基于扫描图像光谱特征和模式识别的水稻叶片磷素诊断研究

Diagnosis Study of Rice Leaf under Phosphorus Insufficiency Based on Spectral Features of Scan Image and Pattern Recognition

磷的缺乏对水稻产量有很大影响,磷的无损快速营养诊断对缺磷水稻生产有十分重要的意义。该文以水稻不同磷营养水平的顶部三张完全展开叶图像为研究对象,综合提取图像光谱信息表现出的颜色、纹理和形状三类共26个叶片特征指数并进行单因子特征分析,结合CfsSubset Eval+Scattersearch方法对26个特征属性进行优化组合、评价和选择,根据不同叶位的特征指数选择结果,利用粗糙集理论将不同磷营养水平叶片图像样本分为三类:极缺、微缺、正常。由识别精度可知,严重缺磷样本识别率最高,第三叶为水稻磷营养诊断的最佳叶位。

Insufficiency of phosphorus could greatly effect rice production,thus it is significant to adopt quick and nondestructive diagnosis of phosphorus content.The present paper focused on first expanded leaves with different phosphorus fertilization levels,comprehensively extracted 26 features＇ spectral information such as color,texture and shape etc.Single feature index analysis was conducted.Then features were collected to integrate CfsSubsetEval＋Scattersearch method for optimizing,evaluation and choosing.Based on the feature selection for different leave positions,leaves in different phosphorus fertilization levels were finally classified into three grades（extremly insufficient,significant insufficient and normal） according to rough set theory.Results showed that the accuracy of recognition was very high while few phosphorus contained in the leaves.Moreover,the third expanded leaf is the best part for phosphorus-nutrient diagnosis.