基于水稻扫描叶片图像特征的氮素营养诊断研究

选用扫描仪获取水稻叶片的数字图像,运用数字图像处理技术研究不同氮素水平水稻的叶色、纹理和形态特征,尝试建立基于叶色、纹理和形态特征的综合水稻氮素营养诊断模型.研究表明:(1)通过水稻叶绿索a含量和叶片颜色特征之间的相关性分析,得到有效颜色特征参量B,b,b/(r+g),b/r,b/g,同时分析叶绿素a含量随叶位的空间分布特征,选择较为稳定的第三完全展开叶作为指示叶或诊断叶;(2)通过与高光谱遥感数据的相关性分析,从机理上说明基于机器视觉的水稻氮素营养诊断是有理论依据的,是可行的;(3)提取水稻叶片颜色、形态综合特征信息,与YIQ电视信号彩色坐标系统的CB值建立水稻氮营养水平的识别模型,得到4个氮素水平的正确识别率为:N0:74.9%;N1:52%;N2:84.7%;N3:75%.初步研究结果表明基于扫描图像特征的田间水稻氮素水平诊断方法是有优势和前景的,有可能建立一种新的实时、快速、准确的田间水稻氮素营养诊断的方法.

Diagnoses of Rice Nitrogen Status Based on Characteristics of Scanning Leaf

In the present research,the scanner was adopted as the digital image sensor,and a new method to diagnose the status of rice based on image processing technology was established. The main results are as follows: (1) According to the analysis of relations between leaf percentage nitrogen contents and color parameter,the sensitive color parameters were abstracted as B,b,b/(r+g),b/r and b/g. The leaf position (vertical spatial variation) effects on leaf chlorophyll contents were investigated,and the third fully...