基于高光谱图像和遗传优化神经网络的茶叶病斑识别  

Recognition of tea disease spot based on hyperspectral image and genetic optimization neural network

作　　者：张帅堂 王紫烟 邹修国 钱燕 余磊 

机构地区：[1]南京农业大学工学院/江苏省智能化农业装备重点实验室,南京210031

出　　处：《农业工程学报》2017年第22期200-207,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家博士后科学基金资助项目(2015M571782);中央高校科研业务基本业务费资助项目(KYTZ201661);江苏省农机基金资助项目(GXZ14002)

摘　　要：为实现茶叶病害的快速高效识别,提出了基于高光谱成像技术和图像处理技术融合的茶叶病斑识别方法。利用高光谱成像技术采集了炭疽病、赤叶斑病、茶白星病、健康叶片等4类样本的高光谱图像。提取感兴趣区域敏感波段的相对光谱反射率作为光谱特征。通过2次主成分分析,确定第二次主成分分析后的第二主成分图像为特征图像,基于颜色矩和灰度共生矩阵提取特征图像的颜色特征和纹理特征。利用BP神经网络对颜色、纹理和光谱特征向量融合数据进行检验,识别率为89.59%;为提高识别率,提出遗传算法优化BP神经网络的方法,使病斑识别率提高到94.17%,建模时间也缩短至1.7 s。试验结果表明:高光谱成像技术和遗传优化神经网络可以快速准确的实现对茶叶病斑的识别,可为植保无人机超低空遥感病害监测提供参考。In order to achieve fast and efficient identification of tea diseases, the method of identifying tea diseases based on hyperspectral imaging technology was put forward. Four kinds of samples, including anthracnose, brown leaf spot disease, white star disease and healthy leaf, were collected in Pingshan tea plantation of Nanjing. Hyperspectral images of these samples, ranging from 358 to 1 021 nm, were collected by hyperspectral imaging system. Among them, there were 80 samples of anthracnose, 72 samples of brown leaf spot disease, 80 samples of white star disease and 60 samples of healthy leaves. The region of interest （ROI） was an area of 200 pixels × 200 pixels near the tip of the tea leaf. The average spectral reflectance curves of the effective band of ROI were extracted to analyze the spectral characteristics. For the purpose of decreasing the redundancy of hyperspectral data, and reducing the computational complexity, this study used principal component analysis （PCA） to process the original hyperspectral images, and obtained 4 kinds of principal component images for the samples with the maximum weight coefficients, and the wavelengths of 762, 700, 721, 719 nm corresponded were taken as the characteristic wavelengths. The test showed that direct use of 481 bands for the first PCA resulted in low calculation speed and low processing efficiency. Thus, the second principal components with the 4 characteristic wavelengths were employed, and the second principal component image was selected as the feature image through comparing the characteristics of lesion and non lesion regions. To get the accurate extraction of tea leaf spots, OTSU algorithm for image segmentation was adopted, the optimal threshold of 4 kinds of leaf samples was determined, and finally the sample images containing only leaf lesion regions were extracted. After image segmentation, 3 color feature parameters were extracted from the single-channel first moments, second moments and three-order moments of each feature image based on co

关 键 词：算法 优化 神经网络 高光谱成像技术 主成分分析 光谱特征 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S435.711[自动化与计算机技术—计算机科学与技术]