基于光谱结构特性的马铃薯干腐和疮痂病识别方法研究

马铃薯干腐病和疮痂病的检测通过人工目测，结果存在主观性，该工作研究正常、干腐和疮痂病马铃薯的光谱分类识别方法。试验获取116个样本，光谱采集范围860~1 745 nm。经过一阶导数(first derivative, FD)处理后的光谱数据，主成分（PCA）分类识别效果较好，FD作为光谱预处理方法。光谱曲线上的极值点、极值点间的中点和极值点间连线的斜率决定光谱曲线的形状和变化，是曲线上的关键点，光谱曲线形状变化代表着内部物质的变化，具有指纹特性，利用极值点和中点对应的光谱或极值点间连线的斜率组成模式特征向量。分别用3种样本关键点的平均光谱形成标准模式特征向量，通过计算待测样本关键点组成的特征向量和标准模式特征向量之间的马氏距离，以最小马氏距离判定样本的归属，通过错误识别率检验模型识别性能。正常、干腐、疮痂样本分别有13，12，15个关键点，由各自关键点对应的反射率组成的模式特征向量，3类样本的错误识别率为0。去掉冗余关键点整合成一个标准模式特征向量，正常和疮痂样本的错误识别率为0，干腐样本的错误识别率为14.3%，全部错误识别为疮痂样本，特征向量数据点的增多，增加了病害样本之间的贴合度，降低了两类病害样本之间的区分度。利用波长911, 1 269和1 455 nm处两点间的斜率形成模式特征向量，正常和疮痂样本的错误识别率为0，干腐样本的错误识别率为2.4%。利用前2个主成分得分作为参数，采用线性判别分析(LDA)和贝叶斯分类器（BC）建模，提供不同角度的分类模型，对比检验基于模式特征向量建立的分类模型的有效性，2种识别方法的错误识别率均为0。实验结果表明，可以利用表征光谱曲线结构特征的模式特征向量作为分类参数，结合距离法建模，与常见识别方法具有同等识别精度。

Study on Identification of Common Diseases in Potato Storage Period Based on Spectral Structure

At present, the detection of dry rot and potato scab was completed by manual visual inspection, and the detection results were subjective. This experiment studied the spectral detection method for classification and recognition of normal, dry rot and scab of potato. 116 potato samples were collected in the experiment, and the spectrum collection range was 860~1 745 nm. After the first derivative (FD) processing, the principal component analysis (PCA) classification recognition effect was better, and FD was used as the spectral preprocessing method. The shape of the spectral curve was determined by the extreme points on the spectral curve, the midpoint between the extreme points and the slope line between the extreme points. The shape change of the spectral curve represented the change of the internal substance and had fingerprint characteristics. The mode eigenvector was composed of the spectrum corresponding to the key points or the line slope between the extreme points. The average spectra of the key points of the three samples were used to form the standard pattern feature vectors. By calculating the Mahalanobis distance between the feature vectors composed of the key points of the tested samples and the standard pattern feature vectors, the minimum Mahalanobis distance was used to determine the attribution of the samples, and the error recognition rate tested the recognition performance of the model. There were 13, 12 and 15 key points in normal, dry rot and scab samples, respectively. The pattern feature vector was composed of the reflectance corresponding to each key point, and the error recognition rate of the three types of samples was zero. By removing redundant key points and integrating them into a standard pattern feature vector, the error recognition rate of normal and scab samples was zero, that of dry rot samples was 14.3%, and all were scab samples. The feature vector data points increase the fit degree between disease samples and reduces the discrimination between two types of disease samples. Using the slope between two points at the wavelength of 911, 1 269 and 1 455 nm to form the pattern feature vector, the error recognition rate of normal and scab samples was zero, and the error recognition rate of dry rot samples was 2.4%. Linear discriminant analysis (LDA) and Bayesian classifier (BC) were used to build the classification model by using the scores of the first two principal components as the parameters. Different classification models were provided. The effectiveness of the classification model based on the pattern feature vector was compared and verified. The error recognition rate of the two recognition methods was zero. The experimental results show that the pattern feature vectors representing the structural features of spectral curves could be used as the classification parameters, and the distance method could be used for modeling, which had the same recognition accuracy as the standard recognition methods.