及快速无损检测度的研究鲜有报道。为解决苗期芸豆盐碱胁迫程度快速检测的难题,基于近红外光谱技术,以苗期芸豆为研究对象,研究苗期芸豆健康和多等级盐碱胁迫的光谱曲线特性,提出一种盐碱胁迫下芸豆冠层NIR光谱特性分析及检测的新方法。首先选取吸光度值较强的990~2452 nm范围内苗期健康和受盐碱胁迫的芸豆冠层光谱数据,选用二次多项式自动拟合去趋势算法(DT)对原始光谱数据进行预处理,然后优选竞争性自适应重加权采样算法(CARS),从预处理后的数据中提取出95个对盐碱胁迫敏感的特征波长。利用径向基函数作为隐含神经元,构建三层前馈神经网络结构为95-282-7型(RBF),通过训练集样本确定网络参数,将网络前向输出值编码为二进制向量,最后解析输出向量至盐碱胁迫等级,完成苗期芸豆盐碱胁迫程度快速检测方法。结果表明:(1)对原始光谱曲线进行多种预处理,研究结果中相关性范围为0.3394~0.9461,其中DT预处理光谱的相关性范围为0.9433~0.9461,平均值为0.9447,能够提高快速检测芸豆盐碱胁迫的精度。(2)针对DT预处理后的芸豆冠层近红外光谱曲线,优选CARS算法提取出95维度的光谱特征波长向量,芸豆波长总数减少了93.51%,有效保留了对盐碱胁迫敏感的特征信息源。(3)应用CARS-RBF模型进行自动快速检测芸豆盐碱胁迫程度中学习次数为282次,均方误差(MSE)为0.00993859,模型检测准确率达到97.73%,因此该方法是一种芸豆盐碱胁迫程度的快速无损检测的新途径,能够为其他农作物盐碱胁迫程度的快速无损检测提供技术借鉴。

Salinity-alkalinity stress is one of the important of adversity factors that affect the kidney bean production and quality,the research of crop salinity-alkalinity stress is commonly by conventional chemical milling extraction method,the operation is complicated and time-consuming and destructive,such as for kidney bean canopy under salinity stress near-infrared(NIR)spectrum feature extraction,and quick nondestructive testing its salinity-alkalinity stress degree research rarely reported.In order to solve the problem of rapid detection of salt and alkali stress of kidney bean at the seedling stage,a new method for analyzing and detecting the NIR spectral characteristics of the canopy of kidney bean under salt and alkali stress was proposed based on near-infrared spectroscopy to study the characteristics of healthy and multi-grade salt and alkali stress of kidney bean at the seedling stage.Firstly,the spectral data of kidney bean canopy with healthy seeding stage and saline-alkali stress in the range of 990~2452 nm with strong absorbance value were selected for study,and the original spectral data were pre-processed by using the automatic fitting detrend algorithm(DT)with a quadratic polynomial.Then a competitive adaptive reweighted sampling algorithm(CARS)was selected to extract 95 characteristic wavelengths sensitive to saline-alkali stress from the pre-processed data.The radial basis function was used as the hidden neuron to construct a three-layer feedforward neural network structure of type 95-282-7(RBF).The network parameters were determined through the training set of samples,and the forward output value of the network was coded as a binary vector.Finally,the output vector was analyzed to the saline-alkali stress degree and the rapid detection method of saline-alkali stress degree of a kidney bean at the seeding stage was completed.The results showed that:(1)the original spectral curve was preprocessed in a variety of ways,and the correlation range of the study results was 0.3394~0.9461,the correlation range of DT pretreatment spectrum was 0.9433~0.9461,and the mean value was only 0.9447,which could improve the accuracy of rapid detection of salt and alkali stress of kidney bean.(2)aiming at the near-infrared spectrum curve of kidney bean canopy pretreated by DT,CARS algorithm was optimized to extract the spectral characteristic wavelength vector of 95 dimensions.The total wavelength of kidney bean was reduced by 93.51%,effectively preserving the characteristic information source sensitive to salt and alkali stress.(3)application of CARS-RBF model for automatic rapid detection of kidney bean salinity-alkalinity stress degree in the study of 282 times,the mean square error(MSE)is 0.00993859,model checking accuracy reached 97.73%,so,this method is a new way of rapid non-destructive detection of saline-alkali stress degree of kidney bean,and can provide a technical reference for the rapid non-destructive detection of the saline-alkali stress degree of other crops.