基于近红外高光谱成像的粮虫生命体征检测研究  

Vital Signs Detection of Stored-grain Insects Based on Near-infrared Hyperspectral Imaging

作　　者：张红涛[1] 胡玉霞[2] 毛罕平[3] 韩绿化[3] 

机构地区：[1]华北水利水电大学电力学院,郑州450011 [2]郑州大学电气工程学院,郑州450001 [3]江苏大学现代农业装备与技术省部共建教育部重点实验室/江苏省重点实验室,江苏镇江212013

出　　处：《农机化研究》2014年第8期165-168,173,共5页Journal of Agricultural Mechanization Research

基　　金：国家自然科学基金项目(31101085);河南省基础与前沿技术研究计划(122300410145);河南省高等学校青年骨干教师资助计划(2011GGJS-094);华北水利水电大学高层次人才科研启动项目(201118)

摘　　要：快速、准确地检测出粮虫的生命体征是粮虫有效综合防治的关键。用液氮低温猝死法杀死粮虫谷蠹,利用构建的近红外高光谱成像系统采集谷蠹的高光谱图像。随着粮虫死亡时间的延长,粮虫相对光谱反射率逐渐增大。提出了基于最大离差法的最优特征波长提取方法,提取出可区分活虫和死虫的最优波长为1 417.2nm。应用区域生长法分割粮虫的图像,并由固定阈值法进行粮虫的自动判别。自粮虫死亡后的第2.5天开始,活虫的识别率达到100%。粮虫图像平均灰度值和其体内的ATP合成酶活性、蛋白质含量之间具有很强的负指数相关关系,与水分含量之间具有很强的负线性相关关系。由此表明,利用近红外高光谱成像系统鉴别粮虫的生命体征是可行的。Detecting vital signs of stored-grain insects rapidly and accurately is critical for making integrated management decisions. The low-temperature sudden death method was adopted to kill Rhyzopertha dominica （ F. ） by liquid nitrogen. The hyperspectral images of R. dominica were acquired by the near-infrared hyperspectral imaging system. The relative spectral reflectance of the insects increased gradually with the duration of the death time. The maximum deviation method was proposed to extract the optimal spectral wavelength, and the optimal characteristic wavelength to distinguish the live and the dead was 1,417.2 nm. The images of insects were segmented by the region-growing method, and the fixed threshold was proposed to identify the live insects. The identification accuracy of the live was 100% since the second and a half day after the death. The mean grey value of the insect images at 1 417.2nm showed a significant negative exponential correlation with the activity of ATP-synthesizing enzyme and the protein content, and a strongly significant negative linear correlation with the moisture content in bodies. The results show that it is feasible to detect the vital signs of the stored-grain insects with the NIR hyperspeetral imaging system.

关 键 词：储粮害虫 检测 生命体征 高光谱成像 近红外 

分 类 号：S123[农业科学—农业基础科学] TP391.41[自动化与计算机技术—计算机应用技术]