基于高光谱成像的水稻穗瘟病害程度分级方法  

Grading method of rice panicle blast severity based on hyperspectral image

作　　者：黄双萍[1,2] 齐龙[1,2] 马旭[1,2] 薛昆南 汪文娟[3] 

机构地区：[1]华南农业大学南方农业机械与装备关键技术省部共建教育部重点实验室,广州510642 [2]华南农业大学工程学院,广州510642 [3]广东省农业科学院植物保护研究所,广州510640

出　　处：《农业工程学报》2015年第1期212-219,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金(31101087);广东省自然科学基金项目(S2013010014240);国家科技支撑计划子课题(2013BAJ13B05-01)资助

摘　　要：为了快速、准确地进行水稻穗瘟病害程度分级,以实现水稻品种抗性评价或精准的田间化学防治,该研究提出了一种光谱词袋(bag of spectrum words,Bo SW)模型分析方法,分析稻穗的高光谱图像,自动评判穗瘟病害程度。首先,稠密规整地将高光谱图像分割成小立方格,计算每个立方格像素的平均全波段包络矢量,用K-Means算法聚类形成典型光谱包络词典。词典中光谱包络"词"(word)用作高光谱图像表达的"基",直方图统计各光谱"词"在高光谱图像样本中的出现频度,形成光谱图像的词袋表达。采用Hyper SIS-VNIR-QE光谱成像仪获取田间采集的170株稻穗样本高光谱图像,用Bo SW方法生成其词袋表达;植保专家根据病害程度类别确定光谱图像样本标签。随机选择2/3"词袋表达-病害程度等级标签"数据对构成训练集,采用卡方-支持矢量机(chi-square support vector machine,Chi-SVM)分类算法建立穗瘟病害程度分级模型。余下的1/3样本构成测试集,测试穗瘟病害等级模型的预测性能,分类识别精度为94.72%,高于主成分分析(principle component analysis,PCA)、敏感波段选择等传统光谱分析方法,其识别精度分别为83.83%和79.83%。该研究提高了穗瘟病分级的自动化程度和准确率,也可为其他病害分级检测提供参考。Estimation of panicle blast level plays an important role in high-quality production of rice. It helps to quantitatively assess the level of blast resistance and severity in the field to make appropriate decisions in gauging cultivar resistance in rice breeding or precisely controlling blast epidemic. However, it is difficult to evaluate the blast disease degree automatically and accurately. In this study, a novel grading method for panicle blast severity based on hyperspectral imaging technology is proposed. The method defines a bag of spectrum words （BoSW） model for hyperspectral image data representation. The BoSW model based on hyperspectral image data representation is used as the input of a Chi-square kernel support vector machine （Chi-SVM） classifier for predicting the rice panicle blast level. More precisely, dense grids are firstly extracted over the spatial X-and Y-axes across the whole spectral Z-axis. The average spectrum curve of all the pixels within a grid cube is calculated. Then, K-Means clustering would be performed on the large collection of average spectrum curves from the training samples to form the dictionary of spectrum words. Next, each spectrum curve on the grid cube is quantized into one of spectrum words. Each hyperspectral image of rice panicle is transformed into a map of spectrum words. All the spectrum words are distributed evenly on the spatial XY-axis plane. BoSW model for each hyperspectral data cube is then formed by means of histogram statistics of spectrum word occurrences. Finally, a Chi-SVM classifier is trained using the BoSW representations of rice panicle hyperspectral images for predicting panicle blast infection levels. The proposed BoSW method uses both the image and full-spectrum information by means of regular grid cube extraction, which utilizes the full potential of the imaging sensing system. Meanwhile, the representation dimension for each hyperspectral image is significantly reduced, i.e. 100 here, and thus relieving modeling difficulty. The procedure o

关 键 词：图像处理 病害 分级 高光谱成像 穗瘟 病害程度分析 光谱词袋模型 

分 类 号：S24[农业科学—农业电气化与自动化]