基于高光谱成像的寒地水稻叶瘟病与缺氮识别  

Identification of rice leaf blast and nitrogen deficiency in cold region using hyperspectral imaging

作　　者：袁建清[1,2] 苏中滨[1] 贾银江[1] 张雨[1] 章宗鑫 

机构地区：[1]东北农业大学电气与信息学院,哈尔滨150030 [2]黑龙江东方学院计算机科学与电气工程学部,哈尔滨150066

出　　处：《农业工程学报》2016年第13期155-160,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家"863"计划项目(2013AA102303);黑龙江省重大科技研发项目(GY2014ZB0011);哈尔滨市科技攻关项目(2013AA6BN010)

摘　　要：为进行水稻叶瘟病与养分缺失的区分、实现叶瘟病及时、准确的诊断,以大田试验为基础,利用高光谱成像仪获取2个品种的健康、缺氮、轻度感病和重度感病共4类水稻叶片的反射率光谱,对其光谱特性进行分析,并采用多种预处理方法、分别结合偏最小二乘判别分析(partial least squares-discriminate analysis,PLS-DA)和主成分加支持向量机(principle component analysis-support vector machine,PCA-SVM)方法构建水稻叶瘟病识别模型。试验结果显示6个判别模型都获得了较高的识别准确率,经标准正态变量(standard normal variate,SNV)变换预处理的PLS-DA模型获得了最佳的识别结果,预测准确率达100%,经多元散射校正(multiplicative scatter correction,MSC)预处理的PCA-SVM模型的预测准确率也达到97.5%。本研究为水稻叶瘟病的判别和分级提供了新方法,也为稻瘟病大范围遥感监测提供了基础。Rice blast is one of the most serious rice diseases and significantly impacts rice yields. In recent years, it is a hotspot to use hyperspectral imaging technology for the non-destructive identification of rice blast. However, nutrient deficiency in rice （such as nitrogen, potassium, etc.） will probably result in the chlorosis similar to rice blast. Therefore, to differentiate between them is very important for field management. In this study, field trials of rice blast and nitrogen stress were carried out in Fangzheng, Harbin, and 2 rice varieties with weak resistance were involved. From 8 to 10 in July, 2015, 4 types of rice leaves from both 2 varieties, including 60 in group of health, 60 in group of nitrogen deficiency, 60 in group of mild infection and 60 in group of severe infection, were collected and their hyperspectral images were captured with the HeadWall hyperspectral imaging system, and then the average reflectance spectrum of interest region of different leaves were acquired using the environment for visualizing images. In order to explore 4 types of spectral characteristics, the average spectrum of each type sample data, which was smoothed with polynomial convolution smoothing（Savitzky-Golay, SG）, were calculated as a spectral curve of each category. Significant differences were found at the following three positions： the range around 560 nm in the reflection peak of green wavelength region; the range from 620 nm to 670 nm in red wavelength region; and particularly remarkable in the range around 760 nm in high reflectance of the near infrared region. The models of rice leaf blast recognition were established by taking advantage of a partial least squares-discriminate analysis method （PLS-DA） and the principle component analysis plus support vector machine （PCA-SVM）, and using three different data pretreatment methods to preprocess original reflectance spectrum data, i.e., SG, standard normal variate transformation（SNV） and multiplicative scatter correetion（MSC）. The models

关 键 词：光谱分析 水稻叶瘟病 主成分分析 算法 高光谱成像 支持向量机 偏最小二乘判别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]