应用数字图像进行小麦氮素营养诊断中图像分析方法的研究  

Image analysis method in application of digital image on diagnosing wheat nitrogen status

作　　者：李红军[1] 张立周[2] 陈曦鸣[3] 张玉铭[1] 程一松[1] 胡春胜[1] 

机构地区：[1]中国科学院遗传与发育生物学研究所农业资源研究中心,石家庄050021 [2]河北农业大学资源与环境科学学院,保定071001 [3]石家庄市第24中学,石家庄050051

出　　处：《中国生态农业学报》2011年第1期155-159,共5页Chinese Journal of Eco-Agriculture

基　　金：国家自然科学基金项目(40971025);国家科技支撑计划项目(2008BADA4B02-04;2006BAD17B05);中国科学院知识创新工程重大项目(KSCX-WY-09)资助

摘　　要：简便、快速、经济地诊断作物氮素营养状况是实施氮肥用量调控的关键。利用数码相机对作物冠层进行拍照,通过图像处理软件获得作物色彩参数,根据色彩参数与作物氮素营养状况的关系可以对其氮素丰缺进行诊断。针对作物数字图像色彩参数的获取方法,结合小麦多水平氮肥试验,采用遥感软件PCIGeo-matics的非监督分类功能,将小麦图像分为土壤、反光叶面和不反光叶面,与AdobePhotoshop软件普通图像处理方法对照,比较分析了小麦图像不同类别叶片的8种色彩参数与SPAD值及植株全氮的相关性。结果表明,返青期小麦反光叶面的G/R与R/(R+G+B)色彩参数能较好地反映小麦的氮素营养状况;拔节期不反光叶面和反光叶面的R/(R+G+B)色彩参数与植株全氮相关性较好。利用普通图像处理软件获得色彩参数的方法有待改进,图像分类后能够提高其色彩参数对作物氮素营养诊断的准确性。Easy, fast and cheap diagnosis of crop nitrogen status is a key to optimize nitrogen fertilization. Crop nitrogen status can be measured by relating it to color parameters retrieved form digital images. In this paper, color parameters of digital image of winter wheat under different levels of nitrogen supply were analyzed by using Adobe Photoshop and PCI Geomatics remote sensing image processing software and the results compared. Based on color differences, winter wheat image was divided into three parts （soil, leaves with and without reflection）. Correlations among eight color parameters retrieved by PCI and Photoshop, SPAD values and total nitrogen content of wheat were analyzed. The results showed that G/R and R/（R＋G＋B） ratios of leaves with reflection were significantly correlated with wheat SPAD at reviving stage. R/（R＋G＋B） ratio of leaves with and without reflection was significantly correlated with wheat total nitrogen content at jointing stage. It was noted that improvements in image processing for getting color parameters of software were required. Color parameters obtained via image classification could increase the diagnosis accuracy of wheat nitrogen status.

关 键 词：冬小麦 氮素营养诊断 数字图像 图像分类 色彩参数 

分 类 号：S127[农业科学—农业基础科学]