为建立基于高光谱成像技术的茄子叶片灰霉病早期检测方法,利用高光谱成像系统获取120个茄子叶片在380～1031nm范围的高光谱图像数据,通过主成分分析(PCA)对高光谱数据进行降维,并从中优选出3个特征波段下的特征图像,截取200×150的感兴趣区域图像(ROI),并从每幅特征图像中分别提取均值、方差、同质性、对比度、差异性、熵、二阶矩和相关性等8个基于灰度共生矩阵的纹理特征变量,通过连续投影算法(SPA)提取13个特征变量,利用最小二乘支持向量机(LS-SVM)构建茄子叶片灰霉病早期鉴别模型,模型判别准确率为97.5％.说明高光谱成像技术可以用于茄子叶片灰霉病的早期检测.%Early detection of gray mold on eggplant leaves using hyperspectral imaging technique was proposed. Hyperspectral images of 120 eggplant samples were captured by hyperspectral imaging system, and the spectral region was from 380 to 1 031 run. The pictures on three feature wavelengths were selected by principal component analysis (PCA), which was a good method to reduce the dimension of hyperspectral data. Eight feature variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) after choosing the region of interest (ROD of 200 X ISO, which were mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, correlation respectively, thus 24 feature variables in total for three feature images. Successive projections algorithm (SPA) was executed on 24 feature variables, 13 feature variables in which were extracted as the input of the least square support vector machines (LS-SVM) model, and the accurate rate of the model was 97.5%. It is showed that it is feasible for early detection of gray mold on eggplant leaves by hyperspectral imaging technique.
展开▼
